Paper Info
Title
Improved Artificial Bee Colony Using Sine-Cosine Algorithm for Multi-Level Thresholding Image Segmentation
Abstract
Multilevel-thresholding is an efficient method used in image segmentation. This paper presents a hybrid meta-heuristic approach for multi-level thresholding image segmentation by integrating both the artificial bee colony (ABC) algorithm and the sine-cosine algorithm (SCA). The proposed algorithm, called ABCSCA, is applied to segment images and it utilizes Otsu's function as the objective function. The proposed ABCSCA uses ABC to optimize the threshold and to reduce the search region. Thereafter, the SCA algorithm uses the output of ABC to determine the global optimal solution, which represents the thresholding values. To evaluate the performance of the proposed ABCSCA, a set of experimental series is performed using nineteen images. In the first experimental series, the proposed ABCSCA is assessed at the low threshold levels and compared with the ABC and SCA as traditional methods. Moreover, the second experimental series aims to evaluate the ABCSCA at high threshold levels and it is compared with six algorithms in addition to the SCA and ABC. Besides, the proposed method is evaluated uyysing the fuzzy entropy. The results demonstrate the effectiveness of the proposed algorithm and showed that it outperforms other algorithms in terms of performance measures, such as Peak Signal-to-Noise Ratio (PSNR) and the Structural Similarity Index (SSIM).
Year
DOI
Venue
2020
10.1109/ACCESS.2020.2971249
IEEE ACCESS
Keywords
DocType
Volume
Image segmentation,multi-level thresholding,artificial bee colony (ABC),sine-cosine algorithm (SCA)
Journal
8
ISSN
Citations 
PageRank 
2169-3536
1
0.34
References 
Authors
0
5
Name
Order
Citations
PageRank
Ahmed A. Ewees121115.49
Mohamed Abd Elaziz221.36
Mohammed A A Al-Qaness3606.48
Hassan A. Khalil410.68
Sunghwan Kim5199.67