Department of Computer Architecture and Technology, University of Girona, Girona, Spain.
J Ultrasound Med. 2013 Sep;32(9):1659-70. doi: 10.7863/ultra.32.9.1659.
Breast sonography currently provides a complementary diagnosis when other modalities are not conclusive. However, lesion segmentation on sonography is still a challenging problem due to the presence of artifacts. To solve these problems, Markov random fields and maximum a posteriori-based methods are used to estimate a distortion field while identifying regions of similar intensity inhomogeneity. In this study, different initialization approaches were exhaustively evaluated using a database of 212 B-mode breast sonograms and considering the lesion types. Finally, conclusions about the relationship between the segmentation results and lesions types are described.
乳腺超声目前在其他影像学检查不明确时提供补充诊断。然而，由于伪影的存在，超声上的病灶分割仍然是一个具有挑战性的问题。为了解决这些问题，使用马尔可夫随机场和最大后验概率方法来估计失真场，同时识别具有相似强度不均匀性的区域。在这项研究中，使用包含 212 个 B 型乳腺超声图像的数据库，针对不同的病灶类型，对各种初始化方法进行了全面评估。最后，描述了分割结果与病灶类型之间的关系。
