# A Systematic Overview of Recent Methods for Non-Contact Chronic Wound Analysis
## 5 Tissue Classification
DSC segmentation accuracy of 90.4% was obtained on 24 images with a very high computational time of 4.84 min, which is not suitable for real-time applications. In another paper by [PERSON] et al. [41] new approach has been used based on CNN, where the input image is divided into small sub-images of \(5\times 5\) which are used as input for the network. Classification of three different tissue types gave an overall accuracy of 92.01%. In [PERSON] et al. [93], the authors developed a mobile application for wound assessment which used morphological reconstruction to prepare the image for image decomposition that has been performed by toroidal vertical and horizontal decomposition (TVD and THD). Images were decomposed in different contrast layers and using the [PERSON]'s thresholding method, pixels are separated into two classes. Segmentation of the wound has been tested on 40 images with an average time of 9.04 s.  
[PERSON] et al. [60] presented the segmentation process in three stages using the k-means algorithm. In the first stage, a preliminary segmentation is performed where adjacent pixels with similar color features are separated in groups, while in the second stage, thresholding strategies are applied, and preliminary ROI is obtained. The final stage clusters pixels with the k-means algorithm into two regions, wound region and surrounding skin region, which results with 104 features extracted from segmented regions. The classification task uses standard MLP neural network, SVM, decision trees, and naive Bayes classifier specifically trained to classify independently the regions in the estimated wound (four tissue classes) and the estimated surrounding healing tissue (two tissue classes) areas. MLP architecture showed the best performance with a tissue separation accuracy rate of 91.9%. In further research by [PERSON] et al. [56] wound was segmented using color histograms, and the probability of belonging to a certain tissue class is calculated with Bayes rule. With a very low runtime of 0.3214 s method obtained an accuracy rate of 87.77% on 322 wound images. In another research [PERSON] et al. [40] compared three different machine learning techniques SVM, NN, and random forest separately on a wound and surrounding tissue area for classification purposes. On 113 tested images efficacy scores are very similar, and there is no clear winner. Even so, SVM showed a slightly better overall accuracy rate of 88.08% but used all 104 extracted features. Furthermore, SVM gave the best efficacy scores of 92.68% and 87.71% when detecting necrotic and slough tissues, respectively. In [PERSON] and [PERSON] [78] authors segmented the ulcer contour with region growing algorithm and the four classes of ulcer wound image parts (severe region, background region, normal wound region, and skin region) are separated using k-means. Based on extracted color and texture features the image is classified to aforementioned classes with kNN algorithm. Wound is segmented with an average accuracy of 94.85% on 1770 wound images. In newer research, [PERSON] et al. [12] performed an analysis of varicose ulcer images. Segmentation was performed with a gradient descent algorithm which was used by the active contour method, while CNN was used for the actual tissue classification task. Testing on 1250 wound images achieved an accuracy rate was 99%. [PERSON] et al. [44] used three CNNs with modified VGG16 architecture where results from each network are joined into a single result matrix for nine classes. The problem with a small database was overcome through the use of data augmentation and transfer learning. Based on 1335 smartphoneimages they achieved an overall accuracy of 82%, 85%, and 83% for wound separation, granulation, and fibrin detection, respectively. Table 2 presents research papers dealing with wound tissue classification which potentially also included wound segmentation as a preprocessing step.