Automating image segmentation verification and validation by learning test oracles

Information and Software Technology

Volumn 53, Issue 12, 2011, Pages 1337-1348

  Frounchi, Kambiz ^a   Briand, Lionel C ^b   Grady, Leo ^c   Labiche, Yvan ^a   Subramanyan, Rajesh ^c  

^a CARLETON UNIVERSITY   (Canada)

^b UNIVERSITY OF OSLO   (Norway)

^c SIEMENS CORPORATE RESEARCH   (United States)

Author keywords

Image processing; Image segmentation; Machine learning; Verification and validation

Indexed keywords

CLASSIFICATION (OF INFORMATION); COMPUTERIZED TOMOGRAPHY; IMAGE PROCESSING; ITERATIVE METHODS; LEARNING ALGORITHMS; LEARNING SYSTEMS; MACHINE LEARNING;

IMAGE SEGMENTATION ALGORITHM; ITERATIVE PROCESS; PARAMETRIC CONFIGURATIONS; REPETITIVE VERIFICATION; SEGMENTATION ALGORITHMS; SIMILARITY MEASURE; TESTING EFFICIENCY; VERIFICATION-AND-VALIDATION;

IMAGE SEGMENTATION;

EID: 84865759232     PISSN: 09505849     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.infsof.2011.06.009     Document Type: Article

References (33)

1. E. Abdou, W.R. Pratt, Quantitave design and evaluation of enhancement/ thresholding edge detectors, in: Proceedings of the IEEE, vol. 67 (5), 1979, pp. 753-763.
2. T. Acharya, A.K. Ray, Image Processing: Principles Applications, Wiley- Interscience, 2005.
3. P. Amman, J. Offut, Introduction to Software Testing, Cambridge University Press, 2008.
4. P.E. Ammann, J.C. Knight, Data diversity: An approach to software fault tolerance, IEEE Transactions on Computers 37 (4) (1988) 418-425.
5. A.J. Baddeley, An error metric for binary images, in: Proceedings of the International Workshop on Robust Computer Vision, 1992.
6. L.C. Briand, Y. Labiche, H. Sun, Investigating the use of analysis contracts to improve the testability of object oriented code, Software Practice and Experience 33 (7) (2003) 637-672.
7. W.K. Chan, S.C. Cheung, J.C.F. Ho, T.H. Tse, PAT: A pattern classification approach to automatic reference oracles for the testing of mesh simplification programs, Journal of Systems and Software 82 (3) (2009) 422-434.
8. W.W. Cohen, Fast effective rule induction, in: Proceedings of the International Conference on Machine Learning, 1995, pp. 115-123.
9. W.R. Crum, P. Camara, D.L.G. Hill, Generalized overlap measures for evaluation and validation in medical image analysis, IEEE Transactions on Medical Imaging 25 (11) (2006) 1451-1461.
10. M.D. Davis, E.I. Weyuker, Pseudo-oracles for non-testable programs, in: Proceedings of the ACM Annual Conference, 1981, pp. 254-257.
11. X. Deng, L. Zhu, Y. Sun, C. Xu, L. Song, J. Chen, R.D. Merges, M.P. Jolly, M. Suehling, X. Xu, On simulating subjective evaluation using combined objective metrics for validation of 3D tumor segmentation, in: Proceedings of the Medical Image Computing and Computer-Assisted Intervention, 2007, pp. 977-984.
12. K. Frounchi, L.C. Briand, L. Grady, Y. Labiche, R. Subramanyan, Automating Image Segmentation Verification and Validation by Learning Test Oracles, Carleton University, Technical Report SCE-09-06, 2009. .
13. L. Grady, Random walks for image segmentation, IEEE Transactions on Pattern Analysis and Machine Intelligence 28 (11) (2006) 1768-1783.
14. M.A. Hall, G. Holmes, Benchmarking attribute selection techniques for discrete class data mining, IEEE Transactions on Knowledge and Data Engineering 15 (6) (2003) 1437-1447.
15. R. Klette, A. Rosenfeld, Digital Geometry: Geometric Methods for Digital Picture Analysis, Morgan Kaufmann, 2004.
16. J.C. Knight, N.G. Leveson, An experimental evaluation of the assumption of independence in multi-version programming, IEEE Transactions on Software Engineering 12 (1) (1986) 96-109.
17. C.P. Lam, D.G. Stork, Evaluating classifiers by means of test data with noisy labels, in: Proceedings of the International Joint Conference on Artificial Intelligence, 1995, pp. 513-518.
18. P.D.L. Machado, W.L. Andrade, The oracle problem for testing against quantified properties, in: Proceedings of the IEEE International Conference on Quality Software, 2007, pp. 415-418.
19. A.P. Mathur, Foundations of Software Testing, Addison Wesley Professional, 2007.
20. K. McGuinness, G. Keenan, T. Adamek, N.E. O'Conner, Image segmentation evaluation using an integrated framework, in: Proceedings of the IET International Conference on Visual Information Engineering, 2007.
21. C. Murphy, K. Shen, G. Kaiser. Automatic system testing of programs without test oracles, in: Proceedings of the ACM International Symposium on Software Testing and Analysis, 2009, pp. 189-200.
22. E. Neri, D. Caramella, C. Bartolozzi, Image Processing in Radiology: Current Applications, Springer, 2008.
23. C. Odet, B. Belaroussi, H. Benoit-Cattin, Scalable discrepancy measures for segmentation evaluation, in: Proceedings of the IEEE International Conference on Image Processing, vol. 1, 2002, pp. 785-788.
24. T. Peli, D. Malah, A study of edge detection algorithms, Computer Graphics and Image Processing 20 (1) (1982) 1-21.
25. D.L. Pham, C. Xu, J.L. Prince, A survey of current methods in medical image segmentation, Annual Review of Biomedical Engineering 2 (2000) 315-338.
26. D.J. Richardson, S.L. Aha, T.O. O'Malley, Specification based test oracles for reactive systems, in: Proceedings of the ACM International Conference on Software Engineering, 1992, pp. 105-118.
27. C. Rosenberger, S. Chabrier, H. Laurant, B. Emile, Unsupervised and supervised image segmentation evaluation, in: Y. Zhang (Ed.), Advances in Image and Video Segmentation, IGI Global, 2006, pp. 365-393.
28. J.K. Udupa, V.R. Leblanc, Y. Zhuge, C. Imielinska, H. Schmidt, L.M. Currie, B.E. Hirsch, J. Woodburn, A framework for evaluating image segmentation algorithms, Computerized Medical Imaging and Graphics 30 (2) (2006) 75-87.
29. E.J. Weyuker, On testing non-testable programs, The Computer Journal 25 (4) (1982) 465-470.
30. I.H. Witten, E. Frank, Data Mining: Practical Machine Learning Tools and Techniques, second ed., Elsevier, 2005.
31. H. Zhang, S. Cholleti, S.A. Goldman, J. Fritts, Meta-evaluation of image segmentation using machine learning, in: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, 2006, pp. 1138-1145.
32. Y. Zhang, Advances in Image and Video Segmentation, IRM Press, 2006.
33. H. Wang, S. Das, J.W. Suh, M. Altinay, J. Pluta, C. Craige, B. Avants, P. Yushkevich, A learning-based wrapper method to correct systematic errors in automatic image segmentation: Consistently improved performance in hippocampus, cortex and brain segmentation, Neuroimage 55 (3) (2011) 968-985.