Applied Improved Canny Edge Detection for Diagnosis Medical Images of Human Brain Tumors

Authors

  • Sarab M. Taher Medical Instruments Engineering, Ashur University College, Baghdad, IRAQ. https://orcid.org/0000-0002-1895-9151
  • Mustafa Ghanim Department of Communication Engineering, University of Technology, Baghdad, IRAQ.
  • Chen Soong Der Department of Informatics, University of Tenaga Nasional (UNITEN), Selangor, MALAYSIA.

DOI:

https://doi.org/10.23851/mjs.v34i4.1392

Keywords:

Medical tumors images, Median Accumulated Histogram, CED, Sobel

Abstract

Medical image processing has become one of the crucial elements of the diagnostic process because of the increased usage of medical imaging recently, and clinicians' dependence on such computer-processed medical images in diagnosing patients. As the traditional Canny edge detection algorithm is sensitive to noise, it is easy to lose weak edge information when filtering out the noise, and its fixed parameters show poor adaptability. The suggested algorithm introduced the concept of image block intensity operator to replace image gradient. In addition, the computing speed of the suggested algorithm is relatively fast because it works block by block rather than pixel by pixel. Two adaptive threshold selection methods are presented, one based on the median accumulative histogram of image gradient magnitude and the other on the standard deviation for both types of image pixels (one with less edge information and the other with rich edge information). The proposed algorithm can be dividing into four stages: Input the medical digital image, convert the color medical image to gray-scale, applied improved canny edge detection, then calculate the MSE & PSNR Measures, in addition conduct a visual questionnaire by oncologists to find out which method that made the enhancement of the medical image clearer.

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Published

30-12-2023

Submission Dates


Received
04/05/2023

Revised
28/06/2023

Accepted
23/07/2023

 

How to Cite

[1]
S. M. Taher, M. . Ghanim, and C. S. Der, “Applied Improved Canny Edge Detection for Diagnosis Medical Images of Human Brain Tumors”, Al-Mustansiriyah Journal of Science, vol. 34, no. 4, pp. 66–74, Dec. 2023.

Issue

Section

Computer Science