Skip to main content

Advertisement

SpringerLink
Log in

Cell-graph coloring for cancerous tissue modelling and classification

  • Published:
Multimedia Tools and Applications Aims and scope Submit manuscript

Abstract

In traditional cancer diagnosis process, pathologists manually examine biopsies to make diagnostic assessments. The assessments are largely based on visual interpretation of cell morphology and tissue distribution, lacking of quantitative measures. Therefore, they are subject to considerable inter-observer variability. To circumvent this problem, numerous studies aim at quantifying the characteristics of cancerous cells and tissues that distinguish them from their counterparts. Such quantification facilitates to design automated systems that operate on quantitative measures, and in turn, to reduce the inter-observer variability. There is a computational model available that relies solely on the topological features of cancerous cells in a tumor. Despite their complex dynamic nature, the self-organizing clusters of cancerous cells exhibit distinctive graph properties that distinguish the cancerous tissue from non-cancerous tissues; e.g. from a healthy tissue or an inflamed tissue. It is difficult to distinguish a cancerous tissue sample visually from an inflamed one. It is possible to construct a graph of the cells (cell graph) based on the location of the cells in the low-magnification image of a tissue sample surgically removed from a human patient. Assuming the cells present in a sample as the vertices of the cell graphs and the edges connecting those vertices/cells we can construct the cell graphs. There is a possibility of implementing the technique of using cell graphs to detect cancerous sample biopsies using some simple or a little bit complex computational techniques. Here possibly a new way is going to be introduced in this field, which is an application of graph coloring using the cell graphs to classify the normal, cancerous and inflamed sample biopsies. This work intends to automate the solution to the problem of identifying cancerous sample biopsies applying customized graph Coloring method solving by Genetic Algorithm on the cell graphs.

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

Similar content being viewed by others

References

  1. Albert R, Jeong H, Barabasi A-L (1999) Diameter of the World-Wide Web. Nature 401:130–131

    Article  Google Scholar 

  2. Baase S, Gelder AV (1999) COMPUTER ALGORITHMS:INTRODUCTION TO DESIGN AND ANALYSIS. Addison-Wesley

  3. Barabasi A-L (2002) Linked: The New Science of Networks. Perseus Books Group; 1ST edition

  4. Ben-Dor A, Bruhn L, Friedman N, Nachman I, Schummer M, Yakhini Z (2000) Tissue classification with gene expression pro_les. Comput Biol 7(3–4):559–583

    Article  Google Scholar 

  5. Broder A, Kumar R, Maghoul F, Raghavan P, Stata R (2000) Graph structure in the Web. Proceedings of the 9th International World Wide Web Conference 247–256

  6. Choi H-K, Jarkrans T, Bengtsson E, Vasko J, Wester K, Malmstrom P-U, Busch C (1997) Image analysis based grading of bladder carcinoma. Comparison of object, texture and graph based methods and their reproducibility. Anal Cell Pathol 15:1–18

    Google Scholar 

  7. Davis L (ed) (1991) Handbook of Genetic Algorithms. Van Nostrand Reinhold, New York

    Google Scholar 

  8. Einstein AJ, Wu HS, Sanchez M, Gil J (1998) Fractal characterization of chromatin appearance for diagnosis in breast cytology. J Pathol 185:366–381

    Article  Google Scholar 

  9. Esgiar AN, Naguib RN, Bennett MK, Murray A (1998) Automated feature extraction and identification of colon carcinoma. J Anal Quant Cytol Histol 20(4):297–301

    Google Scholar 

  10. Esgiar AN, Naguib RNG, Sharif BS, Bennett MK, Murray A (1998) Microscopic image analysis for quantitative measurement and feature identification of normal and cancerous colonic mucosa. IEEE Trans Inf Technol Biomed 2(3):197–203

    Article  Google Scholar 

  11. Esgiar AN, Naguib RNG, Sharif BS, Bennett MK, Murray A (2002) Fractal analysis in the detection of colonic cancer images. IEEE Trans Inf Technol Biomed 6(1):54–58

    Article  Google Scholar 

  12. Faloutsos M, Faloutsos P, Faloutsos C (1999) On power-law relationships of the Internet topology. in Proceedings of ACM/SIGCOMM, 251–262

  13. R. Feldman, MC. Golumbic (1990) Optimization Algorithms for student Scheduling via Constraint Satisfiability. The computer Journal 33(4)

  14. Furey TS, Christianini N, Duffy N, Bednarski DW, Schummer M, Hauessler D (2000) Support vector machine classi_cation and validation of cancer tissue samples using microarray expression data. Bioinformatics 16:906914

    Article  Google Scholar 

  15. Ganster H, Pinz P, Rohrer R, Wildling E, Binder M, Kittler H (2001) Automated melanoma recognition. IEEE Trans Med Imag 20(3):233–239

    Article  Google Scholar 

  16. Garey MR, Johnson DS (1979) COMPUTERS AND INTRACTABILITY : A GUIDE TO THE THEORY OF NP-COMPLETENESS. Newyork, W. H. Freeman and Co

  17. Glotsos D, Spyridonos P, Petalas P, Nikiforidis G, Cavouras D, Ravazoula P, Dadioti P, Lekka I (2003) Support Vector Machines for Classification of Histopathological Images of Brain Tumour Astrocytomas. Proc. Intl Conf. Computational Methods in Sciences and Eng, pp. 192–195

  18. Goldberg DE (1989) Genetic Algorithms in Search, Optimization & Machine Learning. Addison Wesley, Reading

    MATH  Google Scholar 

  19. Goldberg DE (1990) A note on Boltzmann tournament selection for genetic algorithms and population oriented simulated annealing. Complex Syst 4:445–460

    MATH  Google Scholar 

  20. Goldberg M, Horn P, Magdon-Ismail M, Riposo J, Siebecker D, Wallace W, Yener B (2003), Statistical modeling of social groups on communication networks First conference of the North American Association for Computational Social and Organizational Science (CASOS 03).

  21. Golub TR, Slonim DK, Tamayo P, Huard C, Gaasenbeek M, Mesirov JP, Coller H, Loh ML, Downing JR, Caligiuri MA, Bloom_eld CD, Lander ES (1999) Molecular classification of cancer: class discovery and class prediction by gene expression monitoring. Science 286:531537

    Article  Google Scholar 

  22. Gunduz C, Yener B (2003) Accuracy and sampling trade-offs for inferring Internet router graph. Rensselaer Polytechnic Institute, Department of Computer Science, TR-03-09

  23. Gunduz C, Yener B, Gultekin SH (2004) The cell graphs of cancer. Bioinformatics 20:i145–i151

    Article  Google Scholar 

  24. Guyon I, Weston J, Barnhill S, Vapnik V (2002) Gene selection for cancer classification using support vector machines. Mach Learn 46(1–3):389–422

    Article  MATH  Google Scholar 

  25. Guyon I, Weston J, Barnhill S, Vapnik V (2002) Gene selection for cancer classification using support vector machines. Mach Learn 46:389422

    Article  Google Scholar 

  26. Hamilton PW, Allen DC, Watt PC, Patterson CC, Biggart JD (1987) Classification of normal colorectal mucosa and adenocarcinoma by morphometry. Histopathology 11(9):901–911

    Article  Google Scholar 

  27. Hamilton PW, Bartels PH, Thompson D, Anderson NH, Montironi R (1997) Automated location of dysplastic fields in colorectal histology using image texture analysis. J Pathol 182(1):68–75

    Article  Google Scholar 

  28. Jain R, Abraham A (2004) A comparative study of fuzzy classification methods on breast cancer data. Australiasian Physical and Eng. Sciences in Medicine

  29. Jeong B, Tombor R, Albert Z, Oltvai N, Barabasi A-L (2000) The large-scale organization of metabolic networks. Nature 407:651–654

    Article  Google Scholar 

  30. Keenan SJ, Diamond J, McCluggage WG, Bharucha H, Thompson D, Bartels BH, Hamilton PW (2000) An automated machine vision system for the histological grading of cervical intraepithelial neoplasia (CIN). J Pathol 192(3):351–362

    Article  Google Scholar 

  31. Liljeros F, Edling CR, Amaral LAN, Stanley HE, Aberg Y (2001) The web of human sexual contacts. Nature 411:907–908

    Article  Google Scholar 

  32. Mangasarian OL, Street WN, Wolberg WH (1995) Cancer diagnosis and prognosis via linear programming. J Oper Res 43(4):570–577

    Article  MathSciNet  MATH  Google Scholar 

  33. Milgram S (1967) The small-world problem. Psychol Today 2:61–67

    MathSciNet  Google Scholar 

  34. Moscato P (1989) On Evolution, Search, Optimization, Genetic Algorithms and Martial Arts:Towards Memetic Algorithms, Report 826, Caltech Concurrent Computation Program, California Institute of Technology, Pasadena, USA

  35. Moscato P, Norman MG (1992) A Memetic Approach for the Traveling Salesman Problem. Implementation of a Computational Ecology for Combinatorial Optimization on Message-Passing Systems, in Proceedings of the International Conference on Parallel Computing and Transputer Applications, Amsterdam, IOS Press, 1, 177–186

  36. Newman MEJ (2001) Who is the best connected scientist? A study of scientific coauthorship networks. Physics Review, E64

  37. Pal AJ, Pal SK, Sarma SS, Ray B (2008) Graph Coloring with memetic algorithm. Icfai University Journal of Computer Sciences II(2):47–57

  38. Pal AJ, Sen Sarma S, Ray B (2006) Performance profile of a hybrid heuristic search technique using graph coloring as a seed example, Proceedings of The 5th IEEE International Conference on Cognitive Informatics. Beijing, China, vol. 1, pp. 640–645

  39. Pal AJ, Sen Sarma S, Ray B (2007) CCTP, Graph Coloring Algorithms – Soft Computing Solutions, Proceedings of The 6th IEEE International Conference on Cognitive Informatics, Aug, California Lake Tahoe, pp. 364–372

  40. Pena-Reyes CA, Sipper M (1999) A fuzzy genetic approach to breast cancer diagnosis. Artif Intell Med 17(2):131–155

    Article  Google Scholar 

  41. Rifkin R, Mukherjee S, Tamayo P, Ramaswamy S, Yeang C-H, Angelo M, Reich M, Poggio T, Lander ES, Golub TR, Mesirov JP (2003) Ananalytical method for multiclass molecular cancer classi_cation. SIAM Rev 45:706723

    Article  Google Scholar 

  42. Sarma SS, Mondal R, Seth A (1985) Some sequential graph coloring algorithms for restricted channel routing. Int J Electron 77(1):81–93

    Article  Google Scholar 

  43. Schnorrenberg F, Pattichis CS, Schizas CN, Kyriacou K, Vassiliou M (1996) Computer-aided classification of breast cancer nuclei. Technol Health Care 4(2):147–161

    Google Scholar 

  44. Sen Sarma S, Pal AJ (2003) Graph Coloring Scheduling Genetic Algorithm and VLSI algorithms. Conference of Horizon of Telecommunication

  45. Shavitt Y, Sun X, Wool A, Yener B (2003) Computing the unmeasured: an algebraic approach to Internet mapping. IEEE J Sel Areas Commun 22(1):67–78

    Article  Google Scholar 

  46. Tasoulis DK, Spyridonos P, Pavlidis NG, Cavouras D, Ravazoula P, Nikiforidis G, Vrahatis MN (2003) Urinary Bladder Tumor Grade Diagnosis Using On-Line Trained Neural Networks. Proc. Knowledge Based Intelligent Information Eng. Systems Conf, pp. 199–206

  47. Todman AG, Naguib RNG, Bennett MK (2001) Orientational coherence metrics: classification of colonic cancer images based on human form perception. Proc Canadian Conf Electrical and Computer Eng 2:1379–1384

    Google Scholar 

  48. Wasserman S, Faust K (1994) Social network analysis: methods and applications. Cambridge University Press

  49. Watts D, Strogatz S (1998) Collective dynamics of small-world networks. Nature 393:440–442

    Article  Google Scholar 

  50. Weyn B, Van de Wouwer G, Kumar-Singh S, Van Daele A, Scheunders P, Van Marck E, Jacob W (1999) Computer-assisted differential diagnosis of malignant mesothelioma based on syntactic structure analysis. Cytometry 35:23–29

    Article  Google Scholar 

  51. Wolberg WH, Street WN, Heisey DM, Mangasarian OL (1995) Computer- derived nuclear features distinguish malignant from benign breast cytology. Hum Pathol 26(7):792–796

    Article  Google Scholar 

  52. Wu B, Abbott T, Fishman D, McMurray W, Mor G, Stone K, Ward D, Williams K, Zhao H (2003) Comparison of statistical methods for classification of ovarian cancer using mass spectrometry data. Bioinformatics 19:16361643

    Google Scholar 

  53. Wuchty S, Ravasz E, Barabasi A-L (2003) The architecture of biological networks. In Deisboeck TS, Yasha Kresh J, Kepler TB (eds), Complex Systems in Biomedicine. Kluwer Academic Publishing

  54. Zhou ZH, Jiang Y, Yang YB, Chen SF (2002) Lung cancer cell identification based on artificial neural network ensembles. Artif Intell Med 24(1):25–36

    Article  MATH  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Multimedia, Hannam University, Daejeon, South Korea

    Debnath Bhattacharyya & Tai-hoon Kim

  2. Heritage Institute of Technology, Kolkata, India

    Anindya J. Pal

Authors
  1. Debnath Bhattacharyya
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Anindya J. Pal
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Tai-hoon Kim
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Tai-hoon Kim.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Bhattacharyya, D., Pal, A.J. & Kim, Th. Cell-graph coloring for cancerous tissue modelling and classification. Multimed Tools Appl 66, 229–245 (2013). https://doi.org/10.1007/s11042-011-0797-y

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-011-0797-y

Keywords

Search

Navigation