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

Hasan, A; Meziane, F; Aspin, R; Jalab, HA

Abstract

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.

Citation

Hasan, A., Meziane, F., Aspin, R., & Jalab, H. (2016). Segmentation of brain tumors in MRI images using three-dimensional active contour without edge. Symmetry, 8(11), 132. https://doi.org/10.3390/sym8110132

• Journal Article Type: Article
• Acceptance Date: Nov 14, 2016
• Online Publication Date: Nov 18, 2016
• Publication Date: Nov 18, 2016
• Deposit Date: Dec 7, 2016
• Publicly Available Date: Dec 7, 2016
• Journal: Symmetry
• Publisher: MDPI
• Volume: 8
• Issue: 11
• Pages: 132
• DOI: https://doi.org/10.3390/sym8110132
• Publisher URL: http://dx.doi.org/10.3390/sym8110132
• Related Public URLs: http://www.mdpi.com/journal/symmetry