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Walimbe, Vivek SInteractive, quantitative 3D stress echocardiography and myocardial perfusion spect for improved diagnosis of coronary artery disease
Doctor of Philosophy, The Ohio State University, 2006, Biomedical Engineering
Coronary artery disease (CAD), which involves narrowing of the vessels supplying blood to the heart, is the leading cause of death in the United States. Stress testing is a common approach for diagnosing myocardial ischemia, a state of blood supply-demand imbalance resulting from CAD. Physical exercise or pharmacologic agents raise the heart’s oxygen demand, failure to meet which sets in myocardial ischemia leading to left ventricular (LV) dysfunction. Stress echocardiography (echo) and stress single photon emission computed tomography (SPECT), which provide complementary anatomical and perfusion information about the heart, remain the two most commonly prescribed cardiac stress testing procedures. Stress echo manifests the LV dysfunction as abnormal myocardial wall motion and thickening, whereas stress SPECT shows the myocardial perfusion defects. However, these two procedures remain limited in their sensitivity and specificity. This dissertation utilizes real-time three-dimensional (RT-3D) ultrasound - an emerging innovation in ultrasound imaging, and focuses on development of automatic image analysis techniques that will increase the diagnostic accuracy of cardiac stress testing using echo and SPECT. The first hypothesis in this dissertation is that a truly quantitative 3D stress echo procedure, that utilizes advanced quantitative image analysis techniques together with RT-3D ultrasound, is capable of overcoming many of the limitations of conventional stress echo and therefore improving diagnostic accuracy. The current research involves the development of a novel interactive and quantitative stress echo software that combines, for the first time, fully automatic tools for accurate pre-/post-stress image alignment, LV myocardial segmentation and quantification of global and regional left ventricular (LV) function for RT-3D echo images. The second hypothesis states that simultaneous improvement in sensitivity and specificity for detecting CAD can be achieved with a multimodality stress testing approach, wherein diagnosis is based on accurately correlated (temporally and spatially) complementary functional and perfusion information available from RT-3D echo and SPECT, respectively. This dissertation includes development of techniques, including a novel elastic image registration algorithm and quantitative analysis techniques, for automatic multimodality analysis of RT-3D echo and SPECT images. Preliminary validation studies have been described that evaluate the feasibility and effectiveness of the novel procedures for diagnosis of CAD.

Committee:

Cynthia Roberts (Advisor)

Subjects:

Engineering, Biomedical

Keywords:

stress echocardiography; myocardial perfusion SPECT; real-time 3D echocardiography; coronary artery disease; elastic image registration; segmentation; interactive image analysis; quantitative image analysis

Singh, ShantanuQuantitative Phenotyping in Tissue Microenvironments
Doctor of Philosophy, The Ohio State University, 2011, Computer Science and Engineering

In the post-genomic era, there is a growing need for new experimental paradigms for investigating the links between genomics and biology. While entire genome sequences of most model systems are now available, the task of deciphering the genetic code requires characterizing the phenome of these systems in order to establish the genotype-phenotype links. This need has lead to the development of new quantitative phenotyping technologies across different levels of the biological hierarchy.

In this thesis, I present new computational techniques to conduct image-driven in vivo phenotyping at the cellular level. The techniques have been developed in the context of investigating morphological variations of cells in cancer. Recent findings in cancer biology have provided increasing evidence that the normal cells and molecules that surround tumor cells - collectively termed the tumor microenvironment - are involved in the initiation, growth, and spread of tumors. While examples of this phenomenon have been characterized in studies from a genetic standpoint, the lack of appropriate methodologies have precluded quantitative phenotyping studies at the cellular level. The present work addresses this unmet need.

Based on a novel method that uses local metric-learning to integrate different cellular features, I present a framework to identify major cell types in the microenvironment. I further propose a method to generate phenotypic profiles of cell populations and use the technique to detect the subtle global-level changes that occur among certain cells in the microenvironment in gene knock-out experiments that seek to recapitulate human breast cancer. For supporting the larger scope of investigations into the microenvironment, tools for image analysis, visualization, data management and data analysis have been developed.

By proposing new computational methods for cellular-level analysis, and using them to investigate the tumor microenvironment, I demonstrate that image-driven computational phenotyping provides a viable experimental paradigm to investigate the phenomic aspects of complex processes such as cancer.

Committee:

Raghu Machiraju, PhD (Advisor); Jens Rittscher, PhD (Committee Member); Kun Huang, PhD (Committee Member); Gustavo Leone, PhD (Committee Member); Han-Wei Shen, PhD (Committee Member)

Subjects:

Biomedical Research; Computer Science

Keywords:

biomedical image analysis; microscopy; machine-learning; cancer

Kukatla, Harish C.A Study of Strain Elastography Under a Normal Tensile Testing Condition
Master of Computing and Information Systems, Youngstown State University, 2010, Department of Computer Science and Information Systems
Optical elastography is a new imaging modality that is capable of estimating mechanical properties of a deformed object. However, the current method only provides a relative strain distribution map that may not be adequate for certain medical applications. In this investigation, biomechanical tensile testing is used to calibrate the optical elastogram to estimate the absolute Young’s modulus values. This study has two features: (i) Both treated and controlled samples were examined; (ii) Optical flow and strain algorithms were used to compute motion and strain images that characterize tissue property changes during all stages of deformation (liner, plastic and post – rupture). The findings of this study demonstrate that a calibrated optical elastography is a very promising technique to quantify tissue properties in vivo.

Committee:

Yong Zhang, PhD (Advisor); John Sullins, PhD (Committee Member); Graciela Perera, PhD (Committee Member)

Subjects:

Biomedical Research; Computer Science; Materials Science; Mechanical Engineering

Keywords:

bio-mechanical analysis of a soft tissue; optical elastography; image analysis of a soft tissue; study of elastography

Mosaliganti, Kishore RaoMicroscopy Image Analysis Algorithms for Biological Microstructure Characterization
Doctor of Philosophy, The Ohio State University, 2008, Computer and Information Science

Researchers in the medical domain employ microscopy-based image analysis as a tool to make objective, large-scale and verifiable quantitative measurements. The synergistic combination of knowledge on disease pathology with morphological context provided by image analysis leads to a quicker process of discovery. Advancements in medical imaging technologies in combination with the discovery of specific cellular markers have generated datasets that capture very detailed spatial and temporal features. Hence, the onus is on computer science researchers to develop algorithms to process biological imagery and extract relevant information efficiently. Algorithms need to be cognizant of the natural pattern and arrangement unique to biological organization. In this context, this dissertation proposes algorithms for biological microstructure characterization, namely, cell/tissue segmentation and 3D reconstructions of cellular structures.

At a microscopic resolution, biological structures are composed of cells, red blood corpuscles (RBCs), cytoplasm and other microstructural components. These components are re-arranged in a salient tissue to form unique distributions. Microstructure characterization involves the discovery of feature spaces that estimate and spatially delineate component distributions, wherein the tissue layers naturally appear as salient clusters. The clusters are then be suitably classified to provide tissue region segmentations. However, a comprehensive characterization still requires cellular-level descriptions of the microenvironment in 3D.

Early efforts in 3D reconstruction of microscopic biological structures have been impeded by the lack of a rigorous cellular segmentation approach. The primary difficulty in this task is that most of nuclei cluster in regions and seemingly overlap. An automated cell segmentation algorithm is presented that incorporates shape models, geodesic image metrics and image tessellations based on gradient cues in splitting overlapping nuclei. The results of the cellular segmentation step are used in conjunction with a cell shape model to interpolate the 3D cellular locations and shapes onto adjacent slices thereby reconstructing cellular structures.

In this dissertation, data from optical modalities including light, confocal and phase-contrast microscopy are employed to test individual algorithms. Validated results from phenotyping and drug-discovery applications are used to demonstrate the robust performance of these methods.

Committee:

Raghu Machiraju, PhD (Advisor); Kun Huang, PhD (Advisor); Gustavo Leone, PhD (Committee Member); Leona Ayers, PhD (Committee Member)

Subjects:

Computer Science; Electrical Engineering; Molecular Biology

Keywords:

image analysis; phenotyping; 3D anatomic reconstruction; segmentation

Wang, ZhaoIntravascular Optical Coherence Tomography Image Analysis
Doctor of Philosophy, Case Western Reserve University, 2013, Biomedical Engineering
Coronary artery disease (CAD) is the leading cause of death in the world. Most acute coronary events such as heart attacks and sudden deaths are due to the rupture of atherosclerotic plaques inside the arteries. Intravascular Optical Coherence Tomography (IVOCT), a high resolution (10-20µm) imaging modality that performs cross-sectional imaging of coronary arteries by measuring echoes of backscattered light, is rapidly becoming a promising imaging modality for diagnosis of CAD. Compared to alternative technologies such as intravascular ultrasound (IVUS), IVOCT with better resolution allows characterization of atherosclerotic plaques and evaluation of coronary stenting with unprecendented details. Currently, analysis of OCT images has been typically conducted manually in an extremely time-consuming manner. The aim of this PhD dissertation is to develop advanced image processing algorithms and software to automate the task and thereby reduce the image analysis time drastically. In this dissertation, we developed image analysis algorithms for a variety of IVOCT applications, including: (1) 3-D lumen boundary segmentation, (2) Guide wire artifact segmentation, (3) Automated calibration of IVOCT images, (4) Volumetric quantification of fibrous caps, (5) Automated segmentation of calcified plaques, (6) Automated quantification of macrophages and (7) Automated stent analysis, covering almost all the essential tasks performed in the Cardiovascular Imaging Core Laboratories (Core Lab). The algorithms we have developed have been extensively validated using a large number of data sets, and are robust to be used for real world clinical data analysis. Furthermore, we developed the prototype software OCTivat (intravascular OCT image visualization and analysis toolkit) for IVOCT image visualization and analysis, and it is being used by the interventional cardiologists in the Core Lab at the University Hospitals Case Medical Center. These image analysis methods, as well as OCTivat, can reduce the IVOCT image analysis time from tens of hours down to minutes. This may enable large clinical trial analysis and real-time feedback during clinical procedures, and may potentially improve patient care.

Committee:

Andrew Rollins (Advisor); David Wilson (Committee Member); Marco Costa (Committee Member); Guoqiang(GQ) Zhang (Committee Member)

Subjects:

Biomedical Engineering; Information Technology; Optics

Keywords:

coronary artery disease; optical coherence tomography; image analysis; image processing; atherosclerotic plaques; stents

Nakamura, KunioMRI Analysis to Detect Gray Matter Tissue Loss in Multiple Sclerosis
Doctor of Philosophy, Case Western Reserve University, 2011, Biomedical Engineering
Multiple sclerosis (MS) has been traditionally characterized by primary demyelination and inflammation in white matter (WM). However, recent histopathologic studies have shown that gray matter (GM) of MS patients is also abnormal. My aim was to develop methods for quantifying GM damage in terms of GM atrophy and cortical atrophy, to investigate the evolution in various MS disease stages, and to assess relevance to clinical status. First, I developed an automated algorithm that segmented GM and WM in magnetic resonance images (MRI) and measured the normalized GM volume. The algorithm was designed to be applicable to MRI of MS patients, which had focal lesions and significant atrophy. The algorithm was validated and applied in a longitudinal study that included patients with clinically isolated syndrome (CIS), relapsing-remitting (RRMS) and secondary progressive (SPMS) MS as well as healthy normal controls. Other conventional MRI markers of focal damage and clinical measures were available to explore the correlations and predictors of GM atrophy. We found that (1) the rate of GM atrophy increased in a stage-dependent manner, which was similar to that of whole brain atrophy, (2) GM atrophy had moderately strong correlations with the disability measures, and (3) predictors of GM atrophy changed from RRMS to SPMS. Next, I developed a registration and deformable model-based longitudinal method (CLADA, Cortical Longitudinal Atrophy Detection Algorithm) that had high reproducibility and could measure global and regional cortical atrophy in terms of cortical thickness and its change. CLADA was validated and applied to the full longitudinal MRI dataset to explore the evolution of cortical thinning, its clinical correlations, and predictors. The rate of cortical thinning increased with advancing disease, correlated with clinical disability, and distinguished stable and worsening patients with more significance than GM atrophy. In summary, my research showed that GM and cortical atrophy could be measured reliably with the new techniques. Furthermore, application of these methods in MS patients demonstrated that GM and cortical atrophy measurements were (1) relevant both clinically and biologically (2) able to provide insights on MS pathogenesis; and (3) suitable for future clinical trials of potential MS therapies.

Committee:

Andrew Rollins, PhD (Committee Chair); Elizabeth Fisher, PhD (Advisor); Bruce Trapp, PhD (Committee Member); David Wilson, PhD (Committee Member)

Subjects:

Biomedical Engineering

Keywords:

Multiple sclerosis; MRI; image analysis; gray matter; cortex; atrophy;

Buenrostro-Nava, Marco T.Characterization of GFP Gene Expression Using an Automated Image Collection System and Image Analysis
Doctor of Philosophy, The Ohio State University, 2002, Horticulture and Crop Science

Automated systems can be used to facilitate continual collection of biological information from a large number of samples over long periods of time. The use of an automated system and image analysis would allow semi-continual monitoring and non-invasive quantification of the green fluorescent protein (GFP) expression and would therefore provide a better assessment of the levels of gfp gene expression than monitoring GFP at large time intervals. The main aim of this research was to monitor and quantify the expression of the gfp gene from the jellyfish (Aequorea victoria) and in vitro plant growth over time using an automated image acquisition system in combination with image analysis.

The system, developed over the course of this work, consisted of a computer controlled two-dimensional positioning table and a charged-coupled device (CCD) camera mounted on a stereomicroscope equipped with a GFP fluorescence detection system. The image collection system was placed in a horizontal laminar air flow hood to provide an aseptic environment for monitoring in vitro cultures.

In order to compare the pattern of expression of a soluble and an endoplasmic reticulum-targeted gfp gene, images of lima bean (Phaseolus lunatus L.) cotyledons transiently expressing the two different gfp genes, were collected every 30 min for 38 h. Time-lapse animations together with quantification of transient GFP expression using image analysis, showed that expression of the cytoplasmic soluble gfp gene was detected as early as 4 h after bombardment and reached a maximum at 24 h after bombardment. Expression of the endoplasmatic reticulum-targeted gfp gene was first observed 8 h after bombardment and reached its maximum expression after 24 h.

The pattern of GFP expression, driven by the soybean lectin and 35S promoters, was monitored every 12 h for 28 d during somatic embryo development using the automated image collection system. Gene expression was then quantified using image analysis. Quantitative analysis revealed that, even though the lectin: gfp construction showed low levels of expression during early stages of development, expression levels eventually reached levels similar to those recorded from the 35S: gfp construction. Embryos with gfp under the regulatory control of the lectin promoter showed a peak of expression 47 days after embryo development, while GFP expression driven by the 35S promoter gradually increased throughout embryo development. Time-lapse animations were useful in characterization of gfp expression, and revealed a high variability in levels of gfp expression driven by the 35S promoter.

Southern analysis showed the presence of multiple copies of the introduced plasmids for clones generated using particle bombardment. The copy number of clones containing the lectin: gfp construction, was not correlated with levels of gfp expression; however, a high copy number may have led to reduced levels of GFP expression of a clone containing the 35S: gfp construction.

Committee:

John Finer (Advisor)

Subjects:

Biology, Molecular

Keywords:

Plant transformation; Analysis of gene expression; Image analysis; Robotics; Green fluorescent protein; Lima beans; Soybean; Wheat; Arabidopsis

Sertel, OlcayImage Analysis for Computer-aided Histopathology
Doctor of Philosophy, The Ohio State University, 2010, Electrical and Computer Engineering

The recent developments in whole-slide digital scanners have spurred a revolution in imaging technology for histopathology. While these commercially available, high-throughput whole-slide scanners address data acquisition issues, the amount of data provided by them currently far exceeds the rate at which they can be analyzed efficiently. More importantly, the qualitative microscopic visual inspection of tissue slides by human readers (e.g., pathologists) is often subject to significant inter- and intra-reader variations. Using computerized image analysis, it is possible to extract more objective and precise quantitative diagnostic clues that will help improving the current evaluation of histopathological data.

The main goal of this dissertation is to understand and address the challenges associated with the development of image analysis techniques for the computer-aided interpretation of high-resolution histopathology imagery. We aim to design algorithms for key image analysis tasks such as robust and adaptive segmentation of cytological components for higher level processing, construction of biologically relevant and computationally tractable features and their mathematical representations in order to differentiate distinct tissue subtypes, detection of prognostically significant tissue structures, and spatial alignment of tissue sections prepared with different stains in order to incorporate complementary information.

We demonstrate the effectiveness of the proposed approaches on three important histopathology applications: analysis of whole-slide tissue sections for neuroblastoma prognosis, automated grading of follicular lymphoma and quantitative characterization of muscle fiber subtypes from serial transverse skeletal muscle tissue samples.

For computer-aided analysis of whole-slide neuroblastoma tissue sections, we develop a comprehensive, multi-resolution image analysis framework including the establishment of multi-resolution image hierarchy, image segmentation, feature construction and representation, feature extraction, classification and classification evaluation. Within the computer-aided follicular lymphoma grading work, we present a novel cell segmentation approach from the hematoxylin and eosin stained histopathology images with potential applications to other disease domains. We also present a model-based intermediate representation, which models the spatial distribution of cytological components and provides a rich set of features to classify image regions associated with distinct follicular lymphoma grades. In addition, we demonstrate a novel color texture analysis approach based on the non-linear color quantization using self-organizing maps. This approach may also be applicable to the analysis of other natural images with limited color spectrum (e.g., satellite imagery). Finally, we present a complete image analysis workflow including the segmentation of individual muscle fibers, the registration of successive tissue sections with different ATPase activity, and the classification of muscle fiber subtypes to quantitatively characterize the skeletal muscle histology. Each problem gives us an opportunity to explore different challenges associated with histopathological image analysis and propose novel solutions. Overall, proposed systems yield promising results and may provide new ways of characterizing and analyzing histopathology images.

Committee:

Umit V. Catalyurek, PhD (Advisor); Metin N. Gurcan, PhD (Committee Member); Bradley D. Clymer, PhD (Committee Member); Ashok Krishnamurthy, PhD (Committee Member)

Subjects:

Bioinformatics; Computer Science; Electrical Engineering; Pathology

Keywords:

histopathology; image analysis; image segmentation; feature extraction; image registration; classification

Walsh, Colin T.Molecular pharmacodynamics of chemotherapy: fibroblast growth factor (FGF) inhibitors as chemosensitizers
Doctor of Philosophy, The Ohio State University, 2005, Pharmacy
Au and Wientjes et al. recently reported that acidic (aFGF) and basic (bFGF) fibroblast growth factors confer a broad spectrum chemoresistance in solid tumors, and that suramin, a non-specific FGF inhibitor, enhanced the in vitro anti-tumor activity of several anticancer drugs. Based on this initial finding, the studies proposed in this dissertation are focused on improving the understanding of the mechanisms of the FGF-induced resistance and the molecular pharmacodynamics of suramin. Studies in Chapter 2 show suramin can enhance the therapeutic efficacy of chemotherapy in lung cancer, thus establishing the in vivo efficacy of low dose suramin. Studies in Chapter 3 show bFGF is a clinically significant predictor of chemotherapy and suramin effect. Many literature reports show suramin as having anti-angiogenic properties, therefore the possibly of an anti-angiogenic mechanism for suramin was investigated in both in vitro (Chapter 4) and in vivo (Chapter 5) models. Results from Chapter 4 show low doses of suramin alone had no effect compared to control and suramin in combination with chemotherapy had no additional effects as compared to chemotherapy alone in a monolayer endothelial cell model. This finding was extended to an in vitro tumor histoculture model and the results show the suramin effects were vasculature independent. Results from Chapter 5 show that suramin alone had no effect on the in vivo tumor vessel morphology, and suramin did not show any additional effects when combined with chemotherapy. The functionality of the tumor vessels was also tested and the results show that chemotherapy greatly increased the functionality of the tumor vasculature; however there were no additional effects from suramin. Collectively, these studies show suramin is capable of sensitizing tumors to chemotherapy, bFGF is a valid biomarker for chemotherapy and suramin sensitization effect, and that the in vivo mechanism of suramin chemosensitization is not due to anti-angiogenesis.

Committee:

Jessie Au (Advisor)

Subjects:

Health Sciences, Pharmacy

Keywords:

basic fibroblast growth factor; bFGF; image analysis; suramin; angiogenesis

Kang, Wei3-D Volumetric Optical Coherence Tomography Imaging and Image Analysis of Barrett's Esophagus
Doctor of Philosophy, Case Western Reserve University, 2011, Biomedical Engineering
Barrett's esophagus (BE) surveillance remains challenging, because even histopathology, the gold standard, is subject to sampling error. The esophageal mucosal area involved in BE can be 20 square centimeters or more. 3-D volumetric Imaging technology with high diagnostic accuracy may potentially guide and assist the standard histopathology, eliminate the sampling error and improve surveillance efficiency. It has been shown that endoscopic optical coherence tomography (EOCT) of a small mucosal area can obtain interpretable images of gastro-intestinal mucosal microstructure, differentiate mucosal types and detect dysplasia in Barrett’s esophagus. The realization of 3-D EOCT allows for further exploring the potential to fulfill the unmet need of comprehensive surveillance. The dissertation presents the step-by-step work from initially building the 3-D EOCT system for esophageal imaging, to eventually conducting the clinical trial. First, a system based on a spectral-domain OCT configuration will be described. The sample arm with the rotary-joint-pullback unit, double-balloon-based catheter and miniature fiber-optic probe is the main hardware innovation allowing for 3-D imaging. Second, an automated motion artifact correction algorithm will be described. The algorithm successfully reveals the otherwise distorted microstructure in the esophageal mucosa, such as microvasculature network and the layered structure. The feasibility of 3-D imaging and motion artifact correction algorithm will be demonstrated in swine in vivo. Third, the balloon designs will be discussed in terms of safety and diagnostic efficiency for clinical trial. It will be shown that a low pressure level is sufficient for motion artifact suppression, and therefore reduce the risk of perforation. Images appearance is significantly influenced by balloon pressure/contact, which establishes the need to image the mucosa with the double-balloon design. Finally, the first cases in clinical trial of BE patients will be reported. The feasibility of 3-D EOCT imaging system are demonstrated. Images features such as layered structure, surface morphology and glandular structure are observed in BE patients. The 3-D EOCT system provides a platform allowing for comprehensive imaging in high quality, which can potential answer critical questions about what role OCT can play in dysplasia diagnosis during BE screening/surveillance.

Committee:

Andrew Rollins (Committee Chair); Amitabh Chak (Committee Member); Kenneth Singer (Committee Member); David Wilson (Committee Member)

Subjects:

Biomedical Engineering

Keywords:

Optical coherence tomography; Barrett's esophagus; endoscopic imaging; image analysis; dysplasia diagnosis

Woo, JungwonBulk and Surface Characteristics of Model M1 and M2 Phase Catalysts for Propane Ammoxidation to Acrylonitrile
PhD, University of Cincinnati, 2015, Engineering and Applied Science: Chemical Engineering
Direct ammoxidation of propane to acrylonitrile (ACN) has received significant attention of the scientific community in recent decades because propane is cheaper, more abundant and environmentally friendlier than the current propylene feedstock. The MoVTeNbOx M1 phase catalysts display the highest propane conversion and ACN yield (~ 60 mol. %). However, the ACN yield over the M1 phase is insufficiently high in order to replace the current Sohio process, employing the propylene feedstock to produce ACN with a ~ 81 mol. % yield. Therefore, there is currently a global effort in understanding the atomic structure and catalytic behavior of these catalysts in order to design improved M1 phase catalysts for propane ammoxidation. Accordingly, this PhD thesis aimed to pursue the following objectives: 1) improve the High-angle annular dark field scanning transmission electron microscopy (HAADF-STEM) image analysis and provide accurate metal site occupancies in the M1 phase; 2) develop new probability models which correlate the catalytic performance with metal distributions of the M1 phase; 3) elucidate the nature of M1/M2 phase cooperation for all chemical compositions. The HAADF-STEM image simulations were performed in order to determine accurate metal distributions in the MoVTeTaO M1 phase catalyst for propane ammoxidation. QSTEM simulation software was chosen due to the excellent agreement between experimental and simulated HAADF-STEM images. The QSTEM-based HAADF-STEM image analysis method successfully provided accurate metal distributions in the MoVTeTaO M1 phase as compared to previously reported metal occupancies determined by the Z2-based HAADF-STEM image analysis. Three probability models (Model 1-3) were advanced and investigated in this thesis based on metal distributions determined by the QSTEM-based HAADF-STEM image analysis. The correlations between the Mo/V distributions in MoVTeTaO M1 phase catalysts and their catalytic behavior in propane ammoxidation were proposed for Model 1 (based on the probability of finding 1-2 V5+ cations in the proposed S3-S4-S4-S7-S7 catalytic center), Model 2 (based on the total V content in the S2-S4-S4-S7-S7 catalytic center), and Model 3 (based on the probability of finding more than 2 V cations in the S2-S4-S4-S7-S7 catalytic center). Model 1 suggested V5+ in S3 may activate propane. Model 2 and Model 3 emphasized the importance of total V content and multiple VOx sites in the S2-S4-S4-S7-S7 catalytic center for catalytic reactivity in propane ammoxidation, respectively. The kinetic study of the MoV(Te,Sb)(Nb,Ta)Ox M1 and M2 phases in propylene ammoxidation indicated that the M2 phases are less active than the corresponding M1 phases in propylene ammoxidation. The findings of this study do not support the existence of the synergy effect for any M1/M2 compositional variant. Instead, the observed behavior of MoV(Te,Sb)(Nb,Ta)O catalysts was consistent with partial loss of some surface active species from the M1 phase surface during the H2O2 treatment and generation of fresh ab planes of the M1 phase via mechanical grinding of the H2O2-treated M1 phase. These findings provided further evidence that the M1 phase is the only phase required for the activity and selectivity of the MoV(Te,Sb)(Nb,Ta)O catalysts in propane ammoxidation to ACN.

Committee:

Vadim Guliants, Ph.D. (Committee Chair); Qian He, Ph.D. (Committee Member); Ye Xu, Ph.D. (Committee Member); Anastasios Angelopoulos, Ph.D. (Committee Member); Peter Panagiotis Smirniotis, Ph.D. (Committee Member)

Subjects:

Chemical Engineering

Keywords:

Heterogeneous catalysis;Mixed metal oxide catalysts;M1 and M2 phases;HAADF-STEM image;Quantitative HAADF-STEM image analysis;Porpane ammoxidation

Acosta, Jesus-AdolfoPavement surface distress evaluation using video image analysis
Doctor of Philosophy, Case Western Reserve University, 1994, Civil Engineering
Maintenance and repair of the highway network accounts for one of the major expenses in the federal and state budget. Pavement Management Systems (PMS) have been implemented by Departments of Transportation and other transportation agencies to optimize the allocation of these funds. One of the most important inputs to a PMS is the pavement surface evaluation. Rating systems where pavement distress is measured by type, extent and severity, have been used extensively in order to quantify pavement surface condition. In most instances, these systems are both tedious and time consuming. Distress measurement is also subjective, which affects the precision of the rating. Identification and quantification of distress types are possible by automatic analysis of images captured by a microcomputer from video or film recordings. The present research describes the implementation of the PCR-Video System, which allows the identification and classification of most common pavement distress types. Depth and distance measurement devices were installed in a survey vehicle and connected to an on board microcomputer to determine the distance traveled and to allow the identification and quantification of depth related distress types. A bar color code method was developed to inscribe distance and depth readings onto the video S-VHS tape player, an image capturing board and a workstation was assembled. A set of images is digitized by the image capturing board and stored in main memory to remove overlapping areas present in consecutive frames. The Vertical & Horizontal Region Segmentation method was developed to eliminate the drawbacks found in conventional image segmentation approaches. A logic-based classification approach was also developed for cluster classification. The system when combined with a rating procedure, such as the PCR produces a quantitative measurement of pavement condition. Finally, the pavement inventory data file can be updated with the new pavement ratings. The system was validated by rating four roadway sections, previously inspected manually. The automated results showed very good correlation with the visually obtained ratings.

Committee:

J. Figueroa (Advisor)

Subjects:

Engineering, Civil

Keywords:

Pavement surface; distress evaluation; video image analysis

Ausdenmoore, Benjamin D.Synaptic contact localization in three dimensional space using a Center Distance Algorithm
Master of Science (MS), Wright State University, 2011, Physiology and Neuroscience
Spatial distribution of synaptic inputs on the dendritic tree of a neuron can have significant influence on neuronal function. Consequently, accurate anatomical reconstructions of neuron morphology and synaptic localization are critical when modeling and predicting physiological responses of individual neurons. Historically, generation of three-dimensional (3D) neuronal reconstructions together with comprehensive mapping of synaptic inputs has been an extensive task requiring manual identification of putative synaptic contacts directly from tissue samples or digital images. Recent developments in neuronal tracing software applications have improved the speed and accuracy of 3D reconstructions, but localization of synaptic sites through the use of pre- and/or post-synaptic markers has remained largely a manual process. To address this, we developed an algorithm, based on 3D distance measurements between putative pre-synaptic terminals and the post-synaptic dendrite. The algorithm is implemented with custom Matlab routines, and its effectiveness evaluated through analysis of primary sensory afferent terminals on motor neurons.

Committee:

David Ladle, PhD (Committee Chair); Mark Rich, MD,PhD (Committee Member); Christopher Wyatt, PhD (Committee Member)

Subjects:

Anatomy and Physiology; Biology; Biomedical Engineering; Biomedical Research; Computer Science; Medical Imaging; Neurobiology; Neurology; Neurosciences; Physiology; Scientific Imaging

Keywords:

Confocal microscopy; Synapse localization; Dendrite; Tracing; Image analysis; Matlab; Skeleton

Villarruel, Sandra MelissaTHE EFFECT OF OXYGEN TENSION ON THE BIOLOGICAL RESPONSE OF THE HUMAN BONE MARROW DERIVED OSTEOGENIC CONNECTIVE TISSUE PROGENITOR CELL
Doctor of Philosophy, Case Western Reserve University, 2008, Biomedical Engineering
The connective tissue progenitor cell (CTP) is defined as the heterogeneous population of stem cells and progenitor cells found in native tissue that are capable of differentiating into one or more connective tissue phenotype. Tissue engineering strategies for successful bone regeneration are more effective when they enhance the concentration or prevalence of CTPs in the local tissue site or facilitating the attachment, migration, proliferation, differentiation and survival of osteogenic CTPs within the graft site. One of the principal modulators and limiting factors in bone tissue regeneration is oxygen tension. Prior work in measurement and modeling of oxygen tension within fracture sites demonstrates a profound variation of oxygen tension within the wound environment. In vitro work to date using primary CTPs and culture expanded CTP progeny under osteogenic conditions suggest oxygen tension affects every stage of CTPs and their progeny. However, these studies have been flawed by two significant limitations: 1) failure to adequately control, measure and model oxygen tension in in vitro systems and 2) reliance on imprecise manual methods for colony identification and analysis due to a wide variation within and between observers and which fail to extract a full spectrum of information related to biological potential, diversity and response of CTPs and their progeny under conditions of varying oxygen tension. This work seeks to inform and advance the field of musculoskeletal stem cell and progenitor cell biology by a) exploring methods for improved control over oxygen tension in vitro and their effects on the outcome of CTP colony formation and in vitro performance, and b) contributing to the refinement of an automated image analysis system (ColonizeTM) for the colony forming unit (CFU) assay and multidimensional performance assessment in the domains of colony forming unit efficiency, attachment, proliferation, migration, differentiation and survival. In particular, this work adds the capability of utilizing nuclear morphology and distribution within and between colonies as a tool for characterization of heterogeneity and biological response to oxygen tension and other parameters.

Committee:

Steven Eppell, PhD (Committee Chair); George Muschler, MD (Advisor); Roger Marchant, PhD (Committee Member); James Dennis, PhD (Committee Member); Linda Griffith, PhD (Committee Member)

Subjects:

Biomedical Research; Engineering

Keywords:

connective tissue progenitor; oxygen tension; osteogenic; nuclei image analysis

Samsi, Siddharth SadanandComputer Aided Analysis of IHC and H&E Stained Histopathological Images in Lymphoma and Lupus
Doctor of Philosophy, The Ohio State University, 2012, Electrical and Computer Engineering

The use of computers in medical image analysis has seen tremendous growth following the development of imaging technologies that can capture image data in-vivo as well as ex-vivo. While the field of radiology has adopted computer aided image analysis in research as well as clinical settings, the use of similar techniques in histopathology is still in a nascent stage. The current gold standard in diagnosis involves labor-intensive tasks such as cell counting and quantification for disease diagnosis and characterization. This process can be subjective and affected by human factors suach as reader bias and fatigue. Computer based tools such as digital image analysis have the potential to help alleviate some of these problems while also offering insights that may not be readily apparent when viewing glass slides under an optical microscope. Commercially available high-resolution slide scanners now make it possible to obtain images of whole slides scanned at 40x microscope resolution. Additionally, advanced tools for scanning tissue images at 100x resolution are also available. Such scanning tools have led to a large amount of research focused on the development of image analysis techniques for histopathological images. While the availability of high-resolution image data presents innumerable research opportunities, it also leads to several challenges that must be addressed.

This dissertation explores some of the challenges associated with computer-aided analysis of histopathological images. Specifically, we develop a number of tools for Follicular Lymphoma and Lupus. We aim to develop algorithms for detection of salient features in tissue biopsies of follicular lymphoma tumors. We analyze the algorithms from a computational point of view and develop techniques for processing whole slide images efficiently using high performance computing resources. In the application of image analysis for Lupus, we analyze mouse renal biopsies for characterizing the distribution of infiltrates in tissue as well as develop algorithms for identification of tissue components such as the glomeruli, which play a significant role in the diagnosis of the disease. Finally, we explore the development of a web-based system for dissemination of high-resolution images of tissues with the goal of advancing collaboration, research and teaching. Through the use of web technologies and developments in the field of geospatial imaging, we demonstrate the efficacy of an online tissue repository that can enable pathologists, medical students and all researchers to explore these images as well as use high performance computing systems to leverage computer-aided diagnosis tools in their field.

Committee:

Ashok Krishnamurthy, PhD (Advisor); Bradley Clymer, PhD (Committee Member); Kimerly Powell, PhD (Committee Member)

Subjects:

Electrical Engineering; Information Systems; Medical Imaging

Keywords:

Histopathology; Renal image analysis; Lymphoma; IHC; H&38;E;virtual microscopy;high performance computing; HPC

Prescott, Jeffrey WilliamComputer-assisted discovery and characterization of imaging biomarkers for disease diagnosis and treatment planning
Doctor of Philosophy, The Ohio State University, 2010, Biomedical Engineering
The rapid growth of diagnostic medical imaging studies has led to enormous strides in the effective diagnosis and treatment of a myriad of diseases, from chronic diseases to life threatening cancers. The rise of imaging as a major factor in medical decision making has directly led to a drive towards quantification of image findings, in order to augment the qualitative analysis of trained medical professionals, such as radiologists. The overarching goal of this dissertation is to explore, develop, and evaluate imaging biomarkers for both chronic and life threatening diseases. Towards this goal, this dissertation has the following two aims: 1. Discover and characterize imaging biomarkers for diseases which may have either acute/sub-acute presentation or treatment, or diseases which may have a more chronic course and treatment intervention. The former analysis is focused on the case of cervical cancer, and the latter is focused on osteoarthritis (OA). 2. Apply digital image processing methods to the acquired images which leverage the spatial information in the images to characterize the anatomy, physiology, and pathophysiology of a disease process. These aims were designed to improve the characterization of human disease by extracting quantifiable information from the acquired medical images. For the case of cervical cancer, analyses were undertaken of higher order statistics and texture measures as features to be used in the classification of cervical cancer tumor volumes in dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) as either likely to recur or be controlled after treatment. The contributions of these analyses are: 1. A physiologically meaningful measure of tumor vascularity by using the wavelet decomposition to quantify texture, which predicts the outcome of radiation treatment at two-year post-treatment follow-up with 100% accuracy, based on early treatment DCE-MRI studies of the tumor. 2. The demonstration of change in texture measures as more important than change in tumor volume for accurate treatment outcome prediction. For the case of OA, analyses were undertaken of the quadriceps muscle morphology (cross-sectional area (CSA)) and content (intramuscular adipose tissue (IAT)), femur morphology, and meniscus morphology (volume) on MRI as potential biomarkers for OA severity. The analyses of the quadriceps muscle and the femur were further pursued through the development of semi-automated and automated segmentation procedures. The findings and contributions of these analyses are: 1. The association of the CSA of a particular muscle in the quadriceps, the vastus intermedius, with a reduced risk of more severe radiographic OA. 2. The finding that quadriceps lean muscle CSA (anatomical muscle CSA minus IAT) is associated with sex, age, and body mass index (BMI), which are risk factors for OA. 3. The volume of the lateral meniscus has multiple significant associations with the volume of the tibiofemoral articular cartilage in subjects with OA. 4. The development of an image enhancement procedure which standardizes the intensities in MRI images between all subjects and allows for efficient and accurate structure segmentation and imaging biomarker calculation. 5. The development of a semi-automated segmentation algorithm for the individual quadriceps muscles, using atlases and level set contour evolution. 6. A comparison of atlas segmentation procedures, demonstrating that the use of multiple atlases is superior to the use of “representative” atlases selected by a trained human reader. The use of medical imaging allows for the non-invasive localization and characterization of disease, which is especially important for diseases which have significant mortality (such as cervical cancer) or long-term morbidity (such as OA). By studying diseases at these two ends of the spectrum, a unique understanding of the imaging biomarker development and evaluation process can be gained. In addition, the application of digital image processing methods can improve the characterization of the imaging manifestations of disease by providing consistent, accurate, and complicated quantification techniques, which may be difficult, or impossible, for human readers to provide.

Committee:

Metin Gurcan, PhD (Advisor); Bradley Clymer, PhD (Advisor); Thomas Best, MD, PhD (Advisor)

Subjects:

Bioinformatics; Biomedical Research; Electrical Engineering

Keywords:

Imaging biomarkers; Image processing; Image analysis; Cervical cancer; Osteoarthritis

Margolis, Julie AnnaTetracycline Labeled Bone Content Analysis of Ancient Nubian Remains from Kulubnarti
Master of Arts, The Ohio State University, 2015, Anthropology
Armelagos and colleagues (2001) have hypothesized that beer is a conduit for in vivo tetracycline consumption by ancient Nubians. Streptomycetes bacteria has a high prevalence in Sudanese-Nubian soil (60 -70%) and secretes the antibiotic under harsh conditions such as fermentation. At the site of Kulubnarti, 21-S-46 cemetery (716 CE) skeletons likely represent a working underclass contemporaneous with the 21-R-2 cemetery (752 CE) containing the remains of a land-owning class. Interpretations of archaeological and osteological evidence suggest that poorer health and higher mortality occurred in the S population. To test whether an anticipated difference in tetracycline ingestion between S and R cemetery populations existed, the amount of tetracycline-labeled bone was quantified under ultra violet light using image analysis software. Amount of tetracycline labeling was expressed in terms of the total area of labeled bone tissue in square micrometers, number of labeled osteons, and number of grid intersections over labeled bone. No significant differences in percent tetracycline-labeled bone tissue, or percent labeled osteons was observed between cemeteries. These results suggest that tetracycline ingestion was similar for S and R group members, class differences were not mediating tetracycline ingestion, and both sub-groups had equal access to beer.

Committee:

Clark Larsen, Dr. (Committee Co-Chair); Sam Stout, Dr. (Committee Co-Chair); Douglas Crews, Dr. (Committee Member)

Subjects:

African History; Anatomy and Physiology; Ancient Civilizations; Ancient History; Archaeology; Biochemistry; Biology; Biomedical Research; Cellular Biology; Epidemiology; Health; Histology; History; Human Remains; Medical Imaging; Medieval History; Microbiology; Molecular Biology; North African Studies; Nutrition; Pharmacology; Physical Anthropology; Social Structure; World History

Keywords:

tetracycline; Nubia; antibiotics; bioarchaeology; bone histology; Kulubnarti; paleopathology; class differences; Ancient Nubia; Nubian; Ancient Nubian; skeletal remains; bone; skeletal biology; image analysis

Johansen, Richard A.An Automated Approach to Agricultural Tile Drain Detection and Extraction Utilizing High Resolution Aerial Imagery and Object-Based Image Analysis
Master of Arts, University of Toledo, 2015, Geography
Subsurface drainage from agricultural fields in the Maumee River watershed is suspected to adversely impact the water quality and contribute to the formation of harmful algal blooms (HABs) in Lake Erie. In early August of 2014, a HAB developed in the western Lake Erie Basin that resulted in over 400,000 people being unable to drink their tap water due to the presence of a toxin from the bloom. HAB development in Lake Erie is aided by excess nutrients from agricultural fields, which are transported through subsurface tile and enter the watershed. Compounding the issue within the Maumee watershed, the trend within the watershed has been to increase the installation of tile drains in both total extent and density. Due to the immense area of drained fields, there is a need to establish an accurate and effective technique to monitor subsurface farmland tile installations and their associated impacts. This thesis aimed at developing an automated method in order to identify subsurface tile locations from high resolution aerial imagery by applying an object-based image analysis (OBIA) approach utilizing eCognition. This process was accomplished through a set of algorithms and image filters, which segment and classify image objects by their spectral and geometric characteristics. The algorithms utilized were based on the relative location iv of image objects and pixels, in order to maximize the robustness and transferability of the final rule-set. These algorithms were coupled with convolution and histogram image filters to generate results for a 10km² study area located within Clay Township in Ottawa County, Ohio. The eCognition results were compared to previously collected tile locations from an associated project that applied heads-up digitizing of aerial photography to map field tile. The heads-up digitized locations were used as a baseline for the accuracy assessment. The accuracy assessment generated a range of agreement values from 67.20% - 71.20%, and an average agreement of 69.76%. The confusion matrices calculated a range of kappa values from 0.273 - 0.416 with an overall K value of 0.382, considered fair in strength of agreement. This thesis provides a step forward in the ability to automatically identify and extract tile drains, and will assist future research in subsurface agricultural drainage modeling.

Committee:

Kevin Czajkowski (Committee Chair); Patrick Lawrence (Committee Member); Dan Hammel (Committee Member); April Ames (Committee Member)

Subjects:

Agriculture; Geography; Remote Sensing

Keywords:

Object-Based Image Analysis, eCognition, Subsurface Agricultural Drainage, Harmful Algal Blooms

Prasanna, PrateekNOVEL RADIOMICS FOR SPATIALLY INTERROGATING TUMOR HABITAT: APPLICATIONS IN PREDICTING TREATMENT RESPONSE AND SURVIVAL IN BRAIN TUMORS
Doctor of Philosophy, Case Western Reserve University, 2017, Biomedical Engineering
Cancer is not a bounded, self-organized system. Most malignant tumors have heterogeneous growth, leading to disorderly proliferation well beyond the surgical margins. In fact, the impact of certain tumors is observed not just within the visible tumor, but also in the immediate peritumoral, as well as in seemingly normal-appearing adjacent field. Visual inspection is often not a reliable instrument in cancer diagnosis, providing only qualitative analysis of an image, thereby missing subtle disease signatures. These, and other imaging limitations can lead to unnecessary surgical interventions. Computerized image analysis has shown promise in comprehending disease heterogeneity through quantification and detection of sub-visual patterns. In this work, we present novel radiomic tools to identify subtle radiologic cues (radiomic descriptors) and address clinical challenges in cancer diagnosis, prognosis, and treatment-evaluation. The developed tools and techniques are modality- and domain-agnostic. They can be applied in a pan-cancer setting to mine information from radiographic images and discover associations with underlying molecular (radio-genomics) or histological (radio-pathomics) characteristics to provide a holistic characterization of disease. We have demonstrated their efficacy in addressing problems in prognosis and treatment management of brain tumors. The challenges we target specifically include (1) inability to estimate survival at a pre-treatment stage and (2) inability to avoid highly-invasive surgeries in patients with radiation-induced treatment changes that mimic tumor recurrence. Underlying heterogeneity is linked to poor prognosis and tumor recurrence. Cellular level differences associated with the distinct physiological pathways might also manifest at the radiographic (i.e. MRI) length scale. We present two radiomic descriptors, Co-occurrence of Local Anisotropic Gradient Orientations (CoLlAGe) and radiographic-Deformation and Textural Heterogeneity (r-DepTH), which attempt to capture voxel-level textural and structural heterogeneity associated with brain tumors on MRI. These radiomic features are extracted not only from the solid tumor regions, but also from the adjacent tumor habitat and the healthy parenchyma. Subsequently, they are used in a machine learning setting to predict survival on treatment-naive imaging and characterize radiation-induced effects on post-treatment MRI. Further, via human-machine comparison experiments, we demonstrate the utility of radiomic-based frameworks as a second read decision support in cancer management.

Committee:

Anant Madabhushi (Advisor); Pallavi Tiwari (Committee Chair); David Wilson (Committee Member); Lisa Rogers (Committee Member); Charles Lanzieri (Committee Member)

Subjects:

Biomedical Engineering; Biomedical Research

Keywords:

Radiomics; Texture; Brain; Necrosis; Recurrence; Machine Learning; Radiogenomics; GBM; CoLlAGe; Cancer; Image analysis; Habitat; Survival; Prognosis; Diagnosis; treatment evaluation

ATTA-FOSU, THOMASFourier Based Method for Simultaneous Segmentation and Nonlinear Registration
Doctor of Philosophy, Case Western Reserve University, 2017, Applied Mathematics
Image segmentation and registration play active roles in machine vision and medical image analysis of historical data. Individually, the two has seen important research contributions, and the joint treatment of the two problems has become an active area of research. In this thesis we will explore the joint problem of segmenting and registering a template image given a reference image. We formulate the joint problem through an energy functional that integrates two well studied approaches in segmentation and registration: Geodesic Active Contours and nonlinear elastic registration. In the registration regime, the domain is modeled as a St. Venant-Kirchhoff material. We minimize the potential energy of this elastic system using variational methods, and derive an evolution equation which we solve using implicit-explicit integration methods. The numerical discretization of the problem allows us to take advantage of the Fast Fourier Transform. In the segmentation regime, we will adopt an active contours based energy with a weighted total variation penalty on the segmenting front. This particular choice allows for fast solution using the dual formulation of the total variation. The weight of the total variation penalty is an edge stopping function which depends on the deforming template. This allows the segmenting front to accurately track spontaneous changes in the shape of objects embedded in the template image as it deforms.

Committee:

Weihong Guo (Committee Chair); Daniela Calvetti (Committee Member); Julia Dobrotsoskya (Committee Member); Erkki Somersalo (Committee Member); Michael Lewicki (Committee Member)

Subjects:

Applied Mathematics; Biomedical Engineering

Keywords:

Joint segmentation and registration; nonlinear elasticity; Saint Venant-Kirchhoff material; weighted total variation; level sets; Fast Fourier Transform; Implicit-Explicit methods; medical image analysis

Zhu, YoudingModel-Based Human Pose Estimation with Spatio-Temporal Inferencing
Doctor of Philosophy, The Ohio State University, 2009, Computer Science and Engineering
This thesis presents a computational framework for human poseestimation from depth video sequences. The framework has a potential to achieve interesting applications such as robot motion retargeting, activity recognition, etc, wherever joint motion is an appropriate representation of the human motion. On the one hand, feature points that are informative for pose estimation are tracked with depth image analysis. Human poses are reconstructed from these feature points with kinematic constraints including joint limits and self-collision avoidance. On the other hand, human poses could be estimated based on local optimization using dense correspondences between 3D data and the articulated human model. Both could be unified with temporal motion prediction based on Bayesian information integration. We demonstrate our results for humanoid robot motion learning through a novel collision-free retargeting as well as for an example of the human pose estimation with environmental clutters. We show the computational results on a set of challenging motions where limbs interact with each other.

Committee:

Richard Parent, PhD (Advisor); James Davis, PhD (Committee Member); Raghu Machiraju, PhD (Committee Member); Kikuo Fujimura, PhD (Committee Member)

Subjects:

Computer Science

Keywords:

human pose estimation; depth image analysis for robust body part detection labeling and tracking; spatio-temporal inferencing;

Ostapenko, TanyaMagneto-optical and Imaging Studies of Chromonic and Thermotropic Liquid Crystals
PHD, Kent State University, 2011, College of Arts and Sciences / Department of Physics

This dissertation addresses three experimental questions. First, the pretransitional behavior of lyotropic chromonic liquid crystals (LCLCs) is investigated in order to gain further insight into the aggregation mechanism and structure. In order to study the pretransitional behavior of LCLCs in the isotropic phase, a high magnetic field is applied perpendicular to the light propagation direction, which induces birefringence in the material; this is called the Cotton-Mouton effect. The aggregates align with the field, which makes it possible to study how the aggregates form in the isotropic phase. The results of this study indicate that multiple optical effects can be induced, which supports the possibility of a complex aggregate structure.

The second part of this dissertation explores the possibility of a biaxial nematic phase (Nb). The geometry of the liquid crystal mesogen is important and it is thought that banana-shaped liquid crystals will have an Nb phase. However, contradicting reports on different bent-core materials have not determined whether this phase exists in them. Optical techniques usually rely on a sample cell rubbing treatment to homeotropically align the main director, n, but optical misidentification of Nb could occur if the material is in a tilted uniaxial phase, which appears the same as a homeotropically-aligned biaxial phase. Using a high magnetic field to completely align n and measuring the magnetic field-induced optical phase difference perpendicular to n gives a conclusive way to determine whether a material has non-zero biaxial order. None of the materials studied appear to have an Nb phase.

The last part of this dissertation examines director fluctuations in calamitic and bent-core liquid crystals using dynamic imaging analysis. Dynamic image analysis is a relatively new technique where a measurement of nematic phase fluctuations is made in direct space. These measurements are done using a polarizing microscope, heat stage and CCD camera. The advantage of this technique is that it measures small values of q, making it a complementary technique to dynamic light scattering, where large values of q are easily obtained. Basic material properties may be related to the wave vector and decay time of the fluctuations.

Committee:

James Gleeson, PhD (Advisor); Samuel Sprunt, PhD (Advisor); David Allender, PhD (Committee Member); Robert Twieg, PhD (Committee Member); Jonathan Selinger, PhD (Other)

Subjects:

Physics

Keywords:

bent-core liquid crystals; lyotropic chromonic liquid crystals; fluctuations; aggregation; magnetic birefringence; image analysis

CANNING, JENNIFER LASSESSMENT OF THE SKIN CONDITION OF HEALTH CARE WORKERS USING DIGITAL IMAGE PROCESSING
MS, University of Cincinnati, 2006, Pharmacy : Pharmaceutical Sciences
Frequent handwashing has significant effects on the stratum corneum (SC) barrier and can lead to dry skin, irritation and erythema. The effects of hand hygiene procedures on health care workers (HCWs) were investigated by evaluation of skin damage (dryness, erythema) on their hands. Live visual skin evaluation (LSG), digital image analysis (DIA), and visual perception evaluation of high resolution digital images (VPS) were used to measure skin condition in the spring and winter. Compared to non-HCW control subjects, HCW hands are appreciably comprised. The skin was damaged at the start of a work cycle, suggesting that the SC does not have sufficient time to repair itself. Use of test products (TP) resulted in significantly improved skin dryness (LSG) and irritation (VPS) relative to the current products (CP). However, skin erythema observed over a work cycle was similar for both CP and TP. Skin erythema was difficult to assess, most likely due to the compromise of the skin from baseline and the regional heterogeneity of the hands. DIA techniques were used to analyze erythema in the digital images. Redness observed using DIA techniques was significantly higher for the hands, particularly the knuckles, in the winter, an indication of poorer skin condition. The digital images were also viewed on a high resolution monitor in a paired comparison format to examine the effects over a cycle and during regression (VPS Imaging System, P&G). The positive correlations evaluated between LSG and VPS methods verify the fact that images could be collected (much more rapidly) and graded later using a comparison imaging system, i.e. VPS, to establish quantitative results. Only minimal correlations were present between DIA and the visual evaluations (LSG, VPS). High levels of erythema may have affected the correlations between DIA and the visual grades because it is often accompanied by other symptoms, making it more difficult to grade as it becomes more severe. The digital imaging process, used to analyze the digital images, shows to be able to objectively define erythema as a quantitative expression, however, further investigation is needed into the development of this DIA process because of minimal correlations with the visual methods. Further investigation needs to be done so that a quantification system can be established for the investigation of dryness in the digital images collected.

Committee:

R Wickett (Advisor)

Keywords:

Digital Imaging; Health Care Workers; Skin Condition; Erythema; Digital Image Analysis

Chaganti, ShikhaImage Analysis of Glioblastoma Histopathology
MS, University of Cincinnati, 2014, Engineering and Applied Science: Computer Science
Glioblastoma is a form of malignant brain tumor in humans involving glial or non-neuronal cells. The state-of-the-art diagnosis of Glioblastoma is predominantly based on subjective opinion of trained pathologists. However, with the availability of large-scale databases of Glioblastoma histopathology images, it is now possible, in principle, to objectively study and classify this class of tumors via image analysis and pattern recognition techniques. The objective of this work is to develop a quantitative framework for the analysis of Glioblastoma. The first, fundamental step in this process is the identification of histological structures in these images, that is, segmenting the constituent nuclei in the tissue. The work presents a two-step process of iterative thresholding and cleaving (ITC) to identify aforementioned structures. This improves significantly over standard color-based cell segmentation techniques in identifying cellular structures, giving 91.8% precision and 94.7% recall. Furthermore, using various architectural features obtained from each image, it ensures that the identification of regions important for the diagnosis process is distinctly clearer using the ITC approach than with standard approaches such as the Otsu method and adaptive thresholding.

Committee:

Anca Ralescu, Ph.D. (Committee Chair); Fred Annexstein, Ph.D. (Committee Member); Bruce Aronow, Ph.D. (Committee Member)

Subjects:

Computer Science

Keywords:

Image Analysis;Clustering;Computer aided diagnosis;Glioblastoma;Histopathology

Fang, ChenThe Effects of Methiozolin Rates and Nitrogen Fertility Strategies for Annual Bluegrass Control and Creeping Bentgrass Safety on Golf Greens
Master of Science, The Ohio State University, 2015, Horticulture and Crop Science
Annual bluegrass (Poa annua L.) is considered the most problematic weed on golf greens because of its fecund characteristic, low heat and disease tolerance in the summer, massive seed head reproduction, and bright green color. Methiozolin was initially an herbicide for weed control in crop fields and now is being developed for annual bluegrass control on golf greens. It has shown effectiveness and safety on multiple grass species, including creeping bentgrass (Agrostis stolonifera), kentucky bluegrass (Poa pratensis), and bermudagrass (Cynodon dactylon). As a systemic herbicide, methiozolin is mainly taken up by root absorption and shows limited acropetal movement in the plant. It is recommended that methiozolin be watered in immediately after application. Nitrogen, as one of the essential elements of plants, plays an important role in the lateral growth and chlorophyll formation of creeping bentgrass, which can greatly influence the recovery rate, color, and other quality characteristics of the turfgrass surface. Digital Image Analysis (DIA) is a new method for turfgrass surface quality evaluation. DIA has shown efficiency in data analysis with an equal accuracy as the normalized difference vegetation index (NDVI) and better consistency than visual evaluation method (NTEP). Two experiments were conducted on the Ohio State University Golf Club practice green and one on a USGA green at the Ohio Turfgrass Foundation Research and Education Facility. The first project on the OSU Golf Club putting green was designed to study the interaction of methiozolin, nitrogen rate and fertilizing frequency on creeping bentgrass recovery and annual bluegrass suppression/control in the spring with fall only methiozolin treatments and fall/spring methiozolin treatments. The second project consisted of three methiozolin rates and four rates to determine the best combination of spring methiozolin rate and spring nitrogen application strategies that shows best control over annual bluegrass while benefiting creeping bentgrass recovery and safety. The third project at the OSU Research and Education Facility was to study the effects of five nitrogen rates on the lateral growth/recovery and quality of creeping bentgrass with/without methiozolin treatments. The first project found that there was no significant interaction between methiozolin, nitrogen rates and fertilizing frequency in the spring. Spring methiozolin applications had a negative effect on creeping bentgrass color and recovering rate, but also a subsequent control on annual bluegrass after methiozolin fall treatments. Among all nitrogen strategies, the 24.4 kg N ha-1 every two weeks and the 12.2 kg N ha-1 every week were the best for creeping bentgrass green-up and recovery in the spring of 2014. The second experiment project found that methiozolin rates higher than the protocol rate (0.53 kg a.i. ha-1) had significant negative effects on annual bluegrass color and significantly more decrease on annual bluegrass population. Both creeping bentgrass and annual bluegrass color increased significantly with higher nitrogen rates and 24.4 kg N ha-1 had significantly more decrease on annual bluegrass population, which means higher nitrogen rates benefits creeping bentgrass more than annual bluegrass under methiozolin treatments. The third study found that there was no significant negative effects of methiozolin on creeping bentgrass color or lateral growth. According to the regression, there was a quadratic relation between creeping bentgrass color and time. The lateral growth rate of creeping bentgrass was constant through time and was only influenced by the nitrogen rate.

Committee:

David S. Gardner (Advisor); John R. Street (Advisor); T. Karl Danneberger (Committee Member); David J. Barker (Committee Member)

Subjects:

Horticulture

Keywords:

methiozolin; nitrogen; annual bluegrass; creeping bentgrass; digital image analysis

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