An Automatic Bone Disorder Classification Using Hybrid Texture Feature Extraction with Bone Mineral Density

A novel approach has been proposed to classify bone disorders for classifying the radiographic bone image as normal, Osteopenia and Osteoporosis. The proposed system consists of three major stages to predict the accurate bone disorder classification. In the first stage, image preprocessing is performed where bilateral filtering is applied to remove noise and to enhance the image quality. Then, the image is fed to Otsu based segmentation approach for segmenting the abnormal area of the bone image. In the second stage, Discrete Wavelet Transform (DWT) is used to the segmented image. Once the image gets segmented then, the Gray-Level Co-occurrence Matrix (GLCM) method is applied to extract the features in terms of statistical texture-based. Further the image which is applied to Principle Component Analysis (PCA) to reduce size of the feature vector. Besides, Bone Mineral Density (BMD) feature namely calcium volume is estimated from abnormal region in the segmented bone image and it is concatenated with the extracted texture features to obtain the final feature vectors. In the final stage, the Multi-class Support Vector Machine (MSVM) takes feature vectors as a inputto classify bone disorders. The simulation result demonstrates that the proposed system achieved the accuracy of 95.1% and sensitivity of 96.15%.