The malignant melanoma is a deadly form of the skin cancer in humans. It develops quickly, and effortlessly metastasizes. Late identification of the dangerous melanoma is in charge of 75% of deaths connected with skin growths. Early diagnosis is an important factor that increases chances of successful cure as there is a rapid course of the disease. Computer analysis and image processing are efficient tools supporting quantitative medical diagnosis. Therefore it is relevant to develop computer based methods for dermatological images. So, in order to get the effective results and information about distinctive stages of the infected portion, one needs the corresponding features of that particular area in order to decide the stage. So, the feature extraction phase is hugely dependent on the region detected which has the disease. So appropriate segmentation algorithm is required which can affectivity detect the skin melanoma pixels in the information image. In this work, an algorithm is presented which can adequately detect the pixels having melanoma region and ordinary skin. The proposed work uses a hybrid technique in which space complexity of intensity values is reduced by taking pre-segmentation results from Gaussian mixtures posterior algorithm. The algorithm first chooses some candidates from different regions of the images having distinctive intensity values and then Gaussian models are built from the chosen places by taking their neighborhood pixels. After this, posterior testing is carried out to get pre-segmented results. In the end neural network based training and testing is implemented to get final segmentation results. Experimental results show that the proposed algorithm gives 98% accuracy results on the tested database images.