Pattern Recognition and Image Analysis

, Volume 28, Issue 4, pp 792–804 | Cite as

Novel and Efficient Approach for Automated Separation, Segmentation, and Detection of Overlapped Elliptical Red Blood Cells

  • Isam Abu-QasmiehEmail author
Applied Problems


Shape recognition is considered as one of the challenges in automated digital image analysis and computer vision. One of the most commonly used shapes is the ellipse which is of great importance for many industrial and biomedical applications. In this study, a novel technique is proposed for segmenting and separating of overlapped elliptical shape objects using concavity analysis and several morphological image processing techniques. A comparative study of the detection speed and accuracy of elliptical objects between Iterative Random Hough Transformation (IRHT) algorithm approach and Direct Least Squares Fitting (DLSF) of Ellipses method has shown the great superiority of DLSF in both the speed and accuracy of recognition. The validation of the proposed techniques for segmentation and detection along with calculation of the efficiency of the system has shown those techniques to be robust and effective for automation of synthetic and real elliptical shapes. The red blood cells (RBCs) microscopic images of the blood smear in Hereditary Elliptocytosis disorder is studied as real elliptical shapes and a quantitative analysis was implemented on the detected RBCs, where the distribution parameters of the ellipse size (area), Roundness, Eccentricity, and Ellipticity are estimated in addition to RBCs counting. The proposed detection approach is successful in building a fully autonomous and accurate system with ellipse analysis capabilities.


Iterative Random Hough Transformation elliptical-shape detection segmentation Direct Least Squares Fitting red blood cells Hereditary Elliptocytosis disorder 


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  1. 1.
    E. R. Davies, Machine Vision: Theory, Algorithms, Practicalities (Elsevier, Amsterdam, Boston, 2005).Google Scholar
  2. 2.
    P. Nair, “Hough transform based ellipse detection algorithm,” Pattern Recogn. Lett. 17, 777–784 (1996).CrossRefGoogle Scholar
  3. 3.
    N. Guil and E. L. Zapata, “Lower order circle and ellipse Hough transform,” Pattern Recogn. 30, 1729–1744 (1997).CrossRefGoogle Scholar
  4. 4.
    P. K. Ser, “Novel detection of conics using 2–D Hough planes,” IEE Proc.–Vision, Image, Signal Processing 142 (5), 262–270 (1995).CrossRefGoogle Scholar
  5. 5.
    S.–C. Zhang and Z.–Q. Liu, “A robust, real–time ellipse detector,” Pattern Recogn. 38, 273–287 (2005).CrossRefzbMATHGoogle Scholar
  6. 6.
    A. S. Aguado, M. Eugenia Montiel, and M. S. Nixon, “On using directional information for parameter space decomposition in ellipse detection,” Pattern Recogn. 29, 369–381 (1996).CrossRefGoogle Scholar
  7. 7.
    S. Tsuji and F. Matsumoto, “Detection of ellipses by a modified hough transformation,” IEEE Trans. Comput. 8, 777–781 (1978).CrossRefGoogle Scholar
  8. 8.
    L. Xu, E. Oja, and P. Kultanen, “A new curve detection method: randomized Hough transform,” Pattern Recogn. Lett. 11 (5), 331–338 (1990).CrossRefzbMATHGoogle Scholar
  9. 9.
    R. A. McLaughlin, “Randomized Hough transform: improved ellipse detection with comparison,” Pattern Recogn. Lett. 19, 299–305 (1998).CrossRefzbMATHGoogle Scholar
  10. 10.
    T.–C. Chen and K.–L. Chung, “An efficient randomized algorithm for detecting circles,” Comput. Vision Image Understand. 83, 172–191 (2001).CrossRefzbMATHGoogle Scholar
  11. 11.
    C. A. Basca, M. Talos, and R. Brad, “Randomized Hough transform for ellipse detection with result clustering,” in Proc. Int. Conf. on “Computer as a Tool” EUROCON 2005 (Belgrade, Nov. 21–24, 2005), Vol. 2, pp. 1397–1400.CrossRefGoogle Scholar
  12. 12.
    J. K. Lee, B. A. Wood, and T. S. Newman, “Very fast ellipse detection using GPU–based RHT,” in Proc. 19th IEEE Int. Conf. on Pattern Recognition ICPR 2008 (Tampa, FL, 2008), Vol. 5, pp. 3165–3168.Google Scholar
  13. 13.
    C.–T. Ho and L.–H. Chen, “A fast ellipse/circle detector using geometric symmetry,” Pattern Recogn. 28, 117–124 (1995).CrossRefGoogle Scholar
  14. 14.
    C. T. Ho and L. H. Chen, “A high–speed algorithm for elliptical object detection,” IEEE Trans. Image Processing 5, 547–550 (IEEE Signal Processing Soc., 1996).Google Scholar
  15. 15.
    A. Y. S. Chia, M. K. Leung, H. L. Eng, and S. Rahardja, “Ellipse detection with Hough transform in one dimensional parametric space,” in Proc. IEEE Int. Conf. on Image Processing (San Antonio, TX, 2007), Vol. 5, pp. V333–V336.Google Scholar
  16. 16.
    K. Kanatani and N. Ohta, “Automatic detection of circular objects by ellipse growing,” in Proc. 9th Symp. on Sensing via Image Information (SSII2002) (Yokohama, July 2002), Vol. 4, pp. 35–50.Google Scholar
  17. 17.
    D. K. Prasad and M. K. H. Leung, “An ellipse detection method for real images,” in Proc. 25th IEEE Int. Conf. of Image and Vision Computing New Zealand (IVCNZ) (Queenstown, 2010), pp. 1–8.Google Scholar
  18. 18.
    C. M. Chang, “Detecting ellipses via bounding boxes,” Asian J. Health Inf. Sci. 1, 73–84 (2006).Google Scholar
  19. 19.
    P. Y. Yin, “A new circle/ellipse detector using genetic algorithms,” Pattern Recogn. Lett. 20, 731–740 (1999).CrossRefGoogle Scholar
  20. 20.
    A. Y. Chia, S. Rahardja, D. Rajan, and M. K. Leung, “A split and merge based ellipse detector with self–correcting capability,” IEEE Trans. Image Processing 20, 1991–2006 (IEEE Signal Processing Society, 2011).Google Scholar
  21. 21.
    G. Wang, G. Ren, Z. Wu, Y. Zhao, and L. Jiang, “A fast and robust ellipse–detection method based on sorted merging,” Sci. World J. 2014, 481312 (2014).Google Scholar
  22. 22.
    C. Y. Wong, S. C. F. Lin, T. R. Ren, and N. M. Kwok, “A survey on ellipse detection methods,” in Proc. IEEE Int. Symp. on Industrial Electronics (Hangzhou, 2012), pp. 1105–1110.Google Scholar
  23. 23.
    S. J. Ahn, W. Rauh, and H. J. Warnecke, “Leastsquares orthogonal distances fitting of circle, sphere, ellipse, hyperbola, and parabola,” Pattern Recogn. 34, 2283–2303 (2001).CrossRefzbMATHGoogle Scholar
  24. 24.
    A. Fitzgibbon, M. Pilu, and R. B. Fisher, “Direct least square fitting of ellipses,” IEEE Trans. Pattern Anal. Mach. Intellig. 21, 476–480 (1999).CrossRefGoogle Scholar
  25. 25.
    D. Zhu, S. T. Moore, and T. Raphan, “Robust pupil center detection using a curvature algorithm,” Comput. Methods Programs Biomed. 59, 145–157 (1999).CrossRefGoogle Scholar
  26. 26.
    S. Kothari, Q. Chaudry, and M. D. Wang, “Automated cell counting and cluster segmentation using concavity detection and ellipse fitting techniques,” in Proc. IEEE Int. Symp. on Biomedical Imaging: from Nano to Macro, 2009. ISBI’09 (Venice, 2009), pp. 795–798.CrossRefGoogle Scholar
  27. 27.
    W.–H. Zhang, X. Jiang, and Y.–M. Liu, “A method for recognizing overlapping elliptical bubbles in bubble image,” Pattern Recogn. Lett. 33, 1543–1548 (2012).CrossRefGoogle Scholar
  28. 28.
    C. Park, J. Z. Huang, J. X. Ji, and Y. Ding, “Segmentation, inference and classification of partially overlapping nanoparticles,” IEEE Trans. Pattern Anal. Machine Intellig. 35, 669–681 (2013).Google Scholar
  29. 29.
    H. P. Ng, S. H. Ong, K. W. C. Foong, P. S. Goh, and W. L. Nowinski, “Medical image segmentation using K–means clustering and improved watershed algorithm,” in Proc. IEEE Southwest Symp. on. Image Analysis and Interpretation, 2006 (Denver, 2006), pp. 61–65.Google Scholar
  30. 30.
    J. Cheng and J. C. Rajapakse, “Segmentation of clustered nuclei with shape markers and marking function,” IEEE Trans. Bio–Med. Eng. 56, 741–748 (2009).CrossRefGoogle Scholar
  31. 31.
    X. Lou, U. Koethe, J. Wittbrodt, and F. A. Hamprecht, “Learning to segment dense cell nuclei with shape prior,” in Proc. IEEE Conf. on Computer Vision and Pattern Recognition (CVPR 2012) (Providence, 2012), pp. 1012–1018.Google Scholar
  32. 32.
    E. Meijering, “Cell segmentation: 50 years down the road [life Sciences],” IEEE Signal Process Mag. 29, 140–145 (2012).CrossRefGoogle Scholar
  33. 33.
    J. Shu, H. Fu, G. Qiu, P. Kaye, and M. Ilyas, “Segmenting overlapping cell nuclei in digital histopathology images,” in Proc. 35th Annu. IEEE Int. Conf. on Engineering in Medicine and Biology Society (EMBC 2013) (Osaka, 2013), pp. 5445–5448.Google Scholar
  34. 34.
    N. Amoda and R. K. Kulkarni, “Image segmentation and detection using watershed transform and region based image retrieval,” Int. J. Emerging Trends Technol. Comput. Sci. 2, 89–94 (2013).Google Scholar
  35. 35.
    F. Sheeba, R. Thamburaj, J. J. Mammen, and A. K. Nagar, “Splitting of overlapping cells in peripheral blood smear images by concavity analysis,” in Proc. Int. Workshop on Combinatorial Image Analysis (Brno, 2014), pp. 238–249.CrossRefGoogle Scholar
  36. 36.
    S. Zafari, T. Eerola, J. Sampo, H. Kälviäinen, and H. Haario, “Segmentation of partially overlapping nanoparticles using concave points,” in Proc. Int. Symp. on Visual Computing (Las Vegas, 2015), pp. 187–197.Google Scholar
  37. 37.
    S. Zafari, T. Eerola, J. Sampo, H. Kälviäinen, and H. Haario, “Segmentation of overlapping elliptical objects in silhouette images,” IEEE Trans. Image Processing 24, 5942–5952 (IEEE Signal Processing Soc., 2015).Google Scholar
  38. 38.
    O. Daněk, P. Matula, C. Ortiz–de–Solórzano, A. Muñoz–Barrutia, M. Maška, and M. Kozubek, “Segmentation of touching cell nuclei using a two–stage graph cut model,” in Proc. Scandinavian Conf.on Image Analysis (Oslo, 2009), pp. 410–419.Google Scholar
  39. 39.
    Y. Al–Kofahi, W. Lassoued, W. Lee, and B. Roysam, “Improved automatic detection and segmentation of cell nuclei in histopathology images,” IEEE Trans. Bio–Med. Eng. 57, 841–852 (2010).CrossRefGoogle Scholar
  40. 40.
    J. Malcolm, Y. Rathi, and A. Tannenbaum, “Graph cut segmentation with nonlinear shape priors,” in Proc. IEEE Int. Conf. on Image Processing, ICIP 2007 (San Antonio, 2007), Vol. 4, pp. IV365–IV368.Google Scholar
  41. 41.
    S. Vicente, V. Kolmogorov, and C. Rother, “Graph cut based image segmentation with connectivity priors,”in Proc. IEEE Conf. on Computer Vision and Pattern Recognition (Anchorage, 2008).Google Scholar
  42. 42.
    M. E. Plissiti and C. Nikou, “Overlapping cell nuclei segmentation using a spatially adaptive active physical model,” IEEE Trans. Image Processing 21, 4568–4580 (IEEE Signal Processing Society, 2012).Google Scholar
  43. 43.
    L. Yang, P. Meer, and D. J. Foran, “Unsupervised segmentation based on robust estimation and color active contour models,” IEEE Trans. Inf. Technol. Biomed. 9, 475–486 (IEEE Engineering in Medicine and Biology Soc., 2005).Google Scholar
  44. 44.
    I. Rizviand B. K. Mohan, “Wavelet based marker–controlled watershed segmentation technique for high resolution satellite images,” Int. J. Comput. Appl. 14, 61–68 (2011).Google Scholar
  45. 45.
    F. Xing and L. Yang, “Robust nucleus/cell detection and segmentation in digital pathology and microscopy images: a comprehensive review,” IEEE Rev. Biomed. Eng. 9, 234–263 (2016).CrossRefGoogle Scholar
  46. 46.
    B. Venkatalakshmi and K. Thilagavathi, “Automatic red blood cell counting using hough transform,” in Proc. IEEE Conf. on Information & Communication Technologies (ICT) (Vilnius, 2013), pp. 267–271.Google Scholar
  47. 47.
    J. Vromen and B. McCane, “Red blood cell segmentation from SEM images,” in Proc. 24th IEEE Int. Conf. on Image and Vision Computing New Zealand, 2009. IVCNZ’09 (Wellington, 2009), pp. 44–49.Google Scholar
  48. 48.
    R. Wang, B. MacCane, and B. Fang, “RBC image segmentation based on shape reconstruction and multiscale surface fitting,” in Proc. IEEE Int. Symp. on Information Science and Engineering (ISISE 2010) (Shanghai, 2010), pp. 586–589.Google Scholar
  49. 49.
    J. Palek, “Hereditary elliptocytosis and related disorders,” Clinics Haematol. 14, 45–87 (1985).Google Scholar
  50. 50.
    M. Mogra, A. Bansel, and V. Srivastava, “Comparative analysis of extraction and detection of RBCs and WBCs using Hough transform and k–means clustering algorithm,” Int. J. Eng. Res. General Sci. 2, 670–674 (2014).Google Scholar
  51. 51.
    M. Maitra, R. K. Gupta, and M. Mukherjee, “Detection and counting of red blood cells in blood cell images using Hough transform,” Int. J. Comput. Appl. 53, 16 (2012).Google Scholar
  52. 52.
    I. Ersoy, F. Bunyak, K. Palaniappan, and J. M. Higgins, “Coupled edge profile active contours for red 11 blood cell flow analysis,” in Proc. 9th IEEE Int. Symp. on Biomedical Imaging (ISBI 2012) (Barcelona, 2012), pp. 748–751.CrossRefGoogle Scholar
  53. 53.
    A. LaTorre, L. Alonso–Nanclares, S. Muelas, J. M. Peña, and J. DeFelipe, “Segmentation of neuronal nuclei based on clump splitting and a two–step binarization of images,” ESWA Expert Syst. Appl. 40, 6521–6530 (2013).CrossRefGoogle Scholar
  54. 54.
    H. Chang, J. Han, P. T. Spellman, and B. Parvin, “Multireference level set for the characterization of nuclear morphology in glioblastoma multiforme,” IEEE Trans. Bio–Med. Eng. 59, 3460–3467 (2012).CrossRefGoogle Scholar
  55. 55.
    J. Canny, “A computational approach to edge detection,” IEEE Trans. Pattern Anal. Mach. Intellig. PAMI–8 (6), 679–698 (1986).Google Scholar
  56. 56.
    M. R. Hardeman and C. Ince, “Clinical potential of in vitro measured red cell deformability, a myth?,” Clin. Hemorheol. Microcirculat. 21 (3, 4), 277–284 (1999).Google Scholar
  57. 57.
    https://medtextfree.wordpress.comGoogle Scholar
  58. 58.
    I. Andolfo, R. Russo, A. Gambale, and A. Iolascon, “New insights on hereditary erythrocyte membrane defects,” Haematological 101 (11), 1284–1294 (2016).CrossRefGoogle Scholar
  59. 59.
    P. G. Gallagher and P. Jarolim, “Red cell membrane disorders,” in Hematology Basic Principles and Practice, Ed. by R. Hoffman, E. J. Benz, S. J. Shattil, 3rd ed. (Philadelphia, Churchill Livingstone, 2000), p. 576.Google Scholar

Copyright information

© Pleiades Publishing, Ltd. 2018

Authors and Affiliations

  1. 1.Department of Biomedical Systems and Informatics EngineeringYarmouk UniversityIrbidJordan

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