Improving segmentation accuracy for magnetic resonance imaging using a boosted decision tree

Journal of Neuroscience Methods

Volumn 175, Issue 2, 2008, Pages 206-217

  Chao, Wen Hung ^a,b   Chen, You Yin ^a   Cho, Chien Wen ^a   Lin, Sheng Huang ^c   Shih, Yen Yu I ^d   Tsang, Siny ^e  

^a NATIONAL CHIAO TUNG UNIVERSITY   (Taiwan)

^b YUANPEI UNIVERSITY   (Taiwan)

^c BUDDHIST TZU CHI GENERAL HOSPITAL   (Taiwan)

^d INSTITUTE OF BIOMEDICAL SCIENCES   (Taiwan)

^e NATIONAL TAIWAN UNIVERSITY   (Taiwan)

Author keywords

Accuracy rate; Boosted decision tree; Brain tissue classification; Image segmentation; k index; MRI

Indexed keywords

ACCURACY; ALGORITHM; ARTICLE; BRAIN CALCIFICATION; BRAIN TISSUE; CEREBROSPINAL FLUID; DIAGNOSTIC IMAGING; GRAY MATTER; NUCLEAR MAGNETIC RESONANCE IMAGING; PHANTOM; PRIORITY JOURNAL; WHITE MATTER;

ALGORITHMS; BRAIN; BRAIN MAPPING; DECISION TREES; HUMANS; IMAGE PROCESSING, COMPUTER-ASSISTED; MAGNETIC RESONANCE IMAGING;

EID: 53749095558     PISSN: 01650270     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.jneumeth.2008.08.017     Document Type: Article

References (54)

1. Admiraal-Behloul F., Van Den Heuvel D.M.J., Van Osch O.M.J.P., Van Der Grond J., Van Buchem M.A., and Reiber J.H.C. Fully automatic segmentation of white matter hyperintensities in MR images of the elderly. NeuroImage 28 (2005) 607-617
2. Ali A.A., Dale A.M., Badea A., and Johnson G.A. Automatic segmentation of neuroanatomical structures in multispectral MR microscopy of the mouse brain. NeuroImage 27 (2005) 425-435
3. Amato U., Larobina M., Antoniadis A., and Alfano B. Segmentation of magnetic resonance brain images through discriminant analysis. J Neurosci Methods 131 (2003) 65-74
4. Anbeek P., Vincken K.L., Van Osch M.J.P., Bisschops R.H.C., and Van Der Grond J. Probabilistic segmentation of white matter lesions in MR imaging. NeuroImage 21 (2004) 1037-1044
5. Anbeek P., Vincken K.L., Van Bochove G.S., Van Osch M.J.P., Bisschops R.H.C., and Van Der Grond J. Probabilistic segmentation of brain tissue in MR imaging. NeuroImage 27 (2005) 795-804
6. Andersen A.H., Zhang Z., Avison M.J., and Gash D.M. Automated segmentation of multispectral brain MR images. J Neurosci Methods 122 (2002) 13-23
7. Andrey P., and Maurin Y. Free-D: an integrated environment for three-dimensional reconstruction from serial sections. J Neurosci Methods 145 (2005) 233-244
8. Archibald R., Chen K., Gelb A., and Renaut R. Improving tissue segmentation of human brain MRI through preprocessing by the Gegenbauer reconstruction method. NeuroImage 20 (2003) 489-502
9. Arditi D., and Pulket T. Predicting the outcome of construction litigation using boosted decision trees. J Comp Civil Eng 19 (2005) 387-393
10. Barra V., and Boire J.-Y. Tissue segmentation on MR images of the brain by possibilistic clustering on a 3D wavelet representation. J Magn Reson Imag 11 (2000) 267-278
11. Behrens S., Laue H., Altthias M., Boehler T., Kuemmerlan B., Hahn H.K., et al. Computer assistance for MR based diagnosis of breast cancer: present and future challenges. Comput Med Imag Graph 31 (2007) 236-247
12. Bittencourt H.R., and Clarke R.T. Use of classification and regression trees (CART) to classify remotely-sensed digital images. Proc IEEE Int Geosci Remote Sens Symp 6 (2003) 3751-3753
13. Cline H.E., Doumulin C.L., Hart H.R., Lorensen W.E., and Ludke S. 3-D reconstruction of the brain from magnetic resonance images using a connectivity algorithm. Magn Reson Imag 5 (1987) 345-352
14. Dice L.R. Measures of the amount of ecologic association between species. Ecology 26 (1945) 297-302
15. In: Diem K. (Ed). Documenta Geigy Sientific Tables (1962), Geigy Pharmaceuticals, Ardsley, N.Y.
16. Dombi J., and Zsiros A. Learning multicriteria classification models from examples: decision rules in continuous space. Eur J Oper Res 160 (2005) 663-675
17. Dou W., Ruan S., Chen Y., Bloyet D., and Constans J.M. A framework of fuzzy information fusion for the segmentation of brain tumor tissues on MR images. Image Vision Comput 25 (2007) 164-171
18. Freund Y., and Schapire R.E. Experiments with a new boosting algorithm. Proceeding of the 13th international conference on Artificial Intelligence: Machine Learning. International Machine Learning Society. Bari, Italy (1996) 148-156
19. Freund Y., and Schapire R.E. A short introduction to boosting. J Jpn Soc Artif Intell 14 5 (1999) 148-156
20. Frey L., and Fisher D. Identifying Markov blankets with decision tree induction. Proceeding of the Third IEEE International Conference on Data Mining (2003) 59-66
21. Gonzalez R.C., and Woods R.E. Digital image processing. 2nd ed. (2002), Prentice Hall, New Jersey
22. Grajski KA, Breiman L, Di Prisco GV, Freeman WJ. Classification of EEG spatial patterns with a tree structured methodology: CART. IEEE Trans Biomed Eng 1986;BME-33:1076-86.
23. Greenspan H., Ruf A., and Goldberger J. Constrained Gaussian mixture model framework for automatic segmentation of MR brain images. IEEE Trans Med Imag 25 (2006) 1233-1245
24. Gu Y., Hall L.O., Goldgof D., Kanade P.M., and Murtagh F.R. Sequence tolerant segmentation system of brain MRI. Proceedings of the IEEE International Conference on System, Man and Cybermetics. Hawaii October 10-12 (2005)
25. Hautaniemi S., Kharait S., Iwabu A., Wells A., and Lauffenburger D.A. Modeling of signal-response cascades using decision tree analysis. Bioinformatics 21 (2005) 2027-2035
26. He R., Sajia B.R., and Narayana P.A. Implementation of high-dimensional feature map for segmentation of MR images. Ann Biomed Eng 33 (2005) 1439-1448
27. Hu W., Wu O., Chen Z., Fu Z., and Maybank S. Recognition of pornographic web pages by classifying texts and images. IEEE Trans Pattern Anal Mach Intell 29 (2007) 1019-1034
28. Joliot M., and Majoyer B.M. Three dimensional segmentation and interpolation of magnetic resonance brain images. IEEE Trans Med Imag 12 (1993) 269-277
29. Kirchner K., Tölle K.H., and Krieter J. Optimization of the decision tree technique applied to simulated sow herd datasets. Comput Electron Agric 50 (2006) 15-24
30. Liu J., Udupa J.K., Odhner D., Hacney D., and Moonis G. A system for brain tumor volume estimation via MR imaging and fuzzy connectedness. Comput Med Imag Graph 29 (2005) 21-34
31. Macek J. Incremental learning of ensemble classifiers on ECG data. Proceedings of the 18th IEEE Symposium on Computer-Based Medical System (CBMS'05) (2005)
32. Marroquin J.L., Vemuri B.C., Botello S., Calderon F., and Fernadez-Bouzas A. An accurate and efficient Bayesian method for automatic segmentation of brain MRI. IEEE Trans Med Imag 21 (2002) 934-945
33. Mohr J., Hess A., Scholz M., and Obermayer K. A method for automatic segmentation of autoradiographic image stacks and spatial normalization of functional cortical activity patterns. J Neurosci Methods 134 (2004) 45-58
34. Noulhiane M., Samson S., Clemenceau S., Dormont D., Baulac B., and Hasboun D. A volumetric MRI study of the hippocampus and the parahippocampal region after unilaterial medial temporal lobe resection. J Neurosci Methods 156 (2006) 293-304
35. Quinlan J.R. Induction of decision tree. Mach Learn 1 (1986) 81-106
36. Quinlan J.R. C4. 5: programs for machine learning (1993), Morgan Kaufmann, San Mateo, CA
37. Quinlan J.R. Improved use of continuous attributes in C4.5. J Artif Intell Res 4 (1996) 77-90
38. Quinlan JR. Data mining tools See5 and C5.0. Austria: RuleQuest Research, http://www.rulequest.com/see5-info.html, 2003.
39. Ranilla J., Luaces O., and Bahamonde A. A heuristic for learning decision trees and pruning them into classification rules. AI Commun 16 (2003) 71-87
40. Rettmann M.E., Han X., Xu C., and Prince J.L. Automatic sulcal segmentation using watersheds on the cortical surface. NeuroImage 15 (2002) 329-344
41. Schiemann T., Tiede U., and Hohne K.H. Segmentation of visible human for high quality volume-based visualization. Med Imag Anal 1 (1996) 263-270
42. Shan Z.Y., Ji Q., Gajjar A., and Reddick E. A knowledge-guided active contour method of segmentation of cerebella on MR images of pediatric patients with medulloblastoma. Magn Reson Imag 21 (2005) 1-11
43. Shiue Y.R., and Guh R.S. The optimization of attribute selection in decision tree-based production control systems. Int J Adv Manuf Technol 28 (2006) 737-746
44. Stalling D., Westerhoff M., and Hege H.C. Amira - an object oriented system for visual data analysis. In: Johnson C.R., and Hansen C.D. (Eds). Visualization handbook (2005), Academic Press
45. Tang H., Wu E.X., Ma Y.Q., Gallagher D., Perera G.M., and Zhuang T. MRI brain image segmentation by multi-resolution edge detection and region selection. Comput Med Imag Graph 24 (2000) 349-357
46. Wells W.M., Grimson W.E.L., Kikinis R., and Jolesz F.A. Adaptive segmentation of MRI data. IEEE Trans Med Imag 15 (1996) 429-442
47. Wolf I., Vetter M., Wegner I., Böttger T., Nolden M., Schöbinger M., et al. The medical imaging interaction toolkit. Med Imag Anal 9 (2005) 594-604
48. Xia Y., Bettinger K., Shen L., and Reiss A.L. Automatic segmentation of the caudate nucleus from human brain MR images. IEEE Trans Med Imag 26 (2007) 509-517
49. Xue Z., Shen D., and Davatzikos C. Determining correspondence in 3-D MR brain images using attribute vectors as morphological signatures of voxels. IEEE Trans Med Imag 23 (2004) 1276-1291
50. Yoo S.-S., Lee C.-U., Choi B.G., and Saiviroonporn P. Interactive 3-dimensional segmentation of MRI data in personal computer environment. J Neurosci Methods 112 (2001) 75-82
51. Yu Z.Q., Zhu Y., Yang J., and Zhu Y.M. A hybrid region-boundary model for cerebral cortical segmentation in MRI. Comput Med Imag Graph 30 (2006) 197-208
52. Zhou L.Q., Zhu Y.M., Bergot C., Laval-Jeantet A.M., Bousson V., Laredo J.D., et al. A method of radio-frequency inhomogeneity for brain tissue segmentation in MRI. Comput Med Imag Graph 25 (2001) 379-389
53. Zoroofi R.A., Nishii T., Sato Y., Sugano N., Yoshikawa H., and Tamura S. Segmentation of avascular necrosis of the femoral head using 3D MR images. Comput Med Imag Graph 25 (2001) 511-521
54. Zoroofi R.A., Sato Y., Nishii T., Sugano N., Yoshikawa H., and Tamura S. Automated segmentation of necrotic femoral head from 3D MR data. Comput Med Imag Graph 28 (2004) 267-278