A tumour segmentation approach from FLAIR MRI brain images using SVM and genetic algorithm Online publication date: Fri, 14-Aug-2020
by S.U. Aswathy; G. Glan Devadhas; S.S. Kumar
International Journal of Biomedical Engineering and Technology (IJBET), Vol. 33, No. 4, 2020
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.
Existing subscribers:
Go to Inderscience Online Journals to access the Full Text of this article.
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Biomedical Engineering and Technology (IJBET):
Login with your Inderscience username and password:
Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.
If you still need assistance, please email subs@inderscience.com