FPGA-accelerated retinal vessel-tree extraction

Nieto, Alejandro; Brea, V.M.; Vilariño, D.L.

doi:10.1109/fpl.2009.5272498

Cited by 15 publications

(11 citation statements)

References 8 publications

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“…The proposed work offers a significant improvement in terms of classification accuracy and execution time when compared to the existing work ( Table 5). The proposed work offers significantly improved performance in comparison to the work reported in [35][36][37].…”

Section: Methodsmentioning

confidence: 91%

Accelerating Retinal Fundus Image Classification Using Artificial Neural Networks (ANNs) and Reconfigurable Hardware (FPGA)

Ghani

See

Sudhakaran³

et al. 2019

Electronics

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Diabetic retinopathy (DR) and glaucoma are common eye diseases that affect a blood vessel in the retina and are two of the leading causes of vision loss around the world. Glaucoma is a common eye condition where the optic nerve that connects the eye to the brain becomes damaged, whereas DR is a complication of diabetes caused by high blood sugar levels damaging the back of the eye. In order to produce an accurate and early diagnosis, an extremely high number of retinal images needs to be processed. Given the required computational complexity of image processing algorithms and the need for high-performance architectures, this paper proposes and demonstrates the use of fully parallel field programmable gate arrays (FPGAs) to overcome the burden of real-time computing in conventional software architectures. The experimental results achieved through software implementation were validated on an FPGA device. The results showed a remarkable improvement in terms of computational speed and power consumption. This paper presents various preprocessing methods to analyse fundus images, which can serve as a diagnostic tool for detection of glaucoma and diabetic retinopathy. In the proposed adaptive thresholding-based preprocessing method, features were selected by calculating the area of the segmented optic disk, which was further classified using a feedforward neural network (NN). The analysis was carried out using feature extraction through existing methodologies such as adaptive thresholding, histogram and wavelet transform. Results obtained through these methods were quantified to obtain optimum performance in terms of classification accuracy. The proposed hardware implementation outperforms existing methods and offers a significant improvement in terms of computational speed and power consumption.

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Section: Methodsmentioning

confidence: 91%

Accelerating Retinal Fundus Image Classification Using Artificial Neural Networks (ANNs) and Reconfigurable Hardware (FPGA)

Ghani

See

Sudhakaran³

et al. 2019

Electronics

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“…At the end of the image, the sum of the pixel values is divided by the number of pixels to get the final mean value. The standard deviation value is computed according to (6).…”

Section: Mean and Standard Deviation Values Computationmentioning

confidence: 99%

Memory efficient Multi-Scale Line Detector architecture for retinal blood vessel segmentation

Bendaoudi

Cheriet

Langlois

2016

2016 Conference on Design and Architectures for Signal and Image Processing (DASIP)

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This paper presents a memory efficient architecture that implements the Multi-Scale Line Detector (MSLD) algorithm for real-time retinal blood vessel detection in fundus images on a Zynq FPGA. This implementation benefits from the FPGA parallelism to drastically reduce the memory requirements of the MSLD from two images to a few values. The architecture is optimized in terms of resource utilization by reusing the computations and optimizing the bit-width. The throughput is increased by designing fully pipelined functional units. The architecture is capable of achieving a comparable accuracy to its software implementation but 70× faster for low resolution images. For high resolution images, it achieves an acceleration by a factor of 323×.

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“…However, most focus on offline analysis of low-magnification, wide-area images such as fundus images where accuracy is prioritized over speed. With the exception of speed-focused algorithms such as [24], [30], [35] and other hardware-accelerated methods [36], [37], most algorithms require more than 1 s to run, which is insufficiently fast to benefit robotic control loops.…”

Section: Related Workmentioning

confidence: 99%

Real-time retinal vessel mapping and localization for intraocular surgery

Becker

Riviere

2013

2013 IEEE International Conference on Robotics and Automation

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Computer-aided intraocular surgery requires precise, real-time knowledge of the vasculature during retinal procedures such as laser photocoagulation or vessel cannulation. Because vitreoretinal surgeons manipulate retinal structures on the back of the eye through ports in the sclera, voluntary and involuntary tool motion rotates the eye in the socket and causes movement to the microscope view of the retina. The dynamic nature of the surgical workspace during intraocular surgery makes mapping, tracking, and localizing vasculature in real time a challenge. We present an approach that both maps and localizes retinal vessels by temporally fusing and registering individual-frame vessel detections. On video of porcine and human retina, we demonstrate real-time performance, rapid convergence, and robustness to variable illumination and tool occlusion.

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FPGA-accelerated retinal vessel-tree extraction

Cited by 15 publications

References 8 publications

Accelerating Retinal Fundus Image Classification Using Artificial Neural Networks (ANNs) and Reconfigurable Hardware (FPGA)

Accelerating Retinal Fundus Image Classification Using Artificial Neural Networks (ANNs) and Reconfigurable Hardware (FPGA)

Memory efficient Multi-Scale Line Detector architecture for retinal blood vessel segmentation

Real-time retinal vessel mapping and localization for intraocular surgery

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