Medical & Biological Engineering & Computing

, Volume 53, Issue 3, pp 215–226 | Cite as

Effective identification and localization of immature precursors in bone marrow biopsy

  • Guitao Cao
  • Ling Li
  • Weiting Chen
  • Yehua Yu
  • Jun Shi
  • Guixu Zhang
  • Xuehua Liu
Original Article


Abnormal localization of immature precursors (ALIP) aggregating and clustering in bone marrow biopsy appears earlier than that of bone marrow smears in detection of the relapse of acute myelocytic leukemia (AML). But traditional manual ALIP recognition has many shortcomings such as prone to false alarms, neglect of distribution law before three immature precursor cells gathered, and qualitative analysis instead of quantitative one. So, it is very important to develop a novel automatic method to identify and localize immature precursor cells for computer-aided diagnosis, to disclose their patterns before ALIP with the development of AML. The contributions of this paper are as follows. (1) After preprocessing the image with Otsu method, we identify both precursor cells and trabecular bone by multiple morphological operations and thresholds. (2) We localize the precursors in different regions according to their distances with the nearest trabecular bone based on chamfer distance transform, followed by discussion for the presumptions and limitations of our method. The accuracy of recognition and localization is evaluated based on a comparison with visual evaluation by two blinded observers.


Image segmentation Morphology Distance transform Bone marrow biopsy Acute myelocytic leukemia (AML) 



This work was supported by Natural Science Foundation of China (No. 61340036,81170507 and 81101119) and National Key Basic Research Program (No. 2011CB707104).


  1. 1.
    Al-Kofahi Y, Lassoued W, Lee W, Roysam B (2009) Improved automatic detection and segmentation of cell nuclei in histopathology images. IEEE Trans Biomed Eng 57(4):841–852CrossRefPubMedGoogle Scholar
  2. 2.
    Boykov Y, Jolly MP (2001) Interactive graph cuts for optimal boundary and region segmentation of objects in ND images. Proc IEEE Int Conf Comput Vis 1:105–112Google Scholar
  3. 3.
    Cao GT, Zhong C, Li L, Dong J (2009) Detection of red blood cell in urine micrograph. In: The 3rd international conference on bioinformatics and biomedical engineering (ICBBE2009), Beijing, China, pp 1–4. doi: 10.1109/ICBBE.2009.5162609
  4. 4.
    Corneliu T, Nedevschi S (2008) Real-time pedestrian classification exploiting 2D and 3D information. Intell Transp Syst IET 2:201–210CrossRefGoogle Scholar
  5. 5.
    Fernandez DC, Bhargava R, Hewitt SM, Levin IW (2005) Infrared spectroscopic imaging for histopathologic recognition. Nat Biotechnol 23:469–474CrossRefPubMedGoogle Scholar
  6. 6.
    Fu X, You H, Fu K (2012) A statistical approach to detect edges in SAR images based on square successive difference of averages. IEEE Trans Geosci Remote Sens Lett 9:1094–1098CrossRefGoogle Scholar
  7. 7.
    Gopi Krishna S, Sreenivasulu Reddy T, Rajini GK (2012) Removal of high density salt and pepper noise through modified decision based unsymmetric trimmed median filter. Int J Eng Res Appl 2:090–094Google Scholar
  8. 8.
    Hajdu A, Hajdu L, Tijdeman R (2012) Approximation of the Euclidean distance by chamfer distances. Acta Cybern 20(3):399–417Google Scholar
  9. 9.
    Huang PW, Lai YH (2010) Effective segmentation and classification for HCC biopsy images. Pattern Recognit 43:1550–1563CrossRefGoogle Scholar
  10. 10.
    Huang XW, Li H, Qiu YS, Xie SS (2006) Image processing technology in leukemia diagnosis. Laser Optoelectron Progress 43(10):42–46Google Scholar
  11. 11.
    Kong H, Gurcan M, Belkacem-Boussaid K (2011) Partitioning histopathological images: an integrated framework for supervised color-texture segmentation and cell splitting. IEEE Trans Med Imaging 30(9):1661–1677CrossRefPubMedCentralPubMedGoogle Scholar
  12. 12.
    Li SZ (2001) Markov random field modeling in computer vision. Springer, New YorkCrossRefGoogle Scholar
  13. 13.
    Li L, Cao GT, Shi J, Wu H, Zhang XY (2010) Detecting immature precursor cells in pathological images of bone marrow based on morphology. 7th IEEE International Conference on Fuzzy Systems and Knowledge Discovery (FSKD2010), Engineering in Medicine and Biology Society, Beijing, China, pp 2190–2194. doi: 10.1109/FSKD.2010.5569552
  14. 14.
    Marchand-Maillet S, Sharaiha YM (1999) Euclidean ordering via chamfer distance calculations. Comput Vis Image Underst 73:404–413CrossRefGoogle Scholar
  15. 15.
    Morillas S, Gregori V, Sapena A (2011) Adaptive marginal median filter for colour images. Sensors 11:3205–3213CrossRefPubMedCentralPubMedGoogle Scholar
  16. 16.
    Mullikin JC (1992) The vector distance transform in two and three dimensions. CVGIP: Graph Model Image Process 54:526–535Google Scholar
  17. 17.
    Nedevschi S, Bota S, Tomiuc C (2009) Stereo-based pedestrian detection for collision-avoidance applications. IEEE Trans Intell Transp Syst 10:380–391CrossRefGoogle Scholar
  18. 18.
    Nedzved A, Pitas I (2000) Morphological segmentation of histology cell images. IEEE Int Conf Pattern Recogn 1:500–503CrossRefGoogle Scholar
  19. 19.
    Nilsson B, Heyden A (2005) Segmentation of complex cell clusters in microscopic images: application to bone marrow samples. Cytometry A 66(1):24–31. doi: 10.1002/cyto.a.20153 CrossRefPubMedGoogle Scholar
  20. 20.
    Nipon TU (2005) White blood cell segmentation and classification in microscopic bone marrow images. In: Second International Conference on Fuzzy Systems and Knowledge Discovery (FSKD), pp 787–796Google Scholar
  21. 21.
    Ortiz F, Torres F (2004) Vectorial morphological reconstruction for brightness elimination in colour images. Real-Time Imaging 10:379–387CrossRefGoogle Scholar
  22. 22.
    Otsu N (1979) A threshold selection method from gray-level histogram. IEEE Trans Syst Man Cybern Syst 9:62–66CrossRefGoogle Scholar
  23. 23.
    Rosenfeld A, Pfaltz JL (1966) Sequential operations in digital picture processing. J Assoc Comput Mach 13:471–494CrossRefGoogle Scholar
  24. 24.
    Sertel O, Kong J, Catalyurek U, Lozanski G, Saltz J, Gurcan M (2009) Histopathological image analysis using model-based intermediate representation and color texture: follicular lymphoma grading. J Signal Process Syst 55(1):169–183CrossRefGoogle Scholar
  25. 25.
    Sertel O, Lozanski G, Shana’ah A, Gurcan M (2010) Computer-aided detection of centroblasts for follicular lymphoma grading using adaptive likelihood based cell segmentation. IEEE Trans Biomed Eng 57(10):2613–2616CrossRefPubMedCentralPubMedGoogle Scholar
  26. 26.
    Shi J, Malik J (2000) Normalized cuts and image segmentation. IEEE Trans Pattern Anal Mach Intell 22(8):888–905CrossRefGoogle Scholar
  27. 27.
    Tricot G, De Wolf-Peeters C, Vlietinck R, Verwilghen RL (1984) Bone marrow histology in myelodysplastic syndrome prognostic value of abnormal localization of immature precursors in MDS. Br J Haematol 58(2):217–225CrossRefPubMedGoogle Scholar
  28. 28.
    Vairalkar MK, Nimbhorkar SU (2012) Edge detection of images using sobel operator. Int J Emerg Technol Adv Eng 2:291–293Google Scholar
  29. 29.
    Vese LA, Chan TF (2002) A multiphase level set framework for image segmentation using the mumford and shah model. Int J Comput Vis 50(3):271–293CrossRefGoogle Scholar
  30. 30.
    Wang J, Athitsos V, Sclaroff S, Betke M (2008) Detecting objects of variable shape structure with hidden state shape models. IEEE Trans Pattern Anal Mach Intell 30:477–492CrossRefPubMedGoogle Scholar
  31. 31.
    Wang M, Shi J, Tao Y, Liu Y, Pu Q (2009) Correlation analysis of the characteristics of ALP with AML relapse. J Clin Hematol 22:345–346Google Scholar
  32. 32.
    Wen Q, Chang H, Parvin B (2009) A delaunay triangulation approach for segmenting clumps of nuclei. In: IEEE international symposium on biomedical imaging: from nano to macro, 2009 (ISBI09), pp 9–12. doi: 10.1109/ISBI.2009.5192970
  33. 33.
    Yang DG, Dou WC, Cai SJ et al (2004) The application of HSI color model on the segmentation of karyocyte image. Comput Appl Softw 21(9):72–74Google Scholar
  34. 34.
    Yang L, Meer P, Foran D (2005) Unsupervised segmentation based on robust estimation and color active contour models. IEEE Trans Inf Technol Biomed 9(3):475–486CrossRefPubMedGoogle Scholar
  35. 35.
    Zhou X, Liu KY, Bradley P, Perrimon N, Wong ST (2005) Towards automated cellular image segmentation for RNAI genome-wide screening. IEEE Conf Med Image Comput Comput Assist Interv 1:885–892Google Scholar

Copyright information

© International Federation for Medical and Biological Engineering 2014

Authors and Affiliations

  1. 1.Software Engineering InstituteEast China Normal UniversityShanghaiChina
  2. 2.Sixth People’s HospitalAffiliated to Shanghai Jiao Tong UniversityShanghaiChina
  3. 3.School of Information Science and TechnologyEast China Normal UniversityShanghaiChina

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