Classification on Xinjiang high morbidity of Kazak Esophageal disease based on texture feature
Fan Yang · Xinjiang Yike Daxue xuebao · 2014
Objective To discuss the classification on Xinjiang high morbidity of Kazak Esophageal disease based on texture features extracted by GLCM.Methods For X-ray barium angiogram,we normalizing scale,removing the noise by median filter,enhancing by histogram equalization,then we use the gray level co-occurrence matrix(GLCM)to calculate the mean and variance of angular second moment,entropy,inertia moment,correlation and inverse difference moment in 0°,45°,90°and 135°directions to constitute the texture feature vector,and then evaluate the feature′s classification ability by Bayes discriminant analysis.Results Using Bayes discriminant analysis to classify X-ray barium angiogram of Xinjiang high morbidity of Kazak Esophageal disease,the classification accuracy of coarctate esophageal X-ray is 70%,for ulcer esophageal X-ray is 90%.Conclusion The result shows that feature classification ability is different when classifying different images through GLCM which can discriminate the different Esophageal disease,which will help the doctor to diagnosis the Esophageal disease,as well as laying a foundation for computer diagnosis system of Kazak in Xinjiang Uygur autonomous region.