<title>Comparison of skin patterning feature analysis methods for lesion classification</title>

Abstract

This paper describes a method of distinguishing between early malignant melanoma and benign moles by examining skin pattern texture on an image of the lesion. Skin patterning is a macroscopic texture composed of fine linear elements. This texture is poorly described by standard definitions of texture and poorly detected by existing techniques. Skin line patterning is detected through a new method which looks at small patches spaced equally across the image and constructs a profile of their linear self-similarity over a range of angles. Regions which exhibit skin patterning result in similar profiles for neighbouring patches whereas no such similarity is found in areas where the patterning is disrupted. Interpretation of the profile images for the classification of the lesions is then addressed. Four classification methods are used to cluster the output image of profiles from the skin patterning detector, two are based on neural networks (ART and soFM), the third uses the local variance and the last is a novel region based agglomerative clustering technique. A measure based on the relationship between the classification results and an intensity-based segmentation is calculated and this represents the disruption of the skin line patterning. A set of images containing a variety of malignant and non-malignant lesions are analysed. The computed textural disruption figure is compared to both the histological diagnosis and to a visual estimate of patterning disruption for each image. Comparative results are given for the discriminative ability obtained via each of the classification methods.