Doctor of Philosophy (PhD), Ohio University, 2018, Electrical Engineering & Computer Science (Engineering and Technology)

Changes in the morphology of a skin lesion is indicative of melanoma, a deadly type of skin cancer. This dissertation proposes a temporal analysis method to monitor the vascularity, pigmentation, size and other critical morphological attributes of the lesion. Digital images of a skin lesion acquired during follow-up imaging sessions are input to the proposed system. The images are pre-processed to normalize variations introduced over time. The vascularity is modelled as the skin images' red channel information and its changes by the Kullback-Leibler (KL) divergence of the probability density function approximation of histograms. The pigmentation is quantified as textural energy, changes in the energy and pigment coverage in the lesion. An optical flow field and divergence measure indicates the magnitude and direction of global changes in the lesion. Sub-surface change is predicted based on the surface skin lesion image with a novel approach. Changes in key morphological features such as lesions' shape, color, texture, size, and border regularity are computed. Future trends of the skin lesions features are estimated by an auto-regressive predictor. Finally, the features extracted using deep convolutional neural networks and the hand-crafted lesion features are compared with classification metrics. An accuracy of 80.5%, specificity of 98.14%, sensitivity of 76.9% with a deep learning neural network is achieved. Experimental results show the potential of the proposed method to monitor a skin lesion in real-time during routine skin exams.

Committee: Mehmet Celenk Ph.D. (Advisor); Savas Kaya Ph.D. (Committee Member); Jundong Liu Ph.D. (Committee Member); Razvan Bunescu Ph.D. (Committee Member); Xiaoping Shen Ph.D. (Committee Member); Sergio Lopez-Permouth Ph.D. (Committee Member) Subjects: Computer Science; Electrical Engineering; Medical Imaging; Oncology

Doctor of Philosophy in Engineering, University of Toledo, 2006, Bioengineering

In an effort to investigate viability of using autofluorescence to detect superficial skin cancer (melanoma), polarized fluorescence spectroscopy was employed with the goal of reducing the contribution of the background fluorescence generated from the deep skin. Polarized reflectance was also employed to account for the effects of tissue scattering and absorption on the polarized fluorescence measurements and to measure changes in tissue scattering. An investigation of the skin and its layers using polarized fluorescence spectroscopy has revealed that polarized fluorescence can be generated from the skin, the epidermis, and the dermis using polarized excitation light. The epidermis has the highest retention of fluorescence polarization, while the dermis has the lowest retention of fluorescence polarization. The dependence of the fluorescence anisotropy (a measurement of fluorescence polarization) on dermal thickness was measured, suggesting a role for multiple scattering within the dermis in the depolarization of fluorescence in both the dermis and the skin. A hypothesis of NADH binding change resulting from a metabolic shift in cancer cells was presented. An investigation of normal human melanocytes and melanoma cells using fluorescence anisotropy of the NADH within the cells yielded results consistent with this hypothesis. Normal melanocytes show appreciably higher fluorescence anisotropy than melanoma cells. Since dermal matrix erosion is one of the early stage events in cancer progression, an experimental system was developed to mimic tumor invasion. The process of the enzymatic erosion was investigated with polarized fluorescence, non-polarized fluorescence and polarized reflectance spectroscopy to provide insight into the contrasts between the normal and enzyme-digested dermal matrix. The degradation of the dermal matrix with enzymes results in a decrease in fluorescence emission and light scattering in the matrix. These results confirm the suggestion from lit (open full item for complete abstract)

Committee: Patricia Relue (Advisor) Subjects:

Master of Sciences, Case Western Reserve University, 2013, EECS - Electrical Engineering

If detected early, skin cancer has a 95-100% successful treatment rate; therefore early detection is crucial and several computer-aided methods have been developed to assist dermatologists. In skin images removing hairs without altering the lesion is important to effectively apply detection algorithms. This thesis focuses on the use of image processing techniques to remove hairs by identifying hair pixels contained within a binary image mask using the Generalized Radon Transform. The Radon Transform was adapted to find quadratic curves characterized by rotational angle and scaling. The method detects curved hairs in the image mask for removal and replacement through pixel interpolation. Implementing this technique in MATLAB gives the ability to perform tests rapidly on both simulated and actual images. The quadratic Radon transform performs well in curve detection; however, the research points out the need for better algorithms to improve hair masking, peak detection, and interpolation replacement.

Committee: Marc Buchner PhD (Advisor); Kenneth Loparo PhD (Committee Member); Vira Chankong PhD (Committee Member) Subjects: Electrical Engineering