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Proceedings Paper

Statistical analysis of dynamic sequences for functional imaging
Author(s): Chien-Min Kao; Chin-Tu Chen; Miles N. Wernick
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Paper Abstract

Factor analysis of medical image sequences (FAMIS), in which one concerns the problem of simultaneous identification of homogeneous regions (factor images) and the characteristic temporal variations (factors) inside these regions from a temporal sequence of images by statistical analysis, is one of the major challenges in medical imaging. In this research, we contribute to this important area of research by proposing a two-step approach. First, we study the use of the noise- adjusted principal component (NAPC) analysis developed by Lee et. al. for identifying the characteristic temporal variations in dynamic scans acquired by PET and MRI. NAPC allows us to effectively reject data noise and substantially reduce data dimension based on signal-to-noise ratio consideration. Subsequently, a simple spatial analysis based on the criteria of minimum spatial overlapping and non-negativity of the factor images is applied for extraction of the factors and factor images. In our simulation study, our preliminary results indicate that the proposed approach can accurately identify the factor images. However, the factors are not completely separated.

Paper Details

Date Published: 20 April 2000
PDF: 7 pages
Proc. SPIE 3978, Medical Imaging 2000: Physiology and Function from Multidimensional Images, (20 April 2000); doi: 10.1117/12.383416
Show Author Affiliations
Chien-Min Kao, Univ. of Chicago (United States)
Chin-Tu Chen, Univ. of Chicago (United States)
Miles N. Wernick, Illinois Institute of Technology (United States)

Published in SPIE Proceedings Vol. 3978:
Medical Imaging 2000: Physiology and Function from Multidimensional Images
Chin-Tu Chen; Anne V. Clough, Editor(s)

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