A New Parallel Approach for Accelerating the GPU-Based Execution of Edge Detection Algorithms

Zahra Emrani, Soroosh Bateni, Hossein Rabbani



Real-time image processing is used in a wide variety of applications like those in medical care andindustrial processes. This technique in medical care has the ability to display important patientinformation graphi graphically, which can supplement and help the treatment process. Medical decisionsmade based on real-time images are more accurate and reliable. According to the recent researches,graphic processing unit (GPU) programming is a useful method for improving the speed and quality ofmedical image processing and is one of the ways of real-time image processing. Edge detection is anearly stage in most of the image processing methods for the extraction of features and object segmentsfrom a raw image. The Canny method, Sobel and Prewitt filters, and the Roberts’ Cross technique aresome examples of edge detection algorithms that are widely used in image processing and machinevision. In this work, these algorithms are implemented using the Compute Unified Device Architecture(CUDA), Open Source Computer Vision (OpenCV), and Matrix Laboratory (MATLAB) platforms. Anexisting parallel method for Canny approach has been modified further to run in a fully parallel manner.This has been achieved by replacing the breadth-first search procedure with a parallel method. Thesealgorithms have been compared by testing them on a database of optical coherence tomography images.The comparison of results shows that the proposed implementation of the Canny method on GPU usingthe CUDA platform improves the speed of execution by 2–100× compared to the central processing unitbasedimplementation using the OpenCV and MATLAB platforms.


Algorithms; computer systems; computers; humans computer-assisted; image processing; optical coherence tomography

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