Dynamically reconfigurable dataflow architecture for high-performance digital signal processing

Voigt, Sven-Ole; Baesler, Malte; Teufel, Thomas

doi:10.1016/j.sysarc.2010.07.010

Cited by 6 publications

(1 citation statement)

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“…This dataflow computing method is used in automation applications [ 12 ], digital signal processing [ 13 , 14 ], mathematics for solving systems of equations [ 15 ], floating-point matrix multiplication [ 16 ], financial derivatives pricing [ 17 ], artificial neural networks [ 18 ], and much more.…”

Section: Introductionmentioning

confidence: 99%

Acceleration of Image Segmentation Algorithm for (Breast) Mammogram Images Using High-Performance Reconfigurable Dataflow Computers

Milankovic

Mijailović

Filipović

et al. 2017

Computational and Mathematical Methods in Medicine

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Image segmentation is one of the most common procedures in medical imaging applications. It is also a very important task in breast cancer detection. Breast cancer detection procedure based on mammography can be divided into several stages. The first stage is the extraction of the region of interest from a breast image, followed by the identification of suspicious mass regions, their classification, and comparison with the existing image database. It is often the case that already existing image databases have large sets of data whose processing requires a lot of time, and thus the acceleration of each of the processing stages in breast cancer detection is a very important issue. In this paper, the implementation of the already existing algorithm for region-of-interest based image segmentation for mammogram images on High-Performance Reconfigurable Dataflow Computers (HPRDCs) is proposed. As a dataflow engine (DFE) of such HPRDC, Maxeler's acceleration card is used. The experiments for examining the acceleration of that algorithm on the Reconfigurable Dataflow Computers (RDCs) are performed with two types of mammogram images with different resolutions. There were, also, several DFE configurations and each of them gave a different acceleration value of algorithm execution. Those acceleration values are presented and experimental results showed good acceleration.

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