Fully automated brain tumour segmentation system in 3D‐MRI using symmetry analysis of brain and level sets

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.

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