Fully automated brain tumour segmentation system in 3D‐MRI using symmetry analysis of brain and level sets. Issue 11 (1st November 2018)

• Record Type: Journal Article
• Authors: Kermi, Adel; Andjouh, Khaled; Zidane, Ferhat
• Is Part Of: IET image processing. Volume 12:Issue 11(2018)
• Page Start: 1964
• Page End: 1971
• Publication Date: 2018-11-01
• Subjects: image classification -- brain -- tumours -- biomedical MRI -- medical image processing -- image segmentation -- neurophysiology; brain tumour segmentation method -- image preprocessing -- manual segmentation performances -- three‐dimensional deformable model -- automated tumour detection -- segmentation process -- boundary‐based segmentation methods -- brain symmetry analysis method -- MRI volumes -- magnetic resonance imaging -- brain tumour segmentation system; Image processing -- Periodicals; 621.36705
• DOI: 10.1049/iet-ipr.2017.1124
• Languages: English
• ISSNs: 1751-9659

Abstract

Abstract : This study presents a new fully automated, fast, and accurate brain tumour segmentation method which automatically detects and extracts whole tumours from 3D‐MRI. The proposed method is based on a hybrid approach that relies on a brain symmetry analysis method and a combining region‐based and boundary‐based segmentation methods. The segmentation process consists of three main stages. In the first one, image pre‐processing is applied to remove any noise, and to extract the brain from the head image. In the second stage, automated tumour detection is performed. It is based essentially on FBB method using brain symmetry. The obtained result constitutes the automatic initialisation of a deformable model, thus removing the need of selecting the initial region of interest by the user. Finally, the third stage focuses on the application of region growing combined with 3D deformable model based on geodesic level‐set to detect the tumour boundaries containing the initial region, computed previously, regardless of its shape and size. The proposed segmentation system has been tested and evaluated on 3D‐MRIs of 285 subjects with different tumour types and shapes obtained from BraTS'2017 dataset. The obtained results turn out to be promising and objective as well as close to ground truth data.