Zulueta-Coarasa Teresa, Kurugol Sila, Ross James C, Washko George G, San José Estépar Raúl
Annu Int Conf IEEE Eng Med Biol Soc. 2013;2013:3969-72. doi: 10.1109/EMBC.2013.6610414.
In this article we investigate the suitability of a manifold learning technique to classify different types of emphysema based on embedded Probabilistic PCA (PPCA). Our approach finds the most discriminant linear space for each emphysema pattern against the remaining patterns where lung CT image patches can be embedded. In this embedded space, we train a PPCA model for each pattern. The main novelty of our technique is that it is possible to compute the class membership posterior probability for each emphysema pattern rather than a hard assignment as it is typically done by other approaches. We tested our algorithm with six emphysema patterns using a data set of 1337 CT training patches. Using a 10-fold cross validation experiment, an average recall rate of 69% is achieved when the posterior probability is greater than 75%. A quantitative comparison with a texture-based approach based on Local Binary Patterns and with an approach based on local intensity distributions shows that our method is competitive. The analysis of full lungs using our approach shows a good visual agreement with the underlying emphysema types and a smooth spatial relation.
在本文中，我们研究了一种流形学习技术基于嵌入式概率主成分分析（PPCA）对不同类型肺气肿进行分类的适用性。我们的方法针对每种肺气肿模式，在可以嵌入肺部CT图像块的情况下，找到相对于其余模式最具判别力的线性空间。在这个嵌入式空间中，我们为每种模式训练一个PPCA模型。我们技术的主要新颖之处在于，能够计算每种肺气肿模式的类成员后验概率，而不是像其他方法通常所做的那样进行硬分配。我们使用包含1337个CT训练块的数据集，对六种肺气肿模式测试了我们的算法。通过10折交叉验证实验，当后验概率大于75%时，平均召回率达到69%。与基于局部二值模式的基于纹理的方法以及基于局部强度分布的方法进行定量比较表明，我们的方法具有竞争力。使用我们的方法对全肺进行分析，结果显示与潜在的肺气肿类型具有良好的视觉一致性以及平滑的空间关系。
