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Proceedings Paper

Parallel implementation of the adaptive neighborhood contrast enhancement technique
Author(s): Hilary Alto; Dmitri Gavrilov; Rangaraj M. Rangayyan
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Paper Abstract

An adaptive neighborhood contrast enhancement (ANCE) technique was developed to improve the perceptibility of features in digitized mammographic images for use in breast cancer screening. The computationally intensive algorithm was implemented on a cluster of 30 DEC Alpha processors using the message passing interface. The parallel implementation of the ANCE technique utilizes histogram- based image partitioning with each partition consisting of pixels of the same gray-level value regardless of their location in the image. The master processor allots one set of pixels to each slave processor. The slave returns the results to the master, and the master than sends a new set of pixels to the slave for processing. This procedure continues until there are no sets of pixels left. The subdivision of the original image based on gray-level values guarantees that slave processors do not process the same pixel, and is specifically well-suited to the characteristics of the ANCE algorithm. The parallelism value of the problem is approximately 16, i.e., the performance does not improve significantly when more than 16 processors are used. The result is a substantial improvement in processing time, leading to the enhancement of 4 K X 4 K pixel images in the range of 20 to 60 seconds.

Paper Details

Date Published: 7 October 1999
PDF: 10 pages
Proc. SPIE 3817, Parallel and Distributed Methods for Image Processing III, (7 October 1999); doi: 10.1117/12.365893
Show Author Affiliations
Hilary Alto, Univ. of Calgary (Canada)
Dmitri Gavrilov, Univ. of Calgary (Canada)
Rangaraj M. Rangayyan, Univ. of Calgary (Canada)


Published in SPIE Proceedings Vol. 3817:
Parallel and Distributed Methods for Image Processing III
Hongchi Shi; Patrick C. Coffield, Editor(s)

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