Tree Structured Model of Skin Lesion Growth Pattern via Color Based Cluster Analysis

Abstract

Abstract. This paper presents a novel approach to analysis and classification of skin lesions based on their growth pattern. Our method constructs a tree structure for every lesion by repeatedly subdividing the image into sub-images using color based clustering. In this method, segmentation which is a challenging task is not required. The obtained multi-scale tree structure provides a framework that allows us to extract a variety of features, based on the appearance of the tree structure or sub-images corresponding to nodes of the tree. Preliminary features (the number of nodes, leaves, and depth of the tree, and 9 compactness indices of the dark spots represented by the sub-images associated with each node of the tree) are used to train a supervised learning algorithm. Results show the strength of the method in classifying lesions into malignant and benign classes. We achieved Precision of 0.855, Recall of 0.849, and F-measure of 0.834 using 3-layer perceptron and Precision of 0.829, Recall of 0.832, and F-measure of 0.817 using AdaBoost on a dataset containing 112 malignant and 298 benign lesion dermoscopic images.