Mixture model based color clustering for psoriatic plaque segmentation

Abstract

This paper presents a mixture model based color clustering and then applies this technique for psoriatic plaque segmentation in skin images. For clustering image pixels, two mostly relevant colorspaces namely, CIE Luv(cubic) and CIE Lch(equivalent cylindrical) are considered. Gaussian Mixture Model(GMM) is used for clustering in Luv space. However, Lch space being a circular-linear space does not support the use of GMM. Hence, clustering in Lch makes use of a novel mixture model known as Semi-Wrapped Gaussian Mixture Model(SWGMM). The performance of these clustering methods is evaluated for psoriatic plaque segmentation and results are compared with those obtained by the commonly used Fuzzy C-Means (FCM) clustering algorithm. The comparative study shows that the clustering in Lch using SWGMM outperforms the other approaches. For localizing the plaques, we consider von Mises distribution to find a suitable confidence interval and thereby defining skin and non-skin models. The UCI Skin Segmentation dataset is used for this purpose. This localization approach achieves an average accuracy 79.53%. A real clinical dataset of Psoriasis images is used in this experiment.