Authors: S.U. Aswathy; G. Glan Devadhas; S.S. Kumar
Addresses: Department of Computer Science and Engineering, Jyothi Engineering College, Thrissur, India ' Electronics and Instrumentation Department, Vimal Jyothi Engineering College, Jyothi Nagar, Kannur District, Chemperi, Kerala, India ' Electronics and Instrumentation Department, Noorul Islam University, Kumaracoil, Thuckalay, Kanyakumari, Tamil Nadu, India
Abstract: This paper puts forth a framework of a medical image analysis system for brain tumour segmentation. Image segmentation helps to segregate objects right from the background, thus proving to be a powerful tool in medical image processing. This paper presents an improved segmentation algorithm rooted in support vector machine and genetic algorithm. SVM is the basis technique used for segmentation and classification of medical images. The MRI database used consists of FLAIR images. The proposed system consists of two stages. The first stage performs preprocessing the MRI image, followed by block division. The second stage includes - feature extraction, feature selection and finally, the SVM-based training and testing. The feature extraction is done by first order histogram and co-occurrence matrix and GA using KNN is used to select subset features. The performance of the proposed system is evaluated in terms of specificity, sensitivity, accuracy, time elapsed and figure of merit.
Keywords: segmentation; support vector machine; SVM; genetic algorithm; k nearest neighbours; KNN.
International Journal of Biomedical Engineering and Technology, 2020 Vol.33 No.4, pp.386 - 397
Received: 08 Jul 2017
Accepted: 11 Oct 2017
Published online: 14 Aug 2020 *