Multi-dimensional Color Histograms for Segmentation of Wounds in Images

The work investigates the use of multi dimensional histograms for segmentation of images of chronic wounds. We employ a Support Vector Machine (SVM) classifier for automatic extraction of wound region from an image. We show that the SVM classifier can generalize well on the difficult wound segmentation problem using only 3-D dimensional color histograms. We also show that color histograms of higher dimensions provide a better cue for robust separation of classes in the feature space. A key condition for the successful segmentation is an efficient sampling of multi-dimensional histograms. We propose a multi-dimensional histogram sampling technique for generation of input feature vectors for the SVM classifier. We compare the performance of the multi-dimensional histogram sampling with several existing techniques for quantization of 3-D color space. Our experimental results indicate that different sampling techniques used for the generation of input feature vectors may increase the performance of wound segmentation by about 25%.