Imaging & Computational Intelligence Group (ICI), School of Electrical and Electronic Engineering, Universiti Sains Malaysia, Engineering Campus, 14300 Nibong Tebal, Pulau Pinang, Malaysia.
Comput Math Methods Med. 2013;2013:205384. doi: 10.1155/2013/205384. Epub 2013 Sep 10.
Breast cancer mostly arises from the glandular (dense) region of the breast. Consequently, breast density has been found to be a strong indicator for breast cancer risk. Therefore, there is a need to develop a system which can segment or classify dense breast areas. In a dense breast, the sensitivity of mammography for the early detection of breast cancer is reduced. It is difficult to detect a mass in a breast that is dense. Therefore, a computerized method to separate the existence of a mass from the glandular tissues becomes an important task. Moreover, if the segmentation results provide more precise demarcation enabling the visualization of the breast anatomical regions, it could also assist in the detection of architectural distortion or asymmetry. This study attempts to segment the dense areas of the breast and the existence of a mass and to visualize other breast regions (skin-air interface, uncompressed fat, compressed fat, and glandular) in a system. The graph cuts (GC) segmentation technique is proposed. Multiselection of seed labels has been chosen to provide the hard constraint for segmentation of the different parts. The results are promising. A strong correlation (r = 0.93) was observed between the segmented dense breast areas detected and radiological ground truth.
乳腺癌主要发生在乳腺的腺体（致密）区域。因此，乳腺密度已被发现是乳腺癌风险的一个强有力的指标。因此，需要开发一种能够分割或分类致密乳腺区域的系统。在致密的乳房中，乳房 X 光摄影术对乳腺癌的早期检测的灵敏度降低。在致密的乳房中，很难检测到肿块的存在。因此，一种计算机方法将肿块的存在与腺体组织分开成为一项重要的任务。此外，如果分割结果提供更精确的边界，使乳房解剖区域可视化，也可以帮助检测结构扭曲或不对称。本研究试图在一个系统中分割致密的乳腺区域和肿块的存在，并可视化其他乳腺区域（皮肤-空气界面、未压缩脂肪、压缩脂肪和腺体）。提出了图割（GC）分割技术。多选择种子标签被选择来提供分割不同部分的硬约束。结果是有希望的。检测到的致密乳腺区域的分割与放射学地面实况之间存在很强的相关性（r = 0.93）。
