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Canny Edge Detection Model in MRI Image Segmentation Using Optimized Parameter Tuning Method

Meera Radhakrishnan1,*, Anandan Panneerselvam2, Nandhagopal Nachimuthu3

1 Anna University, Chennai, 600025, India
2 Department of Electronics and Communication Engineering, C. Abdul Hakeem College of Engineering and Technology, Melvisharam, 632509, India
3 Department of Electronics and Communication Engineering, Excel Engineering College, Komaraplayam, 637303, India

* Corresponding Author: Meera Radhakrishnan. Email:

Intelligent Automation & Soft Computing 2020, 26(6), 1185-1199.


Image segmentation is a crucial stage in the investigation of medical images and is predominantly implemented in various medical applications. In the case of investigating MRI brain images, the image segmentation is mainly employed to measure and visualize the anatomic structure of the brain that underwent modifications to delineate the regions. At present, distinct segmentation approaches with various degrees of accurateness and complexities are available. But, it needs tuning of various parameters to obtain optimal results. The tuning of parameters can be considered as an optimization issue using a similarity function in solution space. This paper presents a new Parametric Segmentation Tuning of Canny Edge Detection (PST-CED) model. It is based on the comparison of consecutive segmentation outcomes and the selection of one that yields maximum similarity. Besides, this paper employs an effective pre-processing technique at an earlier stage, i.e., before segmentation to improve the image quality. Here, a hybrid contrast stretching approach was employed depending upon the Top-hat filter and Gaussian function. The PST-CED technique was tested with benchmark MRI images and a detailed comparative analysis was conducted with state-of-the-art methods interms of Peak Signal to Noise Ratio (PSNR), detection accuracy, execution time and Mean Square Error (MSE).


Cite This Article

M. Radhakrishnan, A. Panneerselvam and N. Nachimuthu, "Canny edge detection model in mri image segmentation using optimized parameter tuning method," Intelligent Automation & Soft Computing, vol. 26, no.6, pp. 1185–1199, 2020.


This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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