A novel detection on automatic brain tumor tissue based on hierarchical centroid shape descriptor in t1-weighted Mr images

Author: 
Dr. Sudheer, N. and Hrushi Kesava Raju, S, Nirupama, P.
Abstract: 

The brain tumor tissue detection allows localizinga mass of abnormal cells in a slice of Magnetic Resonance (MR). The automatization of this process is useful for post processing of the extracted region of interest like the tumor segmentation. In order to detect this abnormal growth of tissue in an image, this paper presents a novel scheme which uses a two-step procedure; the k-means method and the Hierarchical Centroid Shape Descriptor (HCSD). The clustering stage is applied to discriminate structures based on pixel intensity while the HCSD allow to select only those having a specific shape. A bounding box is then automatically placed to delineate the region in which the tumor was found. Compared to the tumor delineation performed by an expert, a similarity measure of 91% was reached by using the Dice coefficient. The tests were carried out on 254 T1-weighted MRI images of 14 patients with brain tumors.

Download PDF: 

CHIEF EDITOR

  

           Prof. Dr. Bilal BİLGİN

Call for Papers - 2017

    submit your paper now

   Vol. 07, Issue 01, January 2017

CURRENT ISSUE

 

Article Tracking

Get your Certificate

IMPACT FACTOR CERTIFICATE

 

 

Copyright © 2016 International Journal Development Research. All Rights Reserved.