Parallel implementation of the adaptive neighborhood contrast enhancement technique using histogram-based image partitioning

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 COMPAQ Alpha processors using the message passing interface. The parallel implementation of the ANCE technique utilizes histogram-based image partitioning with each partition consisting of a list of gray-level values. The master processor allots one set of gray-level values to each slave processor. Each slave locates all possible seed pixels in the image with the designated gray-level values, grows a region around each pixel, enhances the contrast of the seed and any redundant seed pixels if required, and returns the results to the master. The master then sends a new set of gray-level values to the slave for processing. The subdivision of the original image based on gray-level values guarantees that slave processors do not process the same pixel, and is particularly well suited to the characteristics of the ANCE algorithm. The parallelism value of the problem is in the range of 16-20; the performance did not improve significantly when more than 16 processors were used. The performance declined when more than 20 processors were used. The result is a substantial improvement in processing time, leading to the enhancement of 4 Kx4 K pixel images in the range of 30-90 s. (C) 2001 SPIE and IS&T.