Computer-aided gleason grading of prostate cancer histopathological images using texton forests

2010 7th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, ISBI 2010 - Proceedings

Volumn , Issue , 2010, Pages 636-639

  Khurd, Parmeshwar ^a   Bahlmann, Claus ^a   Maday, Peter ^a   Kamen, Ali ^a   Gibbs Strauss, Summer ^b   Genega, Elizabeth M ^b   Frangioni, John V ^b  

^a SIEMENS CORPORATE RESEARCH   (United States)

^b BETH ISRAEL DEACONESS MEDICAL CENTER   (United States)

Author keywords

Gleason grading; Prostate cancer; Texture classification

Indexed keywords

ERROR-PRONE PROCESS; FILTER RESPONSE; GLEASON GRADING; GLEASON SCORES; GOLD STANDARDS; HISTOPATHOLOGICAL IMAGES; PROGNOSTIC INDICATORS; PROSTATE CANCERS; PROSTATE TISSUE; RANDOM FORESTS; SVM CLASSIFIERS; TEXTONS; TEXTURE CLASSIFICATION; TEXTURE ELEMENTS; TREATMENT PLANNING; TUMOR GRADES;

DECISION TREES; GRADING; IMAGE PROCESSING; MACROS; MEDICAL IMAGING;

TEXTURES;

EID: 77955217140     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ISBI.2010.5490096     Document Type: Conference Paper

References (14)

1. T. Franiel, L. Ludemann, B. Rudolph, H. Rehbein, C. Stephan, M. Taupitz and D. Beyersdorff, "Prostate MR imaging: Tissue characterization with pharmacokinetic volume and blood flow parameters and correlation with histologic parameters," Radiology, vol. 252, no. 1, pp. 101-108, 2009.
2. Y. Ou, D. Shen, J. Zeng, L. Sun, J. Moul and C. Davatzikos, "Sampling the spatial patterns of cancer: Optimized biopsy procedures for estimating prostate cancer volume and Gleason score," Medical Image Analysis, vol. 28, no. 7, pp. 1037-1050, 2009.
3. M. Varma and A. Zisserman, "Classifying images of materials: Achieving viewpoint and illumination independence," in ECCV (3), 2002, pp. 255-271.
4. Y. Freund, S. Dasgupta, M. Kabra, and N. Verma, "Learning the structure of manifolds using random projections," in NIPS, pp. 255-271, 2007.
5. F. Moosmann, B. Triggs, and F. Jurie, "Fast discriminative visual codebooks using randomized clustering forests," in NIPS, 2006, pp. 985-992.
6. J. Shotton, M. Johnson, and R. Cipolla, "Semantic texton forests for image categorization and segmentation," in CVPR, 2008.
7. S. Lazebnik, C. Schmid, and J. Ponce, "Beyond bags of features: Spatial pyramid matching for recognizing natural scene categories," in CVPR (2), 2006, pp. 2169-2178.
8. L. Yang, W. Chen, P. Meer, G. Salaru, L.A. Goodell, V. Berstis, and D.J. Foran, "Virtual microscopy and grid-enabled decision support for large-scale analysis of imaged pathology specimens," Information Technology in Biomedicine, IEEE Transactions on, vol. 13, no. 4, pp. 636-644, 2009.
9. S. Doyle, M. Hwang, K. Shah, A. Madabhushi, M. Feldman and J. Tomaszeweski, "Automated grading of prostate cancer using architectural and textural image features," in ISBI, 2007, pp. 1284-1287.
10. S. Naik, S. Doyle, S. Agner, A. Madabhushi, M. Feldman and J. Tomaszewski, "Automated gland and nuclei segmentation for grading of prostate and breast cancer histopathology," in ISBI, 2008, pp. 284-287.
11. P-W. Huang and C-H. Lee, "Automatic classification for pathological prostate images based on fractal analysis," Medical Imaging, IEEE Transactions on, vol. 28, no. 7, pp. 1037-1050, 2009.
12. A. Tabesh, M. Teverovskiy, H-Y. Pang, V.P. Kumar, D. Verbel, A. Kotsianti, and O. Saidi, "Multifeature prostate cancer diagnosis and Gleason grading of histological images," Medical Imaging, IEEE Transactions on, vol. 26, no. 10, pp. 1366 - 1378, 2007.
13. L. Yang, R. Jin and R. Sukthankar, "Unifying discriminative visual codebook generation with classifier training for object category recognition," in CVPR, 2008.
14. C-C. Chang and C-J. Lin, LIBSVM: a library for support vector machines, 2001.