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Segmentation of Spine Tumour Using K-Means and Active Contour and Feature Extraction Using GLCM
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Author(s): Malathi M. (Rajalakshmi Institute of Technology, India), Sujatha Kesavan (Dr. M. G. R. Educational Research Institute of Technology, India)and Praveen K. (Chennai Institute of Technology, India)
Copyright: 2021
Pages: 14
Source title:
AI Innovation in Medical Imaging Diagnostics
Source Author(s)/Editor(s): Kalaivani Anbarasan (Department of Computer Science and Engineering, Saveetha School of Engineering, India & Saveetha Institute of Medical and Technical Sciences, Chennai, India)
DOI: 10.4018/978-1-7998-3092-4.ch011
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Abstract
MRI imaging technique is used to detect spine tumours. After getting the spine image through MRI scans calculation of area, size, and position of the spine tumour are important to give treatment for the patient. The earlier the tumour portion of the spine is detected using manual labeling. This is a challenging task for the radiologist, and also it is a time-consuming process. Manual labeling of the tumour is a tiring, tedious process for the radiologist. Accurate detection of tumour is important for the doctor because by knowing the position and the stage of the tumour, the doctor can decide the type of treatment for the patient. Next, important consideration in the detection of a tumour is earlier diagnosis of a tumour; this will improve the lifetime of the patient. Hence, a method which helps to segment the tumour region automatically is proposed. Most of the research work uses clustering techniques for segmentation. The research work used k-means clustering and active contour segmentation to find the tumour portion.
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