As a result, much smaller number of features can lead to better classification results. Table 1 The classification results for all features and the optimal features determined by Survivin Apoptosis feature selection method DISCUSSION This paper presents a new procedure for classifying pigmented skin lesions as benign or malignant using macroscopic images, which are taken by conventional digital cameras with spatial resolution

higher than one megapixel. While imaging the used database, any constraints and specific conditions are avoided that is an important difference between this study and the previous ones in this area and makes the proposed procedure appropriate for implementation by public and nonspecialists. In this study, new methods to enhance the quality of processing and analysis

of macroscopic images of skin lesions have been proposed; including new method which weakened effect of nonuniform illumination on the image in the best way, a new thresholding based algorithm which by review the existing information on the image histogram exploits extractable information of the image and a new method which corrects effect of thick hairs and large glows on the lesion that appear while imaging using flash light and greatly increases accuracy of the boundaries set by the segmentation algorithm. In this study, 187 features representing asymmetry, border irregularity, color variation, diameter and texture which are the maximum number of extractable features from the lesion are extracted and by using the PCA algorithm, 13 optimal features are selected. Finally, SVM classifier predicts lesion types with accuracy of 82.2%, sensitivity of 77% and specificity of 86.93%. Because of dissimilarity between the used databases in this study and the other ones, the achieved results cannot be compared. However the accuracy improved significantly against the 64% accuracy of naked eye specialist, which is a worthy conclusion. According to the dermatologist report, the proposed method in this research due to its sensitivity,

accuracy and specificity may help dermatologists in detection the malignant melanoma in more priority Batimastat stages which may help their treatment more effectively. Moreover, if there was access to the personality and imaging information such as tumor site, patient’s skin and eye color, the distance between camera and skin and the lesion diameter which is a limitation for this procedure, the achieved results could be improved significantly. BIOGRAPHIES Maryam Ramezani received B.Sc. degree in electronics engineering from Isfahan University of Technology, Isfahan, Iran, in 2011, and she received M.Sc. degree in biomedical engineering from University of Isfahan, Isfahan, Iran, in 2014. E-mail: [email protected] Alireza Karimian received his B.Sc. degree in electronics engineering from Ferdowsi University, Mashhad, Iran. He also received his M.Sc. and Ph.D.