Advertisement

Erythrocyte shape classification using integral-geometry-based methods

  • X. Gual-Arnau
  • S. Herold-García
  • A. Simó
Original Article

Abstract

Erythrocyte shape deformations are related to different important illnesses. In this paper, we focus on one of the most important: the Sickle cell disease. This disease causes the hardening or polymerization of the hemoglobin that contains the erythrocytes. The study of this process using digital images of peripheral blood smears can offer useful results in the clinical diagnosis of these illnesses. In particular, it would be very valuable to find a rapid and reproducible automatic classification method to quantify the number of deformed cells and so gauge the severity of the illness. In this paper, we show the good results obtained in the automatic classification of erythrocytes in normal cells, sickle cells, and cells with other deformations, when we use a set of functions based on integral-geometry methods, an active contour-based segmentation method, and a k-NN classification algorithm. Blood specimens were obtained from patients with Sickle cell disease. Seventeen peripheral blood smears were obtained for the study, and 45 images of different fields were obtained. A specialist selected the cells to use, determining those cells which were normal, elongated, and with other deformations present in the images. A process of automatic classification, with cross-validation of errors with the proposed descriptors and with other two functions used in previous studies, was realized.

Keywords

Contour functions Erythrocytes Shape classification Integral geometry 

Notes

Acknowledgments

Work supported by the UJI project P11B2012-24.

Conflict of interest

None.

Ethical standard

This article does not contain any studies with human participants or animals performed by any of the authors.

References

  1. 1.
    Altman F (1994) Practical statistics for medical research. Chapman Hall, LondonGoogle Scholar
  2. 2.
    Al Zahir S, Donker H (2010) A novel regression based model for detecting anemia using color microscopic blood images. J Soft Eng Appl 3:756–761CrossRefGoogle Scholar
  3. 3.
    Apostolopoulos G, Tsinopoulos S, Dermatas E (2010) Recognition and identification of red blood cell size using angular radial transform and neural networks. In: XII mediterranean conference on medical and biological engineering and computing 2010. Springer, Berlin, Heidelberg, pp 707–710Google Scholar
  4. 4.
    Asakura T, Hirota T, Nelson AT, Reilly MP, Ohene-Frempong K (1996) Percentage of reversibly and irreversibly sickled cells are altered by the method of blood drawing and storage conditions. J Blood Cells Mol Dis 22(3):297–306CrossRefGoogle Scholar
  5. 5.
    Bacus JW (1984) Quantitative red cell morphology. Monogr Clin Cytol 9:1–27CrossRefPubMedGoogle Scholar
  6. 6.
    Bacus JW, Belanger MG, Aggarwal RK, Trobaugh FG (1976) Image processing for automated erythrocytes classification. J Hystochem Cytochem 24(1):195–201CrossRefGoogle Scholar
  7. 7.
    Bacus JW, Yasnoff WA, Belanger MG (1977) Computer techniques for cell analysis in hematology. In: Proceedings of the first annual symposium on computer applications in medical care (SCAMC), pp 24–35Google Scholar
  8. 8.
    Bergen T, Steckhan D, Wittenberg T, Zerfass T (2008) Segmentation of leukocytes and erythrocytes in blood smear images. In: Engineering in medicine and biology Society, 2008. EMBS 2008. 30th annual international conference of the IEEE, pp 3075–3078Google Scholar
  9. 9.
    Cover TM, Hart PE (1967) Nearest neighbor pattern classification. IEEE Trans Inf Theory 13:21–27CrossRefGoogle Scholar
  10. 10.
    Das DK, Chakraborty C, Mitra B, Maiti AK, Ray AK (2013) Quantitative microscopy approach for shape-based erythrocytes characterization in anaemia. J Microsc Oxf 249:136–149CrossRefGoogle Scholar
  11. 11.
    Das DK, Ghosh M, Chakraborty C, Pal M, Maity AK (2010) Invariant moment based feature analysis for abnormal erythrocyte recognition. In: Systems in medicine and biology (ICSMB), 2010 international conference on, pp 242–247Google Scholar
  12. 12.
    De-Lin R (1994) Topics in Integral Geometry. World Scientific, SingaporeCrossRefGoogle Scholar
  13. 13.
    Devijver PA, Kittler J (1982) Pattern recognition: a statistical approach. Prentice-Hall, Englewood CliffsGoogle Scholar
  14. 14.
    Díaz G, González F, Romero E (2009) A semi-automatic method for quantification and classification of erythrocytes infected with malaria parasites in microscopic images. J Biomed Inf 42:296–307CrossRefGoogle Scholar
  15. 15.
    Di Ruberto C, Dempster A, Khan S, Jarra B (2002) Analysis of infected blood cell images using morphological operators. Image Vis Comput 20:133–146CrossRefGoogle Scholar
  16. 16.
    Eom S, Kim S, Shin V, Ahn B (2006) Leukocyte segmentation in blood smear images using region-based active contours. In: 8th international conferences on advanced concepts for intelligent vision systems, Belgium. LNCS 4179:867–876Google Scholar
  17. 17.
    Frejlichowski D (2010) Pre-processing, extraction and recognition of binary erythrocyte shapes for computer-assisted diagnosis based on MGG images. In: international conference on computer vision and graphics, Poland, Part I, LNCS 6374:368–375Google Scholar
  18. 18.
    Gual X, Herold S, Simó A (2013) Shape description from generalized support functions. Pattern Recognit Lett 34:619–626CrossRefGoogle Scholar
  19. 19.
    Gundersen HJ, Jensen EB, Kiêu K, Nielsen J (1999) The efficiency of systematic sampling in stereology-reconsidered. J Microsc Oxf 193:199–211CrossRefGoogle Scholar
  20. 20.
    Hart PE, Hart RO, Stork DE (2001) Pattern classification. Wiley, New YorkGoogle Scholar
  21. 21.
    Higgins JM, Eddington DT, Bhatia SN, Mahadevan L (2009) Statistical dynamics of flowing red blood cells by morphological image processing. PLoS Comput Biol 5(2):1–10CrossRefGoogle Scholar
  22. 22.
    Hirimutugoda YM, Wijayarathna G (2010) Image analysis system for detection of red cell disorder using artificial neural networks. Sri Lanka J Bio-med Inf 1:35–42Google Scholar
  23. 23.
    Horiuchi K, Ohata J, Hirano Y, Asakura T (1990) Morphologic studies of sickle erythrocytes by image analysis. J Lab Clin Med 115:613–620PubMedGoogle Scholar
  24. 24.
    Jayavanth S, Lee DH, Pak BC (2010) Multi-shape erythrocyte deformability analysis by imaging technique. J Mech Sci Technol 24(4):931–935CrossRefGoogle Scholar
  25. 25.
    Kass M et al (1987) Snakes: active contours models. Int J Comput Vis 1:321–331CrossRefGoogle Scholar
  26. 26.
    Kavitha A, Ramakrishnan S (2005) Analysis on the erythrocyte shape changes using wavelet transforms. Clin Hemorheol Microcirc 33:327–335PubMedGoogle Scholar
  27. 27.
    Le MT, Bretschneider TR, Kuss C, Preiser PR (2008) A novel semi-automatic image processing approach to determine Plasmodium falciparum parasitemia in Giemsa-stained thin blood smears. BMC Cell Biol 9:15CrossRefPubMedCentralPubMedGoogle Scholar
  28. 28.
    Liu R, Dey DK, Boos D, Marquet P, Javidi B (2011) Recognition and classification of red blood cells using digital holographic microscopy and data clustering with discriminant analysis. J Opt Soc Am A Opt Image Sci Vis 28(6):1204–1210CrossRefPubMedGoogle Scholar
  29. 29.
    Makkapati V, Rao R (2009) Segmentation of malaria parasites in peripheral blood smear images. In: ICASSP, Proceedings. Taipei, Taiwan, pp 1361–1364Google Scholar
  30. 30.
    Nixon MS, Aguado AS (2008) Feature extraction and image processing. Academic Press, MassachusettsGoogle Scholar
  31. 31.
    Osher S, Sethian JA (1998) Fronts propagating with curvature-dependent speed: algorithms based on Hamilton–Jacobi formulation. J Comput Phys 79:12–49CrossRefGoogle Scholar
  32. 32.
    Rauber TW, Steiger-Garcao AS (1992) 2-D form descriptors based on a normalized parametric polar transform (UNL transform). In: Proceeding of MVA 1992 IAPR workshop on machine vision applicationsGoogle Scholar
  33. 33.
    Ross NE, Pritchard CJ, Rubin DM, Duse AG (2006) Automated image processing method for the diagnosis and classification of malaria on thin blood smears. Med Biol Eng Comput 44:427–436CrossRefPubMedGoogle Scholar
  34. 34.
    Sabino DMU, da Fontoura Costa L, Gil Rizzati E, Antonio Zago M (2004) A texture approach to leukocyte recognition. Real-Time Imaging 10(4):205–216CrossRefGoogle Scholar
  35. 35.
    Santaló LA (1976) Integral geometry and geometric probability. Addison-Wesley Publishing Company Inc., LondonGoogle Scholar
  36. 36.
    Scotti F (2005) Automatic morphological analysis for acute leukemia identification in peripheral blood microscope images. In: IEEE CIMSA 2005 Proceedings, pp 96–101Google Scholar
  37. 37.
    Veluchamy M, Perumal K, Ponuchamy T (2012) Feature extraction and classification of blood cells using artificial neural network. Am J Appl Sci 9:615–619CrossRefGoogle Scholar
  38. 38.
    Vromen J, McCane B (2009) Red blood cell segmentation from SEM images. In: 24th international conference on image and vision computing IVCNZ’09. pp 44–49Google Scholar
  39. 39.
    Wheeless L, Robinson R, Lapets O, Cox C, Rubio A, Weintraub M, Benjamin L (1994) Classification of red-blood-cells as normal, sickle, or other abnormal. Using a single image-analysis feature. Cytometry 17(2):59–166CrossRefGoogle Scholar
  40. 40.
    Yao C, Zhang J, Zhang H (2007) Blood cell identification and segmentation by means of statistical models. In: Proceedings of 7th WSEAS international Conference on signal processing, computational geometry and artificial vision, Athens, Greece. pp 177–181Google Scholar
  41. 41.
    Yi F, Moon I, Javidi B, Boss D, Marquet P (2013) Automated segmentation of multiple red blood cells with digital holographic microscopy. J Biomed Opt 18(2):026006CrossRefGoogle Scholar
  42. 42.
    Zhang D, Lu G (2004) Review of shape representation and description techniques. Pattern Recognit 37:1–19CrossRefGoogle Scholar

Copyright information

© International Federation for Medical and Biological Engineering 2015

Authors and Affiliations

  1. 1.Institute of New Imaging TechnologiesUniversitat Jaume ICastellóSpain
  2. 2.Departamento de ComputaciónUniversidad de OrienteSantiago de CubaCuba
  3. 3.Institut Universitari de Matemàtiques i Aplicacions de CastellóUniversitat Jaume ICastellónSpain

Personalised recommendations