Pełnotekstowe zasoby PLDML oraz innych baz dziedzinowych są już dostępne w nowej Bibliotece Nauki.
Zapraszamy na


Preferencje help
Widoczny [Schowaj] Abstrakt
Liczba wyników
2014 | 24 | 1 | 19-31

Tytuł artykułu

Nuclei segmentation for computer-aided diagnosis of breast cancer

Treść / Zawartość

Warianty tytułu

Języki publikacji



Breast cancer is the most common cancer among women. The effectiveness of treatment depends on early detection of the disease. Computer-aided diagnosis plays an increasingly important role in this field. Particularly, digital pathology has recently become of interest to a growing number of scientists. This work reports on advances in computer-aided breast cancer diagnosis based on the analysis of cytological images of fine needle biopsies. The task at hand is to classify those as either benign or malignant. We propose a robust segmentation procedure giving satisfactory nuclei separation even when they are densely clustered in the image. Firstly, we determine centers of the nuclei using conditional erosion. The erosion is performed on a binary mask obtained with the use of adaptive thresholding in grayscale and clustering in a color space. Then, we use the multi-label fast marching algorithm initialized with the centers to obtain the final segmentation. A set of 84 features extracted from the nuclei is used in the classification by three different classifiers. The approach was tested on 450 microscopic images of fine needle biopsies obtained from patients of the Regional Hospital in Zielona Góra, Poland. The classification accuracy presented in this paper reaches 100%, which shows that a medical decision support system based on our method would provide accurate diagnostic information.








Opis fizyczny




  • Institute of Control and Computation Engineering, University of Zielona Góra, ul. Podgórna 50, 65-246 Zielona Góra, Poland
  • Institute of Control and Computation Engineering, University of Zielona Góra, ul. Podgórna 50, 65-246 Zielona Góra, Poland


  • Al-Kofahi, Y., Lassoued, W., Lee, W. and Roysam, B. (2010). Improved automatic detection and segmentation of cell nuclei in histopathology images, IEEE Transactions on Biomedcial Engineering 57(4): 841-852.
  • Alvarez Menendez, L., de Cos Juez, F., Sanchez Lasheras, F. and Alvarez Riesgo, J. (2010). Artificial neural networks applied to cancer detection in a breast screening programme, Mathematical and Computer Modelling 52(7-8): 983-991.
  • Bandyopadhyay, S.K., Maitra, I.K. and Banerjee, S. (2010). Digital imaging in pathology towards detection and analysis of human breast cancer, 2nd International Conference on Computational Intelligence, Communication Systems and Networks, Liverpool, UK, pp. 295-300.
  • Basavanhally, A., Ganesan, S., Feldman, M., Shih, N., Mies, C., Tomaszewski, J. and Madabhushi, A. (2013). Multifield-of-view framework for distinguishing tumor grade in ER+ breast cancer from entire histopathology slides, IEEE Transactions on Biomedical Engineering 60(8): 2089-2099.
  • Birdwell, R.L., Bandodkar, P. and Ikeda, D.M. (2005). Computer-aided detection with screening mammography in a university hospital setting, Radiology 236(2): 451-457.
  • Bishop, C. (2006). Pattern Recognition and Machine Learning, Springer, New York, NY.
  • Boryczka, U. (2009). Finding groups in data: Cluster analysis with ants, Applied Soft Computing 9(1): 61-70.
  • Boryczka, U. and Kozak, J. (2010). Ant colony decision trees-a new method for constructing decision trees based on ant colony optimization, in J-S. Pan, S-M. Chen and N.T. Nguyen (Eds.), Computational Collective Intelligence. Technologies and Applications, Lecture Notes in Computer Science, Vol. 6421, Springer-Verlag, Berlin/Heidelberg, pp. 373-382.
  • Bray, F., Ren, J., Masuyer, E. and Ferlay, J. (2012). Estimates of global cancer prevalence for 27 sites in the adult population in 2008, International Journal of Cancer, DOI: 10.1002/ijc.27711.
  • Breiman, L., Friedman, J., Olshen, R. and Stone, C. (1984). Classification and Regression Trees, Wadsworth & Brooks/Cole Advanced Books & Software, Monterey, CA.
  • Butler, S.A., Gabbay, R.J., Kass, D.A., Siedler, D.E., O'Shaughnessy, K.F. and Castellino, R.A. (2004). Computer-aided detection in diagnostic mammography: Detection of clinically unsuspected cancers, American Journal of Roentgenology 183(5): 1511-1515.
  • Cheng, H.D., Shan, J., Ju, W., Guo, Y. and Zhang, L. (2010). Automated breast cancer detection and classification using ultrasound images: A survey, Pattern Recognition 43(1): 299-317.
  • Christel, D., Rojob, M.G., Klossac, J., Mead, V.D., Bookere, D., Beckwithf, B.A. and Schraderg, T. (2011). Standardizing the use of whole slide images in digital pathology, Computerized Medical Imaging and Graphics 35(7-8): 496-505.
  • Clocksin, W. F. (2003). Automatic segmentation of overlapping nuclei with high background variation using robust estimation and flexible contour models, 12th International Conference Image Analysis and Processing, ICIAP'03, Mantova, Italy, pp. 682-687.
  • Cloppet, F. and Boucher, A. (2008). Segmentation of overlapping/aggregating nuclei cells in biological images, 19th International Conference on Pattern Recognition, ICPR 2008, Tampa, FL, USA, pp. 1-4.
  • Cortes, C. and Vapnik, V. (1995). Support-vector networks, Machine Learning 20(3): 273-297.
  • Cover, T. and Hart, P. (1967). Nearest neighbor pattern classification, IEEE Transactions on Information Theory 13(1): 21-27.
  • Cruz-Ramirez, N., Acosta-Mesa, H.-G., Carrillo-Calvet, H. and Barrientos-Martinez, R.-E. (2009). Discovering interobserver variability in the cytodiagnosis of breast cancer using decision trees and Bayesian networks, Applied Soft Computing 9(4): 1331-1342.
  • Cupples, T.E., Cunningham, J.E. and Reynolds, J.C. (2004). Impact of computer-aided detection in a regional screening mammography program, American Journal of Roentgenology 185(4): 944-950.
  • Dean, J.C. and Ilvento, C.C. (2006). Improved cancer detection using computer-aided detection with diagnostic and screening mammography: Prospective study of 104 cancers, American Journal of Roentgenology 187(1): 20-28.
  • Destounis, S.V., DiNitto, P., Logan-Young, W., Bonaccio, E., Zuley, M.L. and Willison, K. M. (2004). Can computer-aided detection with double reading of screening mammograms help decrease the false-negative rate? Initial experience, Radiology 232(2): 578-584.
  • Devijver, P. and Kittler, J. (1982). Pattern Recognition: A Statistical Approach, Prentice-Hall, London.
  • Doi, K. (2005). Current status and future potential of computeraided diagnosis in medical imaging, British Journal of Radiology 78(1): s3-s19.
  • Duda, R., Hart, P. and Stork, D. (2001). Pattern Classification, 2nd Edn, Wiley-Interscience, New York, NY.
  • Eadie, L.H., Taylor, P. and Gibson, A.P. (2012). A systematic review of computer-assisted diagnosis in diagnostic cancer imaging, European Journal of Radiology 81(1): e70-e76.
  • Fabregue, M., Bringay, S., Poncelet, P., Teisseire, M. and Orsetti, B. (2011). Mining microarray data to predict the histological grade of a breast cancer, Journal of Biomedical Informatics 44(Supp. 1): S12-S16.
  • Fatakdawala, H., Xu, J., Basavanhally, A., Bhanot, G., Ganesan, S., Feldman, M., Tomaszewski, J.E. and Madabhushi, A. (2010). Expectation maximization-driven geodesic active contour with overlap resolution (EMaGACOR): Application to lymphocyte segmentation on breast cancer histopathology, IEEE Transactions on Biomedical Engineering 57(7): 1676-1689.
  • Ferlay, J., Shin, H., Bray, F., Forman, D., Mathers, C. and Parkin, D. (2010). Globocan 2008 v2.0, Cancer incidence and mortality worldwide: IARC cancerbase no. 10,
  • Filipczuk, P., Kowal, M. and Obuchowicz, A. (2011a). Automatic breast cancer diagnostics based on k-means clustering and adaptive thresholding hybrid segmentation, in R.S. Choraś (Ed.), Image Processing and Communications Challenges 3, Advances in Intelligent and Soft Computing, Vol. 102, Springer-Verlag, Berlin/Heidelberg, pp. 295-303.
  • Filipczuk, P., Kowal, M. and Obuchowicz, A. (2011b). Fuzzy clustering and adaptive thresholding based segmentation method for breast cancer diagnosis, in R. Burduk, M. Kurzyński, M. Woźniak and A. Żołnierek (Eds.), Computer Recognition Systems 4, Advances in Intelligent and Soft Computing, Vol. 95, Springer-Verlag, Berlin/Heidelberg, pp. 613-622.
  • Fuchsa, T.J. and Buhmanna, J.M. (2011). Computational pathology: Challenges and promises for tissue analysis, Computerized Medical Imaging and Graphics 35(7): 515-530.
  • Ganesan, K., Acharya, U.R., Chua, C.K., Min, L.C., Abraham, K.T. and Ng, K. (2013). Computer-aided breast cancer detection using mammograms: A review, IEEE Reviews in Biomedical Engineering 6(8): 77-98.
  • Giansanti, D., Grigioni, M., D'Avenio, G., Morelli, S., Maccioni, G., Bondi, A. and Giovagnoli, M.R. (2010). Virtual microscopy and digital cytology: State of the art, Ann Ist Super Sanita 46(2): 115-122.
  • Giger, M.L. (2004). Computerized analysis of images in the detection and diagnosis of breast cancer, Seminars in Ultrasound, CT, and MRI 25(4): 411-418.
  • Gocławski, J., Sekulska-Nalewajko, J. and Kuźniak, E. (2012). Neural network segmentation of images from stained cucurbits leaves with colour symptoms of biotic and abiotic stresses, International Journal of Applied Mathematics and Computer Science 22(3): 669-684, DOI: 10.2478/v10006012-0050-5.
  • Grzegorczyk, T.M., Meaney, P.M., Kaufman, P.A., di FlorioAlexander, R.M. and Paulsen, K.D. (2012). Fast 3-D tomographic microwave imaging for breast cancer detection, IEEE Transactions on Medical Imaging 31(8): 1584-1592.
  • Gurcan, M.N., Boucheron, L.E., Can, A., Madabhushi, A., Rajpoot, N.M. and Yener, B. (2009). Histopathological image analysis: A review, IEEE Reviews in Biomedical Engineering 2: 147-171.
  • Haralick, R., Shanmugam, K. and Dinstein, I. (1973). Textural features for image classification, IEEE Transactions on Systems, Man, and Cybernetics 3(6): 610-621.
  • Hartigan, J.A. and Wong, M.A. (2001). Algorithm as 136: A k-means clustering algorithm, Journal of the Royal Statistical Society, Series C (Applied Statistics) 28(1): 100-108.
  • Hassan, M.R., Hossain, M.M., Begg, R.K., Ramamohanarao, K. and Morsi, Y. (2010). Breast-cancer identification using HMM-fuzzy approach, Computers in Biology and Medicine 40(3): 240-251.
  • Jeleń, L., Fevens, T. and Krzyżak, A. (2010). Classification of breast cancer malignancy using cytological images of fine needle aspiration biopsies, International Journal of Applied Mathematics and Computer Science 18(1): 75-83, DOI: 10.2478/v10006-008-0007-x.
  • Kirshin, E., Oreshkin, B., Zhu, G. K., Popovic, M. and Coates, M. (2013). Microwave radar and microwave-induced thermoacoustics: Dual-modality approach for breast cancer detection, IEEE Transactions on Biomedical Engineering 60(2): 354-360.
  • Kowal, M., Filipczuk, P. and Korbicz, J. (2011a). Hybrid cytological image segmentation method based on competitive neural network and adaptive thresholding, Pomiary, Automatyka, Kontrola 57(11): 1448-1451.
  • Kowal, M., Filipczuk, P., Obuchowicz, A. and Korbicz, J. (2011b). Computer-aided diagnosis of breast cancer using Gaussian mixture cytological image segmentation, Journal of Medical Informatics & Technologies 17: 257-262.
  • Kowal, M. and Korbicz, J. (2010). Segmentation of breast cancer fine needle biopsy cytological images using fuzzy clustering, in J. Kornacki, Z. Raś, S. Wierzchoń and J. Kacprzyk (Eds.), Advances in Machine Learning I, Springer-Verlag, Berlin/Heidelberg, pp. 405-417.
  • Krawczyk, B., Filipczuk, P. and Woźniak, M. (2012). Adaptive splitting and selection algorithm for classification of breast cytology images, in N.T. Nguyen, K. Hoang and P. Jędrzejowicz (Eds.), Computational Collective Intelligence. Technologies and Applications, Lecture Notes in Computer Science, Vol. 7653, Springer-Verlag, Berlin/Heidelberg, pp. 475-484.
  • Li, X.-Z., Williams, S. and Bottema, M.J. (2013). Background intensity independent texture features for assessing breast cancer risk in screening mammograms, Pattern Recognition Letters 34(9): 1053-1062.
  • Lloyd, S.P. (1982). Least squares quantization in PCM, IEEE Transactions on Information Theory 28(2): 129-137.
  • Lopez, A., Graham, A.R., Barker, G.P., Richter, L.C., Krupinski, E.A., Lian, F., Lauren L. Grasso, L.L., Miller, A., Kreykes, L.N. and Henderson, J.T. (2009). Virtual slide telepathology enables an innovative telehealth rapid breast care clinic, Human Pathology 40(8): 1082-1091.
  • Malek, J., Sebri, A., Mabrouk, S., Torki, K. and Tourki, R. (2009). Automated breast cancer diagnosis based on GVFSnake segmentation, wavelet features extraction and fuzzy classification, Journal of Signal Processing Systems 55(1-3): 49-66.
  • Malladi, R. and Sethian, J. (1996). Level set and fast marching methods in image processing and computer vision, Proceedings of the IEEE International Conference on Image Processing, Lausanne, Switzerland, pp. 489-492.
  • Marciniak, A., Obuchowicz, A., Monczak, A. and Kołodziński, M. (2005). Cytomorphometry of fine needle biopsy material from the breast cancer, in M. Kurzyński, E. Puchała and M. Woźniak and A. Żołnierek (Eds.), Computer Recognition Systems, Advances in Soft Computing, Vol. 30, Springer-Verlag, Berlin/Heidelberg, pp. 603-609.
  • Mat-Isa, N.A., Subramaniam, E., Mashor, M.Y. and Othman, N.H. (2007). Fine needle aspiration cytology evaluation for classifying breast cancer using artificial neural network, Signal Processing 4(12): 999-1008.
  • Moghbel, M. and Mashohor, S. (2013). Automated breast cancer detection and classification using ultrasound images: A survey, Artificial Intelligence Review 39(4): 305-313.
  • Mohanty, A.K., Senapati, M.R. and Lenka, S.K. (2013). An improved data mining technique for classification and detection of breast cancer from mammograms, Neural Computing and Applications 22(Supp. 1): S303-S310.
  • Moon, W.K., Shen, Y.W., Huang, C.S. and Chiang, L.R. (2011). Computer-aided diagnosis for the classification of breast masses in automated whole breast ultrasound images, Ultrasound in Medicine & Biology 37(4): 539-548.
  • Morton, M.J., Whaley, D.H., Brandt, K.R. and Amrami, K.K. (2006). Screening mammograms: Interpretation with computer-aided detection prospective evaluation, Radiology 239(2): 357-383.
  • Muniandy, S.V. and Stanslas, J. (2008). Modelling of chromatin morphologies in breast cancer cells undergoing apoptosis using generalized Cauchy field, Computerized Medical Imaging and Graphics 32(7): 631-637.
  • National Cancer Registry in Poland (2012).
  • Nikolova, N.K. (2011). Microwave imaging for breast cancer, IEEE Microwave Magazine 12(7): 78-94.
  • Niwas, S.I., Palanisamy, P., Sujathan, K. and Bengtsson, E. (2013). Analysis of nuclei textures of fine needle aspirated cytology images for breast cancer diagnosis using complex Daubechies wavelets, Signal Processing 93(10): 2828-2837.
  • Nixon, M. and Aguado, A. (2012). Feature Extraction & Image Processing for Computer Vision, 3rd Edn., Academic Press, London.
  • Obuchowicz, A., Hrebień, M., Nieczkowski, T. and Marciniak, A. (2008). Computational intelligence techniques in image segmentation for cytopathology, in T.G. Smoliński, M.G. Milanova and A.-G. Hassanien (Eds.), Computational Intelligence in Biomedicine and Bioinformatics, SpringerVerlag, Berlin, pp. 169-199.
  • Polat, K. and Gunes, S. (2007). Breast cancer diagnosis using least square support vector machine, Digital Signal Processing 17(4): 694-701.
  • Sethian, J. (1996). A fast marching level set method for monotonically advancing fronts, Proceedings of the National Academy of Sciences of the United States of America 93(4): 1591-1595.
  • Sezgin, M. and Sankur, B. (2003). Survey over image thresholding techniques and quantitative performance evaluation, Journal of Electronic Imaging 13(1): 146-165.
  • Sifakis, E. and Tziritas, G. (2001). Moving object localisation using a multi-label fast marching algorithm, Signal Processing: Image Communication 16(10): 963-976.
  • Śmietański, J., Tadeusiewicz, R. and Łuczyńska, E. (2010). Texture analysis in perfusion images of prostate cancer - A case study, International Journal of Applied Mathematics and Computer Science 20(1): 149-156, DOI: 10.2478/v10006-010-0011-9.
  • Steć, P. (2005). Segmentation of Colour Video Sequences Using the Fast Marching Method, University of Zielona Góra Press, Zielona Góra.
  • Tang, X. (1998). Texture information in run-length matrices, IEEE Transactions on Image Processing 7(11): 1602-1609.
  • Ubeyli, E.D. (2007). Implementing automated diagnostic systems for breast cancer detection, Expert Systems with Applications 33(4): 1054-1062.
  • Underwood, J.C.E. (1987). Introduction to Biopsy Interpretation and Surgical Pathology, Springer-Verlag, London.
  • Verma, B., McLeod, P. and Klevansky, A. (2010). Classification of benign and malignant patterns in digital mammograms for the diagnosis of breast cancer, Expert Systems With Applications 37(4): 3344-3351.
  • Woźniak, M. and Krawczyk, B. (2012). Combined classifier based on feature space partitioning, International Journal of Applied Mathematics and Computer Science 22(4): 855-866, DOI: 10.2478/v10006-012-0063-0.
  • Xiong, X., Kim, Y., Baek, Y., Rhee, D. W. and Kim, S.-H. (2005). Analysis of breast cancer using data mining & statistical techniques, 6th International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing/1st ACIS International Workshop on Self-Assembling Wireless Networks, Towson, MD, USA, pp. 82-87.
  • Xu, M., Thulasiraman, P. and Noghanian, S. (2012). Microwave tomography for breast cancer detection on cell broadband engine processors, Journal of Parallel and Distributed Computing 72(9): 1106-1116.
  • Yang, X., Li, H. and Zhou, X. (2006). Nuclei segmentation using marker-controlled watershed, tracking using meanshift, and Kalman filter in time-lapse microscopy, IEEE Transactions on Circuits and Systems I 53(11): 2405-2414.
  • Yasmeen, M.G., Bassant, M.B., Hala, H.Z. and Mohamed, I.R. (2013). Automated cell nuclei segmentation for breast fine needle aspiration cytology, Signal Processing 93(10): 2804-2816.

Typ dokumentu



Identyfikator YADDA

JavaScript jest wyłączony w Twojej przeglądarce internetowej. Włącz go, a następnie odśwież stronę, aby móc w pełni z niej korzystać.