Multi-stage fuzzy swarm intelligence for automatic hepatic lesion segmentation from CT scans

Title

Multi-stage fuzzy swarm intelligence for automatic hepatic lesion segmentation from CT scans

Creator

Anter A.M.; Bhattacharyya S.; Zhang Z.

Description

Segmentation of liver and hepatic lesions using computed tomography (CT) is a critical and challenging task for doctors to accurately identify liver abnormalities and to reduce the risk of liver surgery. This study proposed a novel dynamic approach to improve the fuzzy c-means (FCM) clustering algorithm for automatic localization and segmentation of liver and hepatic lesions from CT scans. More specifically, we developed a powerful optimization approach in terms of accuracy, speed, and optimal convergence based on fast-FCM, chaos theory, and bio-inspired ant lion optimizer (ALO), named (CALOFCM), for automatic liver and hepatic lesion segmentation. We employed ALO to guide the FCM to determine the optimal cluster centroids for segmentation processes. We used chaos theory to improve the performance of ALO in terms of convergence speed and local minima avoidance. In addition, chaos theory-based ALO prevented the FCM from getting stuck in local minima and increased computational performance, thus increasing stability, reducing sensitivity in the iterative process, and allowing the best centroids to be used by FCM. We validated the proposed approach on a group of patients with abdominal liver CT images, and the results showed good detection and segmentation performance compared with other popular techniques. This new hybrid approach allowed for the clinical diagnosis of hepatic lesions earlier and more systematically, thereby helping medical experts in their decision-making. 2020 Elsevier B.V.

Source

Applied Soft Computing Journal, Vol-96

Date

2020-01-01

Publisher

Elsevier Ltd

Subject

CALOFCM; Chaos theory; Computed tomography (CT); Fuzzy C-Means (FCM); Hepatic lesion; Swarm intelligence (SI)

Coverage

Anter A.M., School of Biomedical Engineering, Health Science Center, Shenzhen University, Shenzhen, 518060, China, Faculty of Computers and Artificial Intelligence, Beni-Suef University, Benisuef, 62511, Egypt; Bhattacharyya S., Department of Computer Science and Engineering, CHRIST (Deemed to be University), Bangalore, India; Zhang Z., School of Biomedical Engineering, Health Science Center, Shenzhen University, Shenzhen, 518060, China, Peng Cheng Laboratory, Shenzhen, 518055, China

Rights

Restricted Access

Relation

ISSN: 15684946

Format

Online

Language

English

Type

Article

Identifier

https://doi.org/10.1016/j.asoc.2020.106677

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