Mechrez Roey, Goldberger Jacob, Greenspan Hayit
Biomedical Engineering Department, Tel-Aviv University, 69978 Tel Aviv, Israel.
Engineering Faculty, Bar-Ilan University, 52900 Ramat Gan, Israel.
Int J Biomed Imaging. 2016;2016:7952541. doi: 10.1155/2016/7952541. Epub 2016 Jan 24.
This paper presents an automatic lesion segmentation method based on similarities between multichannel patches. A patch database is built using training images for which the label maps are known. For each patch in the testing image, k similar patches are retrieved from the database. The matching labels for these k patches are then combined to produce an initial segmentation map for the test case. Finally an iterative patch-based label refinement process based on the initial segmentation map is performed to ensure the spatial consistency of the detected lesions. The method was evaluated in experiments on multiple sclerosis (MS) lesion segmentation in magnetic resonance images (MRI) of the brain. An evaluation was done for each image in the MICCAI 2008 MS lesion segmentation challenge. Results are shown to compete with the state of the art in the challenge. We conclude that the proposed algorithm for segmentation of lesions provides a promising new approach for local segmentation and global detection in medical images.
本文提出了一种基于多通道图像块相似性的自动病变分割方法。利用已知标签图的训练图像构建一个图像块数据库。对于测试图像中的每个图像块，从数据库中检索出k个相似的图像块。然后将这k个图像块的匹配标签进行合并，以生成测试病例的初始分割图。最后，基于初始分割图执行基于图像块的迭代标签细化过程，以确保检测到的病变的空间一致性。该方法在脑部磁共振成像（MRI）的多发性硬化症（MS）病变分割实验中进行了评估。对2008年医学图像计算方法与计算机辅助干预国际会议（MICCAI）MS病变分割挑战赛中的每幅图像都进行了评估。结果表明该方法在挑战赛中与当前的先进技术具有竞争力。我们得出结论，所提出的病变分割算法为医学图像中的局部分割和全局检测提供了一种有前景的新方法。
