Abstract
The edges make the image analysis easy by discarding unwanted data and preserving only essential information about the image boundary. In order to improve edge detection accuracy on medical images, this paper presents a novel edge detection algorithm based on Attanassov’s Intuitionistic fuzzy set theory. The proposed intuitionistic divergence measure is applied to medical images, and edge detection was performed. The edge detection results are measured by MSE and PSNR parameters. According to the measurement parameters, the results were analyzed and found to be more accurate and more noise-robust than the methods based on fuzzy and other intuitionistic fuzzy set theory and traditional edge detection methods.
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Tripathi, N., Kumrawat, D., Gottimukkala, V.K., Jeevaraj, S., Godfrey, W.W. (2021). Edge Detection on Medical Images Using Intuitionistic Fuzzy Logic. In: Singh, S.K., Roy, P., Raman, B., Nagabhushan, P. (eds) Computer Vision and Image Processing. CVIP 2020. Communications in Computer and Information Science, vol 1376. Springer, Singapore. https://doi.org/10.1007/978-981-16-1086-8_21
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DOI: https://doi.org/10.1007/978-981-16-1086-8_21
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