Comparative Analysis of Different Algorithms for Brain Tumor DetectionJournal: International Journal of Science and Research (IJSR) (Vol.3, No. 6)
Publication Date: 2014-06-15
Authors : Richa Aggarwal; Amanpreet Kaur;
Page : 1159-1163
Keywords : Histogram Thresholding; region growing; FCM; K-mean and Watershed segmentation;
Brain tumor is one of the major causes of death among people. It is evident that the chances of survival can be increased if the tumor is detected and classified correctly at its early stage. The segmentation of brain tumors in magnetic resonance images (MRI) is a challenging and difficult task because of the variety of their possible shapes, locations, image intensities. In this paper, it is intended to summarize and compare the methods of automatic detection of brain tumor through Magnetic Resonance Image using Histogram Thresholding with FCM, Region growing, K-mean and Watershed segmentation. The proposed method can be successfully applied to detect the contour of the tumor and its geometrical dimension. MRI brain tumor images detection is a difficult task due to the variance and complexity of tumors. This paper presents three techniques for the detection purpose; first one is Histogram Thresholding, Second K-mean, third is FCM, fourth is Region growing technique and fifth is Watershed segmentation. In this paper, the purposed method is more accurate and effective for the brain tumor detection and segmentation for MRI (DICOM) images. For the implementation of this proposed work we use the Image Processing Toolbox under MATLAB Software.
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Last modified: 2014-06-25 20:54:36