Next Article in Journal
The Novel Concept of “Behavioural Instability” and Its Potential Applications
Next Article in Special Issue
Attribute Control Chart Construction Based on Fuzzy Score Number
Previous Article in Journal / Special Issue
Some Invariants of Circulant Graphs
Article Menu

Export Article

Open AccessArticle
Symmetry 2016, 8(11), 132; doi:10.3390/sym8110132

Segmentation of Brain Tumors in MRI Images Using Three-Dimensional Active Contour without Edge

1
School of Computing, Science and Engineering, University of Salford, Manchester M5 4WT, UK
2
Computers Unit, College of Medicine, Al-Nahrain University, Baghdad 64074, Iraq
3
Faculty of Computer Science & Information Technology, University of Malaya, Kuala Lumpur 50603, Malaysia
*
Author to whom correspondence should be addressed.
Academic Editor: Angel Garrido
Received: 13 October 2016 / Revised: 13 November 2016 / Accepted: 14 November 2016 / Published: 18 November 2016
(This article belongs to the Special Issue Symmetry in Complex Networks II)
View Full-Text   |   Download PDF [4834 KB, uploaded 18 November 2016]   |  

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. View Full-Text
Keywords: magnetic resonance imaging; modified gray level co-occurrence matrix; three-dimensional active contour without edge; two-dimensional active contour without edge magnetic resonance imaging; modified gray level co-occurrence matrix; three-dimensional active contour without edge; two-dimensional active contour without edge
Figures

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

Scifeed alert for new publications

Never miss any articles matching your research from any publisher
  • Get alerts for new papers matching your research
  • Find out the new papers from selected authors
  • Updated daily for 49'000+ journals and 6000+ publishers
  • Define your Scifeed now

SciFeed Share & Cite This Article

MDPI and ACS Style

Hasan, A.M.; Meziane, F.; Aspin, R.; Jalab, H.A. Segmentation of Brain Tumors in MRI Images Using Three-Dimensional Active Contour without Edge. Symmetry 2016, 8, 132.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Symmetry EISSN 2073-8994 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top