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Morphological Change Monitoring of Skin Lesions for Early Melanoma Detection

Dhinagar, Nikhil J
http://orcid.org/0000-0003-2424-4854
http://rave.ohiolink.edu/etdc/view?acc_num=ohiou1533911373953079

Abstract Details

2018, Doctor of Philosophy (PhD), Ohio University, 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.
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)
96 p.
Computer Science; Electrical Engineering; Medical Imaging; Oncology
Skin cancer detection; melanoma diagnosis; temporal skin lesion analysis; change detection; medical image analysis; computer-assisted diagnosis; computer vision; image processing; deep learning; artificial intelligence; digital image pre-processing

Recommended Citations

Citations

  • Dhinagar, N. J. (2018). Morphological Change Monitoring of Skin Lesions for Early Melanoma Detection [Doctoral dissertation, Ohio University]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=ohiou1533911373953079

    APA Style (7th edition)

  • Dhinagar, Nikhil. Morphological Change Monitoring of Skin Lesions for Early Melanoma Detection. 2018. Ohio University, Doctoral dissertation. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=ohiou1533911373953079.

    MLA Style (8th edition)

  • Dhinagar, Nikhil. "Morphological Change Monitoring of Skin Lesions for Early Melanoma Detection." Doctoral dissertation, Ohio University, 2018. http://rave.ohiolink.edu/etdc/view?acc_num=ohiou1533911373953079

    Chicago Manual of Style (17th edition)

ohiou1533911373953079
445
© 2018, all rights reserved.
This open access ETD is published by Ohio University and OhioLINK.