Performances of the PS/sup 2/ parallel storage and processing system for tomographic image visualization

Messerli, Vincent; Gennart, Benoit A.; Hersch, Roger D.

doi:10.1109/icpads.1997.652594

Cited by 4 publications

(3 citation statements)

References 7 publications

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“…In [8], a system is described that can process images over 100 times larger used by tomosynthesis. Reconstruction algorithms used by computerized tomography (CT) are not able to produce 3D high-resolution images for practical use.…”

Section: Related Workmentioning

confidence: 99%

Acceleration of maximum likelihood estimation for tomosynthesis mammography

Zhang

Meleis

Kaeli

et al. 2006

12th International Conference on Parallel and Distributed Systems - (ICPADS'06)

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Maximum likelihood (ML) estimation is used during tomosynthesis mammography reconstruction. A single reconstruction involves the processing of highresolution projection images, which is both computeintensive and time-consuming. This workload is presently a bottleneck in the accurate diagnosis of breast cancer during screening. This paper presents our parallelization work on an ML algorithm using three different partitioning models: no inter-communication, overlap with inter-communication and non-overlap model. These models are evaluated to obtain the best reconstruction performance given a range of computing environments with different computational power and network speed. Our test results show that the non-overlap method outperforms the other two methods on all five computing platforms evaluated. This parallelization of ML has enabled tomosynthesis to become a viable technology in the breast screening clinic, reducing reconstruction time from 3 hours on a PentiumIV workstation to 6 minutes on a 32-node PentiumIV cluster.

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Section: Related Workmentioning

confidence: 99%

Acceleration of maximum likelihood estimation for tomosynthesis mammography

Zhang

Meleis

Kaeli

et al. 2006

12th International Conference on Parallel and Distributed Systems - (ICPADS'06)

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“…Combining parallel disk access operations (in Figure 6 reading extents from disks) and processing operations as desired enables building highly pipelined applications incorporating the required parallel file system components [Messerli97].…”

Section: Figure 5 Syntax Of a Parallel While Constructmentioning

confidence: 99%

“…We use IBM-DPES 31080, IBM-DCAS 32160, CONNER CFP1080S, SEAGATE ST52160N and SEAGATE ST32155N disks which have a measured mean read data transfer throughput of 3.5 MBytes/s and a mean latency time, i.e. seek time + rotational latency time, of 12.2 ms [Messerli97]. Thus, when accessing 51 KBytes blocks, i.e.…”

Section: Performances and Scalability Analysis Of The Image Slice Extmentioning

confidence: 99%

Parallelizing I/O-intensive image access and processing applications

et al. 1999

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Abstract. We propose a new approach for developing parallel I/O-and computeintensive applications on distributed memory PC. Using the CAP Computer-Aided Parallelization tool, application programmers create separately the serial program parts and express the parallel behavior of the program at a high level of abstraction. This highlevel parallel program description (CAP) is preprocessed into a compilable and executable C++ source parallel program. Low-level parallel file system components can, thanks to the CAP formalism, be combined with processing operations in order to yield efficient pipelined parallel I/O and compute intensive programs. These programs may run on multiple PC servers offering their access and processing services to clients located over the network. The applicability of the CAP tools on a real application is demonstrated with a parallel 3D tomographic image server application enabling clients to specify and access in parallel image slices having any desired position and orientation. The image slices are extracted from a 14 GByte color 3D tomographic image striped over the available set of disks. On a 5 Bi-Pentium Pro PC server comprising 60 disks, the system is able to extract in parallel, resample and visualize 4.8 512x512 colour image slices per second. At the highest load and when file caching is disabled, an aggregate I/O disk bandwidth of 104 MBytes/s has been obtained. When caching is enabled, an I/O throughput of up to 240 MBytes/s is obtained.

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Parallel unfolding and visualization of curved surfaces extracted from large three-dimensional volumes

Figueiredo

Hersch

2002

J. Electron. Imaging

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Abstract. Although many three-dimensional (3D) medical imaging visualization methods exist, 3D volume slicing remains the most commonly used technique for visualizing medical data from modalities such as CT, MRI, and PET. We propose to extend the possibilities of oblique slicing to developable curved surfaces that can be flattened and displayed in two dimensions without deformation. Such surfaces can be used to follow curved anatomical structures while preserving distance metrics at visualization time. They may also be useful for the staging of tumors, i.e., to evaluate the spatial extension of a tumor. We propose an out of core algorithm that runs in parallel on a multi-PC architecture and is able to extract surfaces from very large 3D datasets such as the visible human data set (man: 13 GB, woman: 49 GB). Experimental performance results are presented which demonstrate that parallel surface extraction is scalable and has a reasonable overhead compared with traditional oblique planar slicing. Surface extraction is made available to the public as one of the services offered by EPFLs visible human web server (http://visiblehuman.epfl.ch).

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Performances of the PS/sup 2/ parallel storage and processing system for tomographic image visualization

Abstract: Abstract. We propose a new approach for developing parallel I/O-and

Cited by 4 publications

References 7 publications

Acceleration of maximum likelihood estimation for tomosynthesis mammography

Acceleration of maximum likelihood estimation for tomosynthesis mammography

Parallelizing I/O-intensive image access and processing applications

Parallel unfolding and visualization of curved surfaces extracted from large three-dimensional volumes

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