Hierarchical normalized cuts: Unsupervised segmentation of vascular biomarkers from ovarian cancer tissue microarrays

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 5761 LNCS, Issue PART 1, 2009, Pages 230-238

  Janowczyk, Andrew ^a   Chandran, Sharat ^a   Singh, Rajendra ^b   Sasaroli, Dimitra ^c   Coukos, George ^c   Feldman, Michael D ^c   Madabhushi, Anant ^d  

^a INDIAN INSTITUTE OF TECHNOLOGY BOMBAY   (India)

^b QUEST DIAGNOSTICS NICHOLS INSTITUTE   (United States)

^c UNIVERSITY OF PENNSYLVANIA   (United States)

^d RUTGERS UNIVERSITY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

AUTOMATED SEGMENTATION; COLOR RESOLUTION; FALSE POSITIVE; IMAGE-BASED; INPUT IMAGE; MEAN SHIFT; NORMALIZED CUTS; OBJECT CLASS; OVARIAN CANCERS; OVARIAN TISSUE; POSITIVE PREDICTIVE VALUES; PROGNOSIS PREDICTION; QUALITATIVE ANALYSIS; TARGET CLASS; UNSUPERVISED SEGMENTATION;

DATA STRUCTURES; LEARNING ALGORITHMS; MEDICAL COMPUTING; SUPERVISED LEARNING;

IMAGE SEGMENTATION;

ANGIOGENIC PROTEIN; ESM1 PROTEIN, HUMAN; PROTEOGLYCAN; TUMOR MARKER; TUMOR PROTEIN;

ALGORITHM; ARTICLE; ARTIFICIAL INTELLIGENCE; AUTOMATED PATTERN RECOGNITION; BIOPSY; COMPUTER ASSISTED DIAGNOSIS; FEMALE; GENE EXPRESSION PROFILING; HUMAN; IMAGE ENHANCEMENT; METABOLISM; METHODOLOGY; MICROSCOPY; OVARY TUMOR; REPRODUCIBILITY; SENSITIVITY AND SPECIFICITY; TISSUE MICROARRAY;

ALGORITHMS; ANGIOGENIC PROTEINS; ARTIFICIAL INTELLIGENCE; BIOPSY; DIAGNOSIS, COMPUTER-ASSISTED; FEMALE; GENE EXPRESSION PROFILING; HUMANS; IMAGE ENHANCEMENT; IMAGE INTERPRETATION, COMPUTER-ASSISTED; MICROSCOPY; NEOPLASM PROTEINS; OVARIAN NEOPLASMS; PATTERN RECOGNITION, AUTOMATED; PROTEOGLYCANS; REPRODUCIBILITY OF RESULTS; SENSITIVITY AND SPECIFICITY; TISSUE ARRAY ANALYSIS; TUMOR MARKERS, BIOLOGICAL;

EID: 79955763075     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-642-04268-3_29     Document Type: Conference Paper

References (10)

1. Doyle, S., Madabhushi, A., Feldman, M.D., Tomaszeweski, J.E.: A boosting cascade for automated detection of prostate cancer from digitized histology. In: Larsen, R., Nielsen, M., Sporring, J. (eds.) MICCAI 2006. LNCS, vol. 4191, pp. 504-511. Springer, Heidelberg (2006)
2. Buckanovich, R., Sasaroli, D., et al.: Tumor vascular proteins as biomarkers in ovarian cancer. Journal Of Clinical Oncology, 852-861 (March 2007) (Pubitemid 350002886)
3. Tu, Z.: Probabilistic boosting-tree: Learning discriminative models for classification, recognition, and clustering. In: ICCV, pp. 1589-1596 (2005)
4. Fukunaga, K., Hostetler, L.: The estimation of the gradient of a density function, with applications in pattern recognition. IEEE Trans. Information Theory 21(1), 32-40 (1975)
5. Comaniciu, D., Meer, P.: Mean shift: a robust approach toward feature space analysis. IEEE Trans. PAMI 24(5), 603-619 (2002) (Pubitemid 34550429)
6. Yang, C., Duraiswami, R., Gumerov, N., Davis, L.: Improved Fast Gauss Transform and efficient kernel density estimation. In: ICCV, pp. 664-671 (2003)
7. Wu, Z., Leahy, R.: An optimal graph theoretic approach to data clustering: theory and its application to image segmentation. IEEE Trans. PAMI 15(11), 1101-1113 (1993)
8. Shi, J., Malik, J.: Normalized cuts and image segmentation. IEEE Trans. PAMI 22(8), 888-905 (2000)
9. Dhillon, I.S., Guan, Y., Kulis, B.: Weighted graph cuts without eigenvectors a multilevel approach. IEEE Trans. PAMI 29(11), 1944-1957 (2007)
10. Silverman, B.: Density Estimation for Statistics and Data Analysis. Chapman and Hall, Boca Raton (1986)