In The international journal of medical robotics + computer assisted surgery : MRCAS
BACKGROUND : Segmentation of brain tumors is a complex problem in medical image processing and analysis. It is a time-consuming and error-prone task. Therefore, computer-aided detection (CAD) systems need to be developed to decrease physicians' workload and improve the accuracy of segmentation.
METHODS : This paper proposes a level set method constrained by an intuitive artificial intelligence-based approach to perform brain tumor segmentation. By studying 3D brain tumor images, a new segmentation technique based on the Modified Particle Swarm Optimization (MPSO), Darwin Particle Swarm Optimization (DPSO), and Fractional Order Darwinian Particle Swarm Optimization (FODPSO) algorithms were developed.
RESULTS : The introduced technique was verified according to the MICCAI RASTS 2013 database for high-grade glioma (HGG) patients. The three algorithms were evaluated using different performance measures: accuracy, sensitivity, specificity, and Dice similarity coefficient (DSC) to prove the performance and robustness of our 3D segmentation technique.
CONCLUSION : The result is that the MPSO algorithm consistently outperforms the DPSO and FO DPSO. This article is protected by copyright. All rights reserved.
Gtifa Wafa, Hamdaoui Fayçal, Sakly Anis
2022-Dec-07
3D brain tumor, DPSO, MPSO, Segmentation
