Time-efficient sparse analysis of histopathological whole slide images

Computerized Medical Imaging and Graphics

Volumn 35, Issue 7-8, 2011, Pages 579-591

  Huang, Chao Hui ^b,c   Veillard, Antoine ^b,c   Roux, Ludovic ^b,e   Loménie, Nicolas ^a,b   Racoceanu, Daniel ^a,b,c,d  

^a CNRS   (France)

^b French National Center for Scientific Research   (Singapore)

^c NATIONAL UNIVERSITY OF SINGAPORE   (Singapore)

^d UNIVERSITÉ DE FRANCHE COMTÉ   (France)

^e UNIV GRENOBLE ALPES   (France)

Author keywords

Breast cancer grading; Digitized histopathology; Dynamic sampling; Graphics processing unit; Multi scale analysis; Virtual microscopy; Whole slide image

Indexed keywords

BREAST CANCER GRADING; DIGITIZED HISTOPATHOLOGY; DYNAMIC SAMPLING; GRAPHICS PROCESSING UNIT; MULTI SCALE ANALYSIS; VIRTUAL MICROSCOPY; WHOLE SLIDE IMAGE;

ALGORITHMS; BIOPSY; COMPUTER GRAPHICS EQUIPMENT; COMPUTER VISION; DISEASES; PROGRAM PROCESSORS; VIRTUAL REALITY;

COMPUTER AIDED ANALYSIS;

ARTICLE; BREAST BIOPSY; BREAST CANCER; CANCER DIAGNOSIS; COMPUTER ASSISTED DIAGNOSIS; DIGITIZED HISTOPATHOLOGY; HISTOPATHOLOGY; HUMAN; IMAGE ANALYSIS; IMAGE PROCESSING; MEDICAL TECHNOLOGY; MICROSCOPY; PATHOLOGIST; PRIORITY JOURNAL; SAMPLING; TIME; VIRTUAL MICROSCOPY; VIRTUAL REALITY; WHOLE SLIDE IMAGE;

ALGORITHMS; DIAGNOSTIC IMAGING; HUMANS; IMAGE INTERPRETATION, COMPUTER-ASSISTED; MICROSCOPY; PATTERN RECOGNITION, AUTOMATED; SOFTWARE; TIME FACTORS; USER-COMPUTER INTERFACE;

EID: 80052312476     PISSN: 08956111     EISSN: 18790771     Source Type: Journal    
DOI: 10.1016/j.compmedimag.2010.11.009     Document Type: Article

References (53)

1. Petersen I., Kotb W., Friedrich K.H., Schlüns K., Böcking A., Dietel M. Core classification of lung cancer: correlating nuclear size and mitoses with ploidy and clinicopathological parameters. Lung Cancer 2009, 65(3):312-318.
2. Ming T. The challenges and pitfalls of biomedical imaging-perspectives of a pathologist. First Singaporean-French biomedical imaging workshop 2006.
3. Gurcan M.N., Boucheron L., Can A., Madabhushi A., Rajpoot N., Yener B. Histopathological image analysis: a review. IEEE Reviews in Biomedical Engineering 2009, 2:147-171.
4. Sertel O., Kong J., Shimada H., Catalyurek U.V., Saltz J.H., Gurcan M.N. Computer-aided prognosis of neuroblastoma on whole-slide images: classification of stromal development. Pattern Recognition 2009, 42(6):1093-1103.
5. Kong J., Sertel O., Shimada H., Boyer K.L., Saltz J.H., Gurcan M.N. Computer-aided evaluation of neuroblastoma on whole-slide histology images: classifying grade of neuroblastic differentiation. Pattern Recognition 2009, 42(6):1080-1092.
6. Estévez J., Alayón S., Moreno L., Aguilar R., Sigut J. Cytological breast fine needle aspirate images analysis with a genetic fuzzy finite state machine. Proceedings of the 15th IEEE symposium on computer-based medical systems 2002, 21-26.
7. Schnorrenberg F. Comparison of manual and computer-aided breast cancer biopsy grading. Proceedings of the 18th IEEE international conference of engineering in medicine and biology society 1996, 1166-1167.
8. Schnorrenberg F., Pattichis C., Kyriacou K., Schizas V.M., Schizas C. Computer-aided classification of breast cancer nuclei. Technology and Health Care 1996, 4:147-161.
9. Petushi S., Garcia F.U., Haber M., Katsinis C., Tozeren A. Large-scale computations on histology images reveal grade-differentiating parameters for breast cancer. BMC Medical Imaging 2006, 6:1-14.
10. Petushi S., Katsinis C., Coward C., Garcia F., Tozeren A. Automated identification of microstructures on histology slides. Proceedings of the IEEE international symposium on biomedical imaging 2004, 424-427.
11. Doyle S., Agner S., Madabhushi A., Feldman M., Tomaszewski J. Automated grading of breast cancer histopathology using spectral clustering with textural and architectural image features. Proceedings of the 5th IEEE international symposium on biomedical imaging: from nano to macro 2008, 496-499.
12. Doyle S., Hwang M., Shah K., Adabhushi A., Feldman M., Tomaszeweski J. Automated grading of prostate cancer using architectural and textural image features. Proceedings of the 5th IEEE international symposium on biomedical imaging: from nano to macro 2007, 1284-1287.
13. Tutac A.E., Racoceanu D., Putti T., Xiong W., Leow W.K., Cretu V. Knowledge-guided semantic indexing of breast cancer histopathology images. Proceedings of biomedical engineering and informatics: new development and the future 2008, 107-112. Y. Peng, Y. Zhang (Eds.).
14. Tavassoli F.A., Devilee P. World Health Organization classification of tumours. Pathology & genetics, tumours of the breast and female genital organs 2003, IARC Press, International Agency for Research on Cancer.
15. Lomenie N., Racoceanu D. Spatial relationships over sparse representations. IEEE Image and Vision Computing New Zealand, Wellington 2009, pp. 226-230.
16. Dalle J.R., Leow W.K., Racoceanu D., Tutac A.E., Putti T.C. Automatic breast cancer grading of histopathological images. Proceedings of the 30th IEEE international conference of engineering in medicine and biology society 2008, 3052-3055.
17. Ta V.T., Lezoray O., Elmoataz A., Schupp S. Graph-based tools for microscopic cellular image segmentation. Pattern Recognition 2009, 42(6):1113-1125.
18. Signolle N., Plancoulaine B., Herlin P., Revenu M. Texture-based multiscale segmentation: application to stromal compartment characterization on ovarian carcinoma virtual slides. Proceedings of the international conference on image and signal processing 2008, 173-182.
19. Oger M., Belhomme P., Klossa J., Michels J.J., Elmoataz A. Automated region of interest retrieval and classification using spectral analysis. New trends in digital pathology: proceedings of the 9th European congress on telepathology and 3rd international congress on virtual microscopy 2008.
20. Miikkulainen R., Bednar J.A., Choe Y., Sirosh J. Computational maps in the visual cortex 2005, Springer, Berlin.
21. Hubel D.H. Eye, brain, and vision 1988, W.H. Freeman.
22. Shevelev I.A. Second-order features extraction in the cat visual cortex: selective and invariant sensitivity of neurons to the shape and orientation of crosses and corners. Biosystems 1998, 48(1):195-204.
23. Pack C., Conway B.R., Born R.T., Livingstone M.S. Spatiotemporal structure of nonlinear subunits in macaque visual cortex. The Journal of Neuroscience 2006, 26(3):893-907.
24. Zhang X. S-CIELAB: a spatial extension to the CIE L*a*b* DeltaE Color Difference Metric. http://white.stanford.edu/brian/scielab/.
25. CIE, An analytical model for describing the influence of lighting parameters upon visual performance. vol. 1, Technical Foundations, CIE 19/2.1. Technical Committee 3.1 1; 1981.
26. Huang C.H., Lin C.T. Bio-inspired computer fovea model based on hexagonal-type cellular neural network. IEEE Transactions on Circuits and Systems-I 2007, 54(1):35-47.
27. Hoang M.A., Geusebroek J.M., Smeulders A.W.M. Color texture measurement and segmentation. Signal Processing: Special Issue on Content Based Image and Video Retrieval 2005, 85(2):265-275.
28. Geusebroek J.M., Boomgaard R., Smeulders A.W.M., Geerts H. Color invariance. IEEE Transactions on Pattern Analysis and Machine Intelligence 2001, 23(12):1338-1350.
29. Geusebroek J.M., Boomgaard R., Smeulders A.W.M. evers T. Color constancy from physical principles. Pattern Recognition Letters: Special Issue on Colour Image Processing and Analysis 2003, 24(11):1653-1662.
30. Faugeras O.D. Digital color image processing within the framework of a human visual model. IEEE Transactions on Acoustics, Speech, and Signal Processing 1979, 27(4):380-393.
31. Kumar N.S.L.P., Satoor S., Buck I. Fast parallel expectation maximization for Gaussian mixture models on GPUs using CUDA. Proceedings of the 11th IEEE international conference on high performance computing and communications 2009, 103-109.
32. Larsson J., Landy M.S., Heeger D.J. Orientation-selective adaptation to first- and second-order patterns in human visual cortex. Journal of Neurophysiology 2006, 95862-95881.
33. Frey H.P., König P., Einhäuser W. The role of first- and second-order stimulus features for human overt attention. Perception & Psychophysics 2007, 69(2):153-161.
34. Olshausen B.A., Field D.J. Emergence of simple-cell receptive field properties by learning a sparse code for nature images. Nature 1996, 381(13):607-609.
35. Lee H., Battle A., Raina R., Ng A.Y. Efficient sparse coding algorithms. Natural information processing systems conference 2007, 801-808.
36. Carpenter A. cuSVM: a CUDA implementation of support vector classification and regression. http://patternsonascreen.net/cuSVMDesc.pdf; 2009.
37. Karklin Y. Hierarchical statistical models of computation in the visual cortex 2007, Carnegie Mellon University.
38. Willmore B., Tolhurst D.J. Characterizing the sparseness of neural codes. Network: Computational Neural Systems 2001, 12:255-270.
39. Karklin Y., Lewicki M.S. Emergence of complex cell properties by learning to generalize in natural scenes. Nature 2009, 457:83-86.
40. Hyvärinen A., Hoyer P.O. A two-layer sparse coding model learns simple and complex cell receptive fields and topography from natural images. Vision Research 2001, 41(18):2413-2423.
41. Madhavan A. Using GPUs to speedup sparse coding algorithms applied to self-taught learning problems 2008, Stanford University.
42. Catanzaro BC, Sundaram N, Keutzer K. Fast support vector machine training and classification on graphics processors. University of California at Berkeley; 2008.
43. Frkovic-Grazio S., Bracko M. Long term prognostic value of Nottingham histological grade and its components in early (pT1N0M0) breast carcinoma. Journal of Clinical Pathology 2002, 55(2):88-92.
44. Cosatto E., Miller M., Graf H.P., Meyer J.S. Grading nuclear pleomorphism in histopathological micrographs. Proceedings of the international conference on pattern recognition 2008.
45. Hatanaka Y., Hashizume K., Kamihara Y., Itoh H., Tsuda H., Osamura R.Y., Tani Y. Quantitative immunohistochemical evaluation of HER2/neu expression with HercepTest in breast carcinoma by image analysis. Pathology international 2001, 51(1):33-36.
46. Jadhav A.S., Banarjee P., Chaudhuri K., Chatterjee J. Quantitative analysis of histopathological features of precancerous lesion and condition using image processing techniques. Proceedings of the 19th IEEE international symposium on computer-based medical systems 2006, 231-236.
47. Masmoudi H., Hewitt S.M., Petrick N., Myers K.J., Gavrielides M.A. Automated quantitative assessment of HER-2/neu immunohistochemical expression in breast cancer. IEEE Transactions on Medical Imaging 2009, 28(6):916-925.
48. Matkowskyj K.A., Schonfeld D., Benya R.V. Quantitative immunochemistry by measuring cumulative signal strength using commercially available software Photoshop and Matlab. Journal of Histochemistry and Cytochemistry 2008, 48(2):303-311.
49. Soltanian-Zadeh H., Jafari-Khouzani K. Multiwavelet grading of prostate pathological images. IEEE Transactions on Biomedical Engineering 2003, 50:697-704.
50. Xiong W., Ong S.H., Lim J.H., Foong K., Liu J., Racoceanu D., Chong A.G.L., Tan K.S.W. Automatic area classification in peripheral blood smears. IEEE Transactions on Biomedical Engineering 2010, 57(8):1982-1990.
51. Dalle J.R., Li H., Huang C.H., Leow W.K., Racoceanu D., Putti T.C. Nuclear pleomorphism scoring by selective cell nuclei detection. IEEE workshop on applications of computer vision. Snowbird, UT, USA 2009.
52. Veillard A., Loménie N., Racoceanu D. An exploration scheme for large images: application to breast cancer grading. Proceedings of the 20th international conference on pattern recognition 2010.
53. Ohya T., Miri M., Murota K. Improvements of the incremental method for the Voronoi diagram with computational comparison of various algorithms. Journal of the Operational Research Society of Japan 1984, 27:306-336.