Valuable Segmentation Strategy for Tumor Detection in MRI Images

Authors

  • Ullas Agrwal Electronics & Telecommunication, Rungta College ofEngineering and Technology, Bhilai
  • Pankaj Kumar Mishra Rungta College of Engineering & Technology, Bhilai

Keywords:

MRI, brain tumor, segmentation, k-means clustering, genetic algorithm, c-means clustering

Abstract

Brain tumor extraction and its evaluation are tough obligations in clinical photograph processing due to the fact mind photograph and its shape is complex that may be analyzed simplest through professional radiologists. Segmentation performs a crucial function within side the processing of clinical images. MRI (magnetic resonance imaging) has come to be a in particular beneficial clinical   diagnostic device for analysis of mind and different clinical images. This paper gives a comparative take a look at of 3 segmentation techniques carried out for tumor detection. The techniques consist of k-method clustering with watershed segmentation set of rules, optimized k-method clustering with genetic set of rules and optimized c- method clustering with genetic set of rules. Traditional k-method set of rules is touchy to the preliminary cluster centers. Genetic c- method and k-method clustering strategies are used to locate tumor in MRI of mind images. At the stop of technique, the tumor is extracted from the MR photograph and its genuine function and the form are determined. The experimental effects imply that genetic c-method now no longer simplest dispose of the over- segmentation problem, however additionally offer rapid and green clustering effects.

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Published

2021-11-18

How to Cite

Agrwal, U., & Mishra, P. K. (2021). Valuable Segmentation Strategy for Tumor Detection in MRI Images. CSVTU International Journal of Biotechnology, Bioinformatics and Biomedical, 6(02). Retrieved from http://csvtujournal.in/index.php/ijbbb/article/view/156

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