Segmentation of cervical cell nuclei in high-resolution microscopic images: A new algorithm and a web-based software framework

Computer Methods and Programs in Biomedicine

Volumn 107, Issue 3, 2012, Pages 497-512

  Bergmeir, Christoph ^a   García Silvente, Miguel ^a   Benítez, José Manuel ^a  

^a UNIVERSITY OF GRANADA   (Spain)

Author keywords

Cervical cell imaging; High resolution microscopic imaging; Nucleus segmentation

Indexed keywords

CANNY EDGE DETECTION; CELL NUCLEUS; CERVICAL CANCERS; CERVICAL CELLS; DATA STORAGE; EXPERT USERS; FULL RESOLUTION IMAGES; HIGH QUALITY; HIGH RESOLUTION; IMAGE CHARACTERISTICS; ISOLATED CELLS; LEVEL SET ALGORITHM; MEAN SHIFT; MEDIAN FILTERING; MEDICAL EXPERTS; MICROSCOPE SLIDE; MICROSCOPIC IMAGE; MICROSCOPIC IMAGING; NOISE REMOVAL; NUCLEUS SEGMENTATION; PRIOR KNOWLEDGE; RANDOMIZED HOUGH TRANSFORM; SEGMENTATION ALGORITHMS; VOTING SCHEMES; WEB-BASED ARCHITECTURE; WEB-BASED SOFTWARES;

ALGORITHMS; COMPUTER PROGRAMMING; CYTOLOGY; DIAGNOSIS; EDGE DETECTION; MEDIAN FILTERS; MICROSCOPIC EXAMINATION; WEBSITES;

IMAGE SEGMENTATION;

ALGORITHM; ARTICLE; CELL MATURATION; CELL NUCLEUS; CERVICAL CELL NUCLEUS SEGMENTATION; COMPUTER PROGRAM; DATA BASE; HUMAN; MEDICAL EXPERT; MICROSCOPY; NOISE REDUCTION;

ALGORITHMS; AUTOMATION; CELL NUCLEUS; CYTOPLASM; EARLY DETECTION OF CANCER; FEMALE; HUMANS; IMAGE PROCESSING, COMPUTER-ASSISTED; INTERNET; MODELS, STATISTICAL; PATTERN RECOGNITION, AUTOMATED; SIGNAL PROCESSING, COMPUTER-ASSISTED; SOFTWARE; UTERINE CERVICAL NEOPLASMS;

EID: 84863869463     PISSN: 01692607     EISSN: 18727565     Source Type: Journal    
DOI: 10.1016/j.cmpb.2011.09.017     Document Type: Article

References (49)

1. World Health Organization Comprehensive cervical cancer control: a guide to essential practice 2006, World Health Organization, Department of Reproductive Health and Research and Department of Chronic Diseases and Health Promotion.
2. Wang T.-Y., Chen H.-S., Yang Y.-C., Tsou M.-C. Comparison of fluid-based, thin-layer processing and conventional Papanicolaou methods for uterine cervical cytology. Journal of the Formosan Medical Association 1999, 98(7):500-505.
3. Cibas E.S., Ducatman B.S. Cytology: Diagnostic Principles and Clinical Correlates, Expert Consult Series 2009, Saunders/Elsevier.
4. Lin Y.K., Fu K.S. Automatic classification of cervical cells using a binary tree classifier. Pattern Recognition 1983, 16(1):69-80.
5. Nguyen N.G., Poulsen R.S., Louis C. Some new color features and their application to cervical cell classification. Pattern Recognition 1983, 16(4):401-411.
6. R.F. Walker, Adaptive multi-scale texture analysis with application to automated cytology, PhD Thesis, The University of Queensland, 1997.
7. E. Martin, Pap-smear classification, Master's Thesis, Technical University of Denmark (DTU), Oersted, Dept. of Automation, 2003.
8. J. Norup, Classification of pap-smear data by transductive neuro-fuzzy methods, Master's Thesis, Technical University of Denmark (DTU), Oersted, Dept. of Automation, 2005.
9. Marinakis Y., Marinaki M., Dounias G. Particle swarm optimization for pap-smear diagnosis. Expert Systems with Applications 2008, 35(4):1645-1656.
10. Bamford P., Lovell B. Unsupervised cell nucleus segmentation with active contours. Signal Processing 1998, 71(2):203-213.
11. Wu H.-S., Gil J., Barba J. Optimal segmentation of cell images. IEE Proceedings: Vision, Image and Signal Processing 1998, 145(1):50-56.
12. Mat-Isa N.A., Mashor M.Y., Othman N.H. Seeded region growing features extraction algorithm; its potential use in improving screening for cervical cancer. International Journal of the Computer, the Internet and Management 2005, 13(1):61-70.
13. Tsai M.-H., Chan Y.-K., Lin Z.-Z., Yang-Mao S.-F., Huang P.-C. Nucleus and cytoplast contour detector of cervical smear image. Pattern Recognition Letters 2008, 29(9):1441-1453.
14. Yang-Mao S.-F., Chan Y.-K., Chu Y.-P. Edge enhancement nucleus and cytoplast contour detector of cervical smear images. IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics 2008, 38(2):353-366.
15. Yang-Mao S.-F., Chen Y.-F., Chan Y.-K., Tsai M.-H., Chu Y.-P. Gradient direction edge enhancement based nucleus and cytoplasm contour detector of cervical smear images. Lecture Notes in Computer Science (LNCS) 2008, 4901:290-297.
16. Malm P., Brun A. Closing curves with Riemannian dilation: application to segmentation in automated cervical cancer screening. ISVC '09: Proceedings of the 5th International Symposium on Advances in Visual Computing 2009, 337-346. Springer-Verlag, Berlin/Heidelberg.
17. Lin C.-H., Chan Y.-K., Chen C.-C. Detection and segmentation of cervical cell cytoplast and nucleus. International Journal of Imaging Systems and Technology 2009, 19(3):260-270.
18. Mouroutis T., Roberts S.J., Bharath A.A. Robust cell nuclei segmentation using statistical modelling. Bioimaging 1998, 6(2):79-91.
19. Won C.S., Nam J.Y., Choe Y. Segmenting cell images: a deterministic relaxation approach. Lecture Notes in Computer Science 2004, 3117:281-291.
20. Dorini L.B., Minetto R., Leite N.J. White blood cell segmentation using morphological operators and scale-space analysis. Proceedings of SIBGRAPI 2007 - 20th Brazilian Symposium on Computer Graphics and Image Processing 2007, 294-301.
21. Liu B., Yin C., Liu Z., Zhang Y. Automatic segmentation on cell image fusing gray and gradient information. Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society 2007, 5624-5627.
22. Li G., Liu T., Tarokh A., Nie J., Guo L., Mara A., Holley S., Wong S.T.C. 3D cell nuclei segmentation based on gradient flow tracking. BMC Cell Biology 2007, 8.
23. Chang H., Yang Q., Parvin B. Segmentation of heterogeneous blob objects through voting and level set formulation. Pattern Recognition Letters 2007, 28(13):1781-1787.
24. Ta V.-T., Lézoray O., Elmoataz A., Schüpp S. Graph-based tools for microscopic cellular image segmentation. Pattern Recognition 2009, 42(6):1113-1125.
25. Edwards A.D.N., Hines G., Hunt A. Segmentation of biological cell images for sonification. Proceedings - 1st International Congress on Image and Signal Processing, CISP 2008, vol.2 2008, 128-132.
26. Harandi N.M., Sadri S., Moghaddam N.A., Amirfattahi R. An automated method for segmentation of epithelial cervical cells in images of ThinPrep. Journal of Medical Systems 2010, 34(6):1043-1058.
27. Maleike D., Nolden M., Meinzer H.-P., Wolf I. Interactive segmentation framework of the medical imaging interaction toolkit. Computer Methods and Programs in Biomedicine 2009, 96(1):72-83.
28. Muench H., Schroeter A., Meinzer H.P., Engelmann U. A web-based approach to distributing medical images for referring physicians outside hospitals without specialised user management. International Journal of Computer Assisted Radiology and Surgery 2006, 1(Suppl. 7):130-132.
29. Mayer A., Meinzer H.-P. High performance medical image processing in client/server-environments. Computer Methods and Programs in Biomedicine 1999, 58(3):207-217.
30. Mahmoudi S.E., Akhondi-Asl A., Rahmani R., Faghih-Roohi S., Taimouri V., Sabouri A., Soltanian-Zadeh H. Web-based interactive 2D/3D medical image processing and visualization software. Computer Methods and Programs in Biomedicine 2010, 98(2):172-182.
31. Bergmeir C., García Silvente M., Esquivias López-Cuervo J., Benítez J.M. Segmentation of cervical cell images using mean-shift filtering and morphological operators. SPIE 2010, 7623:76234C.
32. Bradski G., Kaehler A. Learning OpenCV: Computer Vision with the OpenCV Library 2008, O'Reilly, Cambridge, MA.
33. Pratt W.K. Digital Image Processing: PIKS Inside 2001, Wiley.
34. Otsu N. Threshold selection method from gray-level histograms. IEEE Transactions on Systems, Man, and Cybernetics 1979, SMC-9(1):62-66.
35. Comaniciu D., Meer P. Mean shift analysis and applications. Proceedings of the IEEE International Conference on Computer Vision, vol. 2 1999, 1197-1203.
36. Comaniciu D., Meer P. Mean shift: a robust approach toward feature space analysis. IEEE Transactions on Pattern Analysis and Machine Intelligence 2002, 24(5):603-619.
37. Comaniciu D., Meer P. Cell image segmentation for diagnostic pathology. Advanced Algorithmic Approaches to Medical Image Segmentation: State-of-the-art Applications in Cardiology, Neurology, Mammography and Pathology 2001, pp. 541-558.
38. McLaughlin R.A. Randomized Hough transform: better ellipse detection. IEEE Region 10 Annual International Conference, Proceedings/TENCON, vol. 1 1996, 409-414.
39. Bai X., Sun C., Zhou F. Splitting touching cells based on concave points and ellipse fitting. Pattern Recognition 2009, 42(11):2434-2446.
40. Nixon M.S., Aguado A.S. Feature Extraction & Image Processing January 2008, Academic Press. second ed.
41. Hahn K., Jung S., Han Y., Hahn H. A new algorithm for ellipse detection by curve segments. Pattern Recognition Letters 2008, 29(13):1836-1841.
42. Xu L., Oja E., Kultanen P. A new curve detection method: randomized Hough transform (RHT). Pattern Recognition Letters 1990, 11(5):331-338.
43. Hahn K., Han Y., Hahn H. Ellipse detection using a randomized Hough transform based on edge segment merging scheme. ISPRA 2007.
44. Yuen H.K., Illingworth J., Kittler J. Detecting partially occluded ellipses using the Hough transform. Image and Vision Computing 1989, 7(1):31-37.
45. Ibanez L., Schroeder W., Ng L., Cates J. The ITK Software Guide 2005, Kitware, Inc., ISBN 1-930934-15-7. second ed.
46. Zhang H., Fritts J.E., Goldman S.A. Image segmentation evaluation: a survey of unsupervised methods. Computer Vision and Image Understanding 2008, 110(2):260-280.
47. Cárdenes R., de Luis-García R., Bach-Cuadra M. A multidimensional segmentation evaluation for medical image data. Computer Methods and Programs in Biomedicine 2009, 96(2):108-124.
48. Yasnoff W.A., Mui J.K., Bacus J.W. Error measures for scene segmentation. Pattern Recognition 1977, 9(4):217-231.
49. Sun Y., Kamel M.S., Wong A.K.C., Wang Y. Cost-sensitive boosting for classification of imbalanced data. Pattern Recognition 2007, 40(12):3358-3378.