Bejinariu Silviu Ioan, Costin Hariton
Computer Vision Laboratory, Institute of Computer Science, Romanian Academy, Iasi Branch, Iasi, Romania.
Department of Biomedical Sciences, Grigore T. Popa University of Medicine and Pharmacy, Iasi, Romania.
Methods Inf Med. 2018 Nov;57(5-06):280-286. doi: 10.1055/s-0038-1673693. Epub 2019 Mar 15.
Computational Intelligence Re-meets Medical Image Processing Analysis of Machine Learning Algorithms for Diagnosis of Diffuse Lung Diseases BACKGROUND: In the last decades, new optimization methods based on the nature's intelligence were developed. These metaheuristics can find a nearly optimal solution faster than other traditional algorithms even for high-dimensional optimization problems. All these algorithms have a similar structure, the difference being made by the strategies used during the evolutionary process.
A set of three nature-inspired algorithms, including Cuckoo Search algorithm (CSA), Particle Swarm Optimization (PSO), and Multi-Swarm Optimization (MSO), are compared in terms of strategies used in the evolutionary process and also of the results obtained in case of particular optimization problems.
The three algorithms were applied for biomedical image registration (IR) and compared in terms of performances. The expected geometric transform has seven parameters and is composed of rotation against a point in the image, scaling on both axis with different factors, and translation.
The evaluation consisted of 25 runs of each IR procedure and revealed that (1) PSO offers the most precise solutions; (2) CSA and MSO are more stable in the sense that their solutions are less scattered; and (3) MSO and PSO have a higher convergence speed.
The evaluation of PSO, MSO, and CSA was made for multimodal IR problems. It is possible that for other optimization problems and also for other settings of the optimization algorithms, the results can be different. Therefore, the nature-inspired algorithms demonstrated their efficacy for this class of optimization problems.
计算智能再次邂逅医学图像处理——用于弥漫性肺部疾病诊断的机器学习算法分析
在过去几十年中，基于自然智能的新优化方法得以发展。这些元启发式算法即使对于高维优化问题，也能比其他传统算法更快地找到近乎最优的解决方案。所有这些算法都具有相似的结构，区别在于进化过程中所使用的策略。
比较一组三种受自然启发的算法，包括布谷鸟搜索算法（CSA）、粒子群优化算法（PSO）和多群优化算法（MSO），比较它们在进化过程中所使用的策略以及在特定优化问题情况下所获得的结果。
将这三种算法应用于生物医学图像配准（IR），并在性能方面进行比较。预期的几何变换有七个参数，由针对图像中某一点的旋转、在两个轴上以不同因子进行缩放以及平移组成。
评估包括对每个IR程序进行25次运行，结果表明：（1）PSO提供了最精确的解决方案；（2）从其解决方案分布较少的意义上来说，CSA和MSO更稳定；（3）MSO和PSO具有更高的收敛速度。
对PSO、MSO和CSA针对多模态IR问题进行了评估。对于其他优化问题以及优化算法的其他设置，结果可能会有所不同。因此，受自然启发的算法在这类优化问题中证明了它们的有效性。
