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Segmentation Techniques Based on Image Quality and Edge Detection Algorithms


Affiliations
1 School of Informatics and Computing, Singidunum University, Danijelova 32, 11000 Belgrade, Serbia
2 University of Priština, Faculty of Technical Sciences, Knjaza Miloša 7, 38220 Kosovska Mitrovica, Serbia
 

Segmentation is one of the fundamental tasks in the area of digital image processing and analysis. Segmentation highlights parts of the image that have common features. Such areas of the image are called Region of Interest (ROI). The choice of segmentation algorithm depends on the nature of the origin images and there is no single, universal method that can always be applied. When choosing a segmentation algorithm for a particular image, it is very important to test multiple methods and choose the one that gives the best results. This paper presents a comparison of several segmentation algorithms on different origin images. The comparison was performed based on standard parameters like Mean-Square Error (MSE), Signal to Noise Ratio (SNR), Peak Signal-To-Noise Ratio (PSNR), Structure Similarity Index (SSIM) etc. for image quality assessment. The results of this work can help in the selection of the edge detection algorithm and as a preparation for image segmentation.

Keywords

PSNR, ROI, Segmentation, SNR, SSIM.
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  • Segmentation Techniques Based on Image Quality and Edge Detection Algorithms

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Authors

Petar Biševac
School of Informatics and Computing, Singidunum University, Danijelova 32, 11000 Belgrade, Serbia
Ratko Ivković
University of Priština, Faculty of Technical Sciences, Knjaza Miloša 7, 38220 Kosovska Mitrovica, Serbia
Petar Spalević
University of Priština, Faculty of Technical Sciences, Knjaza Miloša 7, 38220 Kosovska Mitrovica, Serbia

Abstract


Segmentation is one of the fundamental tasks in the area of digital image processing and analysis. Segmentation highlights parts of the image that have common features. Such areas of the image are called Region of Interest (ROI). The choice of segmentation algorithm depends on the nature of the origin images and there is no single, universal method that can always be applied. When choosing a segmentation algorithm for a particular image, it is very important to test multiple methods and choose the one that gives the best results. This paper presents a comparison of several segmentation algorithms on different origin images. The comparison was performed based on standard parameters like Mean-Square Error (MSE), Signal to Noise Ratio (SNR), Peak Signal-To-Noise Ratio (PSNR), Structure Similarity Index (SSIM) etc. for image quality assessment. The results of this work can help in the selection of the edge detection algorithm and as a preparation for image segmentation.

Keywords


PSNR, ROI, Segmentation, SNR, SSIM.

References