Abstract
Medical image segmentation entails the extraction of essential Region of Interests (RoIs) for the further analysis of the image. Segmentation using the thresholding technique partitions an image into multiple objects and one background using multiple threshold values. This paper focuses on the application of dragonfly algorithm and crow search algorithm to optimize between-class variance using Kapur’s entropy as fitness function. The proposed methods have been assessed on benchmarked images for threshold values ranging from 2 to 14 and performance is compared with traditional methods like Kapur and Otsu’s multi-level threshold techniques. Experimental results have been evaluated using well-performed metrics like peak signal to noise ratio, visual information fidelity, structural similarity index matrix, and feature based similarity index matrix. Computational time is also compared. Experimental results show that the proposed method dragonfly algorithm with Kapur’s entropy performed better compared to crow search algorithm and traditional methods.
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Acknowledgement
This research has been financially supported by The Analytical Center for the Government of the Russian Federation (Agreement No. 70–2021-00143 dd. 01.11.2021, IGK 000000D730321P5Q0002). Authors acknowledge the technical support and review feedback from AILSIA symposium held in conjunction with the 22nd International Conference on Intelligent Systems Design and Applications (ISDA 2022).
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Biswas, S., Bajaj, A., Abraham, A. (2023). Multi-level Image Segmentation of Breast Tumors Using Kapur Entropy Based Nature-Inspired Algorithms. In: Abraham, A., Pllana, S., Casalino, G., Ma, K., Bajaj, A. (eds) Intelligent Systems Design and Applications. ISDA 2022. Lecture Notes in Networks and Systems, vol 717. Springer, Cham. https://doi.org/10.1007/978-3-031-35510-3_38
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