Automated brain tumour segmentation from multi‐modality magnetic resonance imaging data based on new particle swarm optimisation segmentation method

• Published in: The international journal of medical robotics + computer assisted surgery Vol. 19; no. 3; pp. e2487 - n/a
• Main Authors: Gtifa, Wafa, Hamdaoui, Fayçal, Sakly, Anis
• Format: Journal Article
• Language: English
• Published: England Wiley Subscription Services, Inc 01.06.2023
• Subjects: 3D brain tumour; Algorithms; Artificial Intelligence; Brain; Brain cancer; Brain Neoplasms - diagnostic imaging; DPSO; Glioma; Humans; Image processing; Image Processing, Computer-Assisted - methods; Image segmentation; Magnetic resonance imaging; Magnetic Resonance Imaging - methods; Medical imaging; MPSO; Particle swarm optimization; Performance evaluation; segmentation; Tumors; DPSO; segmentation; MPSO; 3D brain tumour
• ISSN: 1478-5951; 1478-596X; 1478-596X
• DOI: 10.1002/rcs.2487

Background Segmentation of brain tumours is a complex problem in medical image processing and analysis. It is a time‐consuming and error‐prone task. Therefore, computer‐aided detection 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 tumour segmentation. By studying 3D brain tumour images, a new segmentation technique based on the Modified Particle Swarm Optimisation (MPSO), Darwin Particle Swarm Optimisation (DPSO), and Fractional Order Darwinian Particle Swarm Optimisation (FODPSO) algorithms were developed. Results The introduced technique was verified according to the MICCAI RASTS 2013 database for high‐grade glioma patients. The three algorithms were evaluated using different performance measures: accuracy, sensitivity, specificity, and Dice similarity coefficient 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.