Efficient SVM classifier based on color and texture region features for wound tissue images

This work is part of the ESCALE project dedicated to the design of a complete 3D and color wound assessment tool using a simple hand held digital camera. The first part was concerned with the computation of a 3D model for wound measurements using uncalibrated vision techniques. This article presents the second part, which deals with color classification of wound tissues, a prior step before combining shape and color analysis in a single tool for real tissue surface measurements. We have adopted an original approach based on unsupervised segmentation prior to classification, to improve the robustness of the labelling stage. A database of different tissue types is first built; a simple but efficient color correction method is applied to reduce color shifts due to uncontrolled lighting conditions. A ground truth is provided by the fusion of several clinicians manual labellings. Then, color and texture tissue descriptors are extracted from tissue regions of the images database, for the learning stage of an SVM region classifier with the aid of a ground truth resulting from. The output of this classifier provides a prediction model, later used to label the segmented regions of the database. Finally, we apply unsupervised color region segmentation on wound images and classify the tissue regions. Compared to the ground truth, the result of automatic segmentation driven classification provides an overlap score, (66 % to 88%) of tissue regions higher than that obtained by clinicians.