.png){kind=icon}
返回
Monarch butterfly optimization algorithm for computed tomography image segmentation
.png){kind=icon}
PRE
AI
DOI：10.1007/s11042-020-10147-6.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
In the medical field, image segmentation provides important information for surgical planning and registration, and thus demands accurate segmentation. In order to improve the effectiveness and the threshold accuracy of segmentation, researchers have tended to use a metaheuristic algorithm as the operational algorithm to achieve better exploitation and exploration of the search space and to solve many different medical image problems in an effective manner. This research applies the monarch butterfly optimization (MBO) algorithm for image segmentation at multiple threshold values. To evaluate the performance of the implemented MBO algorithm, a comparison is made with the brute-force (i.e. Otsu) algorithm and two metaheuristic algorithms (i.e. Darwinian particle swarm optimization (DPSO) and fractional-order DPSO). In addition, the quality structural similarity index matrix and the peak signal-to-noise ratio are used to evaluate the accuracy of the resultant segmented images. The experimental results show the advantage of using the MBO algorithm for medical image segmentation in terms of accuracy and speed. Regards the accuracy, MBO algorithm produced an exact match at thresholds 1 and 2 and a very close match at thresholds 3 and 4. Regards the speed, the average of the execution time for threshold 1 was 0.24187 s, for threshold 2 was 0.33831 s, for threshold 3 was 0.95967 s and for threshold 4 it was 1.15308 s.
Keyword:
Image segmentation
Classification problems
Metaheuristics
Monarch butterfly optimization
期刊
IF:
3
论文数:
2.0W
被引数:
3.2W
机构
引用论文
Ab Initio Molecular Dynamics Simulation of Tribochemical Reactions Involving Phosphorus Additives at Sliding Iron Interfaces.png)
Lubricants
OAAI
LubricantsIF0
OAAI分享收藏Trp-dependent auxin biosynthesis in Arabidopsis: involvement of cytochrome P450s CYP79B2 and CYP79B3OAAI
OAAI分享收藏Interactive machine learning: experimental evidence for the human in the algorithmic loop: A case study on Ant Colony Optimization交互式机器学习: 算法循环中人类的实验证据: 蚁群优化的案例研究APPLIED INTELLIGENCEOAAI
APPLIED INTELLIGENCEIF3.5
OAAI分享收藏Fuzzy cluster based neural network classifier for classifying breast tumors in ultrasound images
PREAI
分享收藏
查看更多内容