Communications of the ACM
An Evaluation of the Suitability of FPGAs for Embedded Vision Systems
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Workshops - Volume 03
Enhancing retinal image by the Contourlet transform
Pattern Recognition Letters
Computer Methods and Programs in Biomedicine
IEEE Transactions on Information Technology in Biomedicine - Special section on affective and pervasive computing for healthcare
Real-time medical video processing, enabled by hardware accelerated correlations
Journal of Real-Time Image Processing
A performance and energy comparison of FPGAs, GPUs, and multicores for sliding-window applications
Proceedings of the ACM/SIGDA international symposium on Field Programmable Gate Arrays
Blood vessel segmentation methodologies in retinal images - A survey
Computer Methods and Programs in Biomedicine
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Retinal vessel tree extraction is a complex and computationally intensive task used in several medical and biometric applications. The emergence of portable biometric authentication applications, as well as on-site biomedical diagnostics, raises the need for hardware-accelerated, power-efficient architectures that can satisfy the performance and accuracy requirements of retinal vessel tree extraction. As such, this paper presents a VLSI implementation of a retina vessel segmentation system, in an attempt to illustrate the advantages and performance benefits that result from a dedicated VLSI solution. The proposed design implements an unsupervised, vessel segmentation algorithm, which utilizes match filtering with signed integers to enhance the difference between the blood vessels and the rest of the retina. The design simplifies the process of obtaining a binary map of the vessel tree by using parallel processing and efficient resource sharing, thus offering real-time performance. FPGA-based simulation results indicate significant performance improvements (up to 90x) when compared to existing hardware and software implementations.