  Document Type  :  Latin Dissertation  Language of Document  :  English  Record Number  :  54718  Doc. No  :  TL24672  Call number  :  3232998  Main Entry  :  Muhammad Aleemuddin Siddiqi  Title & Author  :  Statistical image and functional data analysisMuhammad Aleemuddin Siddiqi  College  :  University of California, Santa Barbara  Date  :  2006  Degree  :  Ph.D.  student score  :  2006  Page No  :  90  Abstract  :  This work develops Statistical techniques for image analysis with special focus on bioimages from confocal microscopes. Taking a cue from image processing techniques, statistical features are developed to analyze these images. The central idea is to think of a gray image as a 2d distribution on the mxn lattice of pixels and extract relevant features of interest. These features are used in particular, to analyze the relationships between protein distributions in the retinal images. A closely related topic to image analysis is functional data analysis, since a gray image is essentially a 2d function. Two functional data analysis techniques namely, Growth curve modeling and Functional mixture modeling are studied. Growth curve modeling is developed with Bspline basis functions to model the mean growth curves for various treatments. Mean growth curves are fitted and statistical tests carried out for relevant Biological hypotheses regarding microtubule dynamicity in living cells. These analyses and conclusions have potentially important biological significance. A scheme to detect nonconvex shaped clusters in sparsely sampled functional data is developed. This is based on first fitting a Functional mixture model to the entire data and then performing hierarchical clustering of components using the mixture parameters. Nonconvex clusters can be obtained from the original data by graphtheoretic methods but these are computationally intensive. The method developed here performs graphtheoretic clustering on the mixture components which are far fewer, rather than on the whole functional data and hence is fast. This is implemented on a microtubule data set.  Subject  :  Pure sciences; Bspline basis functions; Functional data analysis; Image analysis; Statistical features; Statistics; 0463:Statistics  Added Entry  :  S. R. Jammalamadaka  Added Entry  :  University of California, Santa Barbara 
     
   
http://lib.clisel.com/site/catalogue/54718

 