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Proceedings Paper

KL transformation of spatially invariant image sequences
Author(s): James B. Farison; Yong-gab Park; Qun Yu; Hong Lu
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Paper Abstract

This paper investigates the special properties and results involved in the application of the Karhunen-Loeve (KL) transformation, also called principal component analysis (PCA) or Hotelling transform, to linearly-additive, spatially-invariant (LA SI) image sequences such as arise in many medical imaging applications (SPECT temporal studies, multi-parameter MRI, etc.), multispectral remote sensing, and elsewhere. The special structure of LA SI image sequences provides some interesting results both for the KL analysis and for the resulting principal component images. Simulated images and mathematical results indicate the KL implications for the special structure of LA SI images, in relation to the statistical order and feature characteristics of the image sequence. Simulated image sequences are understood by the mathematical results and illustrate the characteristics of KL compression and reconstruction for LA SI images. The well-known and widely used KL transform is a general and powerful image compression technique based on the statistical variance of the image data. However, it does not explicitly acknowledge specific features or their individual characteristics in an image set. For LA SI images, this may be an important limitation in relation to other methods of analysis and compression for such images.

Paper Details

Date Published: 25 April 1997
PDF: 12 pages
Proc. SPIE 3034, Medical Imaging 1997: Image Processing, (25 April 1997); doi: 10.1117/12.274108
Show Author Affiliations
James B. Farison, Univ. of Toledo (United States)
Yong-gab Park, Univ. of Toledo (United States)
Qun Yu, Univ. of Toledo (United States)
Hong Lu, Univ. of Toledo (United States)

Published in SPIE Proceedings Vol. 3034:
Medical Imaging 1997: Image Processing
Kenneth M. Hanson, Editor(s)

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