Skip to main content
SpringerLink
Log in

A Survey on the Application of Multi-Objective Optimization Methods in Image Segmentation

  • Chapter
  • First Online:
Multi-Objective Optimization

Abstract

A recent trend in problem formulation in image segmentation is to employ the multi-objective optimization (MOO) methods. The decision-making MOOs are the collection of realistic complex optimization problems, where the objective functions are usually conflicting. Image segmentation is the clustering of pixels applying definite criteria. It is one of the crucial parts in image processing. This chapter provides a comprehensive survey on MOO encompassing image segmentation problems. Here, the segmentation models are categorized by the problem formulation with a relevant optimization scheme. The survey also provides the latest direction and challenges of MOO in image segmentation procedure.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 139.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 179.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 179.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  • K. Ahmadian, M. Gavrilova, Chaotic neural network for biometric pattern recognition. Adv. Artif. Intell. 2012, 1 (2012)

    Article  Google Scholar 

  • K. Ahmadian, A. Golestani, M. Analoui, M.R. Jahed, Evolving ensemble of classifiers in low-dimensional spaces using multi-objective evolutionary approach, in 6th IEEE/ACIS International Conference on Computer and Information Science (ICIS, 2007), pp. 20–27

    Google Scholar 

  • T. Alderliesten, J.J. Sonke, P. Bosman, Multi objective optimization for deformable image registration: proof of concept, in Proceedings of the SPIE Medical Imaging 2012 (54), 32–43 (2012)

    Google Scholar 

  • M. Arulraj, A. Nakib, Y. Cooren, P. Siarry, Multi criteria image thresholding based on multi objective particle swarm optimization. Appl. Math. Sci. 8(4), 131–137 (2014)

    Google Scholar 

  • S. Bandyopadhyay, S. Pal, Multiobjective VGA-classifier and quantitative indices of classification and learning using genetic algorithms, in Applications in Bioinformatics and Web Intelligence (Springer, Berlin, Heidelberg, 2007)

    Google Scholar 

  • S. Bandyopadhyay, S.K. Pal, B. Aruna, Multi objective GAs, quantitative indices, and pattern classification. IEEE Trans. Syst. Man Cybern.-Part B Cybern. 34, 2088–2099 (2004)

    Article  Google Scholar 

  • J.C. Bezdek, L.O. Hall, L.P. Clarke, Review of MR image segmentation techniques using pattern recognition. Med. Phys. 20, 1033–1048 (1993)

    Article  Google Scholar 

  • B. Bhanu, S. Lee, S. Das, Adaptive image segmentation using multi objective evaluation and hybrid search methods. Mach. Learn. Comput. Vis. 3(1993), 30–33 (1993)

    Google Scholar 

  • P. Carla, G. Luís, Manuel Ferreira Exudate segmentation in fundus images using an ant colony optimization approach. Inf. Sci. 296, 14–24 (2015)

    Article  Google Scholar 

  • L. Chen, F.P.T. Henning, A. Raith, Y.A. Shamseldin, Multiobjective optimization for maintenance decision making in infrastructure asset management. J. Manag. 31(6), 1–12 (2015)

    Google Scholar 

  • M. Cococcioni, P. Ducange, B. Lazzerini, F. Marcelloni, A Pareto-based multi objective evolutionary approach to the identification of Mamdani fuzzy systems. Soft Comput. 11(11),1013–1031 (2007)

    Google Scholar 

  • M. Cococcioni, P. Ducange, B. Lazzerini, F. Marcelloni, Evolutionary multi-objective optimization of fuzzy rule-based classifiers in the ROC space. FUZZ-IEEE 1–6 (2011)

    Google Scholar 

  • C.A. Cocosco, V. Kollokian, R.K.S. Kwan, A.C. Evans, BrainWeb: online interface to a 3D MRI simulated brain database. Neuro Image 5 (1997)

    Google Scholar 

  • M.J. Collins, E.B. Kopp, On the design and evaluation of multi objective single-channel SAR image segmentation. IEEE Trans. Geosci. Remote Sens. (46), 1836–1846 (2008)

    Google Scholar 

  • V. Das, N. Puhan, Tsallis entropy and sparse reconstructive dictionary learning for exudates detection in diabetic retinopathy. J. Med. Imaging 4(2), 1121–1129 (2017)

    Article  Google Scholar 

  • N.S. Datta, H.S. Dutta, K. Majumder, An effective contrast enhancement method for identification of microaneurysms at early stage. IETE J. Res. 1–10 (2016)

    Google Scholar 

  • T. Ganesan, I. Elamvazuthi, K.Z.K. Shaari, P. Vasant, An algorithmic framework for multi objective optimization. Sci. World J. 2013, 1–11 (2013)

    Article  Google Scholar 

  • N. Ghoggali, Y. Bazi, F. Melgani, A multi objective genetic data inflation methodology for support vector machine classification, in IEEE International Conference on Geoscience and Remote Sensing Symposium (2006), pp. 3910–3916

    Google Scholar 

  • N. Ghoggali, F. Melgani, Y. Bazi, A multiobjective genetic SVM approach for classification problems with limited training samples. IEEE Trans. Geosci. Remote Sens. 47, 1707–1718 (2009)

    Article  Google Scholar 

  • V. Guliashki, H. Toshev, C. Korsemov, Survey of evolutionary algorithms used in multi objective optimization. Probl. Eng. Cybern. Robot. Bulg. Acad. Sci. 2009, 42–54 (2009)

    Google Scholar 

  • P. Gupta, Contrast enhancement for retinal images using multi-objective genetic algorithm. Int. J. Emerg. Trends Eng. Dev. 6, 7–10 (2017)

    Google Scholar 

  • H. Ishibuchi, Y. Nojima, Performance evaluation of evolutionary multi objective approaches to the design of fuzzy rule-based ensemble classifiers, in Fifth International Conference on Hybrid Intelligent Systems (5) (2015), pp. 16–18

    Google Scholar 

  • G.C. Karmakar, L.S. Dooleya, A Generic fuzzy rule based image segmentation algorithm. Pattern Recogn. Lett. 23, 1215–1227 (2002)

    Article  Google Scholar 

  • K. Kottathra, Y. Attikiouzel, A novel multi criteria optimization algorithm for the structure determination of multilayer feed forward neural networks. J. Netw. Comput. Appl. 19, 135–147 (1996)

    Article  Google Scholar 

  • A. Mukhopadhyay, U. Maulik, Unsupervised pixel classification in satellite imagery using multi objective fuzzy clustering combined with SVM classifier. IEEE Trans. Geosci. Remote Sens. 47, 1132–1138 (2009)

    Article  Google Scholar 

  • A. Mukhopadhyay, S. Bandyopadhyay, U. Maulik, Clustering using multi-objective genetic algorithm and its application to image segmentation. IEEE Int. Conf. Syst. Man Cybern. 3, 1–6 (2007)

    Google Scholar 

  • N. Matake, T. Hiroyasu, M. Miki, T. Senda, Multi objective clustering with automatic k-determination for large-scale data, in Genetic and Evolutionary Computation Conference, London, England (2007), pp. 861–868

    Google Scholar 

  • A. Nakib, H. Oulhadj, P. Siarry, Image histogram thresholding based on multi objective optimization. Signal Process. 87, 2515–2534 (2007)

    Article  Google Scholar 

  • A. Nakib, H. Oulhadj, P. Siarry, Fractional differentiation and non-Pareto multi objective optimization for image thresholding. Eng. Appl. Artif. Intell. 22, 236–249 (2009)

    Article  Google Scholar 

  • A. Nakid, H. Oulhadj, P. Siarry, Fast MRI segmentation based on two dimensional survival exponential entropy and particle swarm optimization, In Proceedings of the IEEE EMBC’07 International Conference, 22–26 August 2007.

    Google Scholar 

  • N. Nedjah, LdM Mourelle, Evolutionary multi-objective optimisation: a survey. Int. J. Bio-Inspired Comput. 7(1), 1–25 (2015)

    Article  Google Scholar 

  • D. Newman, S. Hettich, C. Blake, C. Merz, UCI repository of machine learning databases, University of California, Department of Information and Computer Sciences (1998)

    Google Scholar 

  • Y. Nojima, Designing fuzzy ensemble classifiers by evolutionary multi objective optimization with an entropy-based diversity criterion, in Sixth International Conference on Hybrid Intelligent Systems, vol. 16(4) (IEEE, 2006), pp. 11–17

    Google Scholar 

  • L.S. Oliveira, M. Morita, R. Sabourin, Feature selection for ensembles using the multi-objective optimization approach. Stud. Comput. Intell. (SCI) 16, 49–74 (2006)

    Google Scholar 

  • M.G.H. Omran, A.P. Engelbrecht, A. Salman, Differential evolution methods for unsupervised image classification. Congr. Evol. Comput. 3(8), 331–371 (2005)

    Google Scholar 

  • A. Paoli, F. Melgani, E. Pasolli, Clustering of hyper spectral images based on multi objective particle swarm optimization. IEEE Trans. Geosci. Remote Sens. 47, 4179–4180 (2009)

    Article  Google Scholar 

  • P. Pulkkinen, H. Koivisto, Fuzzy classifier identification using decision tree and multi objective evolutionary algorithms. Int. J. Approx. Reason. 48, 526–543 (2008)

    Article  Google Scholar 

  • P. Punia, M. Kaur, Various genetic approaches for solving single and multi objective optimization problems: a review, Int. J. Adv. Res. Comput. Sci. Softw. Eng. 3(7), 1014–1020 (2017)

    Google Scholar 

  • S. Saha, S. Bandyopadhyay, Unsupervised pixel classification in satellite imagery using a new multi objective symmetry based clustering approach, in IEEE Region 10 Annual International Conference (2008)

    Google Scholar 

  • S. Shirakawa, T. Nagao, Evolutionary image segmentation based on multi objective clustering, in Congress on Evolutionary Computation (CEC ‘09), Trondheim, Norway (2009), pp. 2466–2473

    Google Scholar 

  • S. Saha, S. Bandyopadhyay, A symmetry based multi objective clustering technique for automatic evolution of clusters. Pattern Recogn. 43(3), 738–751 (2010)

    Article  Google Scholar 

  • T. Wen, Z. Zhang, Q. Ming, W. Qingfeng, Li Chunfeng, A multi-objective optimization method for emergency medical resources allocation. J. Med. Imaging Health Inform. 7, 393–399 (2017)

    Article  Google Scholar 

  • T.E. Wong, V. Srikrishnan, D. Hadka, K. Keller, A multi-objective decision-making approach to the journal submission problem. PLOS ONE 12(6), 1–19 (2017)

    Google Scholar 

  • J. Wu, M.R. Mahfauz, Robust X-ray image segmentation by spectral clustering and active shape model. J. Med. Imaging 3(3), 1–9 (2016)

    Article  Google Scholar 

  • R. Xu, D. Wunsch, Survey of clustering algorithms. IEEE Trans. Neural Netw. 16(2005), 645–678 (2005)

    Article  Google Scholar 

  • Y. Zhang, P.I. Rockett, Evolving optimal feature extraction using multi-objective genetic programming: a methodology and preliminary study on edge detection, in Conference on Genetic and Evolutionary Computation (2005), pp. 795–802

    Google Scholar 

  • M.N. Zaitoun, J.M. Aqel, Survey on image segmentation techniques. Int. Conf. CCMIT 65, 797–806 (2015)

    Google Scholar 

  • Y. Zhang, P.I. Rockett, Evolving optimal feature extraction using multi-objective genetic programming, a methodology and preliminary study on edge. Artif. Intell. Rev. 27, 149–163 (2005)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Information Technology, Future Institute of Engineering and Management, Kolkata, West Bengal, India

    Niladri Sekhar Datta

  2. Department of Electronics and Communication Engineering, Kalyani Government Engineering College, Kalyani, West Bengal, India

    Himadri Sekhar Dutta

  3. Department of Computer Science and Engineering, West Bengal University of Technology, Salt Lake, Kolkata, West Bengal, India

    Koushik Majumder

  4. Department of Ophthalmology, Sri Aurobindo Seva Kendra, Kolkata, West Bengal, India

    Sumana Chatterjee

  5. Department of Pathology, Dr. B.C. Roy (PGIPS), 111 Narkel bagan main road, Kolkata, West Bengal, India

    Najir Abdul Wasim

Authors
  1. Niladri Sekhar Datta
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Himadri Sekhar Dutta
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Koushik Majumder
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Sumana Chatterjee
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Najir Abdul Wasim
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Himadri Sekhar Dutta .

Editor information

Editors and Affiliations

  1. University of Kalyani, Kalyani, West Bengal, India

    Jyotsna K. Mandal

  2. Assam University, Silchar, Assam, India

    Somnath Mukhopadhyay

  3. Visva Bharati University, Bolpur, Santiniketan, West Bengal, India

    Paramartha Dutta

Rights and permissions

Reprints and permissions

Copyright information

© 2018 Springer Nature Singapore Pte Ltd.

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Datta, N.S., Dutta, H.S., Majumder, K., Chatterjee, S., Wasim, N.A. (2018). A Survey on the Application of Multi-Objective Optimization Methods in Image Segmentation. In: Mandal, J., Mukhopadhyay, S., Dutta, P. (eds) Multi-Objective Optimization. Springer, Singapore. https://doi.org/10.1007/978-981-13-1471-1_12

Download citation

  • DOI: https://doi.org/10.1007/978-981-13-1471-1_12

  • Published:

  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-13-1470-4

  • Online ISBN: 978-981-13-1471-1

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation