Fast retinal vessel tree extraction: A pixel parallel approach

Alonso-Montes, Carmen; Vilariño, D.L.; Dudek, Piotr; Penedo, Manuel G.

doi:10.1002/cta.512

Cited by 39 publications

(38 citation statements)

References 18 publications

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“…The retinal vessel tree extraction algorithm was proposed by Alonso-Montes et al [9]. This technique uses a set of active contours that fit the external boundaries of the vessels.…”

Section: The Retinal Vessel Tree Extraction Algorithmmentioning

confidence: 99%

“…Different approaches to extract the retinal vessel tree or just some specific features with relevant information for the experts have been proposed [6][7][8][9].…”

Section: Introductionmentioning

confidence: 99%

“…However, in some of them, the computational effort is also the main issue. In this sense, a new technique was proposed in Alonso-Montes et al [9]. This algorithm was designed specifically for its utilization in finegrained SIMD architectures with the purpose of improving the computation time.…”

Section: Introductionmentioning

confidence: 99%

“…However, when using real devices, we have to face certain limitations imposed by the technology (i.e. integration density, noise or accuracy), so the results are worse than expected [9]. At this point, other a priori less suitable solutions can provide similar or even better performance.…”

Section: Introductionmentioning

confidence: 99%

“…In this paper, the automatic method to extract the vessel tree from retinal images presented in AlonsoMontes et al [9] was tested on an FPGA and an MPPA, and the results were compared with the native platform of the algorithm, an MP-SIMD processor.…”

Section: Introductionmentioning

confidence: 99%

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Performance analysis of massively parallel embedded hardware architectures for retinal image processing

Nieto

Brea

Vilariño

et al. 2011

J Image Video Proc.

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This paper examines the implementation of a retinal vessel tree extraction technique on different hardware platforms and architectures. Retinal vessel tree extraction is a representative application of those found in the domain of medical image processing. The low signal-to-noise ratio of the images leads to a large amount of lowlevel tasks in order to meet the accuracy requirements. In some applications, this might compromise computing speed. This paper is focused on the assessment of the performance of a retinal vessel tree extraction method on different hardware platforms. In particular, the retinal vessel tree extraction method is mapped onto a massively parallel SIMD (MP-SIMD) chip, a massively parallel processor array (MPPA) and onto an field-programmable gate arrays (FPGA).

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“…The retinal vessel tree extraction algorithm was proposed by Alonso-Montes et al [9]. This technique uses a set of active contours that fit the external boundaries of the vessels.…”

Section: The Retinal Vessel Tree Extraction Algorithmmentioning

confidence: 99%

“…Different approaches to extract the retinal vessel tree or just some specific features with relevant information for the experts have been proposed [6][7][8][9].…”

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Performance analysis of massively parallel embedded hardware architectures for retinal image processing

Nieto

Brea

Vilariño

et al. 2011

J Image Video Proc.

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Retinal blood vessel segmentation for macula detachment surgery monitoring instruments

Hajabdollahi

Karimi

Soroushmehr

et al. 2018

Circuit Theory & Apps

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In recent years image processing has improved detection and diagnosis in medical application. Image processing applications are now embedded in medical instruments such as MRI and CT. In the case of retinopathy, fast extraction of blood vessels can allow the physician to view injury regions during surgery. Macula detachment surgeries, or computer-aided intraocular surgeries, require precise and real-time knowledge of the vasculature during the operation. Use of artificial neural network has produced good results in image processing applications, but its implementation may not be suitable for real-time applications in small, embedded hardware. Because of error resiliency of the neural network, its structure can be pruned and simplified. In this paper an efficient hardware implementation of neural network for retinal vessel segmentation is proposed. We simplify the neural network structure in such a way that the accuracy of the results is not altered significantly. Simulation results and FPGA implementation show that our proposed network has low complexity and can be applied for segmentation of retinal vessels with acceptable accuracy. This makes the proposed method a good candidate to be implemented in any device such as a binocular indirect ophthalmoscope. KEYWORDS artificial neural networks (ANN), error compensation, hardware implementation, retinal vessel segmentation, retinopathy

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Real‐time removal of impulse noise from MR images for radiosurgery applications

HosseinKhani

Hajabdollahi

Karimi

et al. 2019

Circuit Theory & Apps

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Summary In the recent years, image processing techniques are used as a tool to improve detection and diagnostic capabilities in the medical applications. Among these techniques, medical image enhancement algorithms play an essential role in the removal of the noise, which can be produced by medical instruments and during image transfer. Impulse noise is a major type of noise, which is produced by medical imaging systems, such as MRI, computed tomography (CT), and angiography instruments. An embeddable hardware module, which can denoise medical images before and during surgical operations, could be very helpful. In this paper, an accurate algorithm is proposed for real‐time removal of impulse noise in medical images. Our algorithm categorizes all image blocks into three types of edge, smooth, and disordered areas. A different reconstruction method is applied to each category of blocks for noise removal. The proposed method is tested on MR images. Simulation results show acceptable denoising accuracy for various levels of noise. Also, an field programmable gate array (FPGA) implementation of our denoising algorithm shows acceptable hardware resource utilization. Hence, the algorithm is suitable for embedding in medical hardware instruments such as radiosurgery devices.

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Fast retinal vessel tree extraction: A pixel parallel approach

Cited by 39 publications

References 18 publications

Performance analysis of massively parallel embedded hardware architectures for retinal image processing

Performance analysis of massively parallel embedded hardware architectures for retinal image processing

Retinal blood vessel segmentation for macula detachment surgery monitoring instruments

Real‐time removal of impulse noise from MR images for radiosurgery applications

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