Assessment on Brain Tumor Detection Techniques in Hyperintense Mr Images
B. Jefferson1, R. S. Shanmugasundaram2
1B. Jefferson*, Research Scholar, Department of Computer Science, Vinayaka Mission’s Research Foundation , Ariyanoor, Salem, Tamilnadu, India. Email:
2Dr. R. S. Shunmugasundaram, Professor, Department of Computer Science and Engineering, V.M.K.V. Engineering College, Periya Seeragapadi, Salem, Tamilnadu, India.
Manuscript received on January 05, 2020. | Revised Manuscript received on January 25, 2020. | Manuscript published on January 30, 2020. | PP: 3895-3908 | Volume-8 Issue-5, January 2020. | Retrieval Number: E6156018520/2020©BEIESP | DOI: 10.35940/ijrte.E6156.018520
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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: Brain tumors have different characteristics such as shape, size, location, and image intensities. Magnetic-resonance images (MRIs) typically have a degree of noise and randomness associated with the natural random nature of brain structure. MRI is a profoundly created medical imaging strategy giving a range of data about the individual’s delicate tissue structure. Even though it gives a rich data, the complex dynamics of the tumor evolution cannot be captured perfectly because of the uncertainty in the tumor segmentations. Different methods are available to identify and segment a brain tumor. Stages of medical image processing in brain tumor detection are discussed in this paper and overview of the analogous papers is quoted by analyzing several research papers. This paper provides delving of technologies which can be used to prognosticate brain tumor.
Keywords: Brain Tumor, Classification Techniques, Feature Extraction, MRI, Noise, Segmentation, Tissue Structure.
Scope of the Article: Classification.