title: 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 information for clustering analysis. The widely used methods for removing the irrelevant features are sparse clustering algorithms using a lasso-type penalty to select the features. However, the accuracy of clustering using a lasso-type penalty depends on the selection of the penalty parameters and the threshold value. In practice, they are difficult to determine. Recently, randomized algorithms have received a great deal of attentions in big data analysis. This paper presents a randomized algorithm for accurate feature selection in image clustering analysis. The proposed method is applied to both the liver and kidney cancer histology image data from the TCGA database. The results demonstrate that the randomized feature selection method coupled with functional principal component analysis substantially outperforms the current sparse clustering algorithms in image cluster analysis.
authors.affiliation: Human Genetics Center, Department of Biostatistics, School of Public Health, the University of Texas Health Science Center at Houston, Houston, Texas, 77030, United States of America.
authors.forename: Nan
authors.initials: N
authors.lastname: Lin
authors.affiliation: Human Genetics Center, Department of Biostatistics, School of Public Health, the University of Texas Health Science Center at Houston, Houston, Texas, 77030, United States of America.
authors.forename: Junhai
authors.initials: J
authors.lastname: Jiang
authors.affiliation: Human Genetics Center, Department of Biostatistics, School of Public Health, the University of Texas Health Science Center at Houston, Houston, Texas, 77030, United States of America; State Key Laboratory of Genetic Engineering and Ministry of Education Key Laboratory of Contemporary Anthropology, School of Life Sciences and Institutes of Biomedical Sciences, Fudan University, Shanghai, 200433, China.
authors.forename: Shicheng
authors.initials: S
authors.lastname: Guo
authors.affiliation: Human Genetics Center, Department of Biostatistics, School of Public Health, the University of Texas Health Science Center at Houston, Houston, Texas, 77030, United States of America.
authors.forename: Momiao
authors.initials: M
authors.lastname: Xiong
mesh_terms: D000465:Algorithms; D016000:Cluster Analysis; D016208:Databases, Factual; D003952:Diagnostic Imaging / Q000379:methods*; D005583:Fourier Analysis; D006801:Humans; D007091:Image Processing, Computer-Assisted; D007680:Kidney Neoplasms / Q000175:diagnosis* / Q000473:pathology; D008113:Liver Neoplasms / Q000175:diagnosis* / Q000473:pathology; D025341:Principal Component Analysis; D012984:Software