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2012 (Vol. 4, Issue: 3)
Article Information:

Automatic Brain Tumors Segmentation of MR Images using Fluid Vector Flow and Support Vector Machine

B.Vijayakumar and Ashish Chaturvedi
Corresponding Author:  B. Vijayakumar 

Key words:  Active contour models, fluid vector flow, Gaussian filter, Magnetic Resonance Imaging (MRI), Support Vector Machine (SVM), ,
Vol. 4 , (3): 108-114
Submitted Accepted Published
August 15, 2012 September 08, 2012 October 30, 2012

Manual segmentation of brain tumors by medical practitioners is a time consuming task and has inability to assist in accurate diagnosis. Several automatic methods have been developed to overcome these issues. But Automatic MRI (Magnetic Resonance Imaging) brain tumor segmentation is a complicated task due to the variance and intricacy of tumors; to over by this problem we have developed a new method for automatic classification of brain tumor. In the proposed method the MRI Brain image classification of tumors is done based on Fluid vector flow and support vector machine classifier. In this method Fluid Vector Flow is utilized for segmentation of two dimensional brain tumor MR images to extract the tumor and that tumor can be projected into the three dimensional plane to analyze the depth of the tumor. Finally, Support vector machine classifier is utilized to perform two functions. The first is to differentiate between normal and abnormal. The second function is to classify the type of abnormality in benign or malignant tumor.
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  Cite this Reference:
B.Vijayakumar and Ashish Chaturvedi, 2012. Automatic Brain Tumors Segmentation of MR Images using Fluid Vector Flow and Support Vector Machine.  Research Journal of Information Technology , 4(3): 108-114.
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ISSN (Online):  2041-3114
ISSN (Print):   2041-3106
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