Segmentation of wounds in the combined color-texture feature space

Abstract

In this work we describe an application of the Support Vector Machine (SVM) classifier for the segmentation of wounds in color images. The SVM-based segmentation combines naturally a high dimensional space of image features into a single classification machine. Since particular choice of image features is crucial for the performance of SVM classifier, we investigate the efficiency of color- and texture-based features for the differentiation between skin and wound tissue. We find that color features provide better separation between these two tissues. However, incorporation of even a single textural feature improves an overall quality of the SVM classification. We test the impact of each color feature on the quality of wound segmentation and find optimal combination of these features which produces best segmentation result. We suggest a Histogram Sampling technique, which gives wider separation between wound and skin in the color space. Finally, we find a set of image features, which is typical for most types of wounds. When these features are used as an input to the SVM classifier, a fairly robust segmentation of different wound types is achieved. We evaluate the performance of SVM-based segmentation using ground-truth segmentation carried out by clinicians.