A Pixel-Based Skin Segmentation in Psoriasis Images Using Committee of Machine Learning Classifiers

Abstract

Skin segmentation, which involves detecting human skin areas in an image, is an important process for skin disease analysis. The aim of this paper is to identify the skin regions in a newly collected set of psoriasis images. For this purpose, we present a committee of machine learning (ML) classifiers. A psoriasis training set is first collected by using pixel values in five different color spaces. Experiments are then performed to investigate the impact of both the size of the training set and the number of features per pixel, on the performance of each skin classifier. A committee of classifiers is constructed by combining the classification results obtained from seven distinct skin classifiers using majority voting. Also, we propose a refinement method using morphological operations to improve the resulted skin map. We use a set of 100 psoriasis images for training and testing. For comparative evaluation, we consider 3283 face skin images. Finally, F-measure and accuracy are used to evaluate the performance of the classifiers. The experimental results show that the size of the training set does not greatly influence the overall performance. The results also indicate that the feature vector using pixel values in the five color spaces has higher performance than any subset of these spaces. Comparative study suggests that the proposed method performs reasonably with both psoriasis and faces skin images, with accuracy of 97.4% and 80.41% respectively.