Department of Electrical and Computer Engineering, Illinois Institute of Technology, 3301 South Dearborn Street, Chicago, IL 60616, USA.
Phys Med Biol. 2011 Jan 7;56(1):1-17. doi: 10.1088/0031-9155/56/1/001. Epub 2010 Nov 30.
In this work we propose a spatial point process (SPP) approach to improve the detection accuracy of clustered microcalcifications (MCs) in mammogram images. The conventional approach to MC detection has been to first detect the individual MCs in an image independently, which are subsequently grouped into clusters. Our proposed approach aims to exploit the spatial distribution among the different MCs in a mammogram image (i.e. MCs tend to appear in small clusters) directly during the detection process. We model the MCs by a marked point process (MPP) in which spatially neighboring MCs interact with each other. The MCs are then simultaneously detected through maximum a posteriori (MAP) estimation of the model parameters associated with the MPP process. The proposed approach was evaluated with a dataset of 141 clinical mammograms from 66 cases, and the results show that it could yield improved detection performance compared to a recently proposed support vector machine (SVM) detector. In particular, the proposed approach achieved a sensitivity of about 90% with the FP rate at around 0.5 clusters per image, compared to about 83% for the SVM; the performance of the proposed approach was also demonstrated to be more stable over different compositions of the test images.
在这项工作中，我们提出了一种空间点过程（SPP）方法，以提高乳腺图像中簇状微钙化（MC）的检测准确性。传统的 MC 检测方法是首先独立地检测图像中的单个 MC，然后将它们分组为簇。我们提出的方法旨在在检测过程中直接利用乳腺图像中不同 MC 之间的空间分布（即 MC 倾向于出现在小簇中）。我们通过标记点过程（MPP）对 MC 进行建模，其中空间相邻的 MC 相互作用。然后通过对与 MPP 过程相关的模型参数进行最大后验（MAP）估计来同时检测 MC。该方法在来自 66 个病例的 141 张临床乳腺图像数据集上进行了评估，结果表明，与最近提出的支持向量机（SVM）检测器相比，它可以提高检测性能。特别是，与 SVM 的约 83%相比，该方法的灵敏度约为 90%，假阳性率约为 0.5 个簇/图像；该方法的性能在不同测试图像的组成上也表现出更稳定。
