FPGA-Based Real-Time Image Segmentation for Medical Systems and Data Processing

Dillinger, Peter C.; Vogelbruch, J.; Leinen, Jessica; Suslov, S.; Patzak, R.; Winkler, Hanspeter; Schwan, Karsten

doi:10.1109/tns.2006.877268

Cited by 29 publications

(4 citation statements)

References 16 publications

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“…However, it has two main disadvantages compared with our memristor system. One is that, it is expensive for the hardware price to use FPGA as a co-processor for image processing, and the other is that a customized FPGA system could optimize or speed up only a kind of algorithms and is not general [17,18]. Our memristor architecture is a memory system in nature and only needs some peripheral circuits to generate stimulating voltages.…”

Section: Discussionmentioning

confidence: 99%

A memristor-based architecture combining memory and image processing

Zhou

Yang

et al. 2013

Sci. China Inf. Sci.

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Color image edge extraction using memristor-based CNN SCIENTIA SINICA Informationis 47, 863 (2017); Fixed-time synchronization of delayed memristor-based recurrent neural networks SCIENCE CHINA Information Sciences 60, 032201 (2017); Exponential stabilization of memristor-based neural networks with unbounded time-varying delays SCIENCE CHINA Information Sciences 64, 189205 (2021);. RESEARCH PAPER. SCIENCE CHINA Information Sciences

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Section: Discussionmentioning

confidence: 99%

A memristor-based architecture combining memory and image processing

Zhou

Yang

et al. 2013

Sci. China Inf. Sci.

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“…The evolution of FPGAs has motivated an increase in the use of these devices, whose architecture allows the development of hardware solutions optimized for complex tasks, such as 3D MRI image segmentation [118], 3D discrete wavelet transform [119], tomographic image reconstruction [18,120], or PET/MRI systems [121,122]. The developed solutions can perform intensive computation tasks with parallel processing, are dynamically reprogrammable, and have a low cost, all while meeting the hard real-time requirements associated with medical imaging.…”

Section: Fpga 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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“…Field programmable gate array (FPGA) has been widely used to accelerate the image processing algorithms in biomedical imaging area. Although there are some implementations of image segmentation targeted on FPGAs, there is no FPGA implementation for automated cerebral aneurysm segmentation [24,25,26,27]. In this paper, we design and implement an automated aneurysm segmentation algorithm on Zynq SoC.…”

Section: Introductionmentioning

confidence: 99%

Real-time automated image segmentation technique for cerebral aneurysm on reconfigurable system-on-chip

Zhai

Eslami

Hussein

et al. 2018

Journal of Computational Science

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Cerebral aneurysm is a weakness in a blood vessel that may enlarge and bleed into the surrounding area, which is a life-threatening condition. Therefore, early and accurate diagnosis of aneurysm is highly required to help doctors to decide the right treatment. This work aims to implement a real-time automated segmentation technique for cerebral aneurysm on the Zynq system-on-chip (SoC), and virtualise the results on a 3D plane, utilizing virtual reality (VR) facilities, such as Oculus Rift, to create an interactive environment for training purposes. The segmentation algorithm is designed based on hard thresholding and Haar wavelet transformation. The system is tested on six subjects, for each consists 512 × 512 DICOM slices, of 16 bits 3D rotational angiography. The quantitative and subjective evaluation show that the segmented masks and 3D generated volumes have admitted results. In addition, the hardware implement results show that the proposed implementation is capable to process an image using Zynq SoC in an average time of 5.2 ms.

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FPGA-Based Real-Time Image Segmentation for Medical Systems and Data Processing

Cited by 29 publications

References 16 publications

A memristor-based architecture combining memory and image processing

A memristor-based architecture combining memory and image processing

Hardware Architectures for Real-Time Medical Imaging

Real-time automated image segmentation technique for cerebral aneurysm on reconfigurable system-on-chip

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