Department of Automatic Test and Control, Harbin Institute of Technology, Harbin, China.
J Med Syst. 2012 Aug;36(4):2235-44. doi: 10.1007/s10916-011-9691-4. Epub 2011 Apr 8.
Breast tissue classification is an important and effective way for computer aided diagnosis of breast cancer with digital mammogram. Current methods endure two problems, firstly pectoral muscle influences the classification performance owing to its texture similar to parenchyma, and secondly classification algorithms fail to deal with the nonlinear problem from the digital mammogram. For these problems, we propose a novel framework of breast tissue classification based on kernel self-optimized discriminant analysis combined with the artifacts and pectoral muscle removal with multi-level segmentation based Connected Component Labeling analysis. Experiments on mini-MIAS database are implemented to testify and evaluate the performance of proposed algorithm.
乳腺组织分类是基于数字乳腺图像的计算机辅助乳腺癌诊断的一种重要且有效的方法。目前的方法存在两个问题，一是由于胸肌与实质组织的纹理相似，会影响分类性能；二是分类算法无法处理数字乳腺图像的非线性问题。针对这些问题，我们提出了一种新的基于核自优化判别分析的乳腺组织分类框架，并结合基于多级分割的连通分量标记分析的伪影和胸肌去除方法。在 mini-MIAS 数据库上进行实验，以验证和评估所提出算法的性能。
