In this dissertation, we presented a multiscale retinex (MSR) algorithm to successfully correct the intensity inhomogeneity of brain MR images to obtain clearer deep brain structures and better image quality. The performance of intensity inhomogeneity correction was also evaluated by PSNR and CNR. Next, we proposed two decision trees (CART and BDT) with a supervised method and an automatic segmentation to successfully achieve the brain tissue segmentation on brain MR images to obtain better brain structures for neuroanatomical applications. The results of segmentation were also evaluated with two more indexes (accuracy rate, k index, and other published indexes). Finally, we achieved the high quality of segmentation with greatly improved accuracy based on BDT through preprocessing by the MSR algorithm from SBMR images. The experimental results of a number of simulated and in vivo MR data demonstrate that the proposed methods are valuable for brain MR image segmentation in neurological applications.
Although the experimental results are very promising, some aspects are needed to be addressed for further improving the performance and other applications.
1) The retinex algorithm could also be used to increase the SNR and dynamic-range compression in other types of medical image, such as those captured by computerized tomography (CT), digital X-ray systems, and digital mammography.
2) These algorithms can be also applied to process the images of other organs such as abdomen, chest, and also T2-weight, or PD-weighted MR images.
3) The method used in this research, together with 3D reconstruction method, might be applied for the tissue volume measurements of brain MR image to improve the diagnosis in clinical application. It is worth further studies.
4) A more sophisticated method for a group of experts to construct gold-standard image, which might improve the accuracy of segmentation is also an interest research
- 110 -
category.
5) Owing to the complex of brain edge on MR images, the background removing research and more tissue classes of brain MR image are worth studying issues.
- 111 -
References
[1] F. Bloch, “Nuclear induction,” Phys Rev, vol. 70, pp. 460–474, 1946.
[2] F. Bloch, W. W. Hansen, and M. Packard, “The nuclear induction experiment,” Phys Rev, vol. 70, pp. 474–485, 1946.
[3] F. Bloch, W. W. Hansen, and M. Packard, “Nuclear induction,” Phys Rev, vol. 69, pp.
127–127, 1946.
[4] E. M. Purcell, H. C. Torrey, and R. V. Pound, “Resonance absorption by nuclear magnetic moments in a solid,” Phys Rev, vol. 69, pp. 37–38, 1946.
[5] R. R. Edelman, et al., “Surface coil MR imaging of abdominal viscera. Part 1: theory, technique, and initial results,” Radiology, vol. 157, pp. 425–430, 1985.
[6] E. B. Boskamp, “Improved surface coil imaging in MR: decoupling of the excitation and receiver coils,” Radiology, vol. 157, pp. 449–452, 1985.
[7] Z. Cho, J. P. Jones and M. Singh, Foundations of medical imaging, New York: JohnWiley and Sons, 1993.
[8] M. L. Wood, M. J. Shivji and P. L. Stanchev, “Planar motion correction with use of k-space data acquired in Fourier MR imaging,” J Magn Reson Imaging, vol. 5, pp. 57–64, 1995.
[9] R. A. Zoroofi, Y. Sato, S. Tamura, H. Naito and L. Tang. “An improved L method for MRI artifact correction due to translational motion in the imaging plane,” IEEE Trans Med Imag, vol. 14, pp. 471–479, 1995.
[10] E. A. Vokurka, N. A. Watson, Y. Watson, NA Thacker and A. Jackson, “Improved high resolution MR imaging for surface coils using automated intensity non-uniformity correction: feasibility study in the Orbit,” J Magn Reson Imaging, vol. 14, pp. 540–546, 2001.
[11] C. Haiguang, H. Avram, L. Kaufman, J. Hale, and D. Kramer, “T2 restoration and noise
- 112 -
suppression of hybrid MR images using Wiener and linear prediction techniques,” IEEE Trans Med Imag, vol. 13, pp. 667–676, 1994.
[12] H. S. Zadeh, J. P. Windham, D. J. Peck, and A. E. Yagle, “A comparative analysis of several transforms for enhancement and segmentation of magnetic resonance image scene sequences,” IEEE Trans Med Imag, vol. 11, pp.302–318, 1992.
[13] J. G. Sled, A. P. Zijdenbos, and A. C. Evans, “A nonparametric method for automatic correction of intensity nonuniformity in MRI data,” IEEE Trans Med Imag, vol. 17, pp.
87–97, 1998.
[14] C. B. Ahn, Y. C. Song and D. J. Park, “Adaptive template filtering for signal-to-noise ratio enhancement in magnetic resonance imaging,” IEEE Trans Med Imag, vol. 18, pp.
549–556, 1999.
[15] M. Styner, C. Brechbuhler, G. Szekely, and G. Gerig, “Parametric estimate of intensity inhomogeneities applied to MRI,” IEEE Trans Med Imag, vol. 19, pp. 153–165, 2000.
[16] B. Likar, M. A. Viergever and F. Pernus, “Restrospective correction of MR intensity inhomogeneity by information minimization,” IEEE Trans Med Imag, vol. 20, pp.
1398–1410, 2001.
[17] F. H. Lin, Y. J. Chen, J. W. Belliveau and L. L. Wald, “A wavelet-based approximation of surface coil sensitivity profile for correction of image intensity inhomogeneity and parallel imaging reconstruction,” Human Brain Mapp, vol. 19, pp.96–111, 2003.
[18] C. Han, T. S. Hatsukami and C. Yuan, “A multi-scale method for automatic correction of intensity non-uniformity in MR images,” J Magn Reson Imaging, vol. 13, pp.428–436, 2001.
[19] Z. Hou, “A review on MR image intensity inhomogeneity correction,” International Journal of Biomedical Imaging, vol. 2006, pp. 1–11, 2006
[20] J. Luo, Y. Zhu, P. Clarysse, and I. Magnin, “Correction of bias field in MR images using singularity function analysis,” IEEE Trans Med Imag, vol. 24, pp. 1067–1085, 2005.
- 113 -
[21] M. Julien and Z. Yue-Min, “Model-based intensity nonuniformity correction in brain MRI,” Proceedings ICSP '04. 2004 7th International Conference on Signal Processing, vol. 2, pp. 982–985, 2004.
[22] O. Salvado, C. Hillenbrand, and D. L. Wilson, “Correction of intensity inhomogeneity in MR images of vascular disease,” Proceedings of the 2005 IEEE Engineering in Medicine and Biology 27th Annual International Conference, pp. 4302–4305, Sep.
2005.
[23] O. Salvado, C. Hillenbrand, Z. Shaoxiang, and D. L. Wilson, “Method to correct intensity inhomogeneity in MR images for atherosclerosis characterization,” IEEE Transactions on Medical Imaging, vol. 25, pp. 539–552, 2006.
[24] K. Van Leemput, F. Maes, D. Vandermeulen, and P. Suetens, “Automated model-based bias field correction of MR images of the brain,” IEEE Trans Med Imag, vol. 18, pp.
885–896, 1999.
[25] P. Maji, S. Dasgupta, B. Chanda, and M. K. Kundu, “Deformation correction in brain MRI using mutual information and genetic algorithm,” Proceedings of International Conference on Computing: Theory and Applications (ICCTA’07) pp. 372–376, 2007.
[26] R. A. Zoroofi, T. Nishii, Y. Sato, N. Sugano, H. Yoshikawa, and S. Tamura,
“Segmentation of a vascular necrosis of the femoral head using 3D MR images,”
Comput Med Imaging Graph, vol. 25, pp. 511–521, 2001
[27] R. A. Zoroofi,Y. Sato, T. Nishii, N. Sugano, H. Yoshikawa, and S. Tamura, “Automated segmentation of necrotic femoral head from 3D MR data,” Comput Med Imaging Graph, vol. 28, pp. 267–278, 2004.
[28] J. Liu, J. K. Udupa, D. Odhner, D. Hacney, and G. Moonis “A system for brain tumor volume estimation via MR imaging and fuzzy connectedness,” Comput Med Imaging Graph, vol. 29, pp. 21–34, 2005.
[29] S. Behrens, H. Laue,M. Altthias, T. Boehler, B. Kuemmerlan, H. K. Hahn, and H. O.
- 114 -
Peitgen, “Computer assistance for MR based diagnosis ofbreast cancer: present and future challenges,” Comput Med Imaging Graph, vol. 31, pp. 236–247, 2007.
[30] H. E. Cline, C. L. Doumulin, H. R. Hart, W. E. Lorensen, and S. Ludke, “3-D reconstruction of the brain from magnetic resonance images using a connectivity algorithm,” Magn Reson Imaging, vol. 5, pp.345–352, 1987.
[31] M. Joliot, and B. M. Majoyer, “Three dimensional segmentation and interpolation of magnetic resonance brain images,” IEEE Trans Med Imag; vol. 12, pp.269–277, 1993.
[32] T. Schiemann, U. Tiede, and K. H. Hohne, “Segmentation of visible human for high quality volume-based visualization,” Med Imag Anal, vol. 1, pp. 263–270, 1996.
[33] Z. Y. Shan, Q. Ji, A. Gajjar, and E. Reddick, “A knowledge-guided active contour method of segmentation of cerebella on MR images of pediatric patients with medulloblastoma,” Magn Reson Imaging, vol. 21, pp. 1–11, 2005.
[34] A. H. Andersen, Z. Zhang, M. J. Avison, and D. M. Gash, “Automated segmentation of multispectral brain MR images,” J Neurosci Methods vol. 122, pp. 13–23, 2002.
[35] U. Amato, M. Larobina, A. Antoniadis, and B. Alfano, “Segmentation of magnetic resonance brain images through discriminant analysis,” J Neurosci Methods, vol. 131, pp.
65–74, 2003.
[36] A. A. Ali, A. M. Dale, A. Badea, and G. A. Johnson, “Automatic segmentation of neuroanatomical structures in multispectral MR microscopy of the mouse brain,”
NeuroImage, vol. 27, pp. 425–435, 2005.
[37] J. Mohr, A. Hess, M. Scholz, and K. Obermayer, “A method for automatic segmentation of autoradiographic image stacks and spatial normalization of functional cortical activity patterns,” J Neurosci Methods, vol. 134, pp. 45–58, 2004.
[38] J. Sharma, M. P. Sanfilipo, R. H. B. Benedict, B. Weinstock-Guttman, F. E. Munschauer, and R. Bakshi, “Whole-brain atrophy in multiple sclerosis measured by automated
- 115 -
versus semiautomated MR imaging segmentation,” Am J Neuroradiol, vol. 25, pp.
985–996, 2004.
[39] F. Admiraal-Behloul, D. M. J. Van Den Heuvel, O. M. J. P. Van Osch, J. Van Der Grond, M. A. Van Buchem, and J. H. C. Reiber, “Fully automatic segmentation of white matter hyperintensities in MR images of the elderly,” NeuroImage, vol. 28, pp. 607–617, 2005.
[40] M. Prastawa, J. H. Gilmore, W. Lin, and G. Gerig, “Automatic segmentation of MR images of the developing newborn brain,” Med Imag Analysis, vol. 9, pp. 457–466, 2005.
[41] Y. Xia, K. Bettinger, L. Shen, and A.Reiss, “Automatic segmentation of the caudate nucleus from human brain MR images,” IEEE Trans Med Imag, vol. 26, pp. 509–517, 2007.
[42] J. L. Marroquin, B. C. Vemuri, S. Botello, F. Calderon, and A. Fernadez-Bouzas, “An accurate and efficient Bayesian method for automatic segmentation of brain MRI,”
IEEE Trans Med Imag, vol. 21, pp. 934–945, 2002.
[43] H. Greenspan, A. Ruf, and J. Goldberger, “Constrained Gaussian mixture model framework for automatic segmentation of MR brain images,” IEEE Trans Med Imag, vol. 25, pp. 1233–1245, 2006.
[44] P. Anbeek, K. L. Vincken, M. J. P. Van Osch, R. H. C. Bisschops, and J. Van Der Grond,
“Probabilistic segmentation of white matter lesions in MR imaging,” NeuroImage, vol.
21, pp. 1037–1044, 2004.
[45] W. B. Dou, S. Ruan, Y. P. Chen, D. Bloyet, and J. M. Constans, “A framework of fuzzy information fusion for the segmentation of brain tumor tissues on MR images,” Image and Vision Computing, vol. 25, pp. 164–171, 2007.
[46] L. Q. Zhou, Y. M. Zhu, C. Bergot, A. M. Laval-Jeantet, V. Bousson, and J. D. Laredo,
“A method of radio-frequency inhomogeneity for brain tissue segmentation in MRI,”
Comput Med Imaging Graph, vol. 25, pp.379–789, 2001.
- 116 -
[47] Y. Gu, L. O. Hall, D. Goldgof, P. M. Kanade, and F. R. Murtagh, “Sequence tolerant segmentation system of brain MRI,” Proceedings of the IEEE International Conference on System, Man and Cybermetics, Hawaii October 10-12, 2005.
[48] P. Anbeek, K. L. Vincken, G. S. Van Bochove, M. J. P. Van Osch, R. H. C. Bisschops, and J. Van Der Grond, “Probabilistic segmentation of brain tissue in MR imaging,”
NeuroImage, vol. 27, pp. 795–804, 2005.
[49] V. Barra, and J-Y. Boire, “Tissue segmentation on MR images of the brain by possibilistic clustering on a 3D wavelet representation,” J Magn Reson Imaging, vol. 11, pp. 267–278, 2000.
[50] Z. Xue, D. Shen, and C. Davatzikos, “Determining correspondence in 3-D MR brain images using attribute vectors as morphological signatures of voxels,” IEEE Trans Med Imag, vol. 23, pp. 1276–1291, 2004.
[51] H. Tang, E. X. Wu, Y. O. Ma, D. Gallagher, G. M. Perera, and T. Zhuang, “MRI brain image segmentation by multi-resolution edge detection and region selection,” Comput Med Imaging Graph, vol. 24, pp. 349–357, 2000.
[52] S-S. Yoo, C-U. Lee, B. G. Choi, and P. Saiviroonporn, “Interactive 3-dimensional segmentation of MRI data in personal computer environment,” J Neurosci Methods, vol.
112, pp. 75–82, 2001.
[53] R. Archibald, K. Chen, A. Gelb, and R. Renaut, “Improving tissue segmentation of human brain MRI through preprocessing by the Gegenbauer reconstruction method,”
NeuroImage, vol. 20, pp. 489–502, 2003.
[54] P. Andrey, and Y. Maurin, “Free-D: an integrated environment for three-dimensional reconstruction from serial sections,” J Neurosci Methods, vol. 145, pp. 233–244, 2005.
[55] R. He, B. R. Sajia, and P. A. Narayana, “Implementation of high-dimensional feature map for segmentation of MR images,” Annals Biomed Eng, vol. 33, pp. 1439–148, 2005.
- 117 -
[56] M. Noulhiane, S. Samson, S. Clemenceau, D. Dormont, B. Baulac, and D. Hasboun, “A volumetric MRI study of the hippocampus and the parahippocampal region after unilaterial medial temporal lobe resection,” J Neurosci Methods, vol. 156, pp. 293–304, 2006.
[57] R. B. Lufkin, et al., “Dynamic-range compression in surface-coil MRI,” Am J Nueroradiol, vol. 147, pp. 379–382, 1986.
[58] R. B. Lufkin, The MRI manual, California: Mosby, Inc., 1998.
[59] B. H. Suits, and A. N. Garroway, “Optimizing surface coils and the self-shielded gradiometer,” J Appl Phys vol. 94, pp. 4170–4178, 2003.
[60] D. Gensanne, G. Josse, J. M. Lagarde, and D. Vincensini, “High spatial resolution quantitative MR images: an experimental study of dedicated surface coils,” Phys Med Biol; vol. 51, pp. 2843–2855, 2006.
[61] D. T. Chard, G. J. M. Parker, C. M. B. Griffin, A. J. Thompson, and D. H. Miller, “The reproducibility and sensitivity of brain tissue volume measurements derived from an SPM-based segmentation methodology,” J Magn Reson Imaging, vol. 15, pp. 259–267, 2002.
[62] W. Shufeng, Y. Wenhui, and H. Lili, "A background removing method of MR images and its application in the intensity nonuniformity correction methods," Proceedings of 2008 International Conference on Technology and Applications in Biomedicine( ITAB 2008), pp. 175–178, 2008.
[63] W. Chen and M. L. Giger, “A fuzzy c-means (FCM) based algorithm for intensity inhomogeneity correction and segmentation of MR images,” Proceedings of 2004 IEEE International Symposium on Biomedical Imaging, vol. 2, pp. 1307–1310, 2004.
[64] L. Pan, Y. Yong, Z. Chong-Xun, and G. Jian-Wen, “An efficient automatic framework for segmentation of MRI brain image,” Proceedings of the Fourth International Conference on Computer and Information Technology (CIT '04), pp. 896–900, 2004.
- 118 -
[65] X. Li and S. Luo, "Bias field correction-based tissue classification of MR images of brain," Proceedings of 1998 Fourth International Conference on Signal Processing Proceedings ( ICSP '98), vol.2, pp. 948–950, 1998.
[66] B. M. Dawant, A. P. Zijdenbos, and R. A. Margolin, “Correction of intensity variations in MR images for computer-aided tissue classification,” IEEE Trans Med Imag, vol. 12, pp. 770–781, 1993.
[67] B. Johnston, B. Johnston, M. S. Atkins, B. Mackiewich, and M. A. A. M. Anderson,
“Segmentation of multiple sclerosis lesions in intensity corrected multispectral MRI,”
IEEE Transactions on Medical Imaging,, vol. 15, pp. 154–169, 1996.
[68] R. Guillemaud, “Uniformity correction with homomorphic filtering on region of interest,” Proceedings of 1998 International Conference on Image Processing ( ICIP 98), vol. 2, pp. 872–875, 1998.
[69] R. C. Gonzalez, and R. E. Woods, Digital image processing, 2nd ed., New Jersey:
Prentice Hall, Inc., 2002.
[70] S. D. Chen, and A. R. Ramli, “Preserving brightness in histogram equalization based contrast enhancement techniques,” Digit Sig Proc, vol. 14, pp. 413–428, 2004.
[71] V. Caselles, J. L. Lisani, J. M. Morel and G. Sapiro, “Shape preserving local histogram modification,” IEEE Trans Imag Proc, vol. 8, pp. 220–230, 1999.
[72] H. D. Cheng, and X. J. Shi, “A simple and effective histogram equalization approach to image enhancement,” Digit Sig Proc, vol. 14, pp. 158–170, 2004
[73] Y. Sun, and D. Parker, “Small vessel enhancement in MRA images using local maximum mean processing,” IEEE Trans Imag Proc, vol. 10, pp. 1687–1699, 2001.
[74] J. Y. Kim, L. S. Kim, and S. H. Hwang, “An advanced contrast enhancement using partially overlapped sub-block histogram equalization,” IEEE Trans Cir & Sys for Video Tech, vol. 11, pp. 475–484, 2001.
- 119 -
[75] J. Tang, E. Peli and S Acton, “Image enhancement using a contrast measure in compressed domain,” IEEE Sig Proc Letters, vol. 10, pp. 289–292, 2003.
[76] M. Eramian, and D. Mould. “Histogram equalization using neighborhood metrics,”
Proceedings of the 2nd Canadian Conference on Computer and Robot Vision, pp.
397–404, 2005.
[77] E. Land, “An alternative technique for the computation of the designator in the retinex theory of color vision,” Proc Natl Acad Sci U S A., vol. 83, pp. 3078–3080, 1986.
[78] A. Moore, J. Allman, and R. M. Goodman, “A real-time neural system for color constancy,” IEEE Trans Neural Networks, vol. 2, pp. 237–247, 1991.
[79] A. Moore, G. Fox, J. Allman and R. M. Goodman, “A VLSI neural network for color constancy,” in Advances in Neural Information Processing 3”. D. S. Touretzky, R.
Lippman and E. S. Mateo, CA: Morgan Kaufmann, pp. 370–376, 1991.
[80] A. C. Hurlbert, and T. Poggio, “Synthesizing a color algorithm from examples,” Science, vol. 239, pp. 482–485, 1988.
[81] A. C. Hurlbert, The computation of color, PhD dissertation, Mass. Inst. Technol., Cambridge, MA, 1989.
[82] D. J. Jobson, Z. Rahman and G. A. Woodell, “Properties and performance of a center/surround retinex,” IEEE Trans Imag Proc, vol. 6, pp. 451–462, 1997.
[83] B. R. Conway, and M. S. Livingstone, “Spatial and temporal properties of cone signals in alert macaque primary visual cortex,” J Neuroscience, vol. 26, pp. 10826–10846, 2006.
[84] B. R. Conway, “Spatial structure of cone inputs to color cells in alert macaque primary visual cortex,” J Neuroscience, vol. 21, pp. 2768–2783, 2001.
[85] D. J. Jobson, Z. Rahman and G. A. Woodell, “A multiscale retinex for bridging the gap between color images and the human observation of scenes,” IEEE Trans Imag Proc, vol. 6, pp. 965–976, 1997.
- 120 -
[86] D. E. Bowker, R. E. Davis, D. L. Myrick, K. Stacy and W. L. Jones, “Spectral reflectances of natural targets for use in remote sensing studies,” NASA Ref Pub, 1985.
[87] D. H. Brainard, and B. A. Wandell, “An analysis of the retinex theory of color vision,” J Opt Soc Amer A, vol. 3, pp. 1651–1661, 1986.
[88] G. W. Wei, “Generalized perona malik-equation for image restoration,” IEEE Sig Proc Letters, vol. 6, pp. 165–167, 1999.
[89] K. A. Grajski, L. Breiman, G. V. Di Prisco, and W. J. Freeman, “Classification of EEG spatial patterns with a tree structured methodology: CART,” IEEE Trans Biomed Eng, vol. BME–33, pp. 1076–86, 1986.
[90] H. R. Bittencourt, and R. T. Clarke, “Use of classification and regression trees (CART) to classify remotely-sensed digital images,” Proceedings of the IEEE International Geoscience and Remote Sensing Symposium IGARSS '03, vol. 6, pp. 3751–3753, 2003.
[91] L. Breiman, J. H. Friedman, R. A. Olshen, and C. J. Stone, Classification and regression trees, CA: Wadsworth International, 1984.
[92] R. O. Duda, P. E. Hart, and D. G. Stork, Pattern Classification, New York: Wiley, 2001.
[93] B. Wang, P. K. Saha, J. K. Udupa, M. A. Ferrante, J. Baumgardner, D. A. Roberts, and R.
R. Rizi, “3D airway segmentation via hyperpolarized 3He gas MRI by using scale-based fuzy connectedness,” Comput Med Imaging Graph, vol.28, pp. 77–86, 2004.
[94] Z. Q. Yu, Y. Zhu, J. Yang, and Y. M. Zhu, “A hybrid region-boundary model for cerebral cortical segmentation in MRI,” Comput Med Imaging Graph, vol. 30, pp.
197–208, 2006.
[95] S. K. Warefield, K. H. Zou, and W. M. Wells, “Validation of image segmentation and expert quality with an expectation-maximization algorithm,” Proceeding of MICCAI, of lecture notes in computer science, vol. 2488, pp. 298–306, 2002.
- 121 -
[96] S. Hautaniemi, S. Kharait, A. Iwabu, A. Wells, and D. A. Lauffenburger, “Modeling of signal-response cascades using decision tree analysis,” Bioinformatics, vol. pp.
2027–2035, 2005.
[97] J. R. Quinlan, “Induction of decision tree,” Machine Learning, vol. 1, pp. 81–106, 1986.
[98] J. R. Quinlan, C4.5: programs for machine learning, San Mateo, CA: Morgan Kaufmann, 1993.
[99] J. R. Quinlan, “Improved use of continuous attributes in C4.5,” Journal of Artificial Intelligence Research, vol. 4, pp.77–90, 1996.
[100] J. R. Quinlan, Data mining tools See5 and C5.0, Austria: RuleQuest Research, http://www.rulequest.com/see5-info.html. 2003.
[101] Y. R. Shiue, and R. S. Guh, “The optimization of attribute selection in decision tree-based production control systems,” Int J Adv Manuf Technol, vol. 28, pp. 737–746, 2006.
[102] J. Macek, “Incremental learning of esamble classifiers on ECG data,” Proceedings of the 18th IEEE Symposium on Computer-Based Medical System (CBMS’05), 2005.
[103] J. Dombi, and A. Zsiros, “Learning multicriteria classification models from examples:
decision rules in continuous space,” Eur J Oper Res, vol. 160, pp. 663–675, 2005.
[104] L. Frey, and D. Fisher, “Identifying Markov blankets with decision tree induction,”
Proceeding of the Third IEEE International Conference on Data Mining, pp. 59–66, 2003.
[105] J. Ranilla, O. Luaces, and A. Bahamonde, “A heuristic for learning decision trees and pruning them into classification rules,” AI Communications, vol. 16, pp. 71–87, 2003.
[106] W. Hu, O. Wu O, Z. Chen, Z. Fu, and S. Maybank, “Recognition of pornographic web pages by classifying texts and images,” IEEE Trans Pattern Analysis and Machine Intelligence, vol. 29 pp. 1019–1034, 2007.
- 122 -
[107] Y. Freund, and R. E. Schapire, “Experiments with a new boosting algorithm,”
Proceeding of the 13th international conference on Artificial Intelligence: Machine Learning. International Machine Learning Society, Bari, Italy, pp. 148–156, 1996.
[108] Y. Freund, and R. E. Schapire, “A short introduction to boosting,” J Jpn Soc Artif Intell, vol. 14, pp. 148–156, 1999.
[109] D. Arditi, and T. Pulket, “Predicting the outcome of construction litigation using boosted decision trees,” J Comp in Civ Engrg, vol. 19, pp. 387–393, 2005.
[110] K. Kirchner, K. H. Tölle, and J. Krieter, “Optimization of the decision tree technique applied to simulated sow herd datasets,” Comput Electron Agric, vol. 50, pp. 15–24, 2006.
[111] K. Diem, et al., Documenta Geigy Scientific Tables, Geigy Pharmaceuticals, 1962.
[112] D. Stalling, M. Westerhoff, and H. C. Hege, Amira - an object oriented system for visual data analysis, In C. R. Johnson & C. D. Hansen (Eds.), Visualization Handbook:
Academic Press, 2005.
[113] L. R. Dice, “Measures of the amount of ecologic association between species,” Ecology, vol. 26, pp. 297–302, 1945.
[114] I. Wolf, M. Vetter, I. Wegner, T. Böttger, M. Nolden, M. Schöbinger, et al., “The medical imaging interaction toolkit,” Medical Image Analysis, vol. 9, pp. 594–604, 2005.
[115] W. M. Wells, W. E. L. Grimson, R. Kikinis, and F. A. Jolesz, “Adaptive segmentation of MRI data,” IEEE Trans. Medical Imaging, vol. 15, pp.429–442, 1996.
- 123 -
Curriculum Vitae
ID No. L120443672 Renewed Date: 2008/08/28
Chinese Name 趙 文 鴻 English Name
Chao, Wen-Hung (Last Name) (First Name) Nationality Republic of China Gender Male Birth Date Sep. 30, 1966
Address No. 306, Yuanpei St., Hsinchu, Taiwan 300, R.O.C.
Tel. No. (Office) 886-3-5381183 ext 8626 (Home) 886-3-5619435 Fax No. 886-3-6102347 Email [email protected]
Education
University Nation Major Degree Period(Y/M)
National
Cheng Kung University
Taiwan, R. O. C.
Biomedical M. S. From 1994/09
to 1996/06
Biomedical Engineer, Biomedical Engineering Society of the R. O. C.
Experience
From 1990/08 to 1992/11 R&D Engineer, Truth Instruments Co., Ltd.
From 1992/06 to 1993/09 R&D Engineer, Genemax Medical Products Industry Corp.
From 1996/08 to 2008/- Lecturer, Department of Biomedical Engineering, Yuanpei University
Publications International Journal
1. Wen-Hung Chao, Chien-Wen Cho, Yen-Yu Shih, You-Yin Chen, Chen Chang,
“Correction of inhomogeneous MR images using multiscale retinex,” International of Image Processing, vol. 1, pp. 1–16, 2007.
- 124 -
2.Chien-Wen Cho, Wen-Hung Chao, You-Yin Chen, “A linear-discriminant-analysis-based approach to enhance the performance of fuzzy c-means clustering in spike sorting with low-SNR data,” International Journal of Biometric and Bioinformatics, vol. 1, pp. 1–13, 2007.
3. Wen-Hung Chao, Chien-Wen Cho, Yen-Yu I Shih, Sheng-Huang Lin, You-Yin Chen,
“Improving segmentation accuracy for magnetic resonance imaging using a boosted decision tree,” Journal of Neuroscience Methods (Accepted)
4. Wen-Hung Chao, Chien-Wen Cho, Sheng-Huang Lin, You-Yin Chen, “A vision-based analysis system for gait recognition in patients with parkinson's disease,” Expert Systems With Applications. (Accepted)
5. Wen-Hung Chao, Chien-Wen Cho, Yen-Yu I. Shih, You-Yin Chen, ”Automatic
segmentation of magnetic resonance image using decision tree with spatial information,”
Computerized Medical Imaging and Graphics. (Revised)
6. You-Yin Chen, Hsin-Yi Lai, Sheng-Huang Lin, Wen-Hung Chao, Chia-Hsin Liao, Siny Tsang, “Design and fabrication of polyimide-based microelectrode array: application in neural recording and repeatable electrolytic lesion in rat brain,” Journal of Neuroscience Methods. (Revised)
Conference
1. Shing-Hong Liu, Chin-Teng Lin, and Wen-Hung Chao, “The short-time fractal scaling of heart rate variability to estimate the mental stress of driver,” The Proceedings of 2004 IEEE ICNSC International conference, Taipei, Taiwan, pp. 829-833, Mar 2004.
2. Sheng-Fu Liang, Chin-Teng Lin, Ruei-Cheng Wu, Teng-Yi Huang, and Wen-Hung Chao, “Classification of driver's cognitive responses from EEG analysis,” The Proceedings of 2005 IEEE International Symposium on Circuits and Systems, Kobe, Japan, pp.156-159, May 2005.
3. Chin-Teng Lin, Wen-Hung Chao, and, Sheng-Fu Liang “A single-trial event-related
3. Chin-Teng Lin, Wen-Hung Chao, and, Sheng-Fu Liang “A single-trial event-related