Exploiting color and topological features for region segmentation with recursive fuzzy C-means

In this paper we define a novel approach to image segmentation into regions which focuses on both visual and topological cues, namely color similarity, inclusion and spatial adjacency. Many color clustering algorithms have been proposed in the past for skin lesion images but none exploits explicitly the inclusion properties between regions. Our algorithm is based on a recursive version of fuzzy c-means (FCM) clustering algorithm in the 2D color histogram constructed by Principal Component Analysis (PCA) of the color space. The distinctive feature of the proposal is that recursion is guided by evaluation of adjacency and mutual inclusion properties of extracted regions; then, the recursive analysis addresses only included regions or regions with a not-negligible size. This approach allows a coarse-to-fine segmentation which focuses attention on the inner parts of the images, in order to highlight the internal structure of the object depicted in the image. This could be particularly useful in many applications, especially in biomedical image analysis. In this work we apply the technique to segmentation of skin lesions in dermatoscopic images. It could be a suitable support for diagnosis of skin melanoma, since dermatologists are interested in analysis of spatial relations, symmetrical positions and inclusion of regions.