Graph walks for classification of histopathological images

Proceedings - International Symposium on Biomedical Imaging

Volumn , Issue , 2013, Pages 1126-1129

  Olgun, Gulden ^a   Sokmensuer, Cenk ^b   Gunduz Demir, Cigdem ^a  

^a BILKENT UNIVERSITY   (Turkey)

^b HACETTEPE UNIVERSITY   (Turkey)

Author keywords

automated cancer diagnosis; graph walks; Graphs; histopathological image analysis; subgraphs

Indexed keywords

AUTOMATED CANCER DIAGNOSIS; GRAPH WALKS; GRAPHS; HISTOPATHOLOGICAL IMAGE ANALYSIS; SUBGRAPHS;

MEDICAL IMAGING; TISSUE; TISSUE ENGINEERING;

IMAGE CLASSIFICATION;

EID: 84881658333     PISSN: 19457928     EISSN: 19458452     Source Type: Conference Proceeding    
DOI: 10.1109/ISBI.2013.6556677     Document Type: Conference Paper

References (12)

1. A. Tabesh et al., "Multifeature prostate cancer diagnosis and Gleason grading of histological images," IEEE Trans. Med. Imaging, vol. 26, no. 10, pp. 1366-1378, 2007. (Pubitemid 47525298)
2. S. Doyle et al., "A boosted Bayesian multi-resolution classifier for prostate cancer detection from digitized needle biopsies", IEEE Trans. Biomed. Eng., vol. 59, no. 5, pp. 1205-1218, 2012.
3. K. Jafari-Khouzani and H. Soltanian-Zadeh, "Multiwavelet grading of pathological images of prostate," IEEE T. Biomed. Eng., vol. 50, no. 6, pp. 697-704, 2003. (Pubitemid 36723603)
4. O. Sertel et al., "Computer-aided prognosis of neuroblastoma on whole slide images: classification of stromal development," Pattern Recognit., vol. 42, no. 6, pp. 1093-1103, 2009.
5. P.-W. Huang and C.-H. Lee, "Automatic classification for pathological prostate images based on fractal analysis," IEEE Trans. Med. Imaging, vol. 28, no. 7, pp. 1037-1050, 2009.
6. A. N. Basavanhally et al., "Computerized image-based detection and grading of lymphocytic infiltration in HER2+ breast cancer histopathology," IEEE Trans. Biomed. Eng., vol. 57, no. 3, pp. 642-653, 2010.
7. S. Doyle et al., "Automated grading of breast cancer histopathology using spectral clustering with textural and architectural image features," Proc. Int. Symp. on Biomedical Imaging: From Nano to Macro, 2008.
8. C. Demir et al., "Learning the topological properties of brain tumors," IEEE ACM T. Comput. Bi., vol. 2, no. 3, pp. 262-270, 2005.
9. D. Altunbay et al., "Color graphs for automated cancer diagnosis and grading", IEEE Trans. Biomed. Eng., vol. 57, no. 3, pp. 665-674, 2010.
10. A. C. Ruifrok and D. A. Johnston, "Quantification of histochemical staining by color deconvolution," Anal. Quant. Cytol. Histol., vol. 23, pp. 291-299, 2001. Source code is available at http://www.dentistry.bham.ac.uk/ landinig/software/cdeconv/cdeconv.html. (Pubitemid 32762920)
11. A.B. Tosun et al., "Object-oriented texture analysis for the unsupervised segmentation of biopsy images for cancer detection," Pattern Recognit., vol. 42, no. 6, pp. 1104-1112, 2009.
12. E. Ozdemir et al.,"A resampling-based Markovian model for automated colon cancer diagnosis", IEEE Trans. Biomed. Eng., vol. 59, no. 1, pp. 281-289, 2012.