A Brain Abnormality Detection and Tissue
SegmentationTechnique by Using Dual Mode
Classifier
Angel Viji1 and Jayakumari Jayaraj2
1Department of Computer Science and Engineering, Noorul Islam University, India
2Department of Electronic and Communication, Noorul Islam University, India
Abstract:
In the analysis of brain Magnetic Resonance Images (MRI), tissue
classification is an important issue. Many works have been done to classify the
brain tissues from the brain MRI. This paper presents a new technique to
classify the brain MRI images and to perform tissue classification by using
dual mode classifier. Initially, the brain MRI images are obtained from the
brain databases and features such as covariance and correlation are calculated
from the input brain MRI images. These calculated features are given to Feed
Forward Back Propagation Neural Network (FFBNN) to detect whether the given MRI
brain image is normal or abnormal. After detection, the resultant image is
subjected to the segmentation process with the use of Optimized Region Growing
(ORGW) technique to accomplish efficient segmentation. Following that, by
utilizing Local Binary Pattern (LBP), texture feature is computed from the
segmented brain MRI images. Then this texture feature is given as the input to
the dual mode classifier which has two branches. One branch classifies the
normal tissues such as Grey Matter (GM), White Matter (WM) and Cerebrospinal
Fluid (CF) and the other branch classifies the abnormal tissues such as Tumor
and Edema. The performance of our proposed technique is compared with other
techniques such as Conventional Region Growing (RGW), and MRGW.
Keywords: ORGW, LBP, dual mode classifier, FFBNN, correlation,
covariance.
Received December 24, 2013; accepted November 4, 2014