Title: Automatic segmentation of pathological region (tumour and oedema) in high grade glioma multi-sequence MR images through voted prediction from pixel level feature sets

Authors: R. Geetha Ramani; K. Sivaselvi

Addresses: Department of Information Science and Technology, College of Engineering, Anna University, Guindy, Chennai, India ' Department of Information Science and Technology, College of Engineering, Anna University, Guindy, Chennai, India

Abstract: Automatic region segmentation of brain from the neuroimages is an active research area in the medical domain. Currently, different kinds of magnetic resonance imaging acquisition are performed such that each technique highlights a specific region in the brain making multi-sequence images a better candidate for investigation when compared to single sequence. The abnormal regions (tumour and oedema) in glioma images are segmented through hybrid technology involving preprocessing, feature extraction and classification. The extracted features are grouped and random forest procedure is applied on each set and the prediction is obtained that minimises the randomisation. The final prediction of a pixel is obtained by aggregation of individual predictions from feature set through maximum voting which increases the ensembling and improves the outcome appreciably. The average dice coefficient of tumour and oedema segmentation is 0.96 and 0.94 respectively with three-fold cross validation. The results show significant improvement when compared to earlier methodologies.

Keywords: magnetic resonance imaging; brain tumour segmentation; image analysis; data mining; classification; MICCAI BRATS 2012 challenge dataset; random forest; glioma; tumour; oedema.

DOI: 10.1504/IJITST.2018.095929

International Journal of Internet Technology and Secured Transactions, 2018 Vol.8 No.4, pp.553 - 572

Received: 09 May 2017
Accepted: 27 Jun 2017
Published online: 05 Nov 2018 *

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