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ORCIDGobert N. Lee
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2020 – today
- 2021
[c30]Mariusz Bajger
, Minh-Son To, Gobert N. Lee, Adam Wells, Chee Chong, Marc Agzarian, Santosh Poonnoose:
Lumbar Spine CT synthesis from MR images using CycleGAN - a preliminary study. DICTA 2021: 1-8- [c29]Michael Howes, Mariusz Bajger, Gobert N. Lee, Francesca Bucci, Saulo Martelli:
Texture enhanced Statistical Region Merging with application to automatic knee bones segmentation from CT. DICTA 2021: 1-8
2010 – 2019
- 2019
- [c28]Nolwenn Jegou, Franck Desaize, Gobert N. Lee, Mariusz Bajger, Oscar Acosta, Julie Leseur, Renaud de Crevoisier, Martin Caon:
Organs-at-Risk Contouring on Head CT for RT Planning Using 3D Slicer- A Preliminary Study. BIBE 2019: 503-506 - [c27]Ratna Saha, Mariusz Bajger, Gobert N. Lee:
Prior Guided Segmentation and Nuclei Feature Based Abnormality Detection in Cervical Cells. BIBE 2019: 742-746 - [c26]Ratna Saha, Mariusz Bajger, Gobert N. Lee:
SRM Superpixel Merging Framework for Precise Segmentation of Cervical Nucleus. DICTA 2019: 1-8 - [c25]Shelda Sajeev, Mariusz Bajger, Gobert N. Lee:
Graph Modeling for Identifying Breast Tumor Located in Dense Background of a Mammogram. GLMI@MICCAI 2019: 147-154 - 2018
- [j6]Shelda Sajeev, Mariusz Bajger, Gobert N. Lee:
Superpixel texture analysis for classification of breast masses in dense background. IET Comput. Vis. 12(6): 779-786 (2018) - [c24]Ratna Saha, Mariusz Bajger, Gobert N. Lee:
Circular Shape Prior in Efficient Graph Based Image Segmentation to Segment Nucleus. DICTA 2018: 1-8 - [c23]Ratna Saha, Mariusz Bajger, Gobert N. Lee:
Segmentation of cervical nuclei using SLIC and pairwise regional contrast. EMBC 2018: 3422-3425 - [c22]Shelda Sajeev, Mariusz Bajger, Gobert N. Lee:
Superpixel pattern graphs for identifying breast mass ROIs in dense background: a preliminary study. IWBI 2018: 107180V - [c21]Gobert N. Lee, Mariusz Bajger, Kevin Clark:
Deep learning and color variability in breast cancer histopathological images: a preliminary study. IWBI 2018: 107181E - [c20]Mariusz Bajger, Gobert N. Lee, Martin Caon:
Paediatric Liver Segmentation for Low-Contrast CT Images. DATRA/PIPPI@MICCAI 2018: 169-178 - 2017
- [j5]Ratna Saha, Mariusz Bajger, Gobert N. Lee:
Circular shape constrained fuzzy clustering (CiscFC) for nucleus segmentation in Pap smear images. Comput. Biol. Medicine 85: 13-23 (2017) - [c19]Shelda Sajeev, Mariusz Bajger, Gobert N. Lee:
Structured Micro-Pattern Based LBP Features for Classification of Masses in Dense Breasts. DICTA 2017: 1-8 - [c18]Shaymaa Akraa, Feng Guo, Haifeng Shen, Youhong Tang, Jimmy Li, Gobert N. Lee, Ben Zhong Tang:
On the Feasibility of a Smartphone-based Solution to Rapid Qantitative Urinalysis using Nanomaterial Bioprobes. MobiQuitous 2017: 523-524 - 2016
- [c17]Ratna Saha, Mariusz Bajger, Gobert N. Lee:
Spatial Shape Constrained Fuzzy C-Means (FCM) Clustering for Nucleus Segmentation in Pap Smear Images. DICTA 2016: 1-8 - [c16]Jirí Sedlár, Mariusz Bajger, Martin Caon, Gobert N. Lee:
Model-Guided Segmentation of Liver in CT and PET-CT Images of Child Patients Based on Statistical Region Merging. DICTA 2016: 1-8 - [c15]Shelda Sajeev, Mariusz Bajger, Gobert N. Lee:
Improving Breast Mass Segmentation in Local Dense Background: An Entropy Based Optimization of Statistical Region Merging Method. Digital Mammography / IWDM 2016: 635-642 - 2015
- [c14]Shelda Sajeev, Mariusz Bajger, Gobert N. Lee:
Segmentation of Breast Masses in Local Dense Background Using Adaptive Clip Limit-CLAHE. DICTA 2015: 1-8 - 2014
- [c13]Gobert N. Lee, Mariusz Bajger:
Statistical Temporal Changes for Breast Cancer Detection: A Preliminary Study. Digital Mammography / IWDM 2014: 635-642 - 2012
- [c12]Xi-Zhao Li, Simon Williams, Gobert N. Lee, Min Deng:
Computer-aided mammography classification of malignant mass regions and normal regions based on novel texton features. ICARCV 2012: 1431-1436 - 2010
- [c11]Gobert N. Lee, Toshiaki Okada, Daisuke Fukuoka, Chisako Muramatsu, Takeshi Hara, Takako Morita, Etsuo Takada, Tokiko Endo, Hiroshi Fujita:
Classifying Breast Masses in Volumetric Whole Breast Ultrasound Data: A 2.5-Dimensional Approach. Digital Mammography / IWDM 2010: 636-642 - [c10]Hiroshi Fujita, Jane You, Qin Li, Hidetaka Arimura, Rie Tanaka, Shigeru Sanada, Noboru Niki, Gobert N. Lee, Takeshi Hara, Daisuke Fukuoka, Chisako Muramatsu, Tetsuro Katafuchi, Gen Iinuma, Mototaka Miyake, Yasuaki Arai, Noriyuki Moriyama:
State-of-the-Art of Computer-Aided Detection/Diagnosis (CAD). ICMB 2010: 296-305
2000 – 2009
- 2009
- [j4]Yuji Ikedo, Takako Morita, Daisuke Fukuoka, Takeshi Hara, Gobert N. Lee, Hiroshi Fujita, Etsuo Takada, Tokiko Endo:
Automated analysis of breast parenchymal patterns in whole breast ultrasound images: preliminary experience. Int. J. Comput. Assist. Radiol. Surg. 4(3): 299-306 (2009) - 2008
- [j3]Xiangrong Zhou, Mingxu Han, Takeshi Hara, Hiroshi Fujita, Keiko Sugisaki, Huayue Chen, Gobert N. Lee, Ryujiro Yokoyama, Masayuki Kanematsu, Hiroaki Hoshi:
Automated segmentation of mammary gland regions in non-contrast X-ray CT images. Comput. Medical Imaging Graph. 32(8): 699-709 (2008) - [j2]Hiroshi Fujita, Yoshikazu Uchiyama, Toshiaki Nakagawa, Daisuke Fukuoka, Yuji Hatanaka, Takeshi Hara, Gobert N. Lee, Yoshinori Hayashi, Yuji Ikedo, Xin Gao, Xiangrong Zhou:
Computer-aided diagnosis: The emerging of three CAD systems induced by Japanese health care needs. Comput. Methods Programs Biomed. 92(3): 238-248 (2008) - [c9]Gobert N. Lee, Daisuke Fukuoka, Yuji Ikedo, Takeshi Hara, Hiroshi Fujita, Etsuo Takada, Tokiko Endo, Takako Morita:
Classification of Benign and Malignant Masses in Ultrasound Breast Image Based on Geometric and Echo Features. Digital Mammography / IWDM 2008: 433-439 - [c8]Gobert N. Lee, Masayuki Kanematsu, Hiroki Kato, Hiroshi Kondo, Xiangrong Zhou, Takeshi Hara, Hiroshi Fujita, Hiroaki Hoshi:
Unsupervised classification of cirrhotic livers using MRI data. Computer-Aided Diagnosis 2008: 691514 - 2007
- [c7]Gobert N. Lee, Hiroshi Fujita:
K-means Clustering for Classifying Unlabelled MRI Data. DICTA 2007: 92-98 - [c6]Gobert N. Lee, Yoshikazu Uchiyama, Xuejun Zhang, Masayuki Kanematsu, Xiangrong Zhou, Takeshi Hara, Hiroki Kato, Hiroshi Kondo, Hiroshi Fujita, Hiroaki Hoshi:
Classification of cirrhotic liver in Gadolinium-enhanced MR images. Computer-Aided Diagnosis 2007: 651430 - [c5]Xiangrong Zhou, M. Kan, Takeshi Hara, Hiroshi Fujita, Keiko Sugisaki, Ryujiro Yokoyama, Gobert N. Lee, Hiroaki Hoshi:
Automated segmentation of mammary gland regions in non-contrast torso CT images based on probabilistic atlas. Image Processing 2007: 65123O - [c4]Xuejun Zhang, Gobert N. Lee, Tetsuji Tajima, Teruhiko Kitagawa, Masayuki Kanematsu, Xiangrong Zhou, Takeshi Hara, Hiroshi Fujita, Ryujiro Yokoyama, Hiroshi Kondo, Hiroaki Hoshi, Shigeru Nawano, Kenji Shinozaki:
Segmentation of liver region with tumorous tissues. Image Processing 2007: 651235 - [c3]Hiroshi Fujita, Yoshikazu Uchiyama, Toshiaki Nakagawa, Daisuke Fukuoka, Yuji Hatanaka, Takeshi Hara, Yoshinori Hayashi, Yuji Ikedo, Gobert N. Lee, Xin Gao, Xiangrong Zhou:
CAD on Brain, Fundus, and Breast Images. MIMI 2007: 358-366 - 2006
- [j1]Gobert N. Lee, Murk J. Bottema:
Significance of classification scores subsequent to feature selection. Pattern Recognit. Lett. 27(14): 1702-1709 (2006) - [c2]Gobert N. Lee, Takeshi Hara, Hiroshi Fujita:
Classifying Masses as Benign or Malignant Based on Co-occurrence Matrix Textures: A Comparison Study of Different Gray Level Quantizations. Digital Mammography / IWDM 2006: 332-339 - [c1]Gobert N. Lee, Murk J. Bottema, Takeshi Hara, Hiroshi Fujita:
Effect of quantization on co-occurrence matrix based texture features: An example study in mammography. Image Processing 2006: 61445C
Coauthor Index
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