Automatic Detection of Malignant Melanoma using Macroscopic Images

In order to distinguish between benign and malignant types of pigmented skin lesions, computerized procedures have been developedfor images taken by different equipment that the most available one of them is conventional digital cameras. in this research, a newprocedure to detect malignant melanoma from benign pigmented lesions using macroscopic images is presented. the images aretaken by conventional digital cameras with spatial resolution higher than one megapixel and by considering no constraints and specialconditions during imaging. in the proposed procedure, new methods to weaken the effect of nonuniform illumination, correction of theeffect of thick hairs and large glows on the lesion and also, a new threshold based segmentation algorithm are presented. 187 featuresrepresenting asymmetry, border irregularity, color variation, diameter and texture are extracted from the lesion area and after reducingthe number of features using principal component analysis (pca), lesions are determined as malignant or benign using support vectormachine classifier. according to the dermatologist diagnosis, the proposed processing methods have the ability to detect lesions areawith high accuracy. the evaluation measures of classification have indicated that 13 features extracted by pca method lead to betterresults than all of the extracted features. these results led to an accuracy of 82.2%, sensitivity of 77% and specificity of 86.93%. theproposed method may help dermatologists to detect the malignant lesions in the primary stages due to the minimum constraints duringimaging, the ease of usage by the public and nonexperts, and high accuracy in detection of the lesion type.