Edge detection of noisy digital image using optimization of threshold and self organized map neural network
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Edge detection of noisy digital image using optimization of threshold and self organized map neural network Khadiv Hajipour 1 & Vahid Mehrdad 1 Received: 25 July 2019 / Revised: 11 August 2020 / Accepted: 17 September 2020 # Springer Science+Business Media, LLC, part of Springer Nature 2020
Abstract
The purpose of this research is to find a suitable method for detecting the edges of noisy digital images by eliminating the noise effects. The image will be partitioned into equal partitions and the initial threshold of that image partition will be calculated. By applying all these thresholds into the self-organized map (SOM) neural network input optimized for learning and training based optimization algorithm (TLBO), threshold clustering will be performed. The partitioned image will be edge detected by entropy method. Choosing the threshold for image segmentation is of great importance. The mean of the brightness of digital noise images is not a good representative of the initial threshold. Noise causes the mean intensity of the brightness to take distance from the main range of the intensity of the image so the resulting edge detected image will be severely noisy and truncated. By determining the highest frequency of brightness intensity instead of the mean brightness, the above-mentioned weaknesses will be eliminated. This method outperforms many current methods, such as Tsallis entropy, Singh and Kiani and even Canny Edge Detection which demonstrates the effectiveness of the proposed method, In the Table 1 the PSNR of image 5 of the proposed method is 61.4896, but Singh method which is 55.61, Tsallis method which is 53.9234, Kiani method which is 53.9315 the proposed method is less than the other methods. Keywords Digital image processing . Edge detection . Self-organized map neural network . Noise reduction
* Vahid Mehrdad [email protected] Khadiv Hajipour [email protected]
1
Department of Electrical and Electronics Engineering, Faculty of Engineering, Lorestan University, Khorramabad, Loresatn, Iran
Multimedia Tools and Applications
1 Introduction Today, every technical effort is due to the digital processing of the image. The X-rays are used in the imaging. Among the most well-known cases is the use of the beams in medical diagnosis and medical imaging. Computerized axial tomography (CAT) due to its detection and 3D capabilities has revolutionized medicine. Therefore it has been available since the 1970s by applying x-rays in medical imaging. Any CAT image is a cut that is perpendicular to the patient’s body. Figure 1 shows a sample of a cat image cut from the human head [8]. Similar techniques are used in the industrial processes but x-rays with higher energy are used. Figure 2 is an x-ray image of an electronic circuit board. These images show hundreds of industrial uses of the beams used in the testing of electronic boards to find possible defects, such as unassembled components or unclipped paths. If possible industrial cat scans are useful in recognizing components by the X-ray. In this paper
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