Adapted arithmetic optimization algorithm for multi-level thresholding image segmentation: a case study of chest x-ray images

被引:7
作者
Otair, Mohammad [1 ]
Abualigah, Laith [2 ,3 ,4 ,5 ,6 ,7 ,8 ]
Tawfiq, Saif [1 ]
Alshinwan, Mohammad [9 ]
Ezugwu, Absalom E. [10 ]
Zitar, Raed Abu [11 ]
Sumari, Putra [7 ]
机构
[1] Khawarizmi Univ Tech Coll, Amman 11953, Jordan
[2] Al Al Bayt Univ, Prince Hussein Bin Abdullah Fac Informat Technol, Comp Sci Dept, Mafraq 25113, Jordan
[3] Al Ahliyya Amman Univ, Hourani Ctr Appl Sci Res, Amman 19328, Jordan
[4] Middle East Univ, MEU Res Unit, Amman 11831, Jordan
[5] Appl Sci Private Univ, Appl Sci Res Ctr, Amman 11931, Jordan
[6] Sunway Univ Malaysia, Sch Engn & Technol, Petaling Jaya 27500, Malaysia
[7] Univ Sains Malaysia, Sch Comp Sci, George Town 11800, Malaysia
[8] Lebanese Amer Univ, Dept Elect & Comp Engn, Byblos 135053, Lebanon
[9] Appl Sci Private Univ, Fac Informat Technol, Amman 11931, Jordan
[10] North West Univ, Unit Data Sci & Comp, 11 Hoffman St, ZA-2520 Potchefstroom, South Africa
[11] Sorbonne Univ Abu Dhabi, Sorbonne Ctr Artificial Intelligence, Abu Dhabi 38044, U Arab Emirates
关键词
Image segmentation; Multi-level thresholding; Meta-heuristic algorithms; Arithmetic optimization algorithm;
D O I
10.1007/s11042-023-17221-9
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Particularly in recent years, there has been increased interest in determining the ideal thresholding for picture segmentation. The best thresholding values are found using various techniques, including Otsu and Kapur-based techniques. These techniques work well for bi-level thresholding, but when used to find the appropriate thresholds for multi-level thresholding, there will be issues with long calculation times, high computational costs, and the need for accuracy improvements. This work investigates the capability of the Arithmetic Optimization Algorithm to discover the best multilayer thresholding for picture segmentation to circumvent this issue. The leading mathematical arithmetic operators' distributional nature is used by the AOA method. The picture histogram was used to construct the candidate solutions in the modified algorithms, which were then updated according to the algorithm's features. The solutions are evaluated using Otsu's fitness function throughout the optimization process. The picture histogram is used to display the algorithm's potential solutions. The proposed approach is tested on five frequent photos from the Berkeley University database. The fitness function, root-mean-squared error, peak signal-to-noise ratio, and other widely used assessment metrics were utilized to assess the performance of the suggested segmentation approach. Many benchmark pictures were employed to verify the suggested technique's effectiveness and evaluate it against other well-known optimization methods described in the literature.
引用
收藏
页码:41051 / 41081
页数:31
相关论文
共 32 条
[1]   Hyper-heuristic method for multilevel thresholding image segmentation [J].
Abd Elaziz, Mohamed ;
Ewees, Ahmed A. ;
Oliva, Diego .
EXPERT SYSTEMS WITH APPLICATIONS, 2020, 146
[2]   Efficient computer-aided diagnosis technique for leukaemia cancer detection [J].
Abdulla, Alan Anwer .
IET IMAGE PROCESSING, 2020, 14 (17) :4435-4440
[3]   Improved Reptile Search Algorithm by Salp Swarm Algorithm for Medical Image Segmentation [J].
Abualigah, Laith ;
Habash, Mahmoud ;
Hanandeh, Essam Said ;
Hussein, Ahmad MohdAziz ;
Al Shinwan, Mohammad ;
Abu Zitar, Raed ;
Jia, Heming .
JOURNAL OF BIONIC ENGINEERING, 2023, 20 (04) :1766-1790
[4]   The Arithmetic Optimization Algorithm [J].
Abualigah, Laith ;
Diabat, Ali ;
Mirjalili, Seyedali ;
Elaziz, Mohamed Abd ;
Gandomi, Amir H. .
COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING, 2021, 376
[5]   Advances in Sine Cosine Algorithm: A comprehensive survey [J].
Abualigah, Laith ;
Diabat, Ali .
ARTIFICIAL INTELLIGENCE REVIEW, 2021, 54 (04) :2567-2608
[6]   A comprehensive survey of the Grasshopper optimization algorithm: results, variants, and applications [J].
Abualigah, Laith ;
Diabat, Ali .
NEURAL COMPUTING & APPLICATIONS, 2020, 32 (19) :15533-15556
[7]   Multi-verse optimizer algorithm: a comprehensive survey of its results, variants, and applications [J].
Abualigah, Laith .
NEURAL COMPUTING & APPLICATIONS, 2020, 32 (16) :12381-12401
[8]   Salp swarm algorithm: a comprehensive survey [J].
Abualigah, Laith ;
Shehab, Mohammad ;
Alshinwan, Mohammad ;
Alabool, Hamzeh .
NEURAL COMPUTING & APPLICATIONS, 2020, 32 (15) :11195-11215
[9]   Artificial Ecosystem-Based Optimization with Dwarf Mongoose Optimization for Feature Selection and Global Optimization Problems [J].
Al-Shourbaji, Ibrahim ;
Kachare, Pramod ;
Fadlelseed, Sajid ;
Jabbari, Abdoh ;
Hussien, Abdelazim G. ;
Al-Saqqar, Faisal ;
Abualigah, Laith ;
Alameen, Abdalla .
INTERNATIONAL JOURNAL OF COMPUTATIONAL INTELLIGENCE SYSTEMS, 2023, 16 (01)
[10]   Predicting the Severity of COVID-19 from Lung CT Images Using Novel Deep Learning [J].
Alaiad, Ahmad Imwafak ;
Mugdadi, Esraa Ahmad ;
Hmeidi, Ismail Ibrahim ;
Obeidat, Naser ;
Abualigah, Laith .
JOURNAL OF MEDICAL AND BIOLOGICAL ENGINEERING, 2023, 43 (02) :135-146