Methods Inf Med 2018; 57(05/06): 280-286
DOI: 10.1055/s-0038-1673693
Focus Theme “Computational intelligence” – Original Article
Georg Thieme Verlag KG Stuttgart · New York

A Comparison of Some Nature-Inspired Optimization Metaheuristics Applied in Biomedical Image Registration

Silviu Ioan Bejinariu
1   Computer Vision Laboratory, Institute of Computer Science, Romanian Academy, Iasi Branch, Iasi, Romania
,
Hariton Costin
1   Computer Vision Laboratory, Institute of Computer Science, Romanian Academy, Iasi Branch, Iasi, Romania
2   Department of Biomedical Sciences, Grigore T. Popa University of Medicine and Pharmacy, Iasi, Romania
› Author Affiliations
Further Information

Publication History

04 November 2017

26 August 2018

Publication Date:
15 March 2019 (online)

Computational Intelligence Re-meets Medical Image Processing

Analysis of Machine Learning Algorithms for Diagnosis of Diffuse Lung Diseases

Abstract

Background In the last decades, new optimization methods based on the nature's intelligence were developed. These metaheuristics can find a nearly optimal solution faster than other traditional algorithms even for high-dimensional optimization problems. All these algorithms have a similar structure, the difference being made by the strategies used during the evolutionary process.

Objectives A set of three nature-inspired algorithms, including Cuckoo Search algorithm (CSA), Particle Swarm Optimization (PSO), and Multi-Swarm Optimization (MSO), are compared in terms of strategies used in the evolutionary process and also of the results obtained in case of particular optimization problems.

Methods The three algorithms were applied for biomedical image registration (IR) and compared in terms of performances. The expected geometric transform has seven parameters and is composed of rotation against a point in the image, scaling on both axis with different factors, and translation.

Results The evaluation consisted of 25 runs of each IR procedure and revealed that (1) PSO offers the most precise solutions; (2) CSA and MSO are more stable in the sense that their solutions are less scattered; and (3) MSO and PSO have a higher convergence speed.

Conclusions The evaluation of PSO, MSO, and CSA was made for multimodal IR problems. It is possible that for other optimization problems and also for other settings of the optimization algorithms, the results can be different. Therefore, the nature-inspired algorithms demonstrated their efficacy for this class of optimization problems.

 
  • References

  • 1 Kennedy J, Eberhart R. Particle swarm optimization. In: Proceedings of the IEEE International Conference on Neural Networks, IV; 1995 :1942–1948
  • 2 Hendtlass T. WoSP: a multi-optima particle swarm algorithm. In: Proceedings of the IEEE Congress on Evolutionary Computation; 2005 :727–734
  • 3 Yang XS. Nature-Inspired Optimization Algorithms. Amsterdam, The Netherlands: Elsevier; 2014
  • 4 Ilunga-Mbuyamba E, Cruz-Duarte JM. , et al. Active contours driven by Cuckoo Search strategy for brain tumour images segmentation. Expert Syst Appl 2016; 56: 59-68
  • 5 Gao H, Pun C-M, Kwong S. An efficient image segmentation method based on a hybrid particle swarm algorithm with learning strategy. Inf Sci 2016; 369: 500-521
  • 6 Li H, He H, Wen Y. Dynamic particle swarm optimization and K-means clustering algorithm for image segmentation. Int J Light Electron Optics 2015; 126 (24) 4817-4822
  • 7 Li Y, Bai X, Jiao L, Xue Y. Partitioned-cooperative quantum-behaved particle swarm optimization based on multilevel thresholding applied to medical image segmentation. Appl Soft Comput 2017; 56: 345-356
  • 8 Pare S, Kumar A, Bajaj V, Singh GK. A multilevel color image segmentation technique based on cuckoo search algorithm and energy curve. Appl Soft Comput 2016; 47: 76-102
  • 9 Vijay V, Kavitha AR, Rebecca SR. Automated brain tumor segmentation and detection in MRI using enhanced Darwinian particle swarm optimization (EDPSO). Procedia Comput Sci 2016; 92: 475-480
  • 10 Anter AM, Hassenian AE. Computational intelligence optimization approach based on particle swarm optimizer and neutrosophic set for abdominal CT liver tumor segmentation. J Comput Sci 2018; 25: 376-387
  • 11 Daniel E, Anitha J. Optimum wavelet based masking for the contrast enhancement of medical images using enhanced cuckoo search algorithm. Comput Biol Med 2016; 71: 149-155
  • 12 Shanmugavadivu P, Balasubramanian K. Particle swarm optimized multi-objective histogram equalization for image enhancement. Opt Laser Technol 2014; 57: 243-251
  • 13 Paramanandham N, Rajendiran K. Infrared and visible image fusion using discrete cosine transform and swarm intelligence for surveillance applications. Infrared Phys Technol 2018; 88: 13-22
  • 14 Daniel E, Anitha J, Gnanaraj J. Optimum Laplacian wavelet mask based medical image using hybrid cuckoo search – grey wolf optimization algorithm. Knowl Base Syst 2017; 131: 58-69
  • 15 Mohapatra P, Chakravarty S, Dash PK. An improved cuckoo search based extreme learning machine for medical data classification. Swarm Evol Comput 2015; 24: 25-49
  • 16 Alswaitti M, Albughdadi M, Isa NAM. Density-based particle swarm optimization algorithm for data clustering. Expert Syst Appl 2018; 91: 170-186
  • 17 Wang J, Wang L, Shen J. A hybrid discrete cuckoo search for distributed permutation flowshop scheduling problem. In: 2016 IEEE Congress on Evolutionary Computation (CEC), Vancouver, BC; 2016 :2240–2246
  • 18 Bibiks K, Hu YF, Li JP, Pillai P, Smith A. Improved discrete cuckoo search for the resource-constrained project scheduling problem. 2018;69: 493–503 [Elsevier B.V., Amsterdam, The Netherlands]
  • 19 Suresh S, Lal S. An efficient cuckoo search algorithm based multilevel thresholding for segmentation of satellite images using different objective functions. Expert Syst Appl 2016; 58: 184-209
  • 20 Mekhmoukh A, Mokrani K. Improved fuzzy C-Means based Particle Swarm Optimization (PSO) initialization and outlier rejection with level set methods for MR brain image segmentation. Comput Methods Programs Biomed 2015; 122 (02) 266-281
  • 21 Chander A, Chatterjee A, Siarry P. A new social and momentum component adaptive PSO algorithm for image segmentation. Expert Syst Appl 2011; 38: 4998-5004
  • 22 Bejinariu SI, Costin H, Rotaru F, Luca R, Niţă C, Costin D. Image enhancement by multiobjective optimization and bio-inspired heuristics. In: 2017 E-Health and Bioengineering Conference (EHB), Sinaia; 2017: 442 445
  • 23 Zheng L, Tong R. Image registration algorithm using an improved PSO algorithm. In: Wu Y, ed. Computing and Intelligent Systems. ICCIC 2011. Communications in Computer and Information Science, Vol. 234. Springer-Verlag Berlin Heidelberg; 2011. :198–203
  • 24 Rundo L, Tangherloni A, Militello C, Gilardi MC, Mauri G. Multimodal medical image registration using Particle Swarm Optimization: a review. In: 2016 IEEE Symposium Series on Computational Intelligence (SSCI), Athens; 2016 :1–8
  • 25 Muzaffer G, Ulutaş G, Gedikli E. PSO and SURF based digital image forgery detection. In: 2017 International Conference on Computer Science and Engineering (UBMK), Antalya; 2017 :688–692
  • 26 Xue B, Zhang M, Browne WN. Particle swarm optimization for feature selection in classification: a multi-objective approach. IEEE Trans Cybern 2013; 43 (06) 1656-1671
  • 27 Raghavendra R, Dorizzi B, Rao A, Kumar GH. Particle swarm optimization based fusion of near infrared and visible images for improved face verification. Pattern Recognit 2011; 44: 401-411
  • 28 Bejinariu S-I, Luca R, Costin H. Nature-inspired Algorithms based Multispectral Image Fusion. In: Proceedings of the 2016 International Conference and Exposition on Electrical and Power Engineering (EPE); 2016 :10–15
  • 29 Nabizadeh S, Faez K, Tavassoli S, Rezvanian A. A novel method for multi-level image thresholding using particle swarm Optimization algorithms. In: 2 nd International Conference on Computer Engineering and Technology, Chengdu; 2010 :V4–271–V4-275
  • 30 Chen C, Liang JJ, Qu BY, Niu B. Using dynamic multi-swarm particle swarm optimizer to improve the image sparse decomposition based on matching pursuit. In: Huang DS, Jo KH, Zhou YQ, Han K. , eds. Intelligent Computing Theories and Technology, ICIC 2013. Lecture Notes in Computer Science, 7996. Springer; 2013. Lecture Notes in Computer Science, 7996. Springer, Berlin, Heidelberg, 2013, pp. 587-595
  • 31 Woźniak M, Połap D. Basic concept of Cuckoo Search Algorithm for 2D images processing with some research results: an idea to apply Cuckoo Search Algorithm in 2D images key-points search. In: 2014 International Conference on Signal Processing and Multimedia Applications (SIGMAP); 2014 :157–164
  • 32 Ghosh S, Roy S, Kumar U, Mallick A. Gray level image enhancement using Cuckoo Search algorithm. In: Thampi S, Gelbukh A, Mukhopadhyay J. , eds. Advances in Signal Processing and Intelligent Recognition Systems. Advances in Intelligent Systems and Computing, Vol. 264. Cham: Springer; 2014: 275-286
  • 33 Mareli M, Twala B. An adaptive Cuckoo search algorithm for optimization, Applied Computing and Informatics, article in press. . Available at: https://www.sciencedirect.com/science/article/pii/S2210832717301679 . Accessed September 12, 2018
  • 34 Bejinariu SI, Costin H, Rotaru F, Luca R, Niţă C, Lazăr C. Parallel processing and bio-inspired computing for biomedical image registration. Comp Sci J Moldova 2014; 22 (65) 253-277
  • 35 Bejinariu S-I, Costin H, Rotaru F, Luca R, Nita CD. Image Processing by means of Some Bio-Inspired Optimization Algorithms. In: Proceedings of the IEEE 5th International Conference on E-Health and Bioengineering (EHB), Iasi; 2015
  • 36 González B, Valdez F, Melin P, Prado-Arechiga G. Fuzzy logic in the gravitational search algorithm for the optimization of modular neural networks in pattern recognition. Expert Syst Appl 2015; 42 (14) 5839-5847
  • 37 González B, Valdez F, Melin P, Prado-Arechiga G. Fuzzy logic in the gravitational search algorithm enhanced using fuzzy logic with dynamic alpha parameter value adaptation for the optimization of modular neural networks in echocardiogram recognition. Appl Soft Comput 2015; 37: 245-254
  • 38 Melin P, Amezcua J, Valdez F, Castillo O. A new neural network model based on the LVQ algorithm for multi-class classification of arrhythmias. Inf Sci 2014; 279: 483-497
  • 39 Castillo O, Melin P, Ramírez E, Soria J. Hybrid intelligent system for cardiac arrhythmia classification with Fuzzy K-Nearest Neighbors and neural networks combined with a fuzzy system. Expert Syst Appl 2012; 39 (03) 2947-2955
  • 40 Echavarría-Heras H, Leal-Ramírez C, Villa-Diharce E, Castillo O. Using the value of Lin's concordance correlation coefficient as a criterion for efficient estimation of areas of leaves of eelgrass from noisy digital images. Source Code Biol Med 2014; 9 (01) 29
  • 41 Yang XS. Mathematical analysis of nature-inspired algorithms. In Yang XS. , eds. Nature-Inspired Algorithms and Applied Optimization, Studies in Computational Intelligence, Vol. 744. Cham: Springer; 2018: 1-25
  • 42 DICOM sample image sets. Available at: http://www.osirix-viewer.com/datasets/ . Accessed on July 15, 2015
  • 43 Eberhart RC, Shi Y. Comparing inertia weights and constriction factors in particle swarm optimization. In: Proceedings of the Congress on Evolutionary Computation, Vol. 1; 2000 :84–88
  • 44 Zitova B, Flusser F. Image registration methods: a survey. In: Frahm JM, Pantic M, Todorovic S. (eds.-in-chief), Image and Vision Computing, 21(11). The Netherlands: Elsevier B.V. Amsterdam; 2003: 977-1000