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Non-Invasive Skin Cancer Classification from Surface Scanned Lesion Images

Abstract Details

2013, Master of Science (MS), Ohio University, Electrical Engineering (Engineering and Technology).
This work describes a semi-supervised approach to analyze the spectral data content of the samples of surface scanned skin lesion images for cancer diagnosis. As the first step, a surface image of the skin nevi is segmented utilizing Otsu’s grayscale histogram thresholding technique. The anomalous part of the skin tissue is isolated from the surrounding normal skin cells appearing in the background of the skin sample image. A cost estimation function based on the mean and variance of localized windows extracts sub samples from the lesion that has the highest discriminative information. The coordinates of the LAB color channels of the segmented skin nevi are used to obtain the spectral information in each of the windows. The Euclidian distance is employed as the similarity measure tool to compare the color feature vectors of the samples of the three classes, benign, precancerous and malignant in the training and the testing phases of the pattern recognition system. Seventy five surface skin samples have been used for this distance based classification. Classifier performance metrics such as accuracy of 80%, sensitivity of 100% and specificity of 60% have been achieved during the implementation of this computer assisted clinical skin cancer diagnosis system. An adaptive homomorphic filter is designed to eliminate speckle noise in ultrasound scans and thereby increase the ability to perceive morphological characteristics of cells, tissue and organs.
Mehmet Celenk, PhD (Advisor)
100 p.

Recommended Citations

Citations

  • Dhinagar, N. J. (2013). Non-Invasive Skin Cancer Classification from Surface Scanned Lesion Images [Master's thesis, Ohio University]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=ohiou1366384987

    APA Style (7th edition)

  • Dhinagar, Nikhil. Non-Invasive Skin Cancer Classification from Surface Scanned Lesion Images. 2013. Ohio University, Master's thesis. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=ohiou1366384987.

    MLA Style (8th edition)

  • Dhinagar, Nikhil. "Non-Invasive Skin Cancer Classification from Surface Scanned Lesion Images." Master's thesis, Ohio University, 2013. http://rave.ohiolink.edu/etdc/view?acc_num=ohiou1366384987

    Chicago Manual of Style (17th edition)