Faculty of Biomedical Engineering, Silesian University of Technology, Roosevelta 40, 41-800 Zabrze, Poland.
Comput Med Imaging Graph. 2018 Apr;65:152-166. doi: 10.1016/j.compmedimag.2017.04.004. Epub 2017 Apr 26.
Several heuristic, biologically inspired strategies have been discovered in recent decades, including swarm intelligence algorithms. So far, their application to volumetric imaging data mining is, however, limited. This paper presents a new flexible swarm intelligence optimization technique for segmentation of various structures in three- or two-dimensional images. The agents of a self-organizing colony explore their host, use stigmergy to communicate themselves, and mark regions of interest leading to the object extraction. Detailed specification of the bacterium colony segmentation (BCS) technique in terms of both individual and social behaviour is described in this paper. The method is illustrated and evaluated using several experiments involving synthetic data, computed tomography studies, and ultrasonography images. The obtained results and observations are discussed in terms of parameter settings and potential application of the method in various segmentation tasks.
最近几十年，人们发现了几种启发式、受生物启发的策略，包括群体智能算法。然而，到目前为止，它们在体积成像数据挖掘中的应用是有限的。本文提出了一种新的灵活的群体智能优化技术，用于分割三维或二维图像中的各种结构。自组织群体的个体探索它们的宿主，使用信息素进行自我交流，并标记感兴趣的区域，从而提取对象。本文详细说明了细菌群体分割(BCS)技术的个体和社会行为规范。该方法使用涉及合成数据、计算机断层扫描研究和超声图像的几个实验进行了说明和评估。根据参数设置和该方法在各种分割任务中的潜在应用讨论了所得到的结果和观察结果。
