Skip to main content
SpringerLink
Log in

Real-time abnormal cell detection using a deformable snake model

  • Original Paper
  • Published:
Health and Technology Aims and scope Submit manuscript

Abstract

Abnormal cell detection by optical microscopy is widely used, and a large number of biopsies required where the process of determining cell’s position in the histological sample image are very time consuming. In this paper, a proof-of-concept study was done to segmented abnormal cells in real time with high performance metrics focusing on minimizing cost and maximizing efficiency. We developed an improved snake/active contour method for fast cancer cells detection by integrated Canny approach. The implementation of this algorithm on Field-Programmable Gate Array (FPGA) technology substantially decreases the required time for identifying abnormal cells. It also allows an efficient and fast computation of active contour in high throughput image analysis applications where time performance is critical. In order to demonstrate the feasibility of this approach, we implemented the architecture on Xilinx Virtex-FPGAs technology which offers for a scalable and a totally embedded on Chip FPGA. The proposed architecture was validated using 30 images from each histopathological type of colon cells, namely, Benign Hyperplasia (BH), Intraepithelial Neoplasia (IN) and Carcinoma (Ca). This method successfully detected the different cell types with computation time in the order of milliseconds. It was compared with manual cells segmented by using the performance metrics of similarity. The experimental results showed that the proposed snake implementation on FPGA produced accurate and stable results. It was able to rapidly identify multiple cellular types from optical microscope images, and effectively addressed difficult problems such as irregular shapes in carcinoma cells. The short computation time in this method makes it applicable to real-time cancer cell detection.

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

Access this article

Log in via an institution

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
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12

Similar content being viewed by others

References

  1. Chen CH. Computer vision in medical imaging. WORLD SCIENTIFIC. 2014;2.

  2. Srivastava R, Singh SK, Shukla KK (Éd). Research developments in computer vision and image processing: methodologies and applications. IGI Global. 2014.

  3. Castañón CAB, Fraga JS, Fernandez S, Gruber A, da F Costa L. Biological shape characterization for automatic image recognition and diagnosis of protozoan parasites of the genus Eimeria. Pattern Recognit. 2007;40(7):1899–910.

    Article  Google Scholar 

  4. Wang X, Li S, Liu H, Wood M, Chen WR, Zheng B. Automated identification of analyzable metaphase chromosomes depicted on microscopic digital images. J Biomed Inform. 2008;41(2):264–71.

    Article  Google Scholar 

  5. Sabino DMU, da Fontoura Costa L, Gil Rizzatti E, Antonio Zago M. A texture approach to leukocyte recognition. Real-Time Imaging. 2004;10(4):205–16.

    Article  Google Scholar 

  6. Chaddad A, Tanougast C, Dandache A, Houseini A Al, Bouridane A. Improving of colon cancer cells detection based on Haralick’s features on segmented histopathological images. Computer Applications and Industrial Electronics (ICCAIE). 2011. p. 87–90.

  7. Chaddad A, Tanougast C, Dandache A, Bouridane A. Classification of cancer cells based on morphological features from segmented multispectral bio-images. 4th International Conference on Biomedical Electronics and Biomedical Informatics. 2011. p. 92–97.

  8. Wienert S, Heim D, Saeger K, Stenzinger A, Beil M, Hufnagl P, Dietel M, Denkert C, Klauschen F. Detection and segmentation of cell nuclei in virtual microscopy images: a minimum-model approach. Sci. Rep. 2012;2.

  9. Chaddad A, Tanougast C, Dandache A, Bouridane A. Extracted haralick’s texture features and morphological parameters from segmented multispectrale texture bio-images for classification of colon cancer cells. WSEAS Trans Biol Biomed. 2011;8(2):39–50.

    Google Scholar 

  10. Steffen Klupsch ME. Real time image processing based on reconfigurable hardware acceleration. 2002.

  11. Isabel PNF, Figueiredo N. Variational image segmentation for endoscopic human colonic aberrant crypt foci. IEEE Trans Med Imaging. 2009;29(4):998–1011.

    Google Scholar 

  12. Dejnožková Eva DP. Embedded real-time architecture for level-set-based active contours. EURASIP J. Adv. Signal Process. 2005.

  13. Tsuyama H, Maruyama T. An FPGA acceleration of a level set segmentation method. 22nd International Conference on Field Programmable Logic and Applications (FPL), 2012. p. 414–20.

  14. Sugiura T, Takeguchi T, Sakata Y, Nitta S, Okazaki T, Matsumoto N, et al. Automatic model-based contour detection of left ventricle myocardium from cardiac CT images. Int J Comput Assist Radiol Surg. 2013;8(1):145–55.

    Article  Google Scholar 

  15. Farmaki C, Marias K, Sakkalis V, Graf N. Spatially adaptive active contours: a semi-automatic tumor segmentation framework. Int J Comput Assist Radiol Surg. 2010;5(4):369–84.

    Article  Google Scholar 

  16. Hosntalab M, Babapour-Mofrad F, Monshizadeh N, Amoui M. Automatic left ventricle segmentation in volumetric SPECT data set by variational level set. Int J Comput Assist Radiol Surg. 2012;7(6):837–43.

    Article  Google Scholar 

  17. Zheng Y, Barbu A, Georgescu B, Scheuering M, Comaniciu D. Four-chamber heart modeling and automatic segmentation for 3-D cardiac CT volumes using marginal space learning and steerable features. IEEE Trans Med Imaging. 2008;27(11):1668–81.

    Article  Google Scholar 

  18. Hiraoka Y, Sedat JW, Agard DA. The use of a charge-coupled device for quantitative optical microscopy of biological structures. Science. 1987;238(4823):36–41.

    Article  Google Scholar 

  19. Miller PJ, Hoyt CC. Multispectral imaging with a liquid crystal tunable filter. 1995;2345:354–365.

  20. Roula M, Diamond J, Bouridane A, Miller P, Amira A. A multispectral computer vision system for automatic grading of prostatic neoplasia. In: 2002 I.E. International Symposium on Biomedical Imaging, 2002. Proceedings,2002. p. 193‑196.

  21. Kass M, Witkin A, Terzopoulos D. Snakes: active contour models. Int J Comput Vis. 1988;1(4):321–31.

    Article  Google Scholar 

  22. Caselles V, Catté F, Coll T, Dibos F. A geometric model for active contours in image processing. Numer Math. 1993;66(1):1–31.

    Article  MATH  MathSciNet  Google Scholar 

  23. Malladi R, Sethian JA, Vemuri BC. Shape modeling with front propagation: a level set approach. IEEE Trans Pattern Anal Mach Intell. 1995;17(2):158–75.

    Article  Google Scholar 

  24. He L, Peng Z, Everding B, Wang X, Han CY, Weiss KL, et al. A comparative study of deformable contour methods on medical image segmentation. Image Vis Comput. 2008;26(2):141–63.

    Article  Google Scholar 

  25. Chen SY, Guan Q. Parametric shape representation by a deformable NURBS model for cardiac functional measurements. IEEE Trans Biomed Eng. 2011;58(3):480–7.

    Article  Google Scholar 

  26. Chen S, Zhao M, Wu G, Yao C, Zhang J. Recent advances in morphological cell image analysis. Comput Math Methods Med. 2012;2012.

  27. Canny J. A computational approach to edge detection. IEEE Trans Pattern Anal Mach Intell. 1986;PAMI-8(6):679–98.

    Article  Google Scholar 

  28. Jaccard P. The distribution of the flora in the alpine zone.1. New Phytol. 1912;11(2):37–50.

    Article  Google Scholar 

  29. Dice LR. Measures of the amount of ecologic association between species. Ecology. 1945;26(3):297–302.

    Article  Google Scholar 

  30. Virtex-5 FPGA Devices. [En ligne]. Disponible sur: http://www.xilinx.com/onlinestore/silicon/online_store_v5.htm. [Consulté le: 16-juin-2015].

  31. « Tutorials : ISE 9 Software Tutorials ». [En ligne]. Disponible sur: http://www.xilinx.com/support/techsup/tutorials/tutorials9.htm. [Consulté le: 16-juin-2015].

  32. bookfile.book - mb_tutorial_c2bits.pdf.

  33. ARM Architecture Reference Manual - arm_arm.pdf.

  34. Chaddad A, Maamoun M, Tanougast C, Dandache A. Hardware implementation of active contour algorithm for fast cancer cells detection. Biomedical Engineering Conference (SBEC), 2013 29th Southern, 2013, p. 129–130.

  35. Siéler L, Tanougast C, Bouridane A. A scalable and embedded FPGA architecture for efficient computation of grey level co-occurrence matrices and Haralick textures features. Microprocess Microsyst. 2010;34(1):14–24.

    Article  Google Scholar 

  36. Chaddad A, Tanougast C, Golato A, Dandache A. Carcinoma cell identification via optical microscopy and shape feature analysis. J Biomed Sci Eng. 2013;06(11):1029–33.

    Article  Google Scholar 

Download references

Acknowledgments

The authors would like to acknowledge the service Anapat of the CHU hospital of the Nancy-Brabois and the Architecture of Embedded Systems and Smart Sensors (ASEC) team.

Compliance with Ethical Standards

The authors declare that there is no conflict of interest regarding the publication of this article and the materials are in compliance with all applicable laws, regulations and policies for the protection of medical data, and any necessary approvals, authorizations, and informed consent documents were obtained.

Conflict of interest

The authors declare that there is no conflict of interest regarding the publication of this article.

Support

This work was supported by the ASEC-LCOMS laboratory for Scantis project.

Author information

Authors and Affiliations

  1. Laboratory of Design, Optimization and Modeling (LCOMS), University of Lorraine, Metz, France

    Ahmad Chaddad & Camel Tanougast

Authors
  1. Ahmad Chaddad
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Camel Tanougast
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Ahmad Chaddad.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Chaddad, A., Tanougast, C. Real-time abnormal cell detection using a deformable snake model. Health Technol. 5, 179–187 (2015). https://doi.org/10.1007/s12553-015-0115-1

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12553-015-0115-1

Keywords

Search

Navigation