School of Electronic and Information Engineering, Beijing Jiaotong University, Beijing, China.
School of Electronic and Information Engineering, Beijing Jiaotong University, Beijing, China.
Comput Biol Med. 2015 Feb;57:84-95. doi: 10.1016/j.compbiomed.2014.12.007. Epub 2014 Dec 16.
In this paper, a bilateral image analysis scheme is developed for the purpose of reducing false positives (FPs) in the detection of masses in dense mammograms. It consists of two steps: a region matching step for determining the correspondence between a pair of mammograms, and a bilateral similarity analysis step for discarding FPs in the detection. For the first step, a matching cost is defined to quantify the credibility of the corresponding region in a pair of bilateral mammograms. For the second step, a similarity measurement is introduced to discriminate between mass and normal for a pair of bilateral regions based on both global and local image appearances. The proposed scheme is tested on a set of 332 mammograms. The results show that the proposed scheme could obtain better performance when compared with several existing bilateral analysis schemes. With detection sensitivity at 85%, the proposed bilateral scheme could reduce the FP rate of a unilateral scheme from 3.64 to 2.39 per image, a 34% reduction.
本文提出了一种双边图像分析方案，旨在减少致密乳腺 X 线片中肿块检测的假阳性（FP）。它由两个步骤组成：确定一对乳腺 X 线片中对应区域的区域匹配步骤，以及用于丢弃检测中 FP 的双边相似性分析步骤。对于第一步，定义了匹配代价以量化一对双边乳腺 X 线片中对应区域的可信度。对于第二步，引入了相似性度量，以基于全局和局部图像外观来区分一对双边区域中的肿块和正常组织。在一组 332 张乳腺 X 线片中对所提出的方案进行了测试。结果表明，与几种现有的双边分析方案相比，所提出的方案可以获得更好的性能。在检测灵敏度为 85%时，所提出的双边方案可以将单侧方案的 FP 率从每幅图像 3.64 降低到 2.39，降低了 34%。
