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Application of Pattern Recognition Techniques for the Analysis of Histopathological Images

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Book cover Computer Recognition Systems 4

Part of the book series: Advances in Intelligent and Soft Computing ((AINSC,volume 95))

Abstract

In this paper we discuss applications of pattern recognition and image processing to automatic processing and analysis of histopathological images. We focus on two applications: counting of red and white blood cells using microscopic images of blood smear samples and breast cancer malignancy grading from slides of fine needle aspiration biopsies. We provide literature survey and point out new challenges.

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References

  1. Newland, J.: The peripheral blood smear. In: Goldman, L., Ausiello, D. (eds.) Cecil Medicine V, ch. 161, 23rd edn., Saunders Elsevier, Philadelphia (2007)

    Google Scholar 

  2. Agby, G.: Leukopenia and leukocytosis. In: Goldman, L., Ausiello, D. (eds.) Cecil Medicine, ch. 173, 23rd edn., Saunders Elsevier, Philadelphia (2007)

    Google Scholar 

  3. Ramoser, H., Laurain, V., Bischof, H., Ecker, R.: Leukocyte segmentation and classification in blood-smear images. In: 27th IEEE Annual Conference Engineering in Medicine and Biology, Shanghai, China, September 1-4 (2005)

    Google Scholar 

  4. Al-Muhairy, J., Al-Assaf, Y.: Automatic white blood cell segmentation based on image processing. In: 16th IFAC World Congress (2005)

    Google Scholar 

  5. Prasad, B., Prasanna, S.M. (eds.): Speech, Audio, Image and Biomedical Signal Processing using Neural Networks. Studies in Computational Intelligence, vol. 83. Springer, Heidelberg (2008); ISBN 978-3-540-75397-1

    MATH  Google Scholar 

  6. Piuri, V., Scotti, F.: Morphological classification of blood leucocytes by microscope images. In: IEEE International Conference on Computational Intelligence Far Measurement Systems and Applications, Boston, MA, July14-16 (2004)

    Google Scholar 

  7. Bentley, S., Lewis, S.: The use of an image analyzing computer for the quantification of red cell morphological characteristics. British Journal of Hematology 29, 81–88 (1975)

    Article  Google Scholar 

  8. Rowan, R.: Automated examination of the peripheral blood smear. In: Automation and quality assurance in hematology, ch. 5, pp. 129–177. Blackwell Scientific, Oxford (1986)

    Google Scholar 

  9. Costin, H., Rotariu, C., Zbancioc, M., Costin, M., Hanganu, E.: Fuzzy rule-aided decision support for blood cell recognition. Fuzzy Systems & Artificial Intelligence 7(1-3), 61–70 (2001)

    Google Scholar 

  10. Albertini, M., Teodori, L., Piatti, E., Piacentini, M., Accorsi, A., Rocchi, M.: Automated analysis of morphometric parameters for accurate definition of erythrocyte cell shape. Cytometry Part A 52A(1), 12–18 (2003)

    Article  Google Scholar 

  11. Robinson, R., Benjamin, L., Cosgri, J., Cox, C., Lapets, O., Rowley, P., Yatco, E., Wheeless, L.: Textural differences between aa and ss blood specimens as detected by image analysis. Cytometry 17(2), 167–172 (1994)

    Article  Google Scholar 

  12. Gering, E., Atkinson, C.: A rapid method for counting nucleated erythrocytes on stained blood smears by digital image analysis. Journal of Parasitology 90(4), 879–881 (2004)

    Article  Google Scholar 

  13. Martelli, A.: An application of heuristic search methods to edge and contour detection. Communications of the ACM 19(2), 73–83 (1976)

    Article  MATH  MathSciNet  Google Scholar 

  14. Fleagle, S., Johnson, M., Wilbricht, C., Skorton, D., Wilson, R., White, C., Marcus, M., Collins, S.: Automated analysis of coronary arterial morphology in cineangiograms: geometric and physiologic validation in humans. IEEE Transactions on Medical Imaging 8(4), 387–400 (1989)

    Article  Google Scholar 

  15. Fleagle, S., Thedens, D., Ehrhardt, J., Scholz, T., Skorton, D.: Automated identification of left ventricular borders from spin-echo magnetic resonance images. Investigative Radiology 26(4), 295–303 (1991)

    Article  Google Scholar 

  16. Gonzalez, R., Woods, R.: Digital Image Processing (3rd Economy Edition). Prentice–Hall, Englewood Cliffs (2008)

    Google Scholar 

  17. Adjouadi, M., Fernandez, N.: An orientation-independent imaging technique for the classification of blood cells. Particle & Particle Systems Characterization 18(2), 91–98 (2001)

    Article  Google Scholar 

  18. Di Ruberto, C., Dempster, A., Khan, S., Jarra, B.: Segmentation of blood images using morphological operators. In: 15th International Conference on Pattern Recognition (ICPR 2000), Barcelona, Spain, pp. 397–400. IEEE, Los Alamitos (2000)

    Chapter  Google Scholar 

  19. Gauch, J.: Image segmentation and analysis via multiscale gradient watershed hierarchies. IEEE Transactions on Image Processing 8(1), 69–79 (1999)

    Article  Google Scholar 

  20. Kass, M., Witkin, A., Terzopoulos, D.: Snakes: Active contour models. International Journal of Computer Vision 4, 321–331 (1988)

    Article  Google Scholar 

  21. Ongun, G., Halici, U., Leblebicioglu, K., Atalay, V., Beksac, S., Beksac, M.: Automated contour detection in blood cell images by an efficient snake algorithm. Nonlinear Analysis-Theory Methods & Applications 47(9), 5839–5847 (2001)

    Article  MATH  MathSciNet  Google Scholar 

  22. Wang, X., He, L., Wee, W.G.: Deformable contour method: A constrained optimization approach. International Journal of Computer Vision 59(1), 87–108 (2004)

    Article  Google Scholar 

  23. Ongun, G., Halici, U., Leblebicioglu, K., Atalay, V.V., Beksac, M., Beksac, S.: Feature extraction and classification of blood cells for an automated differential blood count system. In: Proc. IJCNN, vol. 4, pp. 2461–2466 (2001)

    Google Scholar 

  24. Leznray, O., Elmoataz, A., Cardot, H., Gougeon, G., Lecluse, M., Elie, H., Revenu, M.: Segmentation of cytological images using color and mathematical morphology. European Congress of Stereology 18(1), 1–14 (1999)

    Google Scholar 

  25. Ravi, B., Kumar, Danny, K., Joseph, Sreenivas, T.V.: Teager energy based blood cell segmentation. In: International Conference on Digital Signal Processing, vol. 2, pp. 619–622 (2002)

    Google Scholar 

  26. Ongun, G., Halici, U., Leblebicioglu, K., Atalay, V., Beksac, M., Beksak, S.: An automated differential blood count system. In: IEEE Int. Conf. on Engineering in Medicine and Biology Society, vol. 3, pp. 2583–2586 (2001)

    Google Scholar 

  27. Sinha, N., Ramakrishnan, A.: Automation of differential blood count. In: Conf. on Convergent Technologies for Asia-Pacific Region, vol. 2, pp. 547–551 (2003)

    Google Scholar 

  28. Comaniciu, D., Meer, P.: Cell image segmentation for diagnostic pathology. In: Suri, J., Setarehdan, S., Singh, S. (eds.) Advanced Algorithmic Approaches to Medical Image Segmentation: state-of-the-art application in cardiology, neurology, mammography and pathology, pp. 541–558. Springer, Heidelberg (2001)

    Google Scholar 

  29. Jiang, K., Liao, Q.-M., Dai, S.-Y.: A novel white blood cell segmentation scheme using scale-space filtering and watershed clustering. In: Int. Conf. on Machine Learning and Cybernetics, vol. 5, pp. 2820–2825 (2003)

    Google Scholar 

  30. Hamghalam, M., Motameni, M., Kelishomi, A.E.: Leukocyte segmentation in giemsa-stained image of peripheral blood smears based on active contour. In: International Conference on Signal Processing Systems, pp. 103–106. IEEE Computer Society, Los Alamitos (2009)

    Chapter  Google Scholar 

  31. Fodor, I., Kamath, C.: On denoising images using wavelet-based statistical techniques. Tech. rep., Lawrence Livermore National Laboratory, uCRL JC-142357 (2001)

    Google Scholar 

  32. Sendur, L., Selesnick, I.: Bivariate shrinkage functions for wavelet-based denoising exploiting interscale dependency. IEEE Transactions on Signal Processing 50(11), 2744–2756 (2002)

    Article  Google Scholar 

  33. Sendur, L., Selesnick, I.: A bivariate shrinkage function for wavelet-based denoising. In: IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), vol. 2, pp. 1261–1264 (2002)

    Google Scholar 

  34. Lim, J.S.: Two-Dimensional Signal and Image Processing. Prentice-Hall, Englewood Cliffs (1990)

    Google Scholar 

  35. Hong, V., Palus, H., Paulus, D.: Edge preserving filters on color images. In: Bubak, M., van Albada, G.D., Sloot, P.M.A., Dongarra, J. (eds.) ICCS 2004. LNCS, vol. 3039, pp. 34–40. Springer, Heidelberg (2004)

    Chapter  Google Scholar 

  36. Babic, Z., Mandic, D.: An efficient noise removal and edge preserving convolution filter. In: 6th International Conference on Telecommunications in Modern Satellite, Cable and Broadcasting Service, vol. 2, pp. 538–541 (2003)

    Google Scholar 

  37. Nikolaou, N., Papamarkos, N.: Color reduction for complex document images. International Journal of Imaging Systems and Technology 19(1), 14–26 (2009)

    Article  Google Scholar 

  38. Tomasi, C., Manduchi, R.: Bilateral filtering for gray and color images. In: Sixth International Conference on Computer Vision, January 4-7, pp. 839–846 (1998)

    Google Scholar 

  39. Papari, G., Petkov, N., Campisi, P.: Artistic edge and corner enhancing smoothing. IEEE Transactions on Image Processing 16(10), 2449–2462 (2007)

    Article  MathSciNet  Google Scholar 

  40. Ntogas, N., Veintzas, D.: A binarization algorithm for historical manuscripts. In: 12th WSEAS International Conference on Communications, pp. 41–51 (2008)

    Google Scholar 

  41. Sauvola, J., Pietikainen, M.: Adaptive document image binarization. The Journal of the Pattern Recognition Society 33(2), 225–236 (2000)

    Article  Google Scholar 

  42. Otsu, N.: A threshold selection method from gray-level histograms. IEEE Transactions on System, Man and Cybernetics 9(1), 62–66 (1979)

    Article  MathSciNet  Google Scholar 

  43. Habibzadeh, M., Krzyzak, A., Fevens, T., Sadr, A.: Counting of RBCs and WBCs in noisy normal blood smear microscopic images. In: SPIE Medical Imaging (February 2011)

    Google Scholar 

  44. Vincent, L.: Fast opening functions and morphological granulometries. Image Algebra and Morphological Image Processing 2300, 253–267 (1994)

    Google Scholar 

  45. Lin, Y.-C., Tsai, Y.-P., Hung, Y.-P., Shih, Z.-C.: Comparison between immersion-based and toboggan-based watershed image segmentation. IEEE Transactions on Image Processing 15(3), 632–640 (2006)

    Article  Google Scholar 

  46. Duda, R., Hart, P., Stork, D.: Pattern Classification, 2nd edn. Wiley Interscience Publishers, Hoboken (2000)

    Google Scholar 

  47. Cheng, H., Shi, X., Min, R., Cai, X., Du, H.N.: Approaches for Automated Detection and Classification of Masses in Mammograms. Pattern Recognition 39(4), 646–668 (2006)

    Article  Google Scholar 

  48. Bottema, M., Slavotinek, J.: Detection and Classification of Lobular and DCIS (small cell) Microcalcifications in Digital Mammograms. Pattern Recognition Letters 21(13-14), 1209–1214 (2000)

    Article  MATH  Google Scholar 

  49. Cheng, H., Cui, M.: Mass Lesion Detection with a Fuzzy Neural Network. Pattern Recognition 37, 1189–1200 (2004)

    Article  Google Scholar 

  50. Cheng, H., Wang, J., Shi, X.: Microcalcification Detection using Fuzzy Logic and Scale Space Approaches. Pattern Recognition 37(2), 363–375 (2004)

    Article  MATH  Google Scholar 

  51. De Santo, M., Molinara, M., Tortorella, F., Vento, M.: Automatic Classification of Clustered Microcalcifications by a Multiple Expert System. Pattern Recognition 36(7), 1467–1477 (2003)

    Article  Google Scholar 

  52. Grohman, W., Dhawan, A.: Fuzzy Convex Set-based Pattern Classification for Analysis of Mammographic Microcalcifications. Pattern Recognition 34(7), 1469–1482 (2001)

    Article  MATH  Google Scholar 

  53. Zhang, P., Verma, B., Kumar, K.: Neural vs. Statistical Classifier in Conjunction with Genetic Algorithm Based Feature Selection. Pattern Recognition Letters 26(7), 909–919 (2005)

    Article  Google Scholar 

  54. Wolberg, W., Mangasarian, O.: Multisurface Method of Pattern Separation for Medical Diagnosis Applied to Breast Cytology. Proceedings of National Academy of Science, USA 87, 9193–9196 (1990)

    Article  MATH  Google Scholar 

  55. Mangasarian, O., Setiono, R., Wolberg, W.: Pattern Recognition via Linear Programming: Theory and Application to Medical Diagnosis. In: Large-Scale Num. Opt., pp. 22–31. SIAM, Philadelphia (1990)

    Google Scholar 

  56. Street, W.N., Wolberg, W.H., Mangasarian, O.L.: Nuclear Feature Extraction for Breast Tumor Diagnosis. In: Imaging Science and Technology/Society of Photographic Instrumentation Engineers 1993 International Symposium on Electronic Imaging: Science and Technology, San Jose, California, vol. 1905, pp. 861–870 (1993)

    Google Scholar 

  57. Street, N.: Cancer Diagnosis and Prognosis via Linear-Programming-Based Machine Learning. Ph.D. thesis, University of Wisconsin (1994)

    Google Scholar 

  58. Street, N.: Xcyt: A System for Remote Cytological Diagnosis and Prognosis of Breast Cancer. In: Jain, L. (ed.) Soft Computing Techniques in Breast Cancer Prognosis and Diagnosis, pp. 297–322. World Scientific Publishing, Singapore (2000)

    Chapter  Google Scholar 

  59. Lee, K., Street, W.: A Fast and Robust Approach for Automated Segmentation of Breast Cancer Nuclei. In: Proceedings of the Second IASTED International Conference on Computer Graphics and Imaging, Palm Springs, CA, pp. 42–47 (1999)

    Google Scholar 

  60. Lee, K., Street, W.: Model-based Detection, Segmentation and Classification for Image Analysis using On-line Shape Learning. Machine Vision and Applications 13(4), 222–233 (2003)

    Article  Google Scholar 

  61. Wolberg, W.H., Street, W.N., Mangasarian, O.L.: Breast Cytology Diagnosis Via Digital Image Analysis. Analytical and Quantitative Cytology and Histology 15, 396–404 (1993)

    Google Scholar 

  62. Wolberg, W.H., Street, W.N., Mangasarian, O.L.: Machine Learning Techniques to Diagnose Breast Cancer from Image-Processed Nuclear Features of Fine Needle Aspirates. Cancer Letters 77, 163–171 (1994)

    Article  Google Scholar 

  63. Walker, H.J., Albertelli, L., Titkov, Y., Kaltsatis, P., Seburyano, G.: Evolution of Neural Networks for the Detection of Breast Cancer. In: Proceedings of International Joint Symposia on Intelligence and Systems, pp. 34–40 (1998)

    Google Scholar 

  64. Walker, H.J., Albertelli, L.: Breast Cancer Screening Using Evolved Neural Networks. In: IEEE International Conference on Systems, Man and Cybernetics, vol. 2, pp. 1619–1624 (1998)

    Google Scholar 

  65. Nezafat, R., Tabesh, A., Akhavan, S., Lucas, C., Zia, M.: Feature Selection and Classification for Diagnosing Breast Cancer. In: Proceedings of International Association of Science and Technology for Development International Conference, pp. 310–313 (1998)

    Google Scholar 

  66. Estevez, J., Alayon, S., Moreno, L.: Cytological Breast Cancer Fine Needle Aspirate Images Analysis with a Genetic Fuzzy Finite State Machine. In: Conference Board of the Mathematical Sciences, CBMS 2002, pp. 21–26 (2002)

    Google Scholar 

  67. Bagui, S., Bagui, S., Pal, K., Pal, N.: Breast Cancer Detection using Rank Nearest Neighbor Classification Rules. Pattern Recognition 36(1), 25–34 (2003)

    Article  MATH  Google Scholar 

  68. Weyn, B., van de Wouwer, G., van Daele, A., Scheunders, P., van Dyck, D., van Marck, E., Jackob, W.: Automated Breast Tumor Diagnisis and Grading Based on Wavelet Chromatin Texture Description. Cytometry 33, 32–40 (1998)

    Article  Google Scholar 

  69. Schnorrenberg, F., Pattichis, C., Kyriacou, K., Schizas, C.: Detection of Cell Nuclei in Breast Cancer Biopsies using Receptive Fields. In: IEEE Proceedings of Engineering in Medicine and Biology Society, pp. 649–650 (1994)

    Google Scholar 

  70. Schnorrenberg, F., Pattichis, C., Kyriacou, K., Schizas, C.: Content-based Description of Breast Cancer Biopsy Slides. In: Proc. Intl. EuroPACS Mtg., pp. 136–140 (1996)

    Google Scholar 

  71. Schnorrenberg, F., Pattichis, C., Kyriacou, K., Vassiliou, M., Schizas, C.: Computer-aided Classification of Breast Cancer Nuclei. Technology & Health Care 4(2), 147–161 (1996)

    Google Scholar 

  72. Schnorrenberg, F., Tsapatsoulis, N., Pattichis, C., Schizas, C., Kollias, S., Vassiliou, M., Adamou, A., Kyriacou, K.: A modular neural network system for the analysis of nuclei in histopathological sections. IEEE Engineering in Medicine and Biology Magazine 19, 48–63 (2000)

    Article  Google Scholar 

  73. Belhomme, P., Elmoataz, A., Herlin, P., Bloyet, D.: Generalized region growing operator with optimal scanning: application to segmentation of breast cancer images. Journal of Microscopy 186, 41–50 (1997)

    Article  Google Scholar 

  74. Adams, R., Bischof, L.: Seeded region growing. IEEE Transactions on Pattern Analysis and Machine Intelligence 16, 641–647 (1994)

    Article  Google Scholar 

  75. Beucher, S.: Segmentation d’images et morphologie mathématique. Ph.D. thesis, Ecole National Supérieur des Mines de Paris (1990)

    Google Scholar 

  76. Beucher, S., Meyer, F.: Mathematical Morphology in Image Processing, ch. 12, pp. 433–481. Marcel Dekker, New York (1992)

    Google Scholar 

  77. Lezoray, O., Elmoataz, A., Cardot, H., Gougeon, G., Lecluse, M., Revenu, M.: Segmentation of cytological images using color and mathematical morphology. In: European Conference on Stereology, Amsterdam, Netherlands, p. 52 (1998)

    Google Scholar 

  78. Schüpp, S., Elmoataz, A., Fadili, J., Herlin, P., Bloyet, D.: Image segmentation via multiple active contour models and fuzzy clustering with biomedical applications. In: The 15th International Conference on Pattern Recognition, ICPR 2000, Barcelona, Spain, vol. 1, pp. 622–625 (2000)

    Google Scholar 

  79. Bloom, H., Richardson, W.: Histological Grading and Prognosis in Breast Cancer. British Journal of Cancer 11, 359–377 (1957)

    Article  Google Scholar 

  80. Mangasarian, O., Street, W., Wolberg, W.: Breast Cancer Diagnosis and Prognosis via Linear Programming. Operations Research 43(4), 570–576 (1994)

    Article  MathSciNet  Google Scholar 

  81. Cheng, H., Li, X., Riodan, D. S., Scrimger, J.N.: A Parallel Approach to Tubule Grading in Breast Cancer Lesions and its VLSI Implementation. In: Fourth Annual IEEE Symposium on Computer-Based Medical Systems, pp. 322–329 (1991)

    Google Scholar 

  82. Cheng, H., Wu, C., Hung, D.: VLSI for Moment Computation and its Application to Breast Cancer Detection. Pattern Recognition 31(8), 1391–1406 (1998)

    Article  Google Scholar 

  83. MacAulay, M., Scrimger, J., Riodan, D., Cheng, H.: An Interactive Graphics Package with Standard Examples of the Bloom and Richardson Histological Grading Technique. In: Fourth Annual IEEE Symposium on Computer-Based Medical Systems, pp. 108–112 (1991)

    Google Scholar 

  84. Gurevich, I., Murashov, D.: Method for early diagnostics of lymphatic system tumors on the basis of the analysis of chromatin constitution in cell nucleus images. In: The 17th International Conference on Pattern Recognition, ICPR 2004, Cambridge, UK, pp. 806–809 (2004)

    Google Scholar 

  85. Florack, L., Kuijper, A.: The topological structure of scale–space images. Journal of Mathematical Imaging and Vision 12(1), 65–80 (2000)

    Article  MATH  MathSciNet  Google Scholar 

  86. Rodenacker, K.: Applications of topology for evaluating pictorial structures. In: Theoretical Foundations of Computer Vision, pp. 35–46. Akademie–Verlag, Berlin (1993)

    Google Scholar 

  87. Rodenacker, K.: Quantitative microscope image analysis for improved diagnosis and prognosis of tumors in pathology. In: Creaso Info Medical Imaging, Creaso GmbH, Gilching, vol. 22 (1995)

    Google Scholar 

  88. Rodenacker, K., Bengtsson, E.: A feature set for cytometry on digitized microscopic images. Anal. Cell Pathol. 25(1), 1–36 (2003)

    Google Scholar 

  89. Weyn, B., Van de Wouwer, G., Koprowski, M., et al.: Value of morphometry, texture analysis, densitometry and histometry in the differential diagnosis and prognosis of malignant mesthelioma. Journal of Pathology 4(189), 581–589 (1999)

    Article  Google Scholar 

  90. Young, I., Verbeek, P., Mayall, B.: Characterization of chromatin distribution in cell nuclei. Cytometry 7(5), 467–474 (1986)

    Article  Google Scholar 

  91. Gurevich, I., Kharazishvili, D., Murashov, D., Salvetti, O., Vorobjev, I.: Technology for automated morphologic analysis of cytological slides. methods and results. In: The 18th International Conference on Pattern Recognition (ICPR 2006), Hong Kong, China, pp. 711–714 (2006)

    Google Scholar 

  92. Churakova, Z., Gurevich, I., Jernova, I., et al.: Selection of diagnostically valuable features for morphological analysis of blood cells. Pattern Recognition and Image Analysis: Advances in Mathematical Theory and Applications 13(2), 381–383 (2003)

    Google Scholar 

  93. Qineti, Q.: Automated Histopathology Breast Cancer Analysis and Diagnisis System.Data Sheet (2005), http://www.qinetiq.com/

  94. Jeleń, Ł.: Computerized Cancer Malignancy Grading of Fine Needle Aspirates. Ph.D. thesis, Concordia University (2009)

    Google Scholar 

  95. Naik, S., Doyle, S., Agner, S., Madabhushi, A., Feldman, M., Tomaszewski, J.: Automated gland and nuclei segmentation for grading of prostate and breast cancer histopathology. In: Proceedings of the IEEE International Symposium on Biomedical Imaging, pp. 284–287 (2008)

    Google Scholar 

  96. Jeleń, Ł., Fevens, T., Krzyżak, A.: Classification of breast cancer malignancy using cytological images of fine needle aspiration biopsies. Int. J. Math. Comput. Sci. 18, 75–83 (2008)

    Article  Google Scholar 

  97. Jeleń, Ł., Krzyżak, A., Fevens, T.: Comparison of pleomorphic and structural features used for breast cancer malignancy classification. In: Bergler, S. (ed.) Canadian AI. LNCS (LNAI), vol. 5032, pp. 138–149. Springer, Heidelberg (2008)

    Chapter  Google Scholar 

  98. Jeleń, Ł., Fevens, T., Krzyżak, A., Jeleń, M.: Discriminatory power of cells grouping features for breast cancer malignancy classification. In: Proceedings of the International Federation for Medical and Biological Engineering, vol. 21(3), pp. 559–562. Springer, Heidelberg (2008)

    Google Scholar 

  99. Jeleń, Ł., Fevens, T., Krzyżak, A.: Influence of nuclei segmentation on breast cancer malignancy classification. In: Proceedings of SPIE, vol. 7260, pp. 726014–726014–9 (2009)

    Google Scholar 

  100. Jeleń, Ł., Lipiński, A., Detyna, J., Jeleń, M.: Clinical verification of computerized breast cancer malignancy grading. Bio–Algorithms and Med–Systems 6(12), suppl. 1, 81–82 (2010)

    Google Scholar 

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Authors and Affiliations

  1. Department of Computer Science and Software Engineering, Concordia University, 1455 De Maisonneuve Blvd. West, Montréal, Québec, Canada, H3G 1M8

    Adam Krzyżak, Thomas Fevens & Mehdi Habibzadeh

  2. Faculty of Life Science and Technology, Wrocław University of Environmental and Life Science, Norwida 2527, 50-375, Wrocław, Poland

    Łukasz Jeleń

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  1. Adam Krzyżak
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  2. Thomas Fevens
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  3. Mehdi Habibzadeh
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  4. Łukasz Jeleń
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  1. Department of Systems and Computer Networks, Wroclaw University of Technology, Wybrzeze Wyspianskiego 27, 50-370, Wroclaw, Poland

    Robert Burduk , Marek Kurzyński  & Andrzej Żołnierek ,  & 

  2. Department of Systems and Computer Networks, Faculty of Electronics, Wroclaw University of Technology, Wybrzeze Wyspianskiego 27, 50-370, Wroclaw, Poland

    Michał Woźniak

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Krzyżak, A., Fevens, T., Habibzadeh, M., Jeleń, Ł. (2011). Application of Pattern Recognition Techniques for the Analysis of Histopathological Images. In: Burduk, R., Kurzyński, M., Woźniak, M., Żołnierek, A. (eds) Computer Recognition Systems 4. Advances in Intelligent and Soft Computing, vol 95. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-20320-6_65

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