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

Zahra Emrani1, Soroosh Bateni2, Hossein Rabbani1

  • 1Medical Image and Signal Processing Research Center, Isfahan University of Medical Sciences, Isfahan, Iran.

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|May 11, 2017
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Summary
This summary is machine-generated.

This study enhances real-time medical image processing using graphic processing unit (GPU) programming. Optimized Canny edge detection on GPU significantly speeds up analysis for more accurate medical decisions.

Keywords:
Algorithmscomputer systemscomputershumans computer-assisted image processingoptical coherence tomography

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Area of Science:

  • Medical Imaging
  • Computer Vision
  • High-Performance Computing

Background:

  • Real-time image processing is crucial for medical diagnosis and industrial applications.
  • Graphic processing unit (GPU) programming accelerates image analysis, enhancing medical decision-making.
  • Edge detection algorithms are fundamental for feature extraction in image processing.

Purpose of the Study:

  • To implement and compare edge detection algorithms (Canny, Sobel, Prewitt, Roberts' Cross) on GPU using CUDA.
  • To optimize the Canny edge detection algorithm for fully parallel execution.
  • To evaluate the performance improvement of GPU-based implementations against CPU-based methods.

Main Methods:

  • Implementation of Canny, Sobel, Prewitt, and Roberts' Cross edge detection algorithms using CUDA, OpenCV, and MATLAB.
  • Modification of the Canny algorithm to achieve fully parallel processing by replacing breadth-first search.
  • Testing and comparison on a database of optical coherence tomography (OCT) images.

Main Results:

  • The proposed GPU-based Canny method implementation using CUDA achieved a 2-100x speedup compared to CPU-based implementations.
  • The parallelized Canny algorithm demonstrated significant performance gains.
  • Comparative analysis highlighted the efficiency of GPU acceleration for medical image processing tasks.

Conclusions:

  • GPU programming, particularly with CUDA, offers substantial speed improvements for real-time medical image processing.
  • Optimized parallel algorithms enhance the efficiency of edge detection techniques.
  • The findings support the use of GPU acceleration for more accurate and timely medical diagnoses.