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Medical imaging

Generation of head models for brain stimulation using deep convolution networks


Transcranial magnetic stimulation (TMS) is a non-invasive clinical technique used for treatment of several neurological diseases such as depression, Alzheimer’s disease and Parkinson’s disease. However, it is always challenging to accurately adjust the electric field on different specific brain regions due to the requirement of several stimulation parameters’ optimizations.

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Authors:
Essam A. Rashed, Jose Gomez-Tames, Akimasa Hirata
Submitted On:
18 September 2019 - 12:31am
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[1] Essam A. Rashed, Jose Gomez-Tames, Akimasa Hirata, "Generation of head models for brain stimulation using deep convolution networks", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4663. Accessed: Sep. 20, 2019.
@article{4663-19,
url = {http://sigport.org/4663},
author = {Essam A. Rashed; Jose Gomez-Tames; Akimasa Hirata },
publisher = {IEEE SigPort},
title = {Generation of head models for brain stimulation using deep convolution networks},
year = {2019} }
TY - EJOUR
T1 - Generation of head models for brain stimulation using deep convolution networks
AU - Essam A. Rashed; Jose Gomez-Tames; Akimasa Hirata
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4663
ER -
Essam A. Rashed, Jose Gomez-Tames, Akimasa Hirata. (2019). Generation of head models for brain stimulation using deep convolution networks. IEEE SigPort. http://sigport.org/4663
Essam A. Rashed, Jose Gomez-Tames, Akimasa Hirata, 2019. Generation of head models for brain stimulation using deep convolution networks. Available at: http://sigport.org/4663.
Essam A. Rashed, Jose Gomez-Tames, Akimasa Hirata. (2019). "Generation of head models for brain stimulation using deep convolution networks." Web.
1. Essam A. Rashed, Jose Gomez-Tames, Akimasa Hirata. Generation of head models for brain stimulation using deep convolution networks [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4663

COMPRESSED SENSING MRI WITH JOINT IMAGE-LEVEL AND PATCH-LEVEL PRIORS

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10 September 2019 - 9:57pm
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[1] , "COMPRESSED SENSING MRI WITH JOINT IMAGE-LEVEL AND PATCH-LEVEL PRIORS", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4580. Accessed: Sep. 20, 2019.
@article{4580-19,
url = {http://sigport.org/4580},
author = { },
publisher = {IEEE SigPort},
title = {COMPRESSED SENSING MRI WITH JOINT IMAGE-LEVEL AND PATCH-LEVEL PRIORS},
year = {2019} }
TY - EJOUR
T1 - COMPRESSED SENSING MRI WITH JOINT IMAGE-LEVEL AND PATCH-LEVEL PRIORS
AU -
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4580
ER -
. (2019). COMPRESSED SENSING MRI WITH JOINT IMAGE-LEVEL AND PATCH-LEVEL PRIORS. IEEE SigPort. http://sigport.org/4580
, 2019. COMPRESSED SENSING MRI WITH JOINT IMAGE-LEVEL AND PATCH-LEVEL PRIORS. Available at: http://sigport.org/4580.
. (2019). "COMPRESSED SENSING MRI WITH JOINT IMAGE-LEVEL AND PATCH-LEVEL PRIORS." Web.
1. . COMPRESSED SENSING MRI WITH JOINT IMAGE-LEVEL AND PATCH-LEVEL PRIORS [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4580

ADVERSARIAL INPAINTING OF MEDICAL IMAGE MODALITIES

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Authors:
Karim Armanious, Youssef Mecky, Sergios Gatidis, Bin Yang
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12 May 2019 - 4:02pm
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[1] Karim Armanious, Youssef Mecky, Sergios Gatidis, Bin Yang, "ADVERSARIAL INPAINTING OF MEDICAL IMAGE MODALITIES", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4470. Accessed: Sep. 20, 2019.
@article{4470-19,
url = {http://sigport.org/4470},
author = {Karim Armanious; Youssef Mecky; Sergios Gatidis; Bin Yang },
publisher = {IEEE SigPort},
title = {ADVERSARIAL INPAINTING OF MEDICAL IMAGE MODALITIES},
year = {2019} }
TY - EJOUR
T1 - ADVERSARIAL INPAINTING OF MEDICAL IMAGE MODALITIES
AU - Karim Armanious; Youssef Mecky; Sergios Gatidis; Bin Yang
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4470
ER -
Karim Armanious, Youssef Mecky, Sergios Gatidis, Bin Yang. (2019). ADVERSARIAL INPAINTING OF MEDICAL IMAGE MODALITIES. IEEE SigPort. http://sigport.org/4470
Karim Armanious, Youssef Mecky, Sergios Gatidis, Bin Yang, 2019. ADVERSARIAL INPAINTING OF MEDICAL IMAGE MODALITIES. Available at: http://sigport.org/4470.
Karim Armanious, Youssef Mecky, Sergios Gatidis, Bin Yang. (2019). "ADVERSARIAL INPAINTING OF MEDICAL IMAGE MODALITIES." Web.
1. Karim Armanious, Youssef Mecky, Sergios Gatidis, Bin Yang. ADVERSARIAL INPAINTING OF MEDICAL IMAGE MODALITIES [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4470

Learned Mixed Material Models for Efficient Clustering Based Dual-Energy CT Image Decomposition

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Zhipeng Li, Saiprasad Ravishankar, Yong Long, Jeffrey A. Fessler
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21 December 2018 - 3:48pm
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[1] Zhipeng Li, Saiprasad Ravishankar, Yong Long, Jeffrey A. Fessler, "Learned Mixed Material Models for Efficient Clustering Based Dual-Energy CT Image Decomposition", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3705. Accessed: Sep. 20, 2019.
@article{3705-18,
url = {http://sigport.org/3705},
author = {Zhipeng Li; Saiprasad Ravishankar; Yong Long; Jeffrey A. Fessler },
publisher = {IEEE SigPort},
title = {Learned Mixed Material Models for Efficient Clustering Based Dual-Energy CT Image Decomposition},
year = {2018} }
TY - EJOUR
T1 - Learned Mixed Material Models for Efficient Clustering Based Dual-Energy CT Image Decomposition
AU - Zhipeng Li; Saiprasad Ravishankar; Yong Long; Jeffrey A. Fessler
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3705
ER -
Zhipeng Li, Saiprasad Ravishankar, Yong Long, Jeffrey A. Fessler. (2018). Learned Mixed Material Models for Efficient Clustering Based Dual-Energy CT Image Decomposition. IEEE SigPort. http://sigport.org/3705
Zhipeng Li, Saiprasad Ravishankar, Yong Long, Jeffrey A. Fessler, 2018. Learned Mixed Material Models for Efficient Clustering Based Dual-Energy CT Image Decomposition. Available at: http://sigport.org/3705.
Zhipeng Li, Saiprasad Ravishankar, Yong Long, Jeffrey A. Fessler. (2018). "Learned Mixed Material Models for Efficient Clustering Based Dual-Energy CT Image Decomposition." Web.
1. Zhipeng Li, Saiprasad Ravishankar, Yong Long, Jeffrey A. Fessler. Learned Mixed Material Models for Efficient Clustering Based Dual-Energy CT Image Decomposition [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3705

BI-RADS classification of breat cancer: a new pre-processing pipeline for deep models training


One of the main difficulties in the use of deep learning strategies in medical contexts is the training set size. While these methods need large annotated training sets, this data is costly to obtain in medical contexts and suffers from intra and iter-subject variability.

In the present work, two new pre-processing techniques are introduced to improve a classifier performance. First, data augmentation based on co-registration is suggested. Then, multi-scale enhancement based on Difference of Gaussians is proposed.

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Authors:
Inês Domingues, Pedro H. Abreu, João Santos
Submitted On:
4 October 2018 - 12:24pm
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[1] Inês Domingues, Pedro H. Abreu, João Santos, "BI-RADS classification of breat cancer: a new pre-processing pipeline for deep models training", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3452. Accessed: Sep. 20, 2019.
@article{3452-18,
url = {http://sigport.org/3452},
author = {Inês Domingues; Pedro H. Abreu; João Santos },
publisher = {IEEE SigPort},
title = {BI-RADS classification of breat cancer: a new pre-processing pipeline for deep models training},
year = {2018} }
TY - EJOUR
T1 - BI-RADS classification of breat cancer: a new pre-processing pipeline for deep models training
AU - Inês Domingues; Pedro H. Abreu; João Santos
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3452
ER -
Inês Domingues, Pedro H. Abreu, João Santos. (2018). BI-RADS classification of breat cancer: a new pre-processing pipeline for deep models training. IEEE SigPort. http://sigport.org/3452
Inês Domingues, Pedro H. Abreu, João Santos, 2018. BI-RADS classification of breat cancer: a new pre-processing pipeline for deep models training. Available at: http://sigport.org/3452.
Inês Domingues, Pedro H. Abreu, João Santos. (2018). "BI-RADS classification of breat cancer: a new pre-processing pipeline for deep models training." Web.
1. Inês Domingues, Pedro H. Abreu, João Santos. BI-RADS classification of breat cancer: a new pre-processing pipeline for deep models training [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3452

Deep Residual Learning for Model-Based Iterative CT Reconstruction using Plug-and-Play Framework


Model-Based Iterative Reconstruction (MBIR) has shown promising results in clinical studies as they allow significant

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Authors:
Dong Hye Ye, Somesh Srivastava, Jean-Baptiste Thibault, Ken Sauer, Charles Bouman
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19 April 2018 - 7:12pm
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[1] Dong Hye Ye, Somesh Srivastava, Jean-Baptiste Thibault, Ken Sauer, Charles Bouman, "Deep Residual Learning for Model-Based Iterative CT Reconstruction using Plug-and-Play Framework", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3038. Accessed: Sep. 20, 2019.
@article{3038-18,
url = {http://sigport.org/3038},
author = {Dong Hye Ye; Somesh Srivastava; Jean-Baptiste Thibault; Ken Sauer; Charles Bouman },
publisher = {IEEE SigPort},
title = {Deep Residual Learning for Model-Based Iterative CT Reconstruction using Plug-and-Play Framework},
year = {2018} }
TY - EJOUR
T1 - Deep Residual Learning for Model-Based Iterative CT Reconstruction using Plug-and-Play Framework
AU - Dong Hye Ye; Somesh Srivastava; Jean-Baptiste Thibault; Ken Sauer; Charles Bouman
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3038
ER -
Dong Hye Ye, Somesh Srivastava, Jean-Baptiste Thibault, Ken Sauer, Charles Bouman. (2018). Deep Residual Learning for Model-Based Iterative CT Reconstruction using Plug-and-Play Framework. IEEE SigPort. http://sigport.org/3038
Dong Hye Ye, Somesh Srivastava, Jean-Baptiste Thibault, Ken Sauer, Charles Bouman, 2018. Deep Residual Learning for Model-Based Iterative CT Reconstruction using Plug-and-Play Framework. Available at: http://sigport.org/3038.
Dong Hye Ye, Somesh Srivastava, Jean-Baptiste Thibault, Ken Sauer, Charles Bouman. (2018). "Deep Residual Learning for Model-Based Iterative CT Reconstruction using Plug-and-Play Framework." Web.
1. Dong Hye Ye, Somesh Srivastava, Jean-Baptiste Thibault, Ken Sauer, Charles Bouman. Deep Residual Learning for Model-Based Iterative CT Reconstruction using Plug-and-Play Framework [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3038

Restoration of ultrasound images using spatially-variant kernel deconvolution


Most of the existing ultrasound image restoration methods consider a spatially-invariant point-spread function (PSF) model and circulant boundary conditions. While computationally efficient, this model is not realistic and severely limits the quality of reconstructed images. In this work, we address ultrasound image restoration under the hypothesis of vertical variation of the PSF. To regularize the solution, we use the classical elastic net constraint.

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Authors:
Mihai I. Florea, Adrian Basarab, Denis Kouame, Sergiy A. Vorobyov
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17 April 2018 - 9:01pm
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[1] Mihai I. Florea, Adrian Basarab, Denis Kouame, Sergiy A. Vorobyov, "Restoration of ultrasound images using spatially-variant kernel deconvolution", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/2954. Accessed: Sep. 20, 2019.
@article{2954-18,
url = {http://sigport.org/2954},
author = {Mihai I. Florea; Adrian Basarab; Denis Kouame; Sergiy A. Vorobyov },
publisher = {IEEE SigPort},
title = {Restoration of ultrasound images using spatially-variant kernel deconvolution},
year = {2018} }
TY - EJOUR
T1 - Restoration of ultrasound images using spatially-variant kernel deconvolution
AU - Mihai I. Florea; Adrian Basarab; Denis Kouame; Sergiy A. Vorobyov
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/2954
ER -
Mihai I. Florea, Adrian Basarab, Denis Kouame, Sergiy A. Vorobyov. (2018). Restoration of ultrasound images using spatially-variant kernel deconvolution. IEEE SigPort. http://sigport.org/2954
Mihai I. Florea, Adrian Basarab, Denis Kouame, Sergiy A. Vorobyov, 2018. Restoration of ultrasound images using spatially-variant kernel deconvolution. Available at: http://sigport.org/2954.
Mihai I. Florea, Adrian Basarab, Denis Kouame, Sergiy A. Vorobyov. (2018). "Restoration of ultrasound images using spatially-variant kernel deconvolution." Web.
1. Mihai I. Florea, Adrian Basarab, Denis Kouame, Sergiy A. Vorobyov. Restoration of ultrasound images using spatially-variant kernel deconvolution [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/2954

AN ATTENUATION ADAPTED PULSE COMPRESSION TECHNIQUE TO ENHANCE THE BANDWIDTH AND THE RESOLUTION USING ULTRAFAST ULTRASOUND IMAGING


Recent studies suggest that Resolution Enhancement Compression (REC) can provide significant improvements in terms of imaging quality over Classical Pulsed (CP) ultrasonic imaging techniques, by employing frequency and amplitude modulated transmitted signals. However the performance of coded excitations methods degrades drastically deeper into the tissue where the attenuation effects become more significant. In this work, a technique that allows overcoming the effects of attenuation on REC imaging is proposed (REC-Opt).

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Authors:
Yanis Mehdi Benane, Denis Bujoreanu, Roberto Lavarello, Christian Cachard, Olivier Basset
Submitted On:
13 April 2018 - 8:18am
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[1] Yanis Mehdi Benane, Denis Bujoreanu, Roberto Lavarello, Christian Cachard, Olivier Basset, "AN ATTENUATION ADAPTED PULSE COMPRESSION TECHNIQUE TO ENHANCE THE BANDWIDTH AND THE RESOLUTION USING ULTRAFAST ULTRASOUND IMAGING", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/2477. Accessed: Sep. 20, 2019.
@article{2477-18,
url = {http://sigport.org/2477},
author = {Yanis Mehdi Benane; Denis Bujoreanu; Roberto Lavarello; Christian Cachard; Olivier Basset },
publisher = {IEEE SigPort},
title = {AN ATTENUATION ADAPTED PULSE COMPRESSION TECHNIQUE TO ENHANCE THE BANDWIDTH AND THE RESOLUTION USING ULTRAFAST ULTRASOUND IMAGING},
year = {2018} }
TY - EJOUR
T1 - AN ATTENUATION ADAPTED PULSE COMPRESSION TECHNIQUE TO ENHANCE THE BANDWIDTH AND THE RESOLUTION USING ULTRAFAST ULTRASOUND IMAGING
AU - Yanis Mehdi Benane; Denis Bujoreanu; Roberto Lavarello; Christian Cachard; Olivier Basset
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/2477
ER -
Yanis Mehdi Benane, Denis Bujoreanu, Roberto Lavarello, Christian Cachard, Olivier Basset. (2018). AN ATTENUATION ADAPTED PULSE COMPRESSION TECHNIQUE TO ENHANCE THE BANDWIDTH AND THE RESOLUTION USING ULTRAFAST ULTRASOUND IMAGING. IEEE SigPort. http://sigport.org/2477
Yanis Mehdi Benane, Denis Bujoreanu, Roberto Lavarello, Christian Cachard, Olivier Basset, 2018. AN ATTENUATION ADAPTED PULSE COMPRESSION TECHNIQUE TO ENHANCE THE BANDWIDTH AND THE RESOLUTION USING ULTRAFAST ULTRASOUND IMAGING. Available at: http://sigport.org/2477.
Yanis Mehdi Benane, Denis Bujoreanu, Roberto Lavarello, Christian Cachard, Olivier Basset. (2018). "AN ATTENUATION ADAPTED PULSE COMPRESSION TECHNIQUE TO ENHANCE THE BANDWIDTH AND THE RESOLUTION USING ULTRAFAST ULTRASOUND IMAGING." Web.
1. Yanis Mehdi Benane, Denis Bujoreanu, Roberto Lavarello, Christian Cachard, Olivier Basset. AN ATTENUATION ADAPTED PULSE COMPRESSION TECHNIQUE TO ENHANCE THE BANDWIDTH AND THE RESOLUTION USING ULTRAFAST ULTRASOUND IMAGING [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/2477

SEGMENTATION AND TRACKING OF INFERIOR VENA CAVA IN ULTRASOUND IMAGES USING A NOVEL POLAR ACTIVE CONTOUR ALGORITHM


Medical research suggests that the area of the IVC and its temporal variation imaged by bedside ultrasound is useful in guiding resuscitation of the critically-ill. Unfortunately, gaps in the vessel wall and intraliminal artifact represents a challenge for both manual and existing algorithm-based segmentation techniques.

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Ebrahim Karami, Mohamed Shehata, Andrew Smith
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13 November 2017 - 12:50am
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[1] Ebrahim Karami, Mohamed Shehata, Andrew Smith, "SEGMENTATION AND TRACKING OF INFERIOR VENA CAVA IN ULTRASOUND IMAGES USING A NOVEL POLAR ACTIVE CONTOUR ALGORITHM", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/2330. Accessed: Sep. 20, 2019.
@article{2330-17,
url = {http://sigport.org/2330},
author = {Ebrahim Karami; Mohamed Shehata; Andrew Smith },
publisher = {IEEE SigPort},
title = {SEGMENTATION AND TRACKING OF INFERIOR VENA CAVA IN ULTRASOUND IMAGES USING A NOVEL POLAR ACTIVE CONTOUR ALGORITHM},
year = {2017} }
TY - EJOUR
T1 - SEGMENTATION AND TRACKING OF INFERIOR VENA CAVA IN ULTRASOUND IMAGES USING A NOVEL POLAR ACTIVE CONTOUR ALGORITHM
AU - Ebrahim Karami; Mohamed Shehata; Andrew Smith
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/2330
ER -
Ebrahim Karami, Mohamed Shehata, Andrew Smith. (2017). SEGMENTATION AND TRACKING OF INFERIOR VENA CAVA IN ULTRASOUND IMAGES USING A NOVEL POLAR ACTIVE CONTOUR ALGORITHM. IEEE SigPort. http://sigport.org/2330
Ebrahim Karami, Mohamed Shehata, Andrew Smith, 2017. SEGMENTATION AND TRACKING OF INFERIOR VENA CAVA IN ULTRASOUND IMAGES USING A NOVEL POLAR ACTIVE CONTOUR ALGORITHM. Available at: http://sigport.org/2330.
Ebrahim Karami, Mohamed Shehata, Andrew Smith. (2017). "SEGMENTATION AND TRACKING OF INFERIOR VENA CAVA IN ULTRASOUND IMAGES USING A NOVEL POLAR ACTIVE CONTOUR ALGORITHM." Web.
1. Ebrahim Karami, Mohamed Shehata, Andrew Smith. SEGMENTATION AND TRACKING OF INFERIOR VENA CAVA IN ULTRASOUND IMAGES USING A NOVEL POLAR ACTIVE CONTOUR ALGORITHM [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/2330

3D SHAPE ASYMMETRY ANALYSIS USING CORRESPONDENCE BETWEEN PARTIAL GEODESIC CURVES


Analyzing the asymmetry of anatomical shapes is one of the cornerstones of efficient computerized diagnosis. In the application of scoliotic trunk analysis, one major challenge is the high variability and complexity of deformations due to the pathology itself, and to changes of body poses, for instance, torsos acquired in lateral bending poses for surgical planning. In this paper, we present a novel and fully automatic approach to analyzing the asymmetry of deformable trunk shapes.

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15 November 2017 - 11:28am
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[1] , "3D SHAPE ASYMMETRY ANALYSIS USING CORRESPONDENCE BETWEEN PARTIAL GEODESIC CURVES", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/2328. Accessed: Sep. 20, 2019.
@article{2328-17,
url = {http://sigport.org/2328},
author = { },
publisher = {IEEE SigPort},
title = {3D SHAPE ASYMMETRY ANALYSIS USING CORRESPONDENCE BETWEEN PARTIAL GEODESIC CURVES},
year = {2017} }
TY - EJOUR
T1 - 3D SHAPE ASYMMETRY ANALYSIS USING CORRESPONDENCE BETWEEN PARTIAL GEODESIC CURVES
AU -
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/2328
ER -
. (2017). 3D SHAPE ASYMMETRY ANALYSIS USING CORRESPONDENCE BETWEEN PARTIAL GEODESIC CURVES. IEEE SigPort. http://sigport.org/2328
, 2017. 3D SHAPE ASYMMETRY ANALYSIS USING CORRESPONDENCE BETWEEN PARTIAL GEODESIC CURVES. Available at: http://sigport.org/2328.
. (2017). "3D SHAPE ASYMMETRY ANALYSIS USING CORRESPONDENCE BETWEEN PARTIAL GEODESIC CURVES." Web.
1. . 3D SHAPE ASYMMETRY ANALYSIS USING CORRESPONDENCE BETWEEN PARTIAL GEODESIC CURVES [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/2328

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