Centre for Biomedical Engineering, School of Electrical & Electronic Engineering, The University of Adelaide, Adelaide, SA, Australia.
Med Biol Eng Comput. 2012 Jan;50(1):91-101. doi: 10.1007/s11517-011-0772-9. Epub 2011 Apr 19.
In this article, we present a novel approach to localize anatomical features-breast costal cartilage-in dynamic contrast-enhanced MRI using level sets. Current breast MRI diagnosis involves magnetic-resonance compatible needles for localization. However, if the breast costal cartilage structure can be used as an alternative to the MR needle, this will not only assist in avoiding invasive procedures, but will also facilitate monitoring of the movement of breasts caused by cardiac and respiratory motion. This article represents a novel algorithm for achieving reliable detection and extraction of costal cartilage structures, which can be used for the analysis of motion artifacts, with possible shape variations of the structure caused by uptake of contrast agent, as well as a potential for the registration of breast. The algorithm represented in this article is to extract volume features from post-contrast MR images at three different time slices for the analysis of motion artifacts, and we validate the current algorithm according to the anatomic structure. This utilizes the level-set method for the size selection of the region of interest. The variable shape of contours acquired from a level-set-based segment image actually determines the feature region of interest, which is used as a guide to achieve initial masks for feature extraction. Following this, the algorithm uses a K-means method for classification of the feature regions from other types of tissue and morphological operations with a choice of an appropriate structuring element to achieve reliable masks and extraction of features. The segments of features can be therefore obtained with the application of extracted masks for subsequent motion analysis of breast and for potential registration purposes.
在本文中,我们提出了一种使用水平集在动态对比增强 MRI 中定位解剖特征 - 乳房肋软骨的新方法。目前的乳腺 MRI 诊断涉及用于定位的磁共振兼容针。但是,如果可以将乳房肋软骨结构用作磁共振针的替代物,这不仅有助于避免侵入性程序,而且还可以方便地监测由于心脏和呼吸运动引起的乳房运动。本文代表了一种可靠检测和提取肋软骨结构的新算法,该算法可用于分析运动伪影,以及可能由于对比剂摄取引起的结构形状变化,以及乳房的注册潜力。本文中表示的算法是从三个不同时间片的对比后 MR 图像中提取体积特征,用于分析运动伪影,并且我们根据解剖结构验证了当前算法。这利用水平集方法来选择感兴趣区域的大小。从基于水平集的分割图像中获取的可变形状的轮廓实际上确定了特征感兴趣区域,该轮廓用作初始掩模的指南,以实现特征提取的初始掩模。之后,该算法使用 K-均值方法对来自其他类型组织的特征区域进行分类,并进行形态学操作,选择合适的结构元素,以实现可靠的掩模和特征提取。因此,可以应用提取的掩模来获得特征的片段,以用于后续的乳房运动分析和潜在的注册目的。