Detection of skin cancer in the earlier stage is very Important and critical. In recent days, skin cancer is seen as one of the most Hazardous form of the Cancers found in Humans. The detection of Melanoma cancer in early stage can be helpful to cure it. Computer vision can play important role in Medical Image Diagnosis and it has been proved by many existing systems. Skin cancer is found in various types such as Melanoma, Basal, Squamous cell Carcinoma, among which Melanoma is the most unpredictable. Melanoma is a less common cancer but it is a more serious type of skin cancer which leads to death. This paper present a method for the detection of Melanoma Skin Cancer using Image processing tools. Support Vector Machine and Minimum Distance Classifier are the common machine learning algorithms for classification. Both the classification techniques are fused to develop a hybrid classifier. The input to the system is the skin lesion image and then by applying image processing techniques, it analyses to conclude about the presence of skin cancer. The Lesion Image analysis tools checks for the various Melanoma parameters, Color, Area perimeter, diameter etc by texture, size and shape analysis for Boundary Trace Based image segmentation and feature stages. The extracted feature parameters are used to classify the image as Non Melanoma and Melanoma cancer lesion. Our proposed methodology archives encouraging results having 96% accuracy.