Fooladivanda Aida, Shokouhi Shahriar B, Ahmadinejad Nasrin
Department of Electrical Engineering, Iran University of Science and Technology (IUST), Tehran, Iran.
Advanced Diagnostic and Interventional Radiology Research Center (ADIR), Tehran University of Medical Science, Tehran, Iran.
Australas Phys Eng Sci Med. 2017 Mar;40(1):69-84. doi: 10.1007/s13246-016-0513-3. Epub 2017 Jan 23.
Breast-region segmentation is an important step for density estimation and Computer-Aided Diagnosis (CAD) systems in Magnetic Resonance Imaging (MRI). Detection of breast-chest wall boundary is often a difficult task due to similarity between gray-level values of fibroglandular tissue and pectoral muscle. This paper proposes a robust breast-region segmentation method which is applicable for both complex cases with fibroglandular tissue connected to the pectoral muscle, and simple cases with high contrast boundaries. We present a decision-making framework based on geometric features and support vector machine (SVM) to classify breasts in two main groups, complex and simple. For complex cases, breast segmentation is done using a combination of intensity-based and atlas-based techniques; however, only intensity-based operation is employed for simple cases. A novel atlas-based method, that is called localized-atlas, accomplishes the processes of atlas construction and registration based on the region of interest (ROI). Atlas-based segmentation is performed by relying on the chest wall template. Our approach is validated using a dataset of 210 cases. Based on similarity between automatic and manual segmentation results, the proposed method achieves Dice similarity coefficient, Jaccard coefficient, total overlap, false negative, and false positive values of 96.3, 92.9, 97.4, 2.61 and 4.77%, respectively. The localization error of the breast-chest wall boundary is 1.97 mm, in terms of averaged deviation distance. The achieved results prove that the suggested framework performs the breast segmentation with negligible errors and efficient computational time for different breasts from the viewpoints of size, shape, and density pattern.
乳腺区域分割是磁共振成像（MRI）中密度估计和计算机辅助诊断（CAD）系统的重要步骤。由于纤维腺组织和胸肌的灰度值相似，检测乳腺-胸壁边界通常是一项艰巨的任务。本文提出了一种鲁棒的乳腺区域分割方法，该方法适用于纤维腺组织与胸肌相连的复杂病例以及边界对比度高的简单病例。我们提出了一个基于几何特征和支持向量机（SVM）的决策框架，将乳房分为复杂和简单两大类。对于复杂病例，使用基于强度和基于图谱的技术相结合进行乳腺分割；然而，对于简单病例仅采用基于强度的操作。一种称为局部图谱的新型基于图谱的方法，基于感兴趣区域（ROI）完成图谱构建和配准过程。基于胸壁模板进行基于图谱的分割。我们使用包含210个病例的数据集对我们的方法进行了验证。基于自动分割结果和手动分割结果之间的相似性，所提出的方法分别实现了96.3%、92.9%、97.4%、2.61%和4.77%的骰子相似系数、杰卡德系数、总重叠率、假阴性率和假阳性率。就平均偏差距离而言，乳腺-胸壁边界的定位误差为1.97毫米。从大小、形状和密度模式的角度来看，所取得的结果证明，所建议的框架在对不同乳房进行乳腺分割时具有可忽略不计的误差和高效的计算时间。
