Flinders University
Dr Gobert Lee
Dr
Gobert
Lee
Flinders University
http://www.flinders.edu.au/people/gobert.lee
| Position/s: | Lecturer School of Computer Science, Engineering and Mathematics |
| Phone: | +61 8 82012410 |
| Email: | gobert.lee@flinders.edu.au |
| Location: | Information, Science & Technology (237) |
| Postal address: | GPO Box 2100, Adelaide 5001, South Australia |
Qualifications
PhD in Medical Image Analysis, Flinders University, Adelaide, SA., Australia
MSc in Applied Physics, RMIT University, Melbourne, Vic., Australia
Honours, awards and grants
- Channel 7 Children's Research Foundation grant, 2012-2013
- Lyn Wrigley Breast Cancer Research & Development Fund, Breast Cancer Research Grant, 2010
- Certificate of Merit Award, Education Exhibit Category, 95th RSNA (Radiologic Society of North America) meeting, 2009
- Excellence in Design Award, Education Exhibit Category, 92nd RSNA meeting, 2006
- JSPS (Japan Society for Promotion of Sciences) International Post-doctoral Fellowship, 2005-2006
Teaching
Research and consultancy
Research interests
My primary research areas are Computer-Aided-Diagnosis (CAD), Medical Image Analysis, Statistical Pattern Recognition and statistical issues related to radiologic studies. More specifically, my research focuses on CAD on breast cancer detection in screening mammograms, whole-body CT segmentation, computational human anatomy, and construction of human voxel models for radiation dose calculation.
Publications
Refereed journal articles
Fujita, H., Uchiyama, Y., Nakagawa, T., Fukuoka, D., Hatanaka, Y., Hara, T., Lee, G.N., Hayashi, Y., Ikedo, Y., Gao, X., et al., 2008. Computer-aided diagnosis: the emerging of three CAD systems induced by Japanese health care needs. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE, 92(3), 238-248.
Lee, G.N. & Bottema, M.J., 2006. Significance of classification scores subsequent to feature selection. Pattern Recognition Letters, 27(14), 1702-1709.
Refereed conference papers
Lee, G.N., Bajger, M., & Caon, M., 2012. Multi-organ segmentation of CT images using statistical region merging. Proceedings of the Ninth IASTED International Conference on Biomedical Engineering, 199-206.
Fujita, H., You, J., Li, Q., Arimura, H., Tanaka, R., Sanda, S., Niki, N., Lee, G.N., Hara, T., Fukuoka, D., et al., 2010. State-of-the-Art of Computer-Aided Detection/Diagnosis. Medical Biometrics, 296-305.
Lee, G.N., Okada, T., Fukuoka, D., Hara, T., Morita, T., Takada, E., Endo, T., & Fujita, H., 2010. Classifying Breast Masses in Volumetric Whole Breast Ultrasound Data: A 2.5-Dimensional Approach. Digital Mammography: 10th International Workshop on Digital Mammography, LNCS 6136, 636-642.
Lee, G.N., Okada, T., Fukuoka, D., Muramatsu, C., Hara, T., Morita, T., Takada, E., Endo, T., & Fujita, H., 2010. Breast cancer detection in anisotropic ultrasound images. Digital Mammography:10th International Workshop on Digital Mammography (IWDM2010), 6136, 636-642.
Fukuoka, D., Morita, T., Muramatsu, C., Hara, T., Fujita, H., & Lee, G.N., 2009. Automated recognition and registration of breast lesions in whole breast ultrasound data and screening mammography. 95th Scientific Assembly and Annual Meeting Program, Radiological Society of North America, 928-928.
Lee, G.N. & Branford, A.J., 2009. Bias in radiologic studies: a review. 95th Scientific Assembly and Annual Meeting Program, Radiological Society of North America, 1082-1082.
Lee, G.N., Morita, T., Fukuoka, D., Muramatsu, C., Hara, T., & Fujita, H., 2009. Differentiation of mass lesions in whole breast ultrasound images: Volumetric analysis. 95th Scientific Assembly and Annual Meeting Program, Radiological Society of North America, 607-607.
Lee, G.N., Fukuoka, D., Ikedo, Y., Hara, T., & Fujita, H., 2008. Classification of benign and malignant masses in ultrasound breast image based on geometric and echo features. Digital Mammography: 9th International Workshop on Digital Mammography, LNCS 5116, 433-439.
Refereed journal articles
Fujita, H., Uchiyama, Y., Nakagawa, T., Fukuoka, D., Hatanaka, Y., Hara, T., Lee, G.N., Hayashi, Y., Ikedo, Y., Gao, X., et al., 2008. Computer-aided diagnosis: the emerging of three CAD systems induced by Japanese health care needs. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE, 92(3), 238-248.
Lee, G.N. & Bottema, M.J., 2006. Significance of classification scores subsequent to feature selection. Pattern Recognition Letters, 27(14), 1702-1709.
Refereed conference papers
Lee, G.N., Bajger, M., & Caon, M., 2012. Multi-organ segmentation of CT images using statistical region merging. Proceedings of the Ninth IASTED International Conference on Biomedical Engineering, 199-206.
Fujita, H., You, J., Li, Q., Arimura, H., Tanaka, R., Sanda, S., Niki, N., Lee, G.N., Hara, T., Fukuoka, D., et al., 2010. State-of-the-Art of Computer-Aided Detection/Diagnosis. Medical Biometrics, 296-305.
Lee, G.N., Okada, T., Fukuoka, D., Hara, T., Morita, T., Takada, E., Endo, T., & Fujita, H., 2010. Classifying Breast Masses in Volumetric Whole Breast Ultrasound Data: A 2.5-Dimensional Approach. Digital Mammography: 10th International Workshop on Digital Mammography, LNCS 6136, 636-642.
Lee, G.N., Okada, T., Fukuoka, D., Muramatsu, C., Hara, T., Morita, T., Takada, E., Endo, T., & Fujita, H., 2010. Breast cancer detection in anisotropic ultrasound images. Digital Mammography:10th International Workshop on Digital Mammography (IWDM2010), 6136, 636-642.
Fukuoka, D., Morita, T., Muramatsu, C., Hara, T., Fujita, H., & Lee, G.N., 2009. Automated recognition and registration of breast lesions in whole breast ultrasound data and screening mammography. 95th Scientific Assembly and Annual Meeting Program, Radiological Society of North America, 928-928.
Lee, G.N. & Branford, A.J., 2009. Bias in radiologic studies: a review. 95th Scientific Assembly and Annual Meeting Program, Radiological Society of North America, 1082-1082.
Lee, G.N., Morita, T., Fukuoka, D., Muramatsu, C., Hara, T., & Fujita, H., 2009. Differentiation of mass lesions in whole breast ultrasound images: Volumetric analysis. 95th Scientific Assembly and Annual Meeting Program, Radiological Society of North America, 607-607.
Lee, G.N., Fukuoka, D., Ikedo, Y., Hara, T., & Fujita, H., 2008. Classification of benign and malignant masses in ultrasound breast image based on geometric and echo features. Digital Mammography: 9th International Workshop on Digital Mammography, LNCS 5116, 433-439.
Lee, G.N., Kanematsu, M., Kato, H., Kondo, H., Zhou, X., Hara, T., Fujita, H., & Hoshi, H., 2008. Unsupervised classification of cirrhotic livers using MRI data. Proceedings of SPIE Medical Imaging 2008: Computer-Aided Diagnosis, 6915, 6915141-6915149.
Lee, G.N., Uchiyama, Y., Zhang, X., Kanematsu, M., Zhou, X., Hara, T., Kato, H., Kondo, H., Fujita, H., & Hoshi, H., 2007. Classification of cirrhotic liver in Gadolinium-enhanced MR images. Proceedings of SPIE Medical Imaging 2007: Computer-Aided Diagnosis, 6514, 6514301-6514308.
Lee, G.N. & Fujita, H., 2007. K-means clustering for classifying unlabelled MRI data. Proceedings of Digital Imaging Computing Techniques and Applications, 92-98.
Lee, G.N., Hara, T., & Fujita, H., 2006. Classifying masses as benign or malignant based on co-occurrence matrix textures: a comparison study of different gray level quantization. Digital Mammography: 8th International Workshop on Digital Mammography, LNCS 4046, 332-339.
Lee, G.N., Bottema, M.J., Hara, T., & Fujita, H., 2006. Effect of quantisation on co-occurrence matrix based texture features: An example study in mammography. Proceedings of SPIE Medical Imaging 2006: Computer-Aided Diagnosis, 6144, 614451-614459.
Lee, G.N., Zhang, X., Kanematsu, M., Zhou, X., Hara, T., Kato, H., Kondo, H., Fujita, H., & Hoshi, H., 2006. Classification of cirrhotic liver on MR images using texture analysis. International Journal of Computer Assisted Radiology and Surgery, 1, 379-381.
Lee, G.N. & Bottema, M.J., 2005. Classification of ROI as invasive lobular carcinoma or normal. Digital Mammography:7th International Workshop on Digital Mammography, 640-645.
Lee, G.N. & Bottema, M.J., 2003. Statistical significance of Az scores: classification of masses in screening mammograms as benign or malignant based on high dimensional texture feature space. Proceedings of the 2003 APRS Workshop on Digital Image Computing, 105-110.
Journal articles
Lee, G.N., Fukuoka, D., Morita, T., & Fujita, H., 2010. Whole-breast ultrasound brings significant screening benefits. Diagnostic Imaging Asia-Pacific, Winter2010, 7-9.
Conference publications
Lee, G.N., Bajger, M., & Caon, M., 2011. CAnat: An Algorithm for the Automatic Segmentation of Anatomy of Medical Images. EPSM-ABEC 2011 Conference, 34(4).
Lee, G.N., Kanematsu, M., Kato, H., Kondo, H., Fujita, H., & Hoshi, H., 2008. K-means clustering and classification of diffuse disease based on unlabelled ROI data. International Journal of Computer Assisted Radiology and Surgery, 3(S1), S432-S433.
