Emphysema classification based on embedded probabilistic PCA.
Emphysema classification based on embedded probabilistic PCA.
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DOI：
10.1109/embc.2013.6610414
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发表时间：
2013
期刊：
影响因子：
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通讯作者：
San José Estépar R
中科院分区：
文献类型：
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作者：
Zulueta-Coarasa T;Kurugol S;Ross JC;Washko GG;San José Estépar R
在这篇文章中，我们调查的适用性流形学习技术，以分类不同类型的肺气肿的基础上嵌入概率主成分分析（PPCA）。我们的方法找到了最具判别力的线性空间，每个肺气肿模式对其余的模式，肺CT图像补丁可以嵌入。在这个嵌入空间中，我们为每个模式训练PPCA模型。我们的技术的主要新奇是，它是可能的，以计算类成员的后验概率为每个肺气肿的模式，而不是一个硬分配，因为它通常是由其他方法。我们使用1337个CT训练补丁的数据集，用六种肺气肿模式测试了我们的算法。使用10倍交叉验证实验，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纹理方法和基于局部强度分布的方法的定量比较表明，我们的方法是有竞争力的。使用我们的方法对全肺的分析显示出与潜在肺气肿类型的良好视觉一致性和平滑的空间关系。
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.