Computer aided diagnosis of skin lesions

dc.contributor.advisorAgaian, Sos
dc.contributor.authorSanchez, Isaac Armand
dc.contributor.committeeMemberAkopian, David
dc.contributor.committeeMemberJoo, Youngjoong
dc.descriptionThis item is available only to currently enrolled UTSA students, faculty or staff. To download, navigate to Log In in the top right-hand corner of this screen, then select Log in with my UTSA ID.
dc.description.abstractMelanoma is the most deadly form of skin cancer. The World Health Organization estimates that more than 65,000 people a year die worldwide from too much sun, mostly from malignant skin cancer. Early diagnosis of melanoma is essential for the fight against skin cancer and if detected early enough, physicians and specialists can treat the skin cancer while it is still localized in the originating skin lesion. While dermatological methods are objective, the scoring itself is by nature subjective and based on the physician's opinion. Hence, accurate detection, and early prediction of cancer is imperative to medical research today. The visual recognition by clinical inspection of the lesions performed by dermatologists is around 75% accuracy. This thesis develops computer aided methods of detection and diagnosis of skin lesions. It discusses extracting lesion images from a larger image of a skin surface and evaluates the ability of feature sets to classify the extracted skin lesions. In order to accomplish this task, this thesis reviews methods of dermoscopy, segmentation, feature extraction, feature selection and finally classification methods. This thesis includes a review of existing features and an introduction of new features based on 2-dimensional color histograms and the shape-adaptive discrete cosine transform. Combined with an automated segmentation method using a fusion of thresholding methods, the resulting system is designed to be used by dermatologists as a complete integrated dermatological analysis tool to improve the rate of correct diagnosis well above 90%. Simulations are implemented to show extraction of skin lesions and how their features are measured as well as classification experiments. The outcomes showed that the CAD models discussed in this paper have an improved classification performance and are objective diagnostic tools that can be used in medical practice.
dc.description.departmentElectrical and Computer Engineering
dc.format.extent106 pages
dc.subjectFeature Extraction
dc.subjectImage Processing
dc.subjectSkin Cancer
dc.subject.classificationElectrical engineering
dc.subject.classificationBiomedical engineering
dc.subject.classificationComputer engineering
dc.titleComputer aided diagnosis of skin lesions
dcterms.accessRightspq_closed and Computer Engineering of Texas at San Antonio of Science


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