1. Statistical analysis of segmentation techniques for brain tumor detection
Authors : Tejas P, S K Padma
Pages : 36-40
DOI : http://dx.doi.org/10.21172/1.173.05
Keywords : Brain tumor detection, Image segmentation, Threshold segmentation, K means clustering, Watershed segmentation, Level set segmentation Abstract :Image segmentation is the fundamental step in medical image analysis. Segmentation is a procedure to separate similar portions of images showing resemblance in different features such as color, intensity, or texture. Gray scale images are mostly used for the segmentation of medical images. Tumors are commonly stated as the abnormal growth of tissues and the brain tumor is a diseased part in the body tissues that is an abnormal mass in which growth rate of cells is irrepressible. The mortality rate of people has raised over the past years due to brain tumor, hence this area has gained the attention of researchers. Automatic detection of brain tumor is a challenging task because it involves pathology, functional physics of MRI along with intensity and shape analysis of MR image, because tumor shape, size, location and intensity vary for each infected case. In this work, a comparison between Threshold segmentation, K means clustering, Watershed segmentation and Level set-based segmentation methods are performed to detect the tumor region of the brain. Statistical and Visual analysis is performed to figure out the best method. This research could help clinicians in surgical planning, treatment planning and accurately segmenting the tumor part with the most accurate method.
Citing this Journal Article :Tejas P, S K Padma, "Statistical analysis of segmentation techniques for brain tumor detection", Volume 17 Issue 3 - September 2020, 36-40
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