A cooperating metaheuristic approach for MR image segmentation

被引：0

作者：

Thuy Xuan Pham ^{[1
]}

Siarry, Patrick ^{[1
]}

Oulhadj, Hamouche ^{[1
]}

机构：

[1] Univ Paris Est Creteil, Lab Images Signals & Intelligent Syst NSSi, F-94400 Vitry Sur Seine, France

来源：

2019 3RD INTERNATIONAL CONFERENCE ON BIO-ENGINEERING FOR SMART TECHNOLOGIES (BIOSMART) | 2019年

关键词：

Cuckoo search; particle swarm optimization; image segmentation; Markov random field; ALGORITHM; OPTIMIZATION;

D O I：

10.1109/biosmart.2019.8734158

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

This paper presents cooperating Cuckoo Search (CS) and Particle Swarm Optimization (PSO) algorithms for MR image segmentation. The problem can be formulated as an optimization problem and the proposed algorithm has been applied to find the best solution. Since image segmentation requires satisfying several criteria, it is important to know how to optimize them in parallel in the same algorithm. This paper is actually a further step of our works, which applies a new mechanism of using metaheuristic algorithms for optimizing a Markov Random Field (MRF) segmentation criterion. The proposed method is validated on both simulated and real MR images. The results indicate that our method can provide better solutions in terms of segmentation quality and efficiency.

引用

页数：5

共 24 条

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