论文标题
使用遗传算法对处理的CT图像上心包轮廓的自动识别
Automated recognition of the pericardium contour on processed CT images using genetic algorithms
论文作者
论文摘要
这项工作提出了使用遗传算法(GA)在追踪和识别使用计算机断层扫描(CT)图像的人心心包轮廓的过程中。我们假设心包的每个切片都可以通过椭圆建模,椭圆形需要最佳地确定其参数。最佳的椭圆将是紧随心包轮廓的紧密椭圆形,因此,将人心脏的心外膜和纵隔脂肪适当地分开。追踪并自动识别心包轮廓辅助医学诊断。通常,由于所需的努力,此过程是手动完成或根本不完成的。此外,检测心包可能会改善先前提出的自动化方法,这些方法将与人心脏相关的两种类型的脂肪分开。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,我们得出的结论是,GA在可行数量的处理时间内提供了令人满意的解决方案。
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.