IEEE/ACM Trans Comput Biol Bioinform. 2021 Jul-Aug;18(4):1396-1404. doi: 10.1109/TCBB.2019.2953701. Epub 2021 Aug 6.
The purpose of this study was to implement principal component analysis (PCA) on videocapsule endoscopy (VE) images to develop a new computerized tool for celiac disease recognition. Three PCA algorithms were implemented for feature extraction and sparse representation. A novel strip PCA (SPCA) with nongreedy L1-norm maximization is proposed for VE image analysis. The extracted principal components were interpreted by a non-parametric k-nearest neighbor (k-NN) method for automated celiac disease classification. A benchmark dataset of 460 images (240 from celiac disease patients with small intestinal villous atrophy versus 220 control patients lacking villous atrophy) was constructed from the clinical VE series. It was found that the newly developed SPCA with nongreedy L1-norm maximization was most efficient for computerized celiac disease recognition, having a robust performance with an average recognition accuracy of 93.9 percent. Furthermore, SPCA also has a reduced computation time as compared with other methods. Therefore, it is likely that SPCA will be a helpful adjunct for the diagnosis of celiac disease.
本研究旨在对胶囊内镜 (VE) 图像进行主成分分析 (PCA)，以开发用于识别乳糜泻的新型计算机工具。我们实施了三种 PCA 算法进行特征提取和稀疏表示。我们提出了一种新颖的带非贪婪 L1-范数最大化的条状 PCA (SPCA) 用于 VE 图像分析。提取的主成分通过非参数 k-最近邻 (k-NN) 方法进行解释，以实现乳糜泻的自动分类。从临床 VE 系列中构建了一个包含 460 张图像的基准数据集（240 张来自小肠绒毛萎缩的乳糜泻患者，220 张无绒毛萎缩的对照患者）。结果发现，新开发的带非贪婪 L1-范数最大化的 SPCA 对计算机化乳糜泻识别最有效，具有稳健的性能，平均识别准确率为 93.9%。此外，SPCA 的计算时间也比其他方法有所减少。因此，SPCA 很可能成为乳糜泻诊断的有用辅助手段。
