Real-time abnormal cell detection using a deformable snake model

Health and Technology

Volumn 5, Issue 3-4, 2015, Pages 179-187

  Chaddad, Ahmad ^a   Tanougast, Camel ^a  

^a UNIVERSITÉ DE LORRAINE   (France)

Author keywords

Cancer; Cells; FPGA; Optical microscopy; Similarity; Snake

Indexed keywords

ARTICLE; CAMERA; CANCER CELL; CANCER DIAGNOSIS; CELLS BY BODY ANATOMY; COLON CANCER; COMPARATIVE STUDY; CONTROLLED STUDY; FILTER; HISTOPATHOLOGY; IMAGE ANALYSIS; IMAGE PROCESSING; LIQUID CRYSTAL; LIQUID CRYSTAL TUNABLE FILTER; MICROSCOPE IMAGE; MICROSCOPY; PROCEDURES CONCERNING CELLS; PROSTATE HYPERTROPHY; SNAKE ACTIVE CONTOUR METHOD;

EID: 84947590762     PISSN: 21907188     EISSN: 21907196     Source Type: Journal    
DOI: 10.1007/s12553-015-0115-1     Document Type: Article

References (36)

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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
28. Jaccard P. The distribution of the flora in the alpine zone.1. New Phytol. 1912;11(2):37–50.
29. Dice LR. Measures of the amount of ecologic association between species. Ecology. 1945;26(3):297–302.
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.
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.