A Review on Various Image Segmentation Techniques for Brain Tumor Detection

Authors

  • Munmun Saha  Department of Computer Science and Application, Sambalpur University, Jyoti Vihar, Sambalpur, Odisha, India
  • Chandrasekhar Panda   Department of Computer Science and Application, Sambalpur University, Jyoti Vihar, Sambalpur, Odisha, India

Keywords:

MRI, Segmentation, Clustering, K-means algorithm, Fuzzy C-mean algorithm, edge detection, Thresholding, Region Growing, Region Splitting, Watershed Segmentation Algorithm, Entropy, SVM.

Abstract

Segmentation is consider as one of the main step in image processing and it plays and important role in image processing. It is the process of subdividing an image into its constituent parts. In this paper we have reviewed various methods of segmentation and its application in medical image processing i.e. MRI image Ultrasound Image etc, we have focused on Brain Tumor MRI image. Recent medical imaging research faces the challenge of detecting brain tumor through MRI(Magnetic Resonance Image). There is a high diversity in the appearance of tumor tissue among different patients and in many cases similarity with the usual tissue. We have used MRI because it provide accurate visualize of anatomical structure of tissue. In this paper various method that have been used for segmentation of MRI for detecting brain tumor is reviewed.

References

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IInternational Journal of Scientific Research in Computer Science, Engineering and Information Technology

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Published

2018-02-28

Issue

Volume 3, Issue 1, January-February-2018

Section

Research Articles

License

Copyright (c) IJSRCSEIT

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This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite

[1]
Munmun Saha, Chandrasekhar Panda , " A Review on Various Image Segmentation Techniques for Brain Tumor Detection, IInternational Journal of Scientific Research in Computer Science, Engineering and Information Technology(IJSRCSEIT), ISSN : 2456-3307, Volume 3, Issue 1, pp.21-30, January-February-2018.