Image classification using segmentation, functional principal component analysis and sparse sufficient dimension reduction

Wenqian Liang, The University of Texas School of Public Health

Abstract

With the development of digital technologies, image classification has become an important method for improving the accuracy and reliability of disease diagnosis. Automatic image classification can be a powerful tool for computer-aided disease diagnosis which provides fast and reliable disease diagnosis. We proposed a novel method for automatic image classification which combines image segmentation, three-dimensional functional principal component analysis (FPCA) and sparse sufficient dimension reduction (SDR). To evaluate the performance of the proposed method for image classification analysis, we applied it to a total of 118 volunteers’ Magnetic resonance imaging (MRI) data and classify them into ischemic group and normal group. The proposed method was compared with the Wavelet PCA-based method and sparse logistic regression. The results showed that the proposed method outperformed the Wavelet PCA-based method and sparse logistic regression.^

Subject Area

Biostatistics

Recommended Citation

Liang, Wenqian, "Image classification using segmentation, functional principal component analysis and sparse sufficient dimension reduction" (2016). Texas Medical Center Dissertations (via ProQuest). AAI10127399.
http://digitalcommons.library.tmc.edu/dissertations/AAI10127399

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