The segmentation of colorectal MRI images

Medical Image Analysis

Volumn 14, Issue 4, 2010, Pages 494-509

  Joshi, Niranjan ^a   Bond, Sarah ^b   Brady, Michael ^a  

^a UNIVERSITY OF OXFORD   (United Kingdom)

^b SIEMENS AG   (Germany)

Author keywords

Colorectal cancer; Level sets; Monogenic signals; Non parametric density estimation; NP windows

Indexed keywords

2-D SLICE; AUTOMATIC ANALYSIS; COLORECTAL CANCER; IMAGE INTENSITIES; LEVEL SET; LEVEL SET FRAMEWORK; LOCAL PHASE; LYMPH NODE; MONOGENIC SIGNAL; MR IMAGES; MRI IMAGE; NON-PARAMETRIC; NONPARAMETRIC DENSITY ESTIMATION; PATIENT MANAGEMENT; SHORTEST DISTANCE; TREATMENT PLANNING; VISUALISATION;

MAGNETIC RESONANCE IMAGING; MERCURY (METAL); PARAMETER ESTIMATION; THREE DIMENSIONAL; TUMORS; VISUALIZATION;

IMAGE SEGMENTATION;

ACCURACY; ARTICLE; AUTOMATION; CLINICAL DECISION MAKING; COLON; COLORECTAL CANCER; DIAGNOSTIC VALUE; HUMAN; IMAGE ANALYSIS; IMAGE PROCESSING; MEASUREMENT; NONPARAMETRIC TEST; NUCLEAR MAGNETIC RESONANCE IMAGING; PRIORITY JOURNAL; RECTUM; SIGNAL DETECTION; THREE DIMENSIONAL IMAGING; TREATMENT PLANNING;

ALGORITHMS; ARTIFICIAL INTELLIGENCE; COLORECTAL NEOPLASMS; HUMANS; IMAGE ENHANCEMENT; IMAGE INTERPRETATION, COMPUTER-ASSISTED; PATTERN RECOGNITION, AUTOMATED; REPRODUCIBILITY OF RESULTS; SENSITIVITY AND SPECIFICITY;

EID: 77955585606     PISSN: 13618415     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.media.2010.03.002     Document Type: Article

References (33)

1. Andersson, T., Lathen, G., Lenz, R., Borga, M., 2009. A fast optimisation method for level set segmentation. In: The Proceedings of 16th Scandinavian Conference on Image Analysis.
2. Beets-Tan R.G.H., Beets G.L., Vliegen R.F.A., Kessels A.G.H., Boven H.V., Bruine A.D., von Meyenfeldt M.F., Baeten C.G.M.I., van Engelshoven J.M.A. Accuracy of magnetic resonance imaging in prediction of tumour-free resection margin in rectal cancer surgery. The Lancet 2001, 357:497-504.
3. Bond, S., 2006. Image analysis for patient management in colorectal cancer. Ph.D. Thesis, University of Oxford, United Kingdom.
4. Boukerroui D., Noble J.A., Brady M. On the choice of band-pass quadrature filters. Journal of Mathematical Imaging and Vision 2004, 21:53-80.
5. Bracewell R.N. The Fourier Transform and Its Applications 1999, McGraw Hill.
6. Brown G., Richards C.J., Newcombe R.G., Dallimore N.S., Radcliffe A.G., Carey D.P., Bourne M.W., Williams G.T. Rectal carcinoma: thin section MR imaging for staging in 28 patients. Radiology 1999, 211:215-222.
7. Caselles V., Kimmel R., Sapiro G. Geodesic active contours. International Journal of Computer Vision 1997, 22(1):61-79.
8. Chan T.F., Vese L.A. Active contours without edges. IEEE Transactions on Image Processing 2001, 10(2):266-277.
9. Cremers D., Rousson M., Deriche R. A review of statistical approaches to level set segmentation: integrating colour, texture, motion, and shape. International Journal of Computer Vision 2007, 72(2):195-215.
10. CRUK, 2007. Cancerstats factsheet: large bowel (colorectal) cancer.
11. Dowson N., Kadir T., Bowden R. Estimating the joint statistics of images using non-parametric windows with application to registration using mutual information. IEEE Transactions on Pattern Analysis and Machine Intelligence 2008, 30(10):1841-1857.
12. Felsberg, M., 2002. Low-level image processing with the structure multivector. Ph.D. Thesis, Institute of Computer Science and Applied Mathematics, Christian-Albrechts-University of Kiel, TR No. 0203. http://www.informatik.uni-kiel.de/reports/2002/0203.html.
13. Felsberg, M., Sommer, G., 2000. A new extension of linear signal processing for estimating local properties and detecting features. In: DAGM-Symposium, pp. 195-202.
14. Felsberg M., Sommer G. The monogenic signal. IEEE Transactions on Signal Processing 2001, 12:3136-3144.
15. ITK, 2005. The insight segmentation and registration toolkit. http://www.itk.org.
16. Joshi, N.B., 2007. Non-parametric probability density function estimation for medical images. D.phil. Thesis, University of Oxford.
17. Joshi, N.B., Brady, M., 2005. A non-parametric mixture model for partial volume segmentation of MR images. In: The Proceedings of British Machine Vision Conference - BMVC.
18. Joshi, N., Brady, M., 2009. Non-parametric mixture model based evolution of level sets and application to medical images. International Journal of Computer Vision. doi: 10.1007/s11263-009-0290-5.
19. Kass M., Witkin A., Terzopoulos D. Snakes: active contour models. International Journal of Computer Vision 1988, 321-331.
20. Kim J., Fisher J., Yezzi A., Cetin M., Willsky A. A nonparametric statistical method for image segmentation using information theory and curve evolution. IEEE Transactions on Image Processing 2005, 14(10):1486-1502.
21. Kovesi P. Image features from phase congruency. Videre: Journal of Computer Vision Research 1999, 1:2-26.
22. McArdle, C.S., Kerr, D.J., Boyle, P., 2000. Colorectal Cancer. ISIS Medical Media.
23. Mellor M., Hong B.-W., Brady M. Locally rotation, contrast, and scale invariant descriptors for texture analysis. IEEE Transactions on Pattern Analysis and Machine Intelligence 2008, 30:52-61.
24. MERCURYStudyGroup, 2006. Diagnostic accuracy of preoperative magnetic resonance imaging in predicting curative resection of rectal cancer: prospective observational study. British Medical Journal 333, 779.
25. Michailovich O., Rathi Y., Tannenbaum A. Image segmentation using active contours driven by the Bhattacharyya gradient flow. IEEE Transactions on Image Processing 2007, 16(11):2787-2801.
26. Morrone M.C., Owens R.A. Feature detection from local energy. Pattern Recognition Letters 1987, 6:303-313.
27. Paragios, N., 2000. Geodesic active regions and level set methods: contributions and applications in artificial vision. Ph.D. Thesis, University of Nice Sophia Antipolis/INRIA, France.
28. Paragios N., Deriche R. Geodesic active regions: a new framework to deal with frame partition problems in computer vision. Journal of Visual Communication and Image Representation 2002, 13:249-268.
29. Petroudi, S., Brown, G., Bond, S., Brady, M., 2006. Circumferential resection margin assessment on MRI of rectal cancer. In: Proceedings of the 28th IEEE EMBS Annual International Conference, pp. 4881-4884.
30. Smith N., Brown G. Preoperative staging of rectal cancer. Acta Oncologica 2007, 47(1):20-31.
31. Styner M., Brechbuhler C., Szekely G., Gerig G. Parametric estimate of intensity inhomogeneities applied to MRI. IEEE Transactions on Medical Imaging 2000, 19(3):153-165.
32. Tagare, H.D., Chen, Y., Fulbright, R.K., 2008. Comparison of em-based and level set partial volume segmentations of MR brain images. In: Proceedings of SPIE Medical Imaging, vol. 6914.
33. Zhu S.C., Yuille A. Region competition: unifying snakes, region growing, and Bayes/MDL for multiband image segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence 1996, 18(9):884-900.