Hybrid RGSA and Support Vector Machine Framework for Three-Dimensional Magnetic Resonance Brain Tumor Classification

A novel hybrid approach for the identification of brain regions using magnetic resonance images accountable for brain tumor is presented in this paper. Classification of medical images is substantial in both clinical and research areas. Magnetic resonance imaging (MRI) modality outperforms towards d...

Full description

Bibliographic Details
Main Authors: R. Rajesh Sharma, P. Marikkannu
Format: Article
Language:English
Published: Hindawi Limited 2015-01-01
Series:The Scientific World Journal
Online Access:http://dx.doi.org/10.1155/2015/184350
id doaj-1b9d66c6eabc45e0ad7a4b51e1e9974f
record_format Article
spelling doaj-1b9d66c6eabc45e0ad7a4b51e1e9974f2020-11-25T00:50:44ZengHindawi LimitedThe Scientific World Journal2356-61401537-744X2015-01-01201510.1155/2015/184350184350Hybrid RGSA and Support Vector Machine Framework for Three-Dimensional Magnetic Resonance Brain Tumor ClassificationR. Rajesh Sharma0P. Marikkannu1Department of IT, Hindusthan College of Engineering and Technology, Coimbatore, Tamil Nadu 641 032, IndiaDepartment of IT, Anna University Regional Centre, Coimbatore, Tamil Nadu 641 046, IndiaA novel hybrid approach for the identification of brain regions using magnetic resonance images accountable for brain tumor is presented in this paper. Classification of medical images is substantial in both clinical and research areas. Magnetic resonance imaging (MRI) modality outperforms towards diagnosing brain abnormalities like brain tumor, multiple sclerosis, hemorrhage, and many more. The primary objective of this work is to propose a three-dimensional (3D) novel brain tumor classification model using MRI images with both micro- and macroscale textures designed to differentiate the MRI of brain under two classes of lesion, benign and malignant. The design approach was initially preprocessed using 3D Gaussian filter. Based on VOI (volume of interest) of the image, features were extracted using 3D volumetric Square Centroid Lines Gray Level Distribution Method (SCLGM) along with 3D run length and cooccurrence matrix. The optimal features are selected using the proposed refined gravitational search algorithm (RGSA). Support vector machines, over backpropagation network, and k-nearest neighbor are used to evaluate the goodness of classifier approach. The preliminary evaluation of the system is performed using 320 real-time brain MRI images. The system is trained and tested by using a leave-one-case-out method. The performance of the classifier is tested using the receiver operating characteristic curve of 0.986 (±002). The experimental results demonstrate the systematic and efficient feature extraction and feature selection algorithm to the performance of state-of-the-art feature classification methods.http://dx.doi.org/10.1155/2015/184350
collection DOAJ
language English
format Article
sources DOAJ
author R. Rajesh Sharma
P. Marikkannu
spellingShingle R. Rajesh Sharma
P. Marikkannu
Hybrid RGSA and Support Vector Machine Framework for Three-Dimensional Magnetic Resonance Brain Tumor Classification
The Scientific World Journal
author_facet R. Rajesh Sharma
P. Marikkannu
author_sort R. Rajesh Sharma
title Hybrid RGSA and Support Vector Machine Framework for Three-Dimensional Magnetic Resonance Brain Tumor Classification
title_short Hybrid RGSA and Support Vector Machine Framework for Three-Dimensional Magnetic Resonance Brain Tumor Classification
title_full Hybrid RGSA and Support Vector Machine Framework for Three-Dimensional Magnetic Resonance Brain Tumor Classification
title_fullStr Hybrid RGSA and Support Vector Machine Framework for Three-Dimensional Magnetic Resonance Brain Tumor Classification
title_full_unstemmed Hybrid RGSA and Support Vector Machine Framework for Three-Dimensional Magnetic Resonance Brain Tumor Classification
title_sort hybrid rgsa and support vector machine framework for three-dimensional magnetic resonance brain tumor classification
publisher Hindawi Limited
series The Scientific World Journal
issn 2356-6140
1537-744X
publishDate 2015-01-01
description A novel hybrid approach for the identification of brain regions using magnetic resonance images accountable for brain tumor is presented in this paper. Classification of medical images is substantial in both clinical and research areas. Magnetic resonance imaging (MRI) modality outperforms towards diagnosing brain abnormalities like brain tumor, multiple sclerosis, hemorrhage, and many more. The primary objective of this work is to propose a three-dimensional (3D) novel brain tumor classification model using MRI images with both micro- and macroscale textures designed to differentiate the MRI of brain under two classes of lesion, benign and malignant. The design approach was initially preprocessed using 3D Gaussian filter. Based on VOI (volume of interest) of the image, features were extracted using 3D volumetric Square Centroid Lines Gray Level Distribution Method (SCLGM) along with 3D run length and cooccurrence matrix. The optimal features are selected using the proposed refined gravitational search algorithm (RGSA). Support vector machines, over backpropagation network, and k-nearest neighbor are used to evaluate the goodness of classifier approach. The preliminary evaluation of the system is performed using 320 real-time brain MRI images. The system is trained and tested by using a leave-one-case-out method. The performance of the classifier is tested using the receiver operating characteristic curve of 0.986 (±002). The experimental results demonstrate the systematic and efficient feature extraction and feature selection algorithm to the performance of state-of-the-art feature classification methods.
url http://dx.doi.org/10.1155/2015/184350
work_keys_str_mv AT rrajeshsharma hybridrgsaandsupportvectormachineframeworkforthreedimensionalmagneticresonancebraintumorclassification
AT pmarikkannu hybridrgsaandsupportvectormachineframeworkforthreedimensionalmagneticresonancebraintumorclassification
_version_ 1725246728448245760