Application of brain tumor detection on DSP environment using TMS320C6713 DSK

Beddad, Boucif; Hachemi, Kaddour

doi:10.1109/tsp.2017.8076057

Cited by 3 publications

(1 citation statement)

References 4 publications

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“…On another note, Beddad and Hachemi showed encouraging performance results when implementing several medical image algorithms (such as Fuzzy C-Means or Level sets) for brain tumor detection in MR images [105].…”

Section: Dsps In Medical Imagingmentioning

confidence: 99%

Hardware Architectures for Real-Time Medical Imaging

et al. 2021

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Medical imaging is considered one of the most important advances in the history of medicine and has become an essential part of the diagnosis and treatment of patients. Earlier prediction and treatment have been driving the acquisition of higher image resolutions as well as the fusion of different modalities, raising the need for sophisticated hardware and software systems for medical image registration, storage, analysis, and processing. In this scenario and given the new clinical pipelines and the huge clinical burden of hospitals, these systems are often required to provide both highly accurate and real-time processing of large amounts of imaging data. Additionally, lowering the prices of each part of imaging equipment, as well as its development and implementation, and increasing their lifespan is crucial to minimize the cost and lead to more accessible healthcare. This paper focuses on the evolution and the application of different hardware architectures (namely, CPU, GPU, DSP, FPGA, and ASIC) in medical imaging through various specific examples and discussing different options depending on the specific application. The main purpose is to provide a general introduction to hardware acceleration techniques for medical imaging researchers and developers who need to accelerate their implementations.

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Section: Dsps In Medical Imagingmentioning

confidence: 99%