Irishina Natalia, Moscoso Miguel, Dorn Oliver
Universidad Carlos III de Madrid, Leganes 78911, Spain.
IEEE Trans Biomed Eng. 2009 Apr;56(4):1143-53. doi: 10.1109/TBME.2009.2012398. Epub 2009 Jan 23.
In this paper, we propose and analyze a novel shape reconstruction technique for the early detection of breast cancer from microwave data, which is based on a level-set technique. The shape-based approach offers several advantages compared to more traditional pixel-based approaches when targeting the reconstruction of key characteristics of a hidden tumor such as its correct size, shape, and static permittivity value. In addition to these key characteristics of hidden tumors, we aim at estimating the correct interfaces between fatty and fibroglandular tissue in the breast and their internal permittivity profiles. The level set strategy (which is an implicit representation of the shapes) frees us from topological restrictions when reconstructing an a priori arbitrary number of tumors and the often quite complicated interfaces between fatty and fibroglandular regions. The presented strategy is able to detect and, in most cases, characterize tumors whose sizes (diameters) are much smaller than the wavelengths of the electromagnetic waves that are used for illuminating the breast. We present numerical results for a 2-D model with two distinct tissue types (fatty and fibroglandular) in the interior of the breast (in addition to a possible tumor and the surrounding skin). Our results demonstrate the performance and potential of our scheme in various simulated but realistic situations.
在本文中，我们提出并分析了一种基于水平集技术的新型形状重建技术，用于从微波数据中早期检测乳腺癌。与更传统的基于像素的方法相比，基于形状的方法在重建隐藏肿瘤的关键特征（如正确的大小、形状和静态介电常数）时具有多个优势。除了隐藏肿瘤的这些关键特征外，我们旨在估计乳腺中脂肪组织和纤维腺组织之间的正确界面及其内部介电常数分布。水平集策略（它是形状的隐式表示）使我们在重建先验任意数量的肿瘤以及脂肪和纤维腺区域之间通常相当复杂的界面时摆脱了拓扑限制。所提出的策略能够检测并且在大多数情况下表征尺寸（直径）远小于用于照射乳腺的电磁波波长的肿瘤。我们给出了乳腺内部具有两种不同组织类型（脂肪和纤维腺）的二维模型的数值结果（除了可能存在的肿瘤和周围皮肤）。我们的结果展示了我们的方案在各种模拟但现实的情况下的性能和潜力。
