Automated morphological classification of lung cancer subtypes using H&E tissue images

Machine Vision and Applications

Volumn 24, Issue 7, 2013, Pages 1383-1391

  Wang, Ching Wei ^a   Yu, Cheng Ping ^b,c  

^a NATIONAL TAIWAN UNIVERSITY OF SCIENCE AND TECHNOLOGY   (Taiwan)

^b TRI SERVICE GENERAL HOSPITAL   (Taiwan)

^c NATIONAL DEFENSE MEDICAL CENTER   (Taiwan)

Author keywords

Adenocarcinoma; Computer vision; H E; Morphological classification; Squamous carcinoma; Tissue microarray

Indexed keywords

ADAPTIVE BOOSTING; BIOLOGICAL ORGANS; COMPUTER AIDED DIAGNOSIS; COMPUTER VISION; DECISION TREES; DISEASES; IMAGE CLASSIFICATION; LEARNING SYSTEMS; MEDICAL IMAGING; PATIENT TREATMENT; SUPPORT VECTOR MACHINES; TEXTURES; TREES (MATHEMATICS);

ADENOCARCINOMA; COMPUTER VISION SYSTEM; DIAGNOSIS OF LUNG CANCER; MACHINE LEARNING METHODS; MORPHOLOGICAL CLASSIFICATIONS; NON SMALL CELL LUNG CANCER; SQUAMOUS CARCINOMA; TISSUE MICROARRAYS;

TISSUE;

EID: 84885322166     PISSN: 09328092     EISSN: 14321769     Source Type: Journal    
DOI: 10.1007/s00138-012-0457-x     Document Type: Conference Paper

References (24)

1. Argiris, A.; Gadgeel, S.M.; Dacic, S.: Subdividing nsclc: Reflections on the past, present, and future of lung cancer therap. Oncology 23, 1-4 (2009)
2. Ambroise, C.; McLachlan, G.J.: Selection bias in gene extraction on the basis of microarray gene-expression data. Proc. Nat. Acad. Sci. USA 99(10), 6562-6566 (2002)
3. American Cancer Society: http://www.cancer.org/. Accessed June 9 (2009)
4. Bishop, C.M.: Neural Networks for Pattern Recognition. Oxford University Press, Inc.; New York (1995)
5. Chen, W.; Foran, D.J.: Advances in cancer tissue microarray technology: towards improved understanding and diagnostics. Anal. Chim. Acta 564, 74-81 (2006)
6. Chapman, J.; Miller, N.; Lickley, H.; Qian, J.; Christens-Barry, W.; Fu, Y.; Yuan, Y.; Axelrod, D.: Ductal carcinoma in situ of the breast (dcis) with heterogeneity of nuclear grade: prognostic effects of quantitative nuclear assessment. BMC Cancer 7(1), 174 (2007)
7. Dubey, S.; Powell, C.A.: Update in lung cancer 2008. Am. J. Respir. Crit. Care Med. 179(10), 860-868 (2009)
8. Edwards, S.L.; Roberts, C.; McKean, M.A.; Cockburn, J.S.; Jeffrey, R.R.; Kerr, K.M.: Pre-operative histological classification of primary lung cancer: accuracy of diagnosis and use of the non-small cell carcinomas. J. Clin. Pathol. 53, 537-540 (2000)
9. Freund, Y.; Mason, L.: The alternating decision tree learning algorithm. In: Proceedings of the 16th International Conference on Machine Learning, pp 124-133. Morgan Kaufmann, San Francisco (1999)
10. Freund, Y.; Schapire, R.E.: Experiments with a new boosting algorithm. In: Proceedings of the Thirteenth International Conference on Machine Learning, pp. 148-156 (1996)
11. Grilley-Olson, J.E.; Hayes, D.N.; Qaqish, B.F.; Moore, D.T.; Socinski, M.A.; Yin, X.; Travis, W.D.; Funkhouser, W.K.; et al.: Validation of inter-observer agreement in lung cancer assessment. J. Clin. Oncol. 27, 15s (2009)
12. Haralick R.M.; Shanmugam K.; Dinstein (1973) Textural features for image classification. IEEE Trans. Syst. Man Cybern. 3(6):610-621
13. Keerthi, S.S.; Shevade, S.K.; Bhattacharyya, C.; Murthy, K.R.K.: Improvements to platt's smo algorithm for svm classifier design. Neural Comput. 13(3), 637-649 (2001)
14. Kohavi, R.: A study of cross-validation and bootstrap for accuracy estimation and model selection. In: IJCAI, pp. 1137-1145 (1995)
15. Otsu, N.: A threshold selection method from gray-level histograms. IEEE Trans. Syst. Man Cybern. 9(1), 62-66 (1979)
16. Otsu, N.: A threshold selection method from gray level histograms. IEEE Trans. Syst. Man Cybern. 9, 62-66 (1979) (minimize inter class variance)
17. Quinlan, R.J.: C4.5: Programs for Machine Learning (Morgan Kaufmann Series in Machine Learning). Morgan Kaufmann, San Francisco (1993)
18. Sandler, A.; Gray, R.; Perry, M.C.; Brahmer, J.; Schiller, J.H.; Dowlati, A.; Lilenbaum, R.; Johnson, D.H.: Paclitaxel-carboplatin alone or with bevacizumab for non-small-cell lung cancer. N Engl. J. Med. 355(24), 2542-2550 (2006)
19. Selvaggi, G.: Histologic subtype in nsclc: does it matter? Oncology 23, 1-11 (2009)
20. Ullmann, R.; Morbini, P.; Halbwedl, I.; Bongiovanni, M.; Gogg-Kammerer, M.; Papotti, M.; Gabor, S.; Renner, H.; Popper, H.H.: Protein expression profiles in adenocarcinomas and squamous cell carcinomas of the lung generated using tissue microarrays. J Pathol 203(3), 798-807 (2004)
21. Wallace, W.: The challenge of classifying poorly differentiated tumours in the lung. J. Histopathol. 54, 28-42 (2009)
22. Wang, C.-W.; Hunter, A.: A low variance error boosting algorithm. Appl. Intell. 33, 357-369 (2009)
23. Witten, I.H.; Frank, E.: Data Mining: Practical Machine Learning Tools and Techniques, 2nd edn. Morgan Kaugmann Series in Data Management Systems. Morgan Kaufmann, San Francisco (2005)
24. Zapotoczny, P.; Zielinska, M.; Nita, Z.: Application of image analysis for the varietal classification of barley: morphological features. J. Cereal Sci. 48(1), 104-110 (2008)