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

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

Emrani Z¹ ; Bateni S² ; Rabbani H¹

Show Affiliations

1. Medical Image and Signal Processing Research Center, Isfahan University of Medical Sciences, Isfahan, 81745-319, Iran
2. Department of Electrical and Computer Engineering, Isfahan University of Technology, Isfahan, Iran

Source: Journal of Medical Signals and Sensors Published:2017

Abstract

Real-time image processing is used in a wide variety of applications like those in medical care and industrial processes. This technique in medical care has the ability to display important patient information graphi graphically, which can supplement and help the treatment process. Medical decisions made 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 of medical image processing and is one of the ways of real-time image processing. Edge detection is an early stage in most of the image processing methods for the extraction of features and object segments from a raw image. The Canny method, Sobel and Prewitt filters, and the Roberts' Cross technique are some examples of edge detection algorithms that are widely used in image processing and machine vision. In this work, these algorithms are implemented using the Compute Unified Device Architecture (CUDA), Open Source Computer Vision (OpenCV), and Matrix Laboratory (MATLAB) platforms. An existing 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. These algorithms 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 using the CUDA platform improves the speed of execution by 2-100× compared to the central processing unit-based implementation using the OpenCV and MATLAB platforms. © 2017 Journal of Medical Signals and Sensors.

Style	Citing Format
MLA	Emrani Z, Bateni S, Rabbani H. "A New Parallel Approach for Accelerating the Gpu-Based Execution of Edge Detection Algorithms." Journal of Medical Signals and Sensors, vol. 7, no. 1, 2017, pp. 33-42.
APA	Emrani Z, Bateni S, Rabbani H (2017). A New Parallel Approach for Accelerating the Gpu-Based Execution of Edge Detection Algorithms. Journal of Medical Signals and Sensors, 7(1), 33-42.
Chicago	Emrani Z, Bateni S, Rabbani H. "A New Parallel Approach for Accelerating the Gpu-Based Execution of Edge Detection Algorithms." Journal of Medical Signals and Sensors 7, no. 1 (2017): 33-42.
Harvard	Emrani Z, Bateni S, Rabbani H (2017) 'A New Parallel Approach for Accelerating the Gpu-Based Execution of Edge Detection Algorithms', Journal of Medical Signals and Sensors, 7(1), pp. 33-42.
Vancouver	Emrani Z, Bateni S, Rabbani H. A New Parallel Approach for Accelerating the Gpu-Based Execution of Edge Detection Algorithms. Journal of Medical Signals and Sensors. 2017;7(1):33-42.
BibTex	@article{ author = {Emrani Z and Bateni S and Rabbani H}, title = {A New Parallel Approach for Accelerating the Gpu-Based Execution of Edge Detection Algorithms}, journal = {Journal of Medical Signals and Sensors}, volume = {7}, number = {1}, pages = {33-42}, year = {2017} }
RIS	TY - JOUR AU - Emrani Z AU - Bateni S AU - Rabbani H TI - A New Parallel Approach for Accelerating the Gpu-Based Execution of Edge Detection Algorithms JO - Journal of Medical Signals and Sensors VL - 7 IS - 1 SP - 33 EP - 42 PY - 2017 ER -

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