Skip to main content

Advertisement

SpringerLink
Log in

LSGA: combining level-sets and genetic algorithms for segmentation

  • Research Paper
  • Published:
Evolutionary Intelligence Aims and scope Submit manuscript

Abstract

A novel technique is presented to combine genetic algorithms (GAs) with level-set functions to segment objects with known shapes and variabilities on images. The individuals of the GA, also known as chromosomes consist of a sequence of parameters of a level-set function. Each chromosome represents a unique segmenting contour. An initial population of segmenting contours is generated based on the learned variation of the level-set parameters from training images. Each segmenting contour (an individual) is evaluated for its fitness based on the texture of the region it encloses. The fittest individuals are allowed to propagate to future generations of the GA run using selection, crossover and mutation. The GA thus provides a framework for combining texture and shape features for segmentation. Level-set-based segmentation methods typically perform gradient descent minimization on an energy function to deform a segmenting contour. The computational complexity of computing derivatives increases as the number of terms increases in the energy function. In contrast, here the level-set-based curve evolution/deformation is performed derivative-free using a genetic algorithm. The algorithm has been tested for segmenting thermographic images of hands and for segmenting the prostate in pelvic CT and MRI images. In this paper we describe the former; the latter is described in [11, 12]. The LSGA successfully segments entire hands on images in which hands are only partially visible. At the end of the paper we report experimental evaluation of the performance of LSGA and compare it with algorithms using single features: the Gabor wavelet based textural segmentation method [1, 9], and the level-set based segmentation algorithm of Chan and Vese [6].

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

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8

Similar content being viewed by others

References

  1. Ahmadian A, Mostafa A (2003) An efficient texture classification algorithm using Gabor wavelet. In: Proceedings of the 25th annual international conference of the IEEE EMBS Cancun, Mexico, 17–21 Sept, pp 930–933

  2. Ballerini L (1999) Genetic snakes for medical images segmentation. In: Poli R et al. (eds) Proceedings of the first European workshops on evolutionary image analysis, signal processing and telecommunications, Lecture notes in computer science, 1596. Springer-Verlag, London, 26–27 May 1999, pp 59–73

  3. Brunnekreef JJ, Oosterhof J, Thijssen DHJ, Colier WN, Van Uden CJ (2006) Forearm blood flow and oxygen consumption in patients with bilateral repetitive strain injury measured by near-infrared spectroscopy. Clin Physiol Funct Imaging 26:178–184

    Article  Google Scholar 

  4. Bunke H (2000) Graph matching for visual object recognition. Spat Vis 13:335–340

    Article  Google Scholar 

  5. Cagnoni A et al (1999) Genetic algorithm-based interactive segmentation of 3D medical images. Image Vis Comput 17(12):881–895

    Article  Google Scholar 

  6. Chan T, Vese L (2001) Active contours without edges. IEEE Trans Image Proc 10:266–277

    Article  MATH  Google Scholar 

  7. Costa LF, Cesar RM Jr (2001) Shape analysis and classification theory and practice. CRC Press, Boca Raton

    MATH  Google Scholar 

  8. Cremers D, Rousson M, Deriche R (2007) A review of statistical approaches to level set segmentation: integrating color, texture, motion and shape. Intl J Comp Vis 72(2):195–215

    Article  Google Scholar 

  9. Dunn D, Higgins WE (1995) Optimal Gabor filters for texture segmentation. IEEE Trans Image Process 4(7):947–964

    Article  Google Scholar 

  10. Elfadel IM, Picard RW (1993) Gibbs random fields, co-occurences and texture modeling. IEEE Trans Patt Anal Mach Intell 16(1):24–37

    Article  Google Scholar 

  11. Ghosh P, Mitchell M (2006) Segmentation of medical images using a genetic algorithm. In: Proceedings of the 8th annual conference on genetic and evolutionary computation, (Seattle, Washington, USA, 08–12 July 2006). GECCO ‘06, ACM, New York, pp 1171–1178

  12. Ghosh P, Mitchell M, Tanyi J, Hung A (2009) A genetic algorithm-based level-set curve evolution for prostate segmentation on Pelvic CT and MRI Images. In: Gonzalez F, Romero Eduardo R (eds) Biomedical image analysis and machine learning, applications and techniques, chap. 6. IGI Global, Hershey

  13. Gold JE, Cherniack M, Hanlon A, Dennerlein JT, Dropkin J (2009) Skin temperature in the dorsal hand of office workers and severity of upper extremity musculoskeletal disorders. Int Arch Occup Environ Health (epub)

  14. Gold JE, Cherniack M, Buchholz B (2004) Infrared thermography for examination of skin temperature in the dorsal hand of office workers. Eur J Appl Physiol 93(1–2):245–251

    Article  Google Scholar 

  15. Goldberg DE (1989) Genetic algorithms in search, optimization and machine learning. 1st Addison-Wesley Longman Publishing Co Inc

  16. Goldberger J, Greenspan H (2006) Context-based segmentation of image sequences. IEEE Trans Patt Ana Mach Intell 28(3):463–468

    Article  Google Scholar 

  17. Gonzales RC, Woods RE (2002) Digital image processing, 2nd edn. Prentice Hall

  18. Harris C, Buxton B (1996) Evolving edge detectors. Research note RN/96/3, University College London, Dept. of Computer Science, London

  19. Harvey N et al (2002) Comparison of GENIE and conventional supervised classifiers for multispectral image feature extraction. IEEE Trans Geosci Remote Sens 40(2):393–404

    Article  MathSciNet  Google Scholar 

  20. Harvey N, Levenson RM, Rimm DL (2003) Investigation of automated feature extraction techniques for applications in cancer detection from multi-spectral histopathology images. Proc SPIE 5032:557–566

    Article  Google Scholar 

  21. Hill A, Taylor C (1992) Model-based image interpretation using genetic algorithms. Image Vis Comput 10(5):295–300

    Article  Google Scholar 

  22. Holland JH (1975) Adaptation in natural and artificial systems. University of Michigan Press, Ann Arbor

    Google Scholar 

  23. Huttenlocher D, Klanderman G, Rucklidge W (1993) Comparing images using the hausdorff distance. IEEE Trans Patt Analysis Mach Intell 15:850–863

    Article  Google Scholar 

  24. Julesz B (1986) Texton gradients: the texton theory revisited. Biol Cybern 54:245–251

    Article  MATH  Google Scholar 

  25. Kass M, Witkin A, Terzopoulos D (1988) Snakes: active contour models. Intl J Comp Vis 1:321–331

    Article  Google Scholar 

  26. Larsson R, Öberg PÅ, Larsson S-E (1999) Changes of trapezius muscle blood flow and electromyography in chronic neck pain due to trapezius myalgia. Pain 79:45–50

    Article  Google Scholar 

  27. Laws KI (1980) Texture image segmentation. PhD dissertation, University of Southern California

  28. Leventon M, Grimson E, Faugeras O (2000) Statistical shape influence in geodesic active contours. Proc IEEE Conf Comp Vis Patt Recog 1:316–323

    Google Scholar 

  29. Louchet J (2007) Model-based image analysis using evolutionary computation. In: Genetic and evolutionary computation for image processing and analysis. Hindawi

  30. MacEachern L, Manku T (1998) Genetic algorithms for active contour optimization. IEEE Proc Intl Symp Circuits Sys 4:229–232

    Google Scholar 

  31. Malladi R, Sethian JA, Vemuri BC (1995) Shape modeling with front propagation: a level set approach. IEEE Trans Patt Anal Machine Intell 17(2):158–175

    Article  Google Scholar 

  32. Mitchell M (1996) An introduction to genetic algorithms. MIT Press, Cambridge

    Google Scholar 

  33. Mumford D, Shah J (1989) Optimal approximation by piecewise smooth functions and associated variational problems. Commun Pure Appl Math 42:577–685

    Article  MathSciNet  MATH  Google Scholar 

  34. Nordin P, Banzhaf W (1996) Programming compression of images and sound, in genetic programming. In: Koza JR et al (eds) Proceedings of the 1st annual conference. Morgan Kaufmann, San Francisco

    Google Scholar 

  35. Osher SJ, Fedkiw RP (2002) Level set methods and dynamic implicit surfaces, 1st edn. Springer, New York

    Google Scholar 

  36. Oskarsson E, Gustafsson B-E, Pettersson K, Piehl Aulin K (2007) Decreased intramuscular blood flow in patients with lateral epicondylitis. Scand J Med Sci Sports 17:211–215

    Google Scholar 

  37. Poli R, Cagoni S (1997) Genetic programming with user-driven selection: experiments on the evolution of algorithms for image enhancement. In: Koza JR et al. (eds) Genetic programming 1997, proceedings of the 2nd annual conference, Morgan Kaufmann, San Francisco

  38. Pritchard MH, Pugh N, Wright I, Brownlee M (1999) A vascular basis for repetitive strain injury. Rheumatology 38:636–639

    Article  Google Scholar 

  39. Sethian JA (1999) Level set methods and fast marching methods, 2nd edn. Cambridge University Press, Cambridge

    MATH  Google Scholar 

  40. Shotton J, Winn J, Rother C, Criminisi A (2006) Textonboost: joint appearance, shape and context modeling for multi-class object recognition and segmentation. In: European conference on computer vision. pp 1–15

  41. Sonka M, Hlavac V, Boyle R (1994) Image processing, analysis and machine vision. Chapman and Hall, London

    Google Scholar 

  42. Tremeau A, Borel N (1997) A region growing and merging algorithm to color segmentation. Pattern Recogn 30(7):1191–1203

    Article  Google Scholar 

  43. Tsai A et al (2003) A shape-based approach to the segmentation of medical imagery using level sets. IEEE Trans Med Imaging 22:137–154

    Article  Google Scholar 

  44. Van Rijsbergen CJ (2004) The geometry of information retrieval. Cambridge University Press, Cambridge

    Book  MATH  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of ECE, Portland State University, Portland, OR, USA

    Payel Ghosh & Melanie Mitchell

  2. Department of Computer Science, Portland State University, Portland, OR, USA

    Melanie Mitchell

  3. Santa Fe Institute, Santa Fe, NM, USA

    Melanie Mitchell

  4. Department of Public Health, Temple University, Philadelphia, PA, USA

    Judith Gold

Authors
  1. Payel Ghosh
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Melanie Mitchell
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Judith Gold
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Payel Ghosh.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Ghosh, P., Mitchell, M. & Gold, J. LSGA: combining level-sets and genetic algorithms for segmentation. Evol. Intel. 3, 1–11 (2010). https://doi.org/10.1007/s12065-010-0036-x

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12065-010-0036-x

Keywords

Search

Navigation