Melanoma detection using fuzzy C-means clustering coupled with mathematical morphology

This paper proposes a Fuzzy C-Means (FCM) based approach designed for melanoma diagnosis. The methodology comprises the traditional data processing architecture, including pre-processing (contrast stretching), main processing (FCM) and post-processing (morphological erosion). The contrast stretching phase has the purpose of stretching the range of pixel intensities of the input image to occupy a larger dynamic range in the output image. This is followed by the FCM algorithm, which automatically divides the data provided by the contrast stretching phase into two clusters: lesion and skin. This process ends with the morphological erosion of the segmented image, where the structuring element is translated over each pixel of the object, so as to overcome typical irregularities between lesion and skin (e.g., irregular boundaries, dark hair covering the lesions, specular reflections, among others). The proposed approach is evaluated in dermatoscopic images of skin cancer, and results show that it is able to produce accurate identification of lesions.