Skip to main content
Log in

Abnormal localization of immature precursors (ALIP) detection for early prediction of acute myelocytic leukemia (AML) relapse

  • Original Article
  • Published:
Medical & Biological Engineering & Computing Aims and scope Submit manuscript

Abstract

Acute myelocytic leukemia (AML) is a relapsing and deadly disease. Thus, it is important to early predict leukemia relapse. Recent studies have demonstrated strong correlations of relapse with abnormal localization of immature precursors (ALIP). However, there is no related research on automated detection of ALIP so far. To this end, we have proposed an ALIP detection method to investigate the relevance with AML relapse. Kernelized fuzzy C-means clustering is applied first to separate the foreground (with cells) and background (without cells). Image repairing is then used to wipe out noises to mark region of interest. Then, image partition is introduced to separate the overlapping cells. After that, a set of features are extracted for the classification. Thereafter, support vector machine is applied to classify precursors. At last, filtering operations are applied to obtain the binary-precursor detection results. Thirty-seven patients with AML are examined. The results show that ALIP is efficiently detected in a high sensitivity and positive predictive value by our proposed method. The investigation also demonstrates the strong correlations of AML relapse with ALIP.

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

Access this article

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

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5

Similar content being viewed by others

References

  1. Agresti A (1992) A survey of exact inference for contingency tables. Stat Sci 7(1):131–153

    Article  Google Scholar 

  2. Bamford P, Lovell B (1998) Unsupervised cell nucleus segmentation with active contours. Signal process 71(2):203–213

    Article  Google Scholar 

  3. Colantonio S, Gurevich I, Salvetti O (2008) Automatic fuzzy-neural based segmentation of microscopic cell images. Int J Signal Imaging Syst Eng 1(1):18–24

    Article  Google Scholar 

  4. Cortes C, Vapnik V (1995) Support-vector networks. Mach Learn 20(3):273–297

    Google Scholar 

  5. Crum W, Camara O, Hill D (2006) Generalized overlap measures for evaluation and validation in medical image analysis. IEEE Trans Med Imaging 25(11):1451–1461

    Article  PubMed  Google Scholar 

  6. Frisch B, Lewis S, Burkhardt R, Bartl R (1985) Biopsy pathology of bone and bone marrow. Chapman and Hall, London

    Google Scholar 

  7. Garrido A, Pérez de la Blanca N (2000) Applying deformable templates for cell image segmentation. Pattern Recognit 33(5):821–832

    Article  Google Scholar 

  8. Kuehni R (2001) Colour space and its divisions. Colour Res Appl 26(3):209–222

    Article  Google Scholar 

  9. Lewandowska K, Doroszewski J, Haemmerli G, Sträuli P (1979) An attempt to analyze locomotion of leukemia cells by computer image processing. Comput Biol Med 9(4):331–344

    Article  CAS  PubMed  Google Scholar 

  10. Lloyd S (1982) Least squares quantization in PCM. IEEE Trans Inf Theory 28(2):129–137

    Article  Google Scholar 

  11. Lyden D, Hattori K, Dias S, Costa C, Blaikie P, Butros L, Chadburn A, Heissig B, Marks W, Witte L (2001) Impaired recruitment of bone-marrow-derived endothelial and hematopoietic precursor cells blocks tumor angiogenesis and growth. Nat Med 7(11):1194–1201

    Article  CAS  PubMed  Google Scholar 

  12. Malpica N, Ortiz de Solorzano C, Vaquero J, Santos A, Vallcorba I, García-Sagredo J, del Pozo F (1997) Applying watershed algorithms to the segmentation of clustered nuclei. Cytometry 28(4):289–297

    Article  CAS  PubMed  Google Scholar 

  13. Mangi MH, Salisbury JR, Mufti GJ (1991) Abnormal localization of immature precursors (ALIP) in the bone marrow of myelodysplastic syndromes: current state of knowledge and future directions. Leuk Res 15(7):627–639

    Article  CAS  PubMed  Google Scholar 

  14. McAuliffe MJ, Lalonde FM, McGarry D, Gandler W, Csaky K, Trus BL Medical image processing, analysis and visualization in clinical research. In: Proceedings of the Fourteenth IEEE Symposium on Computer-Based Medical Systems, 2001, pp 381

  15. Naeim F, Rao P, Grody W (2008) Hematopathology: morphology, immunophenotype, cytogenetics, and molecular approaches. Academic Press, San Diego

    Google Scholar 

  16. Ngo N, Lampert I, Naresh K (2008) Bone marrow trephine morphology and immunohistochemical findings in chronic myelomonocytic leukaemia. Br J Haematol 141(6):771–781

    Article  PubMed  Google Scholar 

  17. Peel R, Krause J (1981) Bone marrow cellularity and stromal reactions. Churchill Livingstone, Edinburgh

    Google Scholar 

  18. Tricot G, De Wolf-Peeters C, Vlietinck R, Verwilghen R (1984) Bone marrow histology in myelodysplastic syndromes. I. Histological findings in myelodysplastic syndromes and comparison with bone marrow smears. Br J Haematol 57(1):423

    Article  CAS  PubMed  Google Scholar 

  19. Tricot G, De Wolf-Peeters C, Vlietinck R, Verwilghen R (1984) Bone marrow histology in myelodysplastic syndromes. II. Prognostic value of abnormal localization of immature precursors in MDS. Br J Haematol 58(2):217

    Article  CAS  PubMed  Google Scholar 

  20. Tricot G, Mecucci C, Van den Berghe H (1986) Evolution of the myelodysplastic syndromes. Br J Haematol 63(4):609–614

    Article  CAS  PubMed  Google Scholar 

  21. Vapnik V (2000) The nature of statistical learning theory. Springer Verlag, New York

    Book  Google Scholar 

  22. Wang M, Zhou X, Li F, Huckins J, King R, Wong S (2008) Novel cell segmentation and online SVM for cell cycle phase identification in automated microscopy. Bioinformatics 24(1):94

    Article  CAS  PubMed  Google Scholar 

  23. Wang M, Shi J, Tao Y, Liu Y, Pu Q (2009) Correlation analysis of the characteristics of ALIP with AML relapse. J Clin Hematol 22(4):345–347

    CAS  Google Scholar 

  24. Wd H, Barba J, Gil J (1996) An iterative algorithm for cell segmentation using short-time Fourier transform. J Microsc 184(2):127–132

    Article  Google Scholar 

  25. Welfer D, Scharcanski J, Kitamura CM, Dal Pizzol MM, Ludwig LWB, Marinho DR (2010) Segmentation of the optic disk in colour eye fundus images using an adaptive morphological approach. Comput Biol Med 40(2):124–137

    Article  PubMed  Google Scholar 

  26. Wu H, Barba J, Gil J (1998) A parametric fitting algorithm for segmentation of cell images. IEEE Trans Biomed Eng 45(3):400–407

    Article  CAS  PubMed  Google Scholar 

Download references

Acknowledgments

This research is supported by Shanghai International Science and Technology Cooperation Foundation (08410702100), Shanghai Research and Development Projects (08QH14014), National Nature Science Foundation of China (30971108), Shanghai Committee of Science and Technology (08JC1412000, 09DZ1121400), Research Fund for the Doctoral Program of Higher Education (200802480036) and Program for New Century Excellent Talents in University (NCET-08-0361).

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Jun Shi.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Huang, HQ., Fang, XZ., Shi, J. et al. Abnormal localization of immature precursors (ALIP) detection for early prediction of acute myelocytic leukemia (AML) relapse. Med Biol Eng Comput 52, 121–129 (2014). https://doi.org/10.1007/s11517-013-1122-x

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11517-013-1122-x

Keywords

Navigation