in which the algorithm to detect malignant melanoma from benign lesions by the usage of skin lesion macroscopic images is proposed. In this study, for lesion area segmentation, first the elimination of the low frequency spatial component of the image

Vorinostat solubility was used for background correction, and then a thresholding based method which was inspired by Otsu’s algorithm, was applied to segment the lesion area. By considering ABCD criteria, 55 features were defined and extracted from the determined lesion area. Then correlation-based feature selection method and adaboost classifier were used as a feature selection step. In this algorithm, one decision support part was added which lead to the usage of the personal information including skin type, age, gender and part of the body along with the output of image classifier. Finally, 86% accuracy, 94% sensitivity and 68% specificity have been achieved.[6] In 2010, Christensen et al. proposed a procedure in which morphological operators were used for thick and thin

hairs removal, pre and postprocessing. Otsu’s thresholding algorithm was applied on blue channel of red, green and blue (RGB) color space locally to determine the lesion area, and then, 9 features describing the overall shape, border and color distribution were extracted. A prediction model was constructed based on statistical analyses of the algorithm outputs. Finally by applying an optimal threshold on output index score, 77% accuracy was achieved.[7] In 2011, the procedure is presented by Cavalcanti et al. in which shadow was estimated by adjusting a two degree quadric polynomial on normal skin and its effect was attenuated by removing this plane from the image. To determine the lesion area, a new three-channel image was defined, and a thresholding method inspired by Otsu’s algorithm was applied on. Then by the usage of 52 extracted features, which were grouped in ABCD criteria features, and two k nearest neighborhood and decision tree

classifiers in two modes, the lesion type was predicted. Finally, accuracy of 96.71%, sensitivity of 96.26% and specificity of 97.78% has been obtained.[8] In 2013, Cavalcanti et al. introduced 12 features based on the values of eumelanin and pheomelanin of the lesion and added them to the feature Batimastat set which used in the previous study. In this way, the proposed procedure in that study resulted in 100% sensitivity, 97.78% specificity and 99.34% accuracy.[9] The database of the mentioned studies was limited due to the conditions and constraints, which noted previously. This disadvantage prevents the proposed procedures from being appropriate to be applied on publicly available equipments that are the ultimate goal of proposing these procedures.