Abstract
In this work, we are trying to implement the segmentation of an X-ray image for cervical spondylosis detection. One of the major concerns of that particular disease is faster detection and identification of diseases in previous stages. According to the opinion of clinical experts, today’s X-ray tomography method is the most effective technology in medical science domain for easily diagnosis of particular cervical diseases. Segmentation is a kind of approach that is used to identify the unambiguous region from the particular X-ray image. Today, the diagnosis of cervical spondylosis becomes one of the challenging work. MRI and CT scans used by a doctor for manual inspection is already available. So our proposed method which is automatically detect and analyze the cervical spondylosis using morphological segmentation and edge detection and classification-based approach. The results of this study gaining more than 90% accuracy and sensitivity for identifying and classifying the cervical diseases in X-ray images more accurately. Here, the experimental performance shows better PSNR and MSE values for image quality measurement of the detection of cervical spondylosis more accurately.
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Acknowledgements
Authors would like to express our deep and earnest gratitude to Dr. Santanu Banarjee, M.S. (Ortho), Consultant Orthopedic Surgeon for valuable suggestions and support to do this work. We also acknowledge our college, Kalyani government engineering college, and the faculties of computer science and engineering for providing us moral support and the facility of department laboratory. We are also thankful to organizing committee providing the proper support for this purpose. Finally, our thanks go to B. N. Bose Sub Divisional Hospital, Kolkata, for providing me the particular data set of CT scan image which is a very important part of the research study.
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Paul, A., Paul, A., Chanda, P.B. (2020). Detection and Classification of Cervical Spondylosis Using Image Segmentation Techniques. In: Mandal, J., Bhattacharya, K., Majumdar, I., Mandal, S. (eds) Information, Photonics and Communication. Lecture Notes in Networks and Systems, vol 79. Springer, Singapore. https://doi.org/10.1007/978-981-32-9453-0_15
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