Segmentation of brain tumors in MRI images using three-dimensional active contour without edge

Hasan, A, Meziane, F ORCID:, Aspin, R ORCID: and Jalab, HA 2016, 'Segmentation of brain tumors in MRI images using three-dimensional active contour without edge' , Symmetry, 8 (11) , p. 132.

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Brain tumor segmentation in magnetic resonance imaging (MRI) is considered a complex procedure because of the variability of tumor shapes and the complexity of determining the tumor location, size, and texture. Manual tumor segmentation is a time-consuming task highly prone to human error. Hence, this study proposes an automated method that can identify tumor slices and segment the tumor across all image slices in volumetric MRI brain scans. First, a set of algorithms in the pre-processing stage is used to clean and standardize the collected data. A modified gray-level co-occurrence matrix and Analysis of Variance (ANOVA) are employed for feature extraction and feature selection, respectively. A multi-layer perceptron neural network is adopted as a classifier, and a bounding 3D-box-based genetic algorithm is used to identify the location of pathological tissues in the MRI slices. Finally, the 3D active contour without edge is applied to segment the brain tumors in volumetric MRI scans. The experimental dataset consists of 165 patient images collected from the MRI Unit of Al-Kadhimiya Teaching Hospital in Iraq. Results of the tumor segmentation achieved an accuracy of 89% +/- 4.7% compared with manual processes.

Item Type: Article
Schools: Schools > School of Computing, Science and Engineering > Salford Innovation Research Centre
Journal or Publication Title: Symmetry
Publisher: MDPI (Multidisciplinary Digital Publishing Institute)
ISSN: 2073-8994
Related URLs:
Funders: Non funded research
Depositing User: Prof Farid Meziane
Date Deposited: 07 Dec 2016 12:40
Last Modified: 20 May 2019 10:45

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