Functional Principal Component Analysis and Randomized Sparse Clustering Algorithm for Medical Image Analysis.

ABSTRACT: Due to the advancement in sensor technology, the growing large medical image data have the ability to visualize the anatomical changes in biological tissues. As a consequence, the medical images have the potential to enhance the diagnosis of disease, the prediction of clinical outcomes and the characterization of disease progression. But in the meantime, the growing data dimensions pose great methodological and computational challenges for the representation and selection of features in image cluster analysis. To address these challenges, we first extend the functional principal component analysis (FPCA) from one dimension to two dimensions to fully capture the space variation of image the signals. The image signals contain a large number of redundant features which provide no additional i

SUBMITTER: Lin N 

PROVIDER: S-EPMC4510534 | biostudies-literature | 2015

REPOSITORIES: biostudies-literature