In this procedure, a new method in order to weaken the effect of nonuniform illumination and also, a new threshold based algorithm in order Src kinase family to segment lesion area is described and applied on the database. Then,

after introduction and applying new methods to correct the effect of thick hairs and large glows on the lesion, 187 features which indicate asymmetry, border irregularity, color variation, diameter and texture are extracted. The number of features is reduced using principal component analysis (PCA) algorithm and the result is used for predicting the type of lesion as benign or malignant using support vector machine (SVM) classifier. METHODS The proposed procedure has three stages in order to detect malignant melanoma

from benign pigmented lesions. The first stage is preprocessing which includes removing effects of macroscopic images artifacts and determining lesion area with high accuracy. In the second stage, descriptor features of lesions are extracted and in the third stage which is called the classification stage, optimal features are determined and used to predict the type of lesions. Database The used database in this study is a set of 282 macroscopic images of pigmented skin lesions which had been collected from several online dermatology atlases such as dermnet, dermis and dermquest atlases.[10,11,12,13,14,15,16] This set includes RGB images of 149 benign lesions and 133 malignant which have various dimensions of 259 × 382 to 1186 × 1369 pixels. Whole area of the lesion in all of the images is visible, but lesion is not necessarily in the middle of the image and can be connected to image edges. These images are taken by conventional digital cameras with different spatial resolutions which are >1 megapixel. There was no need to

adhere to a predetermined distance between the camera and skin while imaging and in some cases, flashlight is used. Thus, the used database in this study has the least restrictions and requirements for imaging. Batimastat Preprocessing At this stage, the effects of part of artifacts in macroscopic images, including impact noise, skin lines, fine hairs, skin stains and small glows and reflections are removed by applying a median filter with mask size which is calculated using Eq. 1.[17] In this equation, mask size n is determined for an M × N image and the floor function round down the result to the next integer. Then, in order to weaken the effect of nonuniform illumination or shadow, image of original RGB color space is converted to hue, saturation and value (HSV) space because shadow effect in Value channel are more visible than other channels and spaces.