Skip to main content
Log in

A review of metaphase chromosome image selection techniques for automatic karyotype generation

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

Abstract

The karyotype is analyzed to detect the genetic abnormalities. It is generated by arranging the chromosomes after extracting them from the metaphase chromosome images. The chromosomes are non-rigid bodies that contain the genetic information of an individual. The metaphase chromosome image spread contains the chromosomes, but these chromosomes are not distinct bodies; they can either be individual chromosomes or be touching one another; they may be bent or even may be overlapping and thus forming a cluster of chromosomes. The extraction of chromosomes from these touching and overlapping chromosomes is a very tedious process. The segmentation of a random metaphase chromosome image may not give us correct and accurate results. Therefore, before taking up a metaphase chromosome image for analysis, it must be analyzed for the orientation of the chromosomes it contains. The various reported methods for metaphase chromosome image selection for automatic karyotype generation are compared in this paper. After analysis, it has been concluded that each metaphase chromosome image selection method has its advantages and disadvantages.

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.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4

Similar content being viewed by others

References

  1. Abuzenadah A (2010) The causes of mutations, p 1–6

  2. Agam G, Member S, Dinstein H (1997) Geometric separation of partially overlapping nonrigid objects applied to automatic chromosome classification. IEEE Trans Pattern Anal Mach Intell 19(11):1212–1222

    Article  Google Scholar 

  3. Alberts B (2000) Basic genetic mechanism. In: Molecular biology of the cell, 5th edn. Garland Science, New York, pp 191–234

    Google Scholar 

  4. Alfredo J, Costa F, De Souza JG (2011) Image segmentation through clustering based on natural computing techniques. Intech, pp 57–82

  5. Arachchige AS (2014) Human metaphase chromosome analysis using image processing. The University of Western Ontario London, Ontario

    Google Scholar 

  6. Carothers A, Piper J (1994) Computer-aided classification of human chromosomes: a review. Stat Comput 4:161–171

    Article  Google Scholar 

  7. Choi BSHH (2006) Automatic segmentation and classification of multiplex-fluorescence in-situ hybridization chromosome images. The University of Texas at Austin, Austin

    Google Scholar 

  8. Devaraj S, Vijaykumar VR, Soundrarajan GR (2013) Leaf biometrics based karyotyping of G-band chromosomes. Int J Hum Genet 13(3):131–138

    Google Scholar 

  9. Dhir R (2014) An efficient segmentation method for overlapping chromosome images. Int J Comput Appl 95(1):29–32

    Google Scholar 

  10. El Emary IMM (2006) On the application of artificial neural networks in analyzing and classifying the human chromosomes. J Comput Sci 2(1):72–75

    Article  Google Scholar 

  11. Grisan E, Poletti E, Ruggeri A (2009) Automatic segmentation and disentangling of chromosomes in Q-Band prometaphase images. IEEE Trans Inf Technol Biomed 13(4):575–581

    Article  PubMed  Google Scholar 

  12. Grisan E, Poletti E, Ruggeri A (2009) An improved segmentation of chromosomes in Q-band prometaphase images using a region based level set. In: World congress on medical physics and biomedical engineering, September 7–12, Munich, pp. 748–751

  13. Gujendran V, Rodriguez JJ (2004) Chromosome counting via digital image analysis. In: Proceedings of international conference on image processing, pp 2929–2932

  14. Harris H, Bassier B (2005) Inside the cell, vol 5. National Institute of General Medical Sciences, pp 1–74

  15. Hin Tjio J, Levan A (1925) The chromosome number of man. Genetics 10(6):80–85

    Google Scholar 

  16. Huber R, Kulka U, Lörch T, Braselmann H, Bauchinger M (1995) Automated metaphase finding: an assessment of the efficiency of the METAFER2 system in a routine mutagenicity assay. ELSEVIER Mutat Res 334:97–102

    Article  CAS  Google Scholar 

  17. Jahani S, Setarehdan SK, Fatemizadeh E (2011) Automatic identification of overlapping/touching chromosomes in microscopic images using morphological operators. In: 2011 7th Iranian conference on machine vision and image processing

  18. Jahani S, Setarehdan SK, Veronica M (2012) An automatic algorithm for identification and straightening images of curved human chromosomes. Biomed Eng Appl Basis Commun 24(6):1–9

    Article  Google Scholar 

  19. Janani MNS, Nandakumar R (2012) Feature extraction and pairing of G-band chromosome images using K-nearest neighbour classifier. Int J Comput Sci Telecommun 3(2):137–140

    Google Scholar 

  20. Karvelis P, Likas A, Fotiadis DI (2010) Identifying touching and overlapping chromosomes using the watershed transform and gradient paths. Pattern Recognit Lett 31:2474–2488

    Article  Google Scholar 

  21. Khmelinskii A, Ventura R, Sanches J (2008) Automatic chromosome pairing using mutual information. In: Conference on proceedings of IEEE engineering in medicine and biology society

  22. Lerner B (1998) Toward a completely automatic neural-network-based human chromosome analysis. IEEE Trans Syst Man Cybern Part B Cybern 28(4):544–552

    Article  CAS  Google Scholar 

  23. Lerner B, Guterman H, Dinstein I, Romem Y (1995) Medial axis transform-based features and a neural network for human chromosome classification. Pergamon Pattern Recognit 28(11):1673–1683

    Article  Google Scholar 

  24. Li Y, Knoll JH, Wilkins RC, Flegal F, Rogan PK (2015) Automated discrimination of dicentric and monocentric chromosomes by machine learning based image processing. BioDose 1–2

  25. Lijiya A, Mumthas TK, Govindan VK (2013) Chromosome classification using M-FISH images. In: Proceedings of international conference on advances in information technology and mobile communication chromosome, pp 314–320

  26. Loganathan E, Anuja MR, Nirmala Madian (2012) Automated Identification to the centromere position and the centromere index (CI) of human chromosome in G-banded images. Int J Adv Technol Eng Res Autom 2(6):13–17

    Google Scholar 

  27. Makkar A, Govindan Professor LAVK, Professor A (2014) A re-segmentation algorithm for improved M-FISH image segmentation and classification. Int J Adv Inf Sci Technol 32(32):87–92

    Google Scholar 

  28. Markou C, Maramis C Automatic chromosome classification using support vector machines. pp 1–24

  29. Moallem P, Karimizadeh A, Yazdchi M (2013) Using shape information and dark paths for automatic recognition of touching and overlapping chromosomes in G-band images. Int J Image Graph Signal Process 5(5):22–28

    Article  Google Scholar 

  30. Moradi M, Setarehdan SK (2006) New features for automatic classification of human chromosomes: a feasibility study. Pattern Recognit Lett 19:19–28

    Article  Google Scholar 

  31. Nair RM, Remya RS, Sabeena K (2015) Karyotyping techniques of chromosomes: a survey. Int J Comput Trends Technol 22(1)

  32. Piper J, Granum E (1989) On fully automatic feature measurement for banded chromosome classification. Cytometry 10:242–255

    Article  CAS  PubMed  Google Scholar 

  33. Pravina VA (2015) Survey on techniques used for M-FISH image segmentation for classification of chromosomes. Middle East J Sci Res 23(8):1772–1779

    Google Scholar 

  34. Qiu Y (2013) Comprehensive performance evaluation and optimization of high throughput scanning microscopy for metaphase chromosome imaging. University Of Oklahoma, Norman

    Google Scholar 

  35. Qiu Y, Chen X, Li Y, Chen WR, Zheng B, Li S, Liu H (2013) Evaluations of auto-focusing methods under a microscopic imaging modality for metaphase chromosome image analysis. Anal Cell Pathol 36:37–44

    Article  CAS  Google Scholar 

  36. Qiu Y, Song J, Lu X, Li Y, Zheng B, Li S, Liu H (2014) Feature selection for the automated detection of metaphase chromosomes: performance comparison using a receiver operating characteristic method. Anal Cell Pathol 2014:1–10

    Article  Google Scholar 

  37. Ren HL, Li Z, Li Y, Zheng B, Li S, Chen X, Liu H (2014) The impact of the condenser on cytogenetic image quality in digital microscope system. Anal Cell Pathol (Amst). 36:45–59

    Article  Google Scholar 

  38. Schwartzkopf WC, Bovik AC, Evans BL (2005) Maximum-likelihood techniques for joint segmentation-classification of multispectral chromosome images. IEEE Trans Med Imaging 24(12):1593–1610

    Article  PubMed  Google Scholar 

  39. Shemilt L, Verbanis E, Schwenke J, Estandarte AK, Xiong G, Harder R, Parmar N, Yusuf M, Zhang F, Robinson IK (2015) Karyotyping human chromosomes by optical and X-ray ptychography methods. Biophysj 108:706–713

    Article  CAS  Google Scholar 

  40. Somasundaram D, Kumar VRV (2014) Separation of overlapped chromosomes and pairing of similar chromosomes for karyotyping analysis. Measurement 48:274–281

    Article  Google Scholar 

  41. Somasundaram D, Nirmala M (2010) Automatic segmentation and karyotyping of chromosomes using bio-metrics. In: International conference on emerging trends in robotics and communication technologies (INTERACT), pp 42–45

  42. Somasundaram D, Palaniswami S, Vijayabhasker R, Venkatesakumar V (2014) G-band chromosome segmentation, overlapped chromosome separation and visible band calculation. Int J Hum Genet 14(2):73–81

    CAS  Google Scholar 

  43. Sreejini KS, Lijiya A, Govindan VK (2012) M-FISH karyotyping—a new approach based on watershed transform. Int J Comput Sci Eng Inf Technol 2(2):105–117

    Google Scholar 

  44. Sri Balaji V, Vidhya S (2015) A novel and maximum-likelihood segmentation algorithm for touching and overlapping human chromosome images. ARPN J Eng Appl Sci 10(7): 2777–2781

    Google Scholar 

  45. Sri Balaji V, Pragasam G, Sowmiya R, Vijayalakshmi H, Madian N (2012) Segmentation of overlapped and touching human chromosome images. IOSR J VLSI Signal Process 1(5):01–06

    Google Scholar 

  46. Uttamatanin R (2013) Chromosome classification for metaphase selection. In: 13th international symposium on communications and information technologies (ISCIT), pp 464–468

  47. Uttamatanin R, Yuvapoositanon P, Intarapanich A, Kaewkamnerd S, Phuksaritanon R, Assawamakin A, Tongsima S (2013) MetaSel: a metaphase selection tool using a Gaussian-based classification technique. BMC Bioinf 14:S13

    Article  Google Scholar 

  48. Uttamatanin R, Yuvapoositanon P, Intarapanich A, Kaewkamnerd S, Tongsima S (2013) Band classification based on chromosome shapes. In: 13th international symposium on communications and information technologies (ISCIT), pp 464–468

  49. Vijayan V, Remya RS, Sabeena K (2015) Survey on chromosome image analysis for abnormality detection in leukemias. Int J Res Eng Technol 4(4):664–669

    Article  Google Scholar 

  50. Wang X, Li S, Liu H, Wood M, Chen WR, Zheng B (2008) Automated identification of analyzable metaphase chromosomes depicted on microscopic digital images. J Biomed Inform 41(2):264–271

    Article  PubMed  Google Scholar 

  51. Wang X, Zheng B, Li S, Mulvihill JJ, Wood MC, Liu H (2008) Automated classification of metaphase chromosomes: optimization of an adaptive computerized scheme. J Biomed Inform 42:22–31

    Article  PubMed  PubMed Central  Google Scholar 

  52. Wenzhong Y (2009) A counting algorithm for overlapped chromosomes. In: 3rd international conference on bioinformatics and biomedical engineering, pp 1–3

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Tanvi Arora.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Arora, T., Dhir, R. A review of metaphase chromosome image selection techniques for automatic karyotype generation. Med Biol Eng Comput 54, 1147–1157 (2016). https://doi.org/10.1007/s11517-015-1419-z

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11517-015-1419-z

Keywords

Navigation