A Review on 3D-Brain Tumour Segmentation Methods

Vol-3 | Issue-11 | November 2018 | Published Online: 10 November 2018    PDF ( 240 KB )
Author(s)
Himani Parmar 1; Bijal Talati 2

1Research Scholar, Computer Engineering Department, Sardar Vallabhbhai Patel Institute of Technology, Vasad, Gujarat (India)

2Professor Computer Engineering Department, Sardar Vallabhbhai Patel Institute of Technology, Vasad, Gujarat (India)

Abstract

There are different segmentation technique for brain tumor but, The computational time taken by K-means is more than Fuzzy C-Means, making FCM better in the clustering technique. The performance of the Otsu thresholding is better than other thresholding techniques and also the clustering techniques. Morphological opening and closing is better in the showing of the content of the image than the other techniques. In Existing methods Color, Cluster and Morphological Based Approach is used to Segment the Brain Tumor Part. In Color and morphological based method affected by noise Where Cluster based method needs A priori specification of no. of clusters. From above problems a proposed method can be made by Adaptive KWFCM method to segment accurate and fast brain tumor.

Keywords
3D segmentation, KWFCM, 2D segmentation, clustering, k-means, Fuzzy C-Means, otsu segmentations,ROI,3D domain, Brain tumor, MRI data,3D reconstruction
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