In this paper, Conditional Random Field Based Segmentation and different model-based Markov Random Field(MRF) classification for skin lesions in dermoscopic images are proposed. This method is used in the pattern analysis framework for diagnosis of melanoma by dermatologists. A Dermoscopic image is smoothened by Wiener Filer Method and converted into Grayscale Image. Then the image is diluted which gives the contour of an image. The input image is segmented by Conditional Random Field Technique. The Estimated CPU time is calculated which gives less Processing Time. Then classification is carried out by an image retrieval approach with different distance metrics. These features are supposed to follow Gaussian Model, Gaussian Mixture Model, and Bag-of-features Histogram Model. The main aim of this paper is the classification of an entire pigmented lesion and analysis of the texture of an image. The image database is extracted from a public Atlas of Dermoscopy. Receiver Operating Characteristics (ROC) Curve is used to evaluate the performance of Segmentation Process which gives more accuracy. Finally, the skin lesions with their levels were analysed.