Title: Brain tumour segmentation and detection using modified region growing and genetic algorithm in MRI images

Authors: A.R. Kavitha; C. Chellamuthu

Addresses: Information Technology Department, Jerusalem College of Engineering, Anna University, Chennai 600100, India ' EEE Department, R.M.K. Engineering College, Anna University, Chennai 600025, India

Abstract: In modern medical research several approaches of image segmentation are used for the detection of brain tumour. Several pieces of medical equipment such as magnetic resonance imaging (MRI) scans, X-ray and computed tomography (CT) are used for diagnosis of brain tumour. This paper proposes a new segmentation method which combines modified region growing and genetic algorithm for detecting brain tumour. This consists of four steps-pre-processing, segmentation, classification and fitness calculation. Pre-processing uses Gaussian filter for removal of noise present in the image. The pre-processed image is segmented using modified region growing (MRG) method which includes the orientation constraint in addition to the intensity constraint used in region growing (RG) method. Back propagation neural network (BPNN) classifier classifies the tumour as normal or abnormal. Then a genetic approach of initial population and fitness calculation is done to find the optimum value for the best segmented tumour portion of the MRI image. The proposed approach overcomes dark abnormalities as well as over segmentation problem. Implementing the proposed method on MRI image helps in creating awareness to patients and it also serves as a perquisite for Radiologists, doctors in rural areas for providing effective treatment to the brain tumour patients.

Keywords: Gaussian filter; modified region growing; MRG; genetic algorithm; GA; back propagation neural network; BPNN; magnetic resonance imaging; MRI; fitness.

DOI: 10.1504/IJMEI.2017.085052

International Journal of Medical Engineering and Informatics, 2017 Vol.9 No.3, pp.269 - 283

Received: 03 Sep 2015
Accepted: 13 Sep 2016
Published online: 10 Jul 2017 *

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