Journal of Bionic Engineering ›› 2023, Vol. 20 ›› Issue (4): 1766-1790.doi: 10.1007/s42235-023-00332-2
Improved Reptile Search Algorithm by Salp Swarm Algorithm for Medical Image Segmentation
Laith Abualigah1,2,3,4,5; Mahmoud Habash6; Essam Said Hanandeh7; Ahmad MohdAziz Hussein8; Mohammad Al Shinwan9; Raed Abu Zitar10; Heming Jia11
- 1 Computer Science Department, Prince Hussein Bin Abdullah Faculty for Information Technology, Al Al-Bayt University, Mafraq 25113, Jordan 2 Hourani Center for Applied Scientific Research, Al-Ahliyya Amman University, Amman 19328, Jordan 3 Faculty of Information Technology, Middle East University, Amman 11831, Jordan 4 Applied Science Research Center, Applied Science Private University, Amman 11931, Jordan 5 School of Computer Sciences, Universiti Sains Malaysia, 11800 Pulau Pinang, Malaysia 6 Amman Arab University, Amman, Jordan 7 Department of Computer Information System, Zarqa University, P.O. Box 13132, Zarqa, Jordan 8 Deanship of E-Learning and Distance Education, Umm Al-Qura University, Makkah 21955, Saudi Arabia 9 Faculty of Information Technology, Applied Science Private University, Amman 11931, Jordan 10 Sorbonne Center of Artificial Intelligence, Sorbonne University-Abu Dhabi, Abu Dhabi, United Arab Emirates 11 School of Information Engineering, Sanming University, Sanming 365004, China
Improved Reptile Search Algorithm by Salp Swarm Algorithm for Medical Image Segmentation
Laith Abualigah1,2,3,4,5; Mahmoud Habash6; Essam Said Hanandeh7; Ahmad MohdAziz Hussein8; Mohammad Al Shinwan9; Raed Abu Zitar10; Heming Jia11#br#
- 1 Computer Science Department, Prince Hussein Bin Abdullah Faculty for Information Technology, Al Al-Bayt University, Mafraq 25113, Jordan 2 Hourani Center for Applied Scientific Research, Al-Ahliyya Amman University, Amman 19328, Jordan 3 Faculty of Information Technology, Middle East University, Amman 11831, Jordan 4 Applied Science Research Center, Applied Science Private University, Amman 11931, Jordan 5 School of Computer Sciences, Universiti Sains Malaysia, 11800 Pulau Pinang, Malaysia 6 Amman Arab University, Amman, Jordan 7 Department of Computer Information System, Zarqa University, P.O. Box 13132, Zarqa, Jordan 8 Deanship of E-Learning and Distance Education, Umm Al-Qura University, Makkah 21955, Saudi Arabia 9 Faculty of Information Technology, Applied Science Private University, Amman 11931, Jordan 10 Sorbonne Center of Artificial Intelligence, Sorbonne University-Abu Dhabi, Abu Dhabi, United Arab Emirates 11 School of Information Engineering, Sanming University, Sanming 365004, China
摘要： This study proposes a novel nature-inspired meta-heuristic optimizer based on the Reptile Search Algorithm combed with Salp Swarm Algorithm for image segmentation using gray-scale multi-level thresholding, called RSA-SSA. The proposed method introduces a better search space to find the optimal solution at each iteration. However, we proposed RSA-SSA to avoid the searching problem in the same area and determine the optimal multi-level thresholds. The obtained solutions by the proposed method are represented using the image histogram. The proposed RSA-SSA employed Otsu’s variance class function to get the best threshold values at each level. The performance measure for the proposed method is valid by detecting fitness function, structural similarity index, peak signal-to-noise ratio, and Friedman ranking test. Several benchmark images of COVID-19 validate the performance of the proposed RSA-SSA. The results showed that the proposed RSA-SSA outperformed other metaheuristics optimization algorithms published in the literature.