Segmentation of Spine Tumour Using K-Means and Active Contour and Feature Extraction Using GLCM

Segmentation of Spine Tumour Using K-Means and Active Contour and Feature Extraction Using GLCM

Malathi M., Sujatha Kesavan, Praveen K.
Copyright: © 2021 |Pages: 14
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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Manual segmentation refers the human operator or physician performs segmentation and labeling of an image by hand. The separation is performed in a slice by slice method on a 3-D volumetric image. Depending on the artifacts present in the medical image makes the segmentation is an easy or difficult process. But manual segmentation requires a long time to complete the task. Recent days the CT and MRI imaging technique is mostly used in the diagnosis, treatment planning and clinical studies require computers to assist the radiologist experts. In order to perform the segmentation of a large amount of images with the same accuracy, the computer aided diagnosis was implemented

(Shan Shen et al., 2005) The author proposed technique in which noises are present in MRI brain images due to poor operating performance, disturbances, surroundings and poor equipment maintenance. This noise affects the accuracy of the segmentation process. Many conventional algorithms are available for tumour segmentation. Clustering is grouping of pixels depends on the intensity value of a pixel. Conventional clustering method mainly based on the intensity value of a pixel, which leads to poor segmentation. Hence segmentation of the tumour is improved by using a neighborhood attraction method, which will provide comparative position and structures of neighbouring pixels. The proposed methodology does not use intensity as a single parameter to perform segmentation of tumour, but the intensity of neighbouring pixels was also taken into account for segmentation of tumour.

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