TY - JOUR ID - 80141 TI - An Automatic Bone Disorder Classification Using Hybrid Texture Feature Extraction with Bone Mineral Density JO - Asian Pacific Journal of Cancer Prevention JA - APJCP LA - en SN - 1513-7368 AU - S, Ramkumar AU - R, Malathi AD - Department of EIE, Annamalai University, India. Y1 - 2018 PY - 2018 VL - 19 IS - 12 SP - 3517 EP - 3523 KW - Osteoporosis KW - osteopenia KW - DWT (Discrete Wavelet Transform) KW - GLCM (Gray-Level Co-occurrence Matrix) KW - MSVM (Multi-class Support Vector Machine) DO - 10.31557/APJCP.2018.19.12.3517 N2 - A novel approach has been proposed to classify bone disorders for classifying the radiographic bone image asnormal, Osteopenia and Osteoporosis. The proposed system consists of three major stages to predict the accurate bonedisorder classification. In the first stage, image preprocessing is performed where bilateral filtering is applied to removenoise and to enhance the image quality. Then, the image is fed to Otsu based segmentation approach for segmentingthe abnormal area of the bone image. In the second stage, Discrete Wavelet Transform (DWT) is used to the segmentedimage. Once the image gets segmented then, the Gray-Level Co-occurrence Matrix (GLCM) method is applied to extractthe 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 isestimated from abnormal region in the segmented bone image and it is concatenated with the extracted texture featuresto obtain the final feature vectors. In the final stage, the Multi-class Support Vector Machine (MSVM) takes featurevectors as a inputto classify bone disorders. The simulation result demonstrates that the proposed system achieved theaccuracy of 95.1% and sensitivity of 96.15%. UR - https://journal.waocp.org/article_80141.html L1 - https://journal.waocp.org/article_80141_185c6fdd4f26ad59dfce22d96e29736a.pdf ER -