Multi-resolution graph-based analysis of histopathological whole slide images: Application to mitotic cell extraction and visualization

Computerized Medical Imaging and Graphics

Volumn 35, Issue 7-8, 2011, Pages 603-615

  Roullier, Vincent ^a   Lézoray, Olivier ^a   Ta, Vinh Thong ^b   Elmoataz, Abderrahim ^a  

^a UNIVERSITÉ DE CAEN   (France)

^b UNIVERSITÉ DE BORDEAUX   (France)

Author keywords

Breast cancer grading; Histopathology; Multi resolution analysis; Whole slide image

Indexed keywords

BREAST CANCER; BREAST CANCER GRADING; COARSER RESOLUTION; DOMAIN-SPECIFIC KNOWLEDGE; FOCUS OF ATTENTION; GRAPH-BASED; HISTOPATHOLOGY; MITOTIC CELLS; MULTI-RESOLUTION APPROACH; MULTI-RESOLUTIONS; RESOLUTION LEVEL; WHOLE SLIDE IMAGE;

DISEASES; VISUALIZATION;

IMAGE SEGMENTATION;

ARTICLE; BREAST CANCER; CANCER GRADING; HISTOPATHOLOGY; HUMAN; IMAGE ANALYSIS; MICROSCOPE; MITOSIS; MULTI RESOLUTION ANALYSIS; PATHOLOGIST; PRIORITY JOURNAL; SCORING SYSTEM; WHOLE SLIDE IMAGE;

ALGORITHMS; BREAST NEOPLASMS; DIAGNOSTIC IMAGING; FEMALE; HUMANS; IMAGE PROCESSING, COMPUTER-ASSISTED; MITOSIS; NEOPLASM GRADING; PATTERN RECOGNITION, AUTOMATED;

EID: 80052317448     PISSN: 08956111     EISSN: 18790771     Source Type: Journal    
DOI: 10.1016/j.compmedimag.2011.02.005     Document Type: Article

References (32)

1. Tavassoli F., Devilee P. Pathology and genetics tumours of the breast and female genital organ 2003, International Agency for Research on Cancer Press.
2. Gillett CE. Assessment of cellular proliferation by calculation of mitotic index and by immunohistochemistry. In: Metastasis research protocols, volume 57 of methods in molecular medicine. Humana Press; 2001. p. 123-31.
3. Demir C, Yener B. Automated cancer diagnosis based on histopathological images: a systematic survey. Technical Report, Rensselaer Polytechnic Institute; 2005.
4. Petushi S., Garcia F., Haber M., Katsinis C., Tozeren A. Large-scale computations on histology images reveal grade-differentiating parameters for breast cancer. BMC Medical Imaging 2006, 6:1-11.
5. Naik S, Doyle S, Agner S, Madabhushi A, Feldman MD, Tomaszewski J. Automated gland and nuclei segmentation for grading of prostate and breast cancer histopathology. In: International symposium on biomedical imaging; 2008. p. 284-7.
6. Doyle S, Agner S, Madabhushi A, Feldman MD, Tomaszewski J. Automated grading of breast cancer histopathology using spectral clustering with textural and architectural image features. In: International symposium on biomedical imaging; 2008. p. 496-9.
7. Ruiz A, Kong J, Ujaldon M, Boyer K, Saltz J, Gurcan M. Pathological image segmentation for neuroblasma using the gpu. In: International symposium on biomedical imaging; 2008. p. 296-9.
8. Doyle S, Madabhushi A, Feldman M, Tomaszeweski J. A boosting cascade for automated detection of prostate cancer from digitized histology. In: International conference on medical image computing and computer assisted intervention; 2006. p. 504-11.
9. Fuchs TJ, Wild PJ, Moch H, Buhmann JM. Computational pathology analysis of tissue microarrays predicts survival of renal clear cell carcinoma patients. In: International conference on medical image computing and computer assisted intervention, vol. 2; 2008. p. 1-8.
10. Signolle N, Plancoulaine B, Herlin P, Revenu M. Texture-based multiscale segmentation: Application to stromal compartment characterization on ovarian carcinoma virtual slides. In: International conference on image and signal processing, vol. LNCS 5099; 2008. p. 173-82.
11. Elston C.W., Ellis I.O. Pathological prognostic factors in breast cancer. I. The value of histological grade in breast cancer: experience from a large study with long-term follow-up. Histopathology 1991, 19:403-410.
12. Dalle J, Leow W, Racoceanu D, Tutac A, Putti T. Automatic breast cancer grading of histopathological images. In: International conference of the IEEE engineering in medicine and biology society; 2008. p. 3052-5.
13. Kong J., Sertel O., Shimada H., Boyer K., Saltz J., Gurcan M. Computer-aided evaluation of neuroblastoma on whole-slide histology images: classifying grade of neuroblastic differentiation. Pattern Recognition 2009, 42:1080-1092.
14. Elmoataz A., Lézoray O., Bougleux S. Nonlocal discrete regularization an weighted graphs: a framework for image and manifolds processing. IEEE transactions on Image Processing 2008, 17:1047-1060.
15. Ta V-T., Lézoray O., Elmoataz A., Schüpp S. Graph-based tools for microscopic cellular image segmentation. Pattern Recognition 2009, 42:1113-1125.
16. Diestel R. Graph theory volume 173 of graduate texts in mathematics 2005, Springer-Verlag.
17. Chung F.R. Spectral graph theory. CBMS Regional Conference Series in Mathematics 1997, 92:1-212.
18. Chan T., Osher S., Shen J. The digital TV filter and nonlinear denoising. IEEE Transactions on Image Processing 2001, 10:231-241.
19. Wang F., Wang J., Zhang C., Shen H.C. Semi-supervised classification using linear neighborhood propagation. International Conference on Computer Vision and Pattern Recognition 2006, 1:160-167.
20. Grady L. Random walks for image segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence 2006, 28:1768-1783.
21. Sinop AK, Grady L. A seeded image segmentation framework unifying graph cuts and random walker which yields a new algorithm. In: International conference on computer vision; 2007. p. 1-8.
22. Zhou D, Schölkopf B. Regularization on discrete spaces. In: German association for pattern recognition DAGM, vol. LNCS 3663; 2005. p. 361-8.
23. Belkin M., Niyogi P., Sindhwani V., Bartlett P. Manifold regularization: a geometric framework for learning from labeled and unlabeled examples. Journal of Machine Learning Research 2006, 7:2399-2434.
24. Roullier V, Ta V-T, Lézoray O, Elmoataz A. Graph-based multi-resolution segmentation of histological whole slide images. In: International symposium on biomedical imaging; 2010. p. 153-6.
25. Kanungo T., Mount D., Netanyahu N., Piatko C., Silverman R., Wu A. An efficient k-means clustering algorithm: Analysis and implementation. IEEE Transactions on Pattern Analysis and Machine Intelligence 2002, 24:881-892.
26. Elie N., Plancoulaine B., Signolle J-P., Herlin P. A simple way of quantifying immunostained cell nuclei on the whole histologic section. Cytometry Part A 2003, 56A:37-45.
27. Ojala T., Pietikäinen M., Harwood D. A comparative study of texture measures with classification based on feature distributions. Pattern Recognition 1996, 29:51-59.
28. Ojala T., Pietikäinen M., Maenpaa T. Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Transactions on Pattern Analysis and Machine Intelligence 2002, 24:971-987.
29. Belkin M., Niyogi P. Laplacian eigenmaps for dimensionality reduction and data representation. Neural Computation 2003, 15:1373-1396.
30. Shi J., Malik J. Normalized cuts and image segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence 1997, 22:888-905.
31. Howard C, Reed M. Unbiased stereology three-dimensional measurement in microscopy, 2nd ed. QTP Publications; 2010.
32. Herlin P, Lay B, Bouchard H, Pezeril H, Lange H, Plancoulaine B. Benefits of stereology for quality control of fully automated virtual slide analysis. In: 10th European congress on telepathology and 4th international congress on virtual microscopy; 2010.