Automatic Detection of Elbow Abnormalities in X-ray Imagery

Abnormality or deformity in any of the bone disrupts overall function of human skeleton. Hence, orthopedic abnormalities are common reasons for emergency department visits and elbow deformation is one of the common issue seen among emergency patients. Despite high frequency of elbow-related casualties, there is no standardized method for interpretation of digital X-rays. Accordingly, we have proposed a model for automatic deformation detection in elbow and connected forearm bones using Image Processing techniques. The X-ray images were preprocessed and the region of interest is segmented using Multi Class Probabilistic Segmentation in first step. Subsequently, multi-phase canny edge detector was used to highlight the edges and descriptive features were extracted to differentiate among normal and abnormal X-rays. On the basis of those features, three tests were performed to automatically trace deformities in different bones associated with elbow. The publically available Musculoskeletal Radiographs (MURA) dataset has been used in this research. Hence, 250 elbow X-rays from the dataset were investigated for geometrical shape distortions, crack, damage and extra-ordinary distance between the bones. Accordingly, the proposed method shows promising results in terms of 86.20% accuracy.

@article{Afzal2020,
  title     = {Automatic Detection of Elbow Abnormalities in X-ray Imagery},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {11},
  number    = {12},
  year      = {2020},
  publisher = {The Science and Information Organization},
  author    = {Mashal Afzal and M. Moazzam Jawaid and Rizwan Badar Baloch and Sanam Narejo},
  doi       = {10.14569/IJACSA.2020.0111248},
  url       = {https://doi.org/10.14569/IJACSA.2020.0111248}
}