A PRELIMARY APPROACH FOR THE AUTOMATED RECOGNITION OF MALIGNANT MELANOMA

Ezzeddine Zagrouba, Walid Barhoumi

Abstract


In this work, we are motivated by the desire to classify skin lesions as malignants or benigns from color photographic slides of the lesions. Thus, we use color images of skin lesions, image processing techniques and artificial neural network classifier to distinguish melanoma from benign pigmented lesions. As the first step of the data set analysis, a preprocessing sequence is implemented to remove noise and undesired structures from the color image. Second, an automated segmentation approach localizes suspicious lesion regions by region growing after a preliminary step based on fuzzy sets. Then, we rely on quantitative image analysis to measure a series of candidate attributes hoped to contain enough information to differentiate melanomas from benign lesions. At last, the selected features are supplied to an artificial neural network for classification of tumor lesion as malignant or benign. For a preliminary balanced training/testing set, our approach is able to obtain 79.1% of correct classification of malignant and benign lesions on real skin lesion images.

Keywords
fuzzy region growing; Karhunen-Loeve transform; melanoma recognition; neural networks

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DOI: 10.5566/ias.v23.p121-135

Image Analysis & Stereology
EISSN 1854-5165 (Electronic version)
ISSN 1580-3139 (Printed version)