Real-time medical video processing, enabled by hardware accelerated correlations

Authors:
Thiusius Rajeeth Savarimuthu;Anders Kjær-Nielsen;Anders Stengaard Sørensen
Affiliations:
The Maersk Mc-Kinney Moller Institute, University of Southern Denmark, Odense M, Denmark 5230;The Maersk Mc-Kinney Moller Institute, University of Southern Denmark, Odense M, Denmark 5230;The Maersk Mc-Kinney Moller Institute, University of Southern Denmark, Odense M, Denmark 5230
Venue:
Journal of Real-Time Image Processing
Year:
2011

Citing 3
Cited 2

An improved matched filter for blood vessel detection of digital retinal images

Computers in Biology and Medicine
Optical Flow Computation on Compute Unified Device Architecture

ICIAP '07 Proceedings of the 14th International Conference on Image Analysis and Processing
A Real-Time Embedded System for Stereo Vision Preprocessing Using an FPGA

RECONFIG '08 Proceedings of the 2008 International Conference on Reconfigurable Computing and FPGAs

Hardware acceleration of retinal blood vasculature segmentation

Proceedings of the 23rd ACM international conference on Great lakes symposium on VLSI
A practical evaluation of the performance of the Impulse CoDeveloper HLS tool for implementing large-kernel 2-D filters

Journal of Real-Time Image Processing

Quantified Score

Hi-index	0.00

Visualization

Abstract

Image processing involving correlation based filter algorithms have proved extremely useful for image enhancement, feature extraction and recognition, in a wide range of medical applications, but is almost exclusively used with still images due to the amount of computations required by the correlations. In this paper, we present two different practical methods for applying correlation-based algorithms to real-time video images, using hardware accelerated correlation, as well as our results in applying the method to optical venography. The first method employs a GPU accelerated personal computer, while the second method employs an embedded FPGA. We will discuss major difference between the two approaches, and their suitability for clinical use. The system presented detects blood vessels in human forearms in images from NIR camera setup for the use in a clinical environment.