Department Radiology, Faculty of Medicine Siriraj Hospital, Mahidol University, Bangkok, 10700, Thailand.
Faculty of Information and Communication Technology, Mahidol University, Nakhon Pathom, 73170, Thailand.
J Digit Imaging. 2018 Aug;31(4):490-504. doi: 10.1007/s10278-018-0049-z.
Aortic aneurysm segmentation remains a challenge. Manual segmentation is a time-consuming process which is not practical for routine use. To address this limitation, several automated segmentation techniques for aortic aneurysm have been developed, such as edge detection-based methods, partial differential equation methods, and graph partitioning methods. However, automatic segmentation of aortic aneurysm is difficult due to high pixel similarity to adjacent tissue and a lack of color information in the medical image, preventing previous work from being applicable to difficult cases. This paper uses uses a variable neighborhood search that alternates between intensity-based and gradient-based segmentation techniques. By alternating between intensity and gradient spaces, the search can escape from local optima of each space. The experimental results demonstrate that the proposed method outperforms the other existing segmentation methods in the literature, based on measurements of dice similarity coefficient and jaccard similarity coefficient at the pixel level. In addition, it is shown to perform well for cases that are difficult to segment.
主动脉瘤分割仍然是一个挑战。手动分割是一个耗时的过程，不适用于常规使用。为了解决这个限制，已经开发了几种用于主动脉瘤的自动分割技术，例如基于边缘检测的方法、偏微分方程方法和图划分方法。然而，由于与相邻组织的像素相似度高，并且医学图像中缺乏颜色信息，因此主动脉瘤的自动分割很困难，这使得以前的工作无法适用于困难的情况。本文使用基于可变邻域搜索的方法，在基于强度和基于梯度的分割技术之间交替。通过在强度和梯度空间之间交替，搜索可以摆脱每个空间的局部最优解。实验结果表明，基于像素级的骰子相似系数和杰卡德相似系数的测量，所提出的方法优于文献中现有的其他分割方法。此外，它在分割困难的情况下表现良好。
