Various Techniques used for MRI Brain Image Segmentation
Sana Ali1, Jitendra Agrawal2
1Sana Ali*, School of Information Technology, Rajiv Gandhi Proudyogiki Vishwavidyalaya, Bhopal, India.
2Jitendra Agrawal, School of Information Technology, Rajiv Gandhi Proudyogiki Vishwavidyalaya, Bhopal, India.
Manuscript received on November 12, 2019. | Revised Manuscript received on 24 November, 2019. | Manuscript published on December 10, 2019. | PP: 2087-2091 | Volume-9 Issue-2, December 2019. | Retrieval Number: L38611081219/2019©BEIESP | DOI: 10.35940/ijitee.L3861.129219
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Image processing methods stems from processing of images from storing, transmitting and representation for autonomous machine perception. It is widely used technique in all engineering areas including diagnosis and treatment of diseases in Medical Science. Segmentation of non trivial images is one of the most challenging task because it requires more sophisticated method to differentiate between each region. The objective of this paper is to provide comprehensive overview of various segmentation methods used for MRI brain images. Hence, study of existing algorithms is all important for achieving accuracy. First, we briefly discuss about brain tumors, imaging modalities of brain and then several segmentation algorithms surveyed in this work. Complexities of existing algorithms and the segmentation outputs and analysis has also been discussed in observations.
Keywords: Image Processing, Magnetic Resonance Imaging (MRI), Segmentation, Machine Learning Techniques.
Scope of the Article: Signal and Image Processing