Dynamic adaptive residual network for liver CT image segmentation

Computers & Electrical Engineering ◽

10.1016/j.compeleceng.2021.107024 ◽

2021 ◽

Vol 91 ◽

pp. 107024 ◽

Author(s):

Xiwang Xie ◽

Weidong Zhang ◽

Huadeng Wang ◽

Lingqiao Li ◽

Zhengyun Feng ◽

...

Keyword(s):

Image Segmentation ◽

Ct Image ◽

Residual Network ◽

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Fuzzy C-Means Based Liver CT Image Segmentation with Optimum Number of Clusters

Advances in Intelligent Systems and Computing - Proceedings of the Fifth International Conference on Innovations in Bio-Inspired Computing and Applications IBICA 2014 ◽

10.1007/978-3-319-08156-4_14 ◽

2014 ◽

pp. 131-139 ◽

Author(s):

Abder-Rahman Ali ◽

Micael Couceiro ◽

Aboul Ella Hassanien ◽

Mohamed F. Tolba ◽

Václav Snášel

Keyword(s):

Image Segmentation ◽

Optimum Number ◽

Ct Image ◽

Number Of Clusters ◽

Liver Ct ◽

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Liver CT Image Segmentation with an Optimum Threshold Using Measure of Fuzziness

Advances in Intelligent Systems and Computing - Proceedings of the Fifth International Conference on Innovations in Bio-Inspired Computing and Applications IBICA 2014 ◽

10.1007/978-3-319-08156-4_9 ◽

2014 ◽

pp. 83-92 ◽

Author(s):

Abder-Rahman Ali ◽

Micael Couceiro ◽

Ahmed M. Anter ◽

Aboul Ella Hassanien ◽

Mohamed F. Tolba ◽

...

Keyword(s):

Image Segmentation ◽

Ct Image ◽

Liver Ct ◽

Optimum Threshold

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Attention Unet++: A Nested Attention-Aware U-Net for Liver CT Image Segmentation

2020 IEEE International Conference on Image Processing (ICIP) ◽

10.1109/icip40778.2020.9190761 ◽

2020 ◽

Author(s):

Chen Li ◽

Yusong Tan ◽

Wei Chen ◽

Xin Luo ◽

Yuanming Gao ◽

...

Keyword(s):

Image Segmentation ◽

Ct Image ◽

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Performance benchmarking of liver CT image segmentation and volume estimation

10.1117/12.770858 ◽

2008 ◽

Author(s):

Wei Xiong ◽

Jiayin Zhou ◽

Qi Tian ◽

Jimmy J. Liu ◽

Yingyi Qi ◽

...

Keyword(s):

Image Segmentation ◽

Volume Estimation ◽

Ct Image ◽

Liver Ct ◽

Performance Benchmarking

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Liver CT image segmentation by local entropy method

2010 International Conference on Computer Application and System Modeling (ICCASM 2010) ◽

10.1109/iccasm.2010.5623136 ◽

2010 ◽

Author(s):

Yi Xu ◽

Zhon Liu ◽

Ji Lin

Keyword(s):

Image Segmentation ◽

Entropy Method ◽

Ct Image ◽

Local Entropy ◽

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Liver CT image segmentation algorithm research based on CV model

2017 3rd IEEE International Conference on Computer and Communications (ICCC) ◽

10.1109/compcomm.2017.8322866 ◽

2017 ◽

Author(s):

Xiangxin Shao ◽

Xiaomei Lin ◽

Tingting Shang

Keyword(s):

Image Segmentation ◽

Segmentation Algorithm ◽

Ct Image ◽

Liver Ct ◽

Cv Model ◽

Image Segmentation Algorithm

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Fuzzy C-Means based on Minkowski distance for liver CT image segmentation

Intelligent Decision Technologies ◽

10.3233/idt-160266 ◽

2016 ◽

Vol 10 (4) ◽

pp. 393-406 ◽

Author(s):

Abder-Rahman Ali ◽

Micael S. Couceiro ◽

Aboul Ella Hassanien ◽

D. Jude Hemanth

Keyword(s):

Image Segmentation ◽

Ct Image ◽

Minkowski Distance ◽

Liver Ct ◽

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Liver CT Image Segmentation Based on Modified Canny Algorithm

2019 12th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI) ◽

10.1109/cisp-bmei48845.2019.8966082 ◽

2019 ◽

Author(s):

Wei Peng ◽

Yuan Zhao

Keyword(s):

Image Segmentation ◽

Ct Image ◽

Liver Ct ◽

Canny Algorithm

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Application of U-Shaped Convolutional Neural Network Based on Attention Mechanism in Liver CT Image Segmentation

Medical Imaging and Computer-Aided Diagnosis - Lecture Notes in Electrical Engineering ◽

10.1007/978-981-15-5199-4_20 ◽

2020 ◽

pp. 198-206

Author(s):

Chen Li ◽

Wei Chen ◽

Xin Luo ◽

Mingfei Wu ◽

Xiaogang Jia ◽

...

Keyword(s):

Neural Network ◽

Image Segmentation ◽

Convolutional Neural Network ◽

Attention Mechanism ◽

Ct Image ◽

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Survey on liver CT image segmentation methods

Artificial Intelligence Review ◽

10.1007/s10462-011-9220-3 ◽

2011 ◽

Vol 37 (2) ◽

pp. 83-95 ◽

Author(s):

Ahmed M. Mharib ◽

Abdul Rahman Ramli ◽

Syamsiah Mashohor ◽

Rozi Binti Mahmood

Keyword(s):

Image Segmentation ◽

Ct Image ◽

Liver Ct ◽

Segmentation Methods

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