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Proceedings Paper

Optimal restoration of compressed biomedical images: a discrete lattice theoretic approach
Author(s): Vivek Swarnakar; Athimootil V. Mathew; Edward R. Dougherty; Claudio H. Sibata
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Paper Abstract

Biomedical image data, such as obtained from CT or MR imaging modalities, tend to occupy a large amount of storage space. At the cost of losing or distorting salient features, lossy compression techniques can be used to reduce significantly the amount of data storage space. The main aim of this article is to present a novel method for optimal enhancement and restoration of images recovered from data stored using lossy compression techniques. A statistical model of the deformations undergone by the salient features within the original image, when it is stored using lossy compression techniques and then recovered, is generated. This statistical model generates a discrete lattice space. The algorithm presented here designs a set of filters over the statistical lattice space. Due to the statistical nature of the lattice the designed filters are optimal and the best possible recovery of the salient features in the original image is achieved. Results comparing the performance of the presented method to that achieved by median filters are presented. Robustness of the algorithm is tested by applying filters generated using a set of images of one subject, to images of different subjects and images stored using different compression ratios.

Paper Details

Date Published: 16 April 1996
PDF: 9 pages
Proc. SPIE 2710, Medical Imaging 1996: Image Processing, (16 April 1996); doi: 10.1117/12.237949
Show Author Affiliations
Vivek Swarnakar, Roswell Park Cancer Research Institute (United States)
Athimootil V. Mathew, Rochester Institute of Technology (United States)
Edward R. Dougherty, Rochester Institute of Technology (United States)
Claudio H. Sibata, Roswell Park Cancer Institute (United States)


Published in SPIE Proceedings Vol. 2710:
Medical Imaging 1996: Image Processing
Murray H. Loew; Kenneth M. Hanson, Editor(s)

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