An integrated and interactive decision support system for automated melanoma recognition of dermoscopic images.

This paper presents an integrated and interactive decision support system for the automated melanoma recognition of the dermoscopic images based on image retrieval by content and multiple expert fusion. In this context, the ultimate aim is to support the decision making by retrieving and displaying the relevant past cases as well as predicting the image categories (e.g., melanoma, benign and dysplastic nevi) by combining outputs from different classifiers. However, the most challenging aspect in this domain is to detect the lesion from the healthy background skin and extract the lesion-specific local image features. A thresholding-based segmentation method is applied on the intensity images generated from two different schemes to detect the lesion. For the fusion-based image retrieval and classification, the lesion-specific local color and texture features are extracted and represented in the form of the mean and variance-covariance of color channels and in a combined feature space. The performance is evaluated by using both the precision-recall and classification accuracies. Experimental results on a dermoscopic image collection demonstrate the effectiveness of the proposed system and show the viability of a real-time clinical application.