![]() IET Image Proc 13(6):998–1005īhandari AK, Singh VK, Kumar A, Singh GK (2014) Cuckoo search algorithm and wind driven optimization based study of satellite image segmentation for multilevel thresholding using Kapur’s entropy. Shao D, Xu C, Xiang Y, Gui P, Zhu X, Zhang C, Yu Z (2019) Ultrasound image segmentation with multilevel threshold based on differential search algorithm. Pare S, Kumar A, Bajaj V, Singh GK (2019) A context sensitive multilevel thresholding using swarm based algorithms. Segmentations of sample images from the BSDS300 dataset are illustrated based on the proposed method, plus other existing segmentation methods, with the proposed method producing superior results over the current methods.Īgrawal S, Panda R, Abraham A (2018) A novel diagonal class entropy-based multilevel image thresholding using coral reef optimization. In addition, it has been shown that the proposed method reaches the highest fitness value when compared to other methods, thus achieving the optimal thresholds. Improvements in the indicated indices have been achieved by 1.3591% in NAE, 3.2552% in MD, 17.6973% in CC, 0.2176% in RMSE, 0.1939% in SSIM, 0.02551% in FSIM and 1.7236% in BDE for 5-level thresholding. Improvements in the indicated indices have been achieved by 5.4151% in MD, 33.11% in CC, 0.1011% in RMSE, 0.1618% in FSIM, 0.6180% in VOI, 0.0615% in BDE and 0.4557% in PRI for 3-level thresholding. In the segmentation performed with 3-level and 5-level thresholding, it is seen from the studies that 7 out of 12 quality measurement indices give the best results when compared to the other segmentation methods. The results are compared with six other segmentation methods in terms of computational times, fitness values, and optimal thresholding values. Experimental results have been performed with 300 images obtained from the Berkeley-Benchmark dataset. Performance evaluation is done using 12 different image quality measurement indices (BDE, PRI, GCE, SSIM, FSIM, VOI, RMSE, NAE, PSNR, CC, MD, and AD) using proposed the two-dimensional, non-local means golden sinus algorithm II segmentation method (NLM-GoldSa-II). Firstly, a two-dimensional non-local means histogram is constructed and segmentation is carried out based on the gray-level images with various thresholding levels. GoldSa-II narrows the search space and converges to the targeted optimum point (optimal thresholds) more accurately in a shorter time using the decreasing sine function and the golden ratio. ![]() In order to reduce computation time and improve thresholding performance, we propose a new thresholding method based on the golden sinus algorithm II (GoldSa-II) and Renyi’s entropy. The image thresholding methods consume a lot of time due to computational complexity when the number of threshold levels increases. ![]()
0 Comments
Leave a Reply. |
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |