Automatic segmentation and degree identification in burn color images

When burn injury occurs, the most important step is to provide treatment to the injury immediately by identifying degree of the burn which can only be diagnosed by specialists. However, specialists for burn trauma are still inadequate for some local hospitals. Hence, the invention of an automatic system that is able to help evaluating the burn would be extremely beneficial to those hospitals. The aim of this work is to develop an automatic system with the ability of providing the first assessment to burn injury from burn color images. The method used in this work can be divided into 2 parts, i.e., burn image segmentation and degree of burn identification. Burn image segmentation employs the Cr-transformation, Luv-transformation and fuzzy c-means clustering technique to separate the burn wound area from healthy skin and then mathematical morphology is applied to reduce segmentation errors. The segmentation algorithm performance is evaluated by the positive predictive value (PPV) and the sensitivity (S). Burn degree identification uses h-transformation and texture analysis to extract feature vectors and the support vector machine (SVM) is applied to identify the degree of burn. The classification results are compared with that of Bayes and K-nearest neighbor classifiers. The experimental results show that our proposed segmentation algorithm yields good results for the burn color images. The PPV and S are about 0.92 and 0.84, respectively. Degree of burn identification experiments show that SVM yields the best results of 89.29 % correct classification on the validation sets of the 4-fold cross validation. SVM also yields 75.33 % correct classification on the blind test experiment.

[1]  James M. Keller,et al.  Fuzzy Models and Algorithms for Pattern Recognition and Image Processing , 1999 .

[2]  Bernhard Schölkopf,et al.  A tutorial on support vector regression , 2004, Stat. Comput..

[3]  M. Lie UNSUPERVISED LIP SEGMENTATION UNDER NATURAL CONDITIONS , 1999 .

[4]  James C. Bezdek,et al.  Pattern Recognition with Fuzzy Objective Function Algorithms , 1981, Advanced Applications in Pattern Recognition.

[5]  Matti Pietikäinen,et al.  Classification with color and texture: jointly or separately? , 2004, Pattern Recognit..

[6]  Begoña Acha,et al.  Segmentation of burn images using the L*u*v* space and classification of their depths by color and texture imformation , 2002, SPIE Medical Imaging.

[7]  M. Kolesnik,et al.  How robust is the SVM wound segmentation? , 2006, Proceedings of the 7th Nordic Signal Processing Symposium - NORSIG 2006.

[8]  Josef Chaloupka Automatic lips reading for audio-visual speech processing and recognition , 2004, INTERSPEECH.

[9]  Begoña Acha,et al.  A computer assisted diagnosis tool for the classification of burns by depth of injury. , 2005, Burns : journal of the International Society for Burn Injuries.

[10]  W. Peizhuang Pattern Recognition with Fuzzy Objective Function Algorithms (James C. Bezdek) , 1983 .

[11]  Begoña Acha,et al.  Segmentation and classification of burn color images , 2001, 2001 Conference Proceedings of the 23rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[12]  R. Brereton,et al.  Support vector machines for classification and regression. , 2010, The Analyst.

[13]  George J. Klir,et al.  Fuzzy sets and fuzzy logic - theory and applications , 1995 .

[14]  Marina Kolesnik,et al.  Multi-dimensional Color Histograms for Segmentation of Wounds in Images , 2005, ICIAR.