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9 October 2000 Parallel implementation of an algorithm to detect calcifications in mammograms
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A multi-tolerance region-growing algorithm for automatically detecting and circumscribing calcifications in digitized mammographic images was developed. Independent studies comparing various segmentation methods showed that the multi-tolerance technique works well. However, the method is computationally expensive due to the checking of the validity of the grown region at every tolerance level until the optimal region is obtained for each calcification. Furthermore, a single mammogram may contain as many as a few hundred calcifications. In order to reduce processing time, the calcification detection algorithm was implemented on a cluster of processors using the message passing interface. In the parallel implementation, the master processor partitions the image via histogram thresholding, and sends seed pixels to the slaves to execute the multi-tolerance region-growing procedure. The slave processors grow regions, calculating a few shape parameters at each tolerance level. The parameters are used to compute distance measures which are compared until the minimum change in distance is achieved. Shape factors are then computed to describe the roughness of each region's final boundary and returned to the master processor. Initial trails have shown a speedup factor of three to eight when comparing the use of 13 slave processors to the use of one slave processor.
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Hilary Alto, Rangaraj M. Rangayyan, and Dmitri Gavrilov "Parallel implementation of an algorithm to detect calcifications in mammograms", Proc. SPIE 4118, Parallel and Distributed Methods for Image Processing IV, (9 October 2000);

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