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Skin Cancer segmentation and Detection Using Total Variation and Multiresolution Analysis

Abstract : The vast majority of skin cancer deaths are due to malignant melanoma. It is considered as one of the most dangerous cancers. In its early stages, malignant melanoma is completely curable with a simple biopsy. Therefore, an early detection is the best solution to improve skin cancer prognostic. Medical imaging such as dermoscopy and standard camera images are the most suitable tools available to diagnose melanoma at early stages. To help radiologists in the diagnosis of melanoma cases, there is a strong need to develop computer aided diagnosis (CAD) systems. The accurate segmentation and classification of pigment skin lesions still remains a challenging task due to the various colors and structures developed randomly inside the lesions. The current work focused on two main tasks. Inthe first task, a new approach of the segmentation of skin lesions based on Chan and Vese model is developed. The model is adapted to segment the variations of the pigment inside the lesion and not only the main border. The subjective evaluation, applied on a database of standard camera images, obtained a very encouraging results with 97.62% of true detection rate. In the second main task, two feature extraction methods were developed for the analysis of standard camera and dermoscopy images. The method developed for the standard camera skin cancer images is based on border irregularities, introducing two new concepts, which are valleys and crevasses as first and second level of the border irregularity. The method has been implemented on DermIs and DermQues, two databases of standard camera images, and achieved an accuracy of 86.54% with a sensitivity of 80% and a specificity of 95.45%. The second method consisted of a fusion of structural and textural features. The structural features were extracted from wavelet and curvelet coefficients, while the textural features were obtained from the local binary pattern operator. The method has been implemented on the PH2 database for dermoscopy images with 1000-random sampling cross validation. The obtained results achieved an accuracy, a sensitivity and a specificity of 86:07%, 78.93% and 93.25%. Compared to the existing methods, the proposed methods in this work show very good performances.
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  • HAL Id : tel-01813424, version 1

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Faouzi Adjed. Skin Cancer segmentation and Detection Using Total Variation and Multiresolution Analysis. Signal and Image processing. Univeristé Paris-Saclay; Université d'Evry-Val-d'Essonne; UTP Petronas, 2017. English. ⟨NNT : 2017SACLE042⟩. ⟨tel-01813424⟩

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