[en] During the last years, computer vision-based diagnosis systems have been widely used in several hospitals and dermatology clinics, aiming mostly at the early detection of malignant melanoma tumor, which is among the most frequent types of skin cancer, versus other types of nonmalignant cutaneous diseases. They grow in melanocytes, the cells responsible for pigmentation. This type of cancer is increasing rapidly and its related mortality rate is increasing more modestly, and inversely proportional to the tumor's thickness. The mortality rate can be decreased by earlier detection of suspicious lesions and better prevention. In this work, we are interested in extracting all specific attributes which can be used for computer-aided diagnosis of melanoma. In the first step of the proposed work, we applied the Dull Razor [Lee T et al., Dullrazor: A software approach to hair removal from images, Cancer Control Research, British Columbia Cancer Agency, Vancouver, Canada, Vol. 21, No. 6, pp. 533-543, 1997] technique to images to reduce the influence of small structures, hairs, bubbles, light reflection. In the second step, a new fuzzy level set algorithm is proposed in order to facilitate the medical image segmentation task. It is able to directly evolve from the initial segmentation proposed that uses a spatial fuzzy clustering approach. The controlling parameters of the level set evolution are also estimated from the results of the fuzzy clustering step. This step is essential to characterize the shape of the lesion and also to locate the tumor to be analyzed. In this paper, we have also treated the necessity to extract all the specific attributes used to develop a characterization methodology that enables specialists to take the best possible diagnosis. For this purpose, our proposal relies largely on visual observation of the tumor while dealing with some characteristics as color, texture or form. The method used in this paper is called ABCD. It requires calculating four factors: Asymmetry (A), Border (B), Color (C), and Diversity (D). Finally, these parameters are used to construct a classification module based on artificial neural network for the recognition of malignant melanoma.
Disciplines :
Computer science
Author, co-author :
Messadi, Mahammed; Biomedical Engineering Laboratory, Department of Electrical and Electronics, Technology Faculty, Tlemcen University, Tlemcen, Algeria ; Laboratory of Biomedical Engineering, Faculty of Technology, University of Tlemcen, Tlemcen, Algeria
Mahmoudi, Saïd ; Université de Mons - UMONS > Faculté Polytechnique > Service Informatique, Logiciel et Intelligence artificielle
Bessaid, Abdelhafid ; Biomedical Engineering Laboratory, Department of Electrical and Electronics, Technology Faculty, Tlemcen University, Tlemcen, Algeria
Language :
English
Title :
ANALYSIS OF SPECIFIC PARAMETERS FOR SKIN TUMOR CLASSIFICATION
R300 - Institut de Recherche en Technologies de l'Information et Sciences de l'Informatique R450 - Institut NUMEDIART pour les Technologies des Arts Numériques
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