Skip to main content
Springer Nature Link
Log in

Application of Multiobjective Optimization Techniques in Biomedical Image Segmentation—A Study

  • Chapter
  • First Online:
Multi-Objective Optimization

Abstract

Multiobjective optimization methods in image analysis are one of the active research domains in the current years. These methods are used for the decision-making process in case of image segmentation. Multiobjective techniques are popular and suitable model for many difficult optimization problems. In various practical problems, different objectives are to be considered. Now, most of the problems have some objectives those are conflicting in nature. Hence, only one objective cannot be optimized or prioritize because it can result in some adverse effect on other objective, and can produce some undesired results in terms of the other objectives. The main goal of this chapter is to give a comprehensive study of multiobjective optimization techniques in biomedical image analysis problem. The different models are categorized depending on the relevant features. For example, the different aspects of the optimization methods employed, different formulations of the problems, categories of data, and the domain of the application. This study mainly focuses on the multiobjective optimization techniques that can be used to analyze digital images specially biomedical images. Here, some of the problems, and challenges related to images are diagnosed and analyzed with multiple objectives. It is a comprehensive study that consolidated some of the recent works along with future directions.

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

Access this chapter

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

  • H.A. Abbass, Speeding up backpropagation using multiobjective evolutionary algorithms. Neurocomputing 15, 2705–2726 (2003)

    MATH  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)

    Google Scholar 

  • Berkeley University, The Berkeley segmentation dataset and benchmark. Grouping and Ecological Statistics. June 2007, http://www.eecs.berkeley.edu/Research/Projects/CS/vision/grouping/

  • B. Bhanu, S. Lee, S. Das, Adaptive image segmentation using multiobjective evaluation and hybrid search methods. Machine Learning in Computer Vision (1993)

    Google Scholar 

  • Brainweb: simulated brain database, http://www.bic.mni.mcgill.ca/brainweb

  • C.W. Bong, Y.C. Wang, A multi-objective hybrid metaheuristic for zone definition procedure. Int. J. Serv. Oper. Inf. 1(1–2), 146–164 (2006)

    Google Scholar 

  • S. Chakraborty, S. Bhowmik, An efficient approach to job shop scheduling problem using simulated annealing. Int. J. Hybrid Inf. Technol. 8(11), 273–284 (2015a)

    Article  Google Scholar 

  • S. Chakraborty, S. Bhowmik, Blending Roulette wheel selection with simulated annealing for job shop scheduling problem, in Proceedings of Michael Farady IET International Summit MFIIS 2015, Kolkata, India, vol. 2, pp. 579–585 (2015b)

    Google Scholar 

  • S. Chakraborty, A. Seal, M. Roy, An elitist model for obtaining alignment of multiple sequences using genetic algorithm, in Proceedings of 2nd National Conference NCETAS 2015, MIET, Bandel, India, pp. 61–67 (2015)

    Google Scholar 

  • S. Chakraborty, S. Chatterjee, N. Dey, A. Ashour, A.S. Ashour, F. Shi, K. Mali, Modified cuckoo search algorithm in microscopic image segmentation of hippocampus. Microsc. Res. Tech. 00, 1–22 (2017a). Wiley

    Google Scholar 

  • S. Chakraborty, K. Mali, S. Chatterjee, S. Banerjee, K.G. Mazumdar, M. Debnath, K. Roy, Detection of skin disease using metaheuristic supported artificial neural networks, in 2017 8th Annual Industrial Automation and Electromechanical Engineering Conference (IEMECON), pp. 224–229. IEEE, Aug 2017 (2017b)

    Google Scholar 

  • S. Chakraborty, M. Roy, S. Hore, A study on different edge detection techniques in digital image processing, in Proceedings of Feature Detectors and Motion Detection in Video Processing, IGI Global, pp. 100–122 (2016)

    Google Scholar 

  • S. Chakraborty, S. Bhowmik, Job shop scheduling using simulated annealing, in Proceedings of 1st International Conference IC3A 2013, JIS College of Engineering, Kalyani, India (2013)

    Google Scholar 

  • S. Chakraborty, S. Chatterjee, A.S. Ashour, K. Mali, N. Dey, Intelligent Computing in Medical Imaging: A Study. Advancements in Applied Metaheuristic Computing, vol. 143 (2017a)

    Google Scholar 

  • S. Chakraborty, S. Chatterjee, N. Dey, A.S. Ashour, F. Shi, Gradient approximation in retinal blood vessel segmentation, in 4th IEEE Uttar Pradesh Section International Conference on Electrical, Computer and Electronics (UPCON), Mathura, India, 2017, pp. 618–623 (2017b)

    Google Scholar 

  • B. Chin-Wei, M. Rajeswari, Multiobjective optimization approaches in image segmentation–the directions and challenges. Int. J. Adv. Soft Comput. Appl. 2(1) (2010)

    Google Scholar 

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

    Google Scholar 

  • C.A.C. Coello, A comprehensive survey of evolutionary-based multiobjective optimization techniques. Knowl. Inf. Syst. 1(3), 129–156 (1999)

    Google Scholar 

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

    Google Scholar 

  • M. Cococcioni, P. Ducange, B. Lazzerini, F. Marcelloni, Evolutionary multi-objective optimization of fuzzy rule-based classifiers in the ROC space, in FUZZ-IEEE 2007, pp. 1–6 (2007)

    Google Scholar 

  • K. Deb, Multi-objective Optimization Using Evolutionary Algorithms (Wiley, England, 2001)

    MATH  Google Scholar 

  • R. Demirci, Rule-based automatic segmentation of color images. AEU Int. J. Electron. Commun. 60(6), 435–442 (2006)

    Article  Google Scholar 

  • R.H. Erin, Feature selection for self-organizing feature map neural networks with applications in medical image segmentation, MSc thesis, University of Louisville, 2001

    Google Scholar 

  • K. Faceli, M.C.P. De-Souto, A.C. De-Carvalho (2008) A strategy for the selection of solutions of the Pareto front approximation in multi-objective clustering approaches, in 10th Brazilian Symposium on Neural Networks, SBRN 2008

    Google Scholar 

  • N. Ghoggali, F. Melgani, Semi-supervised multitemporal classification with support vector machines and genetic algorithms, in Proceedings of the IEEE-International Geoscience and Remote Sensing Symposium IGARSS-2007, Barcelona, Spain, pp. 2577–2580 (2007)

    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)

    Google Scholar 

  • V. Guliashki, H. Toshev, C. Korsemov, Survey of evolutionary algorithms used in multiobjective optimization. Probl. Eng. Cybern. Robot. 60(1), 42–54 (2009)

    MathSciNet  Google Scholar 

  • J. Handl, J. Knowles, An evolutionary approach to multiobjective clustering. IEEE Trans. Evol. Comput. 11, 56–76 (2007)

    Article  Google Scholar 

  • S. Hore, S. Chakraborty, A.S. Ashour, N. Dey, A.S. Ashour, D.S. Pistolla, T. Bhattacharya, S.R. Chaudhuri, Finding contours of hippocampus brain cell using microscopic image analysis. J. Adv. Microsc. Res. 10(2), 93–103 (2015)

    Article  Google Scholar 

  • S. Hore, S. Chakraborty, S. Chatterjee, N. Dey , A.S. Ashour, L.V. Chung, D.N. Le, An integrated interactive technique for image segmentation using stack based seeded region growing and thresholding. Int. J. Electr. Comput. Eng. 6(6) (2016a)

    Google Scholar 

  • S. Hore, S. Chatterjee, S. Chakraborty, R.K. Shaw, Analysis of different feature description algorithm in object recognition, in Feature Detectors and Motion Detection in Video Processing, IGI Global, pp. 66–99 (2016b)

    Google Scholar 

  • International Skin Imaging Collaboration Website (n.d.). http://www.isdis.net/index.php/isic-project. Accessed 12 July 2017. (19:52 GMT+05:30)

  • H. Ishibuchi, Y. Nojima, Performance evaluation of evolutionary multiobjective approaches to the design of fuzzy rule-based ensemble classifiers, in Fifth International Conference on Hybrid Intelligent Systems (HIS’05). IEEE (2005)

    Google Scholar 

  • D.F. Jones, S.K. Mirrazavi, M. Tamiz, Multi-objective meta-heuristics: an overview of the current state-of-the-art. Eur. J. Oper. Res. 137(1), 1–9 (2002)

    Article  Google Scholar 

  • K.A. Johnson, J.A. Becker, The whole brain atlas (1999), http://www.med.harvard.edu/AANLIB/home.html

  • G.C. Karmakar, L.S. Dooleya, A generic fuzzy rule based image segmentation algorithm. Pattern Recogn. Lett. 23

    Google Scholar 

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

    Article  Google Scholar 

  • R.V.V. Krishna, S.S. Kumar, Hybridizing differential evolution with a genetic algorithm for color image segmentation. Eng. Technol. Appl. Sci. Res. 6(5), 1182–1186 (2016)

    Google Scholar 

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

    Google Scholar 

  • K. Mali, S. Chakraborty, A. Seal, M. Roy, An efficient image cryptographic algorithm based on frequency domain using haar wavelet transform. Int. J. Secur. Appl. 9(12), 265–274 (2015)

    Google Scholar 

  • U. Maulik, I. Saha, Modified differential evolution based fuzzy clustering for pixel classification in remote sensing imagery. Pattern Recogn. 42(9), 2135–2149 (2009)

    Article  Google Scholar 

  • K. Miettinen, Introduction to multiobjective optimization: noninteractive approaches. Multiobjective Optim. 5252, 1–26 (2008)

    Article  Google Scholar 

  • A. Mukhopadhyay, S. Bandyopadhyay, U. Maulik, Clustering using multi-objective genetic algorithm and its application to image segmentation, in IEEE International Conference on Systems, Man and Cybernetics, vol. 3 (2007)

    Google Scholar 

  • A. Mukhopadhyay, S. Bandyopadhyay, U. Maulik, Combining multiobjective fuzzy clustering and probabilistic ANN classifier for unsupervised pattern classification: application to satellite image segmentation, in Congress on Evolutionary Computation. IEEE, pp. 877–883 (2008)

    Google Scholar 

  • A. Mukhopadhyay, U. Maulik, S. Bandyopadhyay, Multiobjective genetic clustering with ensemble among Pareto front solutions: application to MRI brain image segmentation, in 7th International Conference on Advances in Pattern Recognition (2009b)

    Google Scholar 

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

    Google Scholar 

  • A. Nakib, H. Oulhadj, P. Siarry, Non-supervised image segmentation based on multiobjective optimization. Pattern Recogn. Lett. 29 (2008)

    Google Scholar 

  • A. Nakib, H. Oulhadj, P. Siarry, Fractional differentiation and non-Pareto multiobjective optimization for image thresholding. Eng. Appl. Artif. Intell. 22(2), 236–249 (2009a)

    Article  Google Scholar 

  • A. Nakib, H. Oulhadj, P. Siarry, Image thresholding based on Pareto multiobjective optimization. Eng. Appl. Artif. Intell. (2009b)

    Google Scholar 

  • Y. Niu, L. Shen, A novel approach to image fusion based on multi objective optimization, in 6th World Congress on Intelligent Control and Automation (2006)

    Google Scholar 

  • D. Newman, S. Hettich, C. Blake, C. Merz, UCI repository of machine learning databases, University of California Irvine, Dept. of Information and Computer Sciences (1998), http://www.ics.uci.edu/~mlearn/MLRepository.html. Acessado em 06/07/2006 1998

  • M.G.H. Omran, A.P. Engelbrecht, A. Salman, Differential evolution methods for unsupervised image classification, in Congress on Evolutionary Computation (2005a)

    Google Scholar 

  • D.W. Optiz, Feature Selection for ensembles, in 16th National Conference on Artificial Intelligence (AAAI) (1999)

    Google Scholar 

  • M.G.H. Omran, A.P. Engelbrecht, A. Salman, Differential evolution methods for unsupervised image classification, in Proceedings of Congress on Evolutionary Computation (2005b)

    Google Scholar 

  • A. Paoli, F. Melgani, E. Pasolli, Clustering of hyperspectral images based on multiobjective particle swarm optimization. IEEE Trans. Geosci. Remote Sens. Accepted for publication (2009)

    Google Scholar 

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

    Article  Google Scholar 

  • M. Roy, S. Chakraborty, K. Mali, S. Chatterjee, S. Banerjee, A. Chakraborty, K. Roy, Biomedical image enhancement based on modified cuckoo search and morphology, in 2017 8th Annual Industrial Automation and Electromechanical Engineering Conference (IEMECON), pp. 230–235. IEEE, Aug 2017

    Google Scholar 

  • I. Saha, U. Maulik, S. Bandyopadhyay, An improved multi-objective technique for fuzzy clustering with application to IRS image segmentation. Applications of Evolutionary Computing, pp. 426–431 (2009)

    Google Scholar 

  • S. Saha, S. Bandyopadhyay, A multiobjective simulated annealing based fuzzy-clustering technique with symmetry for pixel classification in remote sensing imagery, in 19th International Conference on Pattern Recognition (2008)

    Google Scholar 

  • S. Saha, S. Bandyopadhyay, A new symmetry based multiobjective clustering technique for automatic evolution of clusters. Pattern Recogn. (2010)

    Google Scholar 

  • A. Seal, S. Chakraborty, K. Mali, A new and resilient image encryption technique based on pixel manipulation, value transformation and visual transformation utilizing single–level haar wavelet transform, in Proceedings of Advances in Intelligent Systems and Computing (Springer), pp. 603–611

    Google Scholar 

  • S. Shirakawa, T. Nagao, Evolutionary image segmentation based on multiobjective clustering, in IEEE Congress on Evolutionary Computation, 2009. CEC’09, pp. 2466–2473, May 2009

    Google Scholar 

  • X. Wang, H. Wang, Classification by evolutionary ensembles. Pattern Recogn. 39, 595–607 (2006)

    Article  Google Scholar 

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

    Google Scholar 

Download references

Author information

Authors and Affiliations

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

    Shouvik Chakraborty & Kalyani Mali

Authors
  1. Shouvik Chakraborty
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Kalyani Mali
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Shouvik Chakraborty .

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

Chakraborty, S., Mali, K. (2018). Application of Multiobjective Optimization Techniques in Biomedical Image Segmentation—A Study. In: Mandal, J., Mukhopadhyay, S., Dutta, P. (eds) Multi-Objective Optimization. Springer, Singapore. https://doi.org/10.1007/978-981-13-1471-1_8

Download citation

  • DOI: https://doi.org/10.1007/978-981-13-1471-1_8

  • 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