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A Comparative Analysis of Brain Tumor Segmentation Techniques
Brain tumor is one among the conscientious diseases in medical discipline. A brain tumor is an assembly of anomalous cells that develops in or around the brain area. These Tumors can candidly wreck firm brain cells. They can likewise by allusion harm sound cells by swarming different parts of the brain and bringing on irritation, brain swelling and weight inside the skull. Brain tumors are either dangerous or harmless. Brain tumor identification and segmentation is one of the trickiest and tedious undertaking in restorative image handling. MRI (Magnetic Resonance Imaging) is a therapeutic system, fundamentally utilized by the radiologist for representation of the inward structure of the human body without any surgery. MRI gives copious data about the delicate human tissue, which helps in the analysis of brain tumor. The exact segmentation of MRI image is essential for the analysis of brain tumor by system supported clinical apparatus. This paper focuses on various technologies and implementations for segmenting brain tumor images. Besides summarizing those classification techniques, this paper also provides a basic evaluation of these facets of segmenting tumor images.
Fuzzy Clustering, K-Means Clustering, Linear Svm, Morphological Filtering, Mri Brain Tumor Segmentation, Neutrosophic Set Approach, Pnn and Grnn.
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