Department of Biomedical Informatics, Columbia University, New York, NY 10032, USA.
J Biomed Inform. 2009 Dec;42(6):1056-64. doi: 10.1016/j.jbi.2009.07.003. Epub 2009 Jul 15.
Although clinicians have long sought to integrate computer-aided diagnostic (CAD) systems into routine clinical practice, it has proven to be extremely difficult to perform fully automated algorithmic analyses on lesions, based solely on the information contained in images. To increase the utility of computerized tools, it would be intuitive to incorporate anatomical and pathological knowledge and heuristics to help the system draw diagnostic inferences. In neuro-imaging applications, for example, one way to perform this knowledge integration is to uncover symmetry/asymmetry information from the corresponding regions of the head and to explore its implication to positive clinical findings. To correctly quantify asymmetric patterns in brain images, however, the symmetry axis, or the symmetry plane, needs to be appropriately oriented in space; i.e., the symmetry plane needs to be correctly identified either manually or using computerized methods. This review will provide an overview of the current state of knowledge of both symmetry axis/plane detection, and asymmetry quantification in neuro-images.
尽管临床医生长期以来一直试图将计算机辅助诊断(CAD)系统集成到常规临床实践中,但仅基于图像中包含的信息,对病变进行全自动算法分析已被证明极其困难。为了提高计算机工具的实用性,直观的方法是将解剖学和病理学知识和启发式方法纳入其中,以帮助系统得出诊断推论。例如,在神经影像学应用中,执行这种知识集成的一种方法是从头部的相应区域中揭示对称/不对称信息,并探索其对阳性临床发现的意义。然而,为了正确量化脑图像中的不对称模式,对称轴或对称面需要在空间中适当定向;即,需要手动或使用计算机化方法正确识别对称面。本综述将概述神经图像中对称轴/面检测和不对称量化的当前知识状态。