关节软骨磁共振(MR)图像的分析和分割属于膝关节区域肌肉骨骼系统诊断中最常见的常规任务之一。传统的区域分割方法,它们基于直方图分区(例如,Otsu方法)或聚类方法(例如,K-means),经常被用于区域分割的任务。这种方法是众所周知的在环境中快速和良好的工作,其中软骨图像特征是可靠可识别的。众所周知的事实是,这些方法的性能容易出现图像噪声和伪影。在这种情况下,区域细分战略,由遗传算法或选定的进化计算策略驱动,有可能克服这些传统方法,如Otsu阈值或K-means在其性能的背景下。这些优化策略连续生成一组可能的直方图阈值的金字塔,通过使用基于Kapur的熵最大化的适应度函数来评估其质量,以找到关节软骨分割的最佳阈值组合。另一方面,这样的优化策略通常对计算要求很高,这是针对MR图像的堆叠使用这种方法的限制。在这项研究中,我们发布了基于模糊软分割的优化方法的综合分析,由人工蜂群(ABC)驱动,粒子群优化(PSO),达尔文粒子群优化(DPSO)以及一种遗传算法,用于针对常规分割Otsu和K均值进行最佳阈值选择,以进行分析以及从MR图像中提取关节软骨的特征。本研究客观地分析了分割策略在动态强度可变噪声下的性能,以报告在各种图像条件下的分割鲁棒性,适用于各种数量的分割类(4、7和10)。软骨特征(面积,周边,和骨架)针对常规分割策略的提取精度,最后是计算时间,这代表了分割性能的一个重要因素。我们在单个优化策略上使用相同的设置:100次迭代和50次总体。这项研究表明,从附加动态噪声影响的分割影响的角度来看,与其他方法相比,模糊阈值与ABC算法的结合具有最佳性能。还用于软骨特征提取。另一方面,在某些情况下,使用遗传算法进行软骨分割并不能提供良好的性能。在大多数情况下,分析的优化策略显著克服了常规的分割方法,除了计算时间,对于常规算法,这通常较低。我们还发布了显著性统计检验,显示了个体优化策略与Otsu和K-means方法的性能差异。最后,作为这项研究的一部分,我们发布一个软件环境,整合了本研究的所有方法。
The analysis and segmentation of articular cartilage magnetic resonance (MR) images belongs to one of the most commonly routine tasks in diagnostics of the musculoskeletal system of the knee area. Conventional regional segmentation methods, which are based either on the histogram partitioning (e.g., Otsu method) or clustering methods (e.g., K-means), have been frequently used for the task of regional segmentation. Such methods are well known as fast and well working in the environment, where cartilage image features are reliably recognizable. The well-known fact is that the performance of these methods is prone to the image noise and artefacts. In this context, regional segmentation strategies, driven by either genetic algorithms or selected evolutionary computing strategies, have the potential to overcome these traditional methods such as Otsu thresholding or K-means in the context of their performance. These optimization strategies consecutively generate a pyramid of a possible set of histogram thresholds, of which the quality is evaluated by using the fitness function based on Kapur\'s entropy maximization to find the most optimal combination of thresholds for articular cartilage segmentation. On the other hand, such optimization strategies are often computationally demanding, which is a limitation of using such methods for a stack of MR images. In this
study, we publish a comprehensive analysis of the optimization methods based on fuzzy soft segmentation, driven by artificial bee colony (ABC), particle swarm optimization (PSO), Darwinian particle swarm optimization (DPSO), and a genetic algorithm for an optimal thresholding selection against the routine segmentations Otsu and K-means for analysis and the features extraction of articular cartilage from MR images. This
study objectively analyzes the performance of the segmentation strategies upon variable noise with dynamic intensities to report a segmentation\'s robustness in various image conditions for a various number of segmentation classes (4, 7, and 10), cartilage features (area, perimeter, and skeleton) extraction preciseness against the routine segmentation strategies, and lastly the computing time, which represents an important factor of segmentation performance. We use the same settings on individual optimization strategies: 100 iterations and 50 population. This
study suggests that the combination of fuzzy thresholding with an ABC algorithm gives the best performance in the comparison with other methods as from the view of the segmentation influence of additive dynamic noise influence, also for cartilage features extraction. On the other hand, using genetic algorithms for cartilage segmentation in some cases does not give a good performance. In most cases, the analyzed optimization strategies significantly overcome the routine segmentation methods except for the computing time, which is normally lower for the routine algorithms. We also publish statistical tests of significance, showing differences in the performance of individual optimization strategies against Otsu and K-means method. Lastly, as a part of this
study, we publish a software environment, integrating all the methods from this
study.