Department of Computer Science, Universidade Federal Fluminense, Niterói, Rio de Janeiro, Brazil.
School of Pharmacy, Universidade Federal Fluminense, Niterói, Rio de Janeiro, Brazil.
Comput Biol Med. 2017 Aug 1;87:38-45. doi: 10.1016/j.compbiomed.2017.05.013. Epub 2017 May 17.
This work proposes the use of Genetic Algorithms (GA) in tracing and recognizing the pericardium contour of the human heart using Computed Tomography (CT) images. We assume that each slice of the pericardium can be modelled by an ellipse, the parameters of which need to be optimally determined. An optimal ellipse would be one that closely follows the pericardium contour and, consequently, separates appropriately the epicardial and mediastinal fats of the human heart. Tracing and automatically identifying the pericardium contour aids in medical diagnosis. Usually, this process is done manually or not done at all due to the effort required. Besides, detecting the pericardium may improve previously proposed automated methodologies that separate the two types of fat associated to the human heart. Quantification of these fats provides important health risk marker information, as they are associated with the development of certain cardiovascular pathologies. Finally, we conclude that GA offers satisfiable solutions in a feasible amount of processing time.
本研究提出使用遗传算法（GA）来追踪和识别心脏心包轮廓，所使用的图像为计算机断层扫描（CT）。我们假设心包的每一层都可以建模为一个椭圆，其参数需要进行最优确定。最优的椭圆应尽可能贴合心包轮廓，从而恰当分离心脏的心外膜和纵隔脂肪。追踪和自动识别心包轮廓有助于医学诊断。通常，由于需要付出努力，这一过程要么手动完成，要么完全不做。此外，检测心包可能会改进先前提出的自动分离与心脏相关的两种脂肪的方法。这些脂肪的量化提供了重要的健康风险标志物信息，因为它们与某些心血管病理的发展有关。最后，我们得出结论，GA 在可行的处理时间内提供了令人满意的解决方案。
