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Medical image analysis

An algorithm for multi subject fMRI analysis based on the SVD and penalized rank-1 matrix approximation


In recent years, data driven methods have been successfully used for analyzing multi-subject functional magnetic resonance imaging (fMRI) datasets. These methods attempt to learn shared spatial activation maps (SM) or voxel time courses (TC) from temporally or spatially concatenated fMRI datasets respectively. Most of the methods proposed so far do not distinguish whether a particular SM/TC is a group level component or only present in a certain subject dataset.

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Authors:
Asif Iqbal, Abd-Krim Seghouane
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14 April 2018 - 9:28pm
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Poster_SVD_1970.pdf

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[1] Asif Iqbal, Abd-Krim Seghouane, "An algorithm for multi subject fMRI analysis based on the SVD and penalized rank-1 matrix approximation", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/2868. Accessed: May. 25, 2018.
@article{2868-18,
url = {http://sigport.org/2868},
author = {Asif Iqbal; Abd-Krim Seghouane },
publisher = {IEEE SigPort},
title = {An algorithm for multi subject fMRI analysis based on the SVD and penalized rank-1 matrix approximation},
year = {2018} }
TY - EJOUR
T1 - An algorithm for multi subject fMRI analysis based on the SVD and penalized rank-1 matrix approximation
AU - Asif Iqbal; Abd-Krim Seghouane
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/2868
ER -
Asif Iqbal, Abd-Krim Seghouane. (2018). An algorithm for multi subject fMRI analysis based on the SVD and penalized rank-1 matrix approximation. IEEE SigPort. http://sigport.org/2868
Asif Iqbal, Abd-Krim Seghouane, 2018. An algorithm for multi subject fMRI analysis based on the SVD and penalized rank-1 matrix approximation. Available at: http://sigport.org/2868.
Asif Iqbal, Abd-Krim Seghouane. (2018). "An algorithm for multi subject fMRI analysis based on the SVD and penalized rank-1 matrix approximation." Web.
1. Asif Iqbal, Abd-Krim Seghouane. An algorithm for multi subject fMRI analysis based on the SVD and penalized rank-1 matrix approximation [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/2868

Dictionary learning algorithm for Multi-Subject fMRI analysis via temporal and spatial concatenation


In recent history, dictionary learning (DL) methods have been successfully used for analyzing multi-subject functional magnetic resonance imaging. These algorithms try to learn group-level spatial activation maps (SM) or voxel time courses (TC) from temporally or spatially concatenated fMRI datasets respectively. However, in multi-subject fMRI studies, we are interested in both group-level TCs as well as SMs.

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Authors:
Asif Iqbal, Abd-Krim Seghouane
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14 April 2018 - 9:20pm
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Poster_DL_1964.pdf

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[1] Asif Iqbal, Abd-Krim Seghouane, "Dictionary learning algorithm for Multi-Subject fMRI analysis via temporal and spatial concatenation", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/2867. Accessed: May. 25, 2018.
@article{2867-18,
url = {http://sigport.org/2867},
author = {Asif Iqbal; Abd-Krim Seghouane },
publisher = {IEEE SigPort},
title = {Dictionary learning algorithm for Multi-Subject fMRI analysis via temporal and spatial concatenation},
year = {2018} }
TY - EJOUR
T1 - Dictionary learning algorithm for Multi-Subject fMRI analysis via temporal and spatial concatenation
AU - Asif Iqbal; Abd-Krim Seghouane
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/2867
ER -
Asif Iqbal, Abd-Krim Seghouane. (2018). Dictionary learning algorithm for Multi-Subject fMRI analysis via temporal and spatial concatenation. IEEE SigPort. http://sigport.org/2867
Asif Iqbal, Abd-Krim Seghouane, 2018. Dictionary learning algorithm for Multi-Subject fMRI analysis via temporal and spatial concatenation. Available at: http://sigport.org/2867.
Asif Iqbal, Abd-Krim Seghouane. (2018). "Dictionary learning algorithm for Multi-Subject fMRI analysis via temporal and spatial concatenation." Web.
1. Asif Iqbal, Abd-Krim Seghouane. Dictionary learning algorithm for Multi-Subject fMRI analysis via temporal and spatial concatenation [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/2867

PULMONARY TEXTURES CLASSIFICATION USING A DEEP NEURAL NETWORK WITH APPEARANCE AND GEOMETRY CUES

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14 April 2018 - 8:41am
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icassp2018poster-cz-new.pdf

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[1] , "PULMONARY TEXTURES CLASSIFICATION USING A DEEP NEURAL NETWORK WITH APPEARANCE AND GEOMETRY CUES", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/2828. Accessed: May. 25, 2018.
@article{2828-18,
url = {http://sigport.org/2828},
author = { },
publisher = {IEEE SigPort},
title = {PULMONARY TEXTURES CLASSIFICATION USING A DEEP NEURAL NETWORK WITH APPEARANCE AND GEOMETRY CUES},
year = {2018} }
TY - EJOUR
T1 - PULMONARY TEXTURES CLASSIFICATION USING A DEEP NEURAL NETWORK WITH APPEARANCE AND GEOMETRY CUES
AU -
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/2828
ER -
. (2018). PULMONARY TEXTURES CLASSIFICATION USING A DEEP NEURAL NETWORK WITH APPEARANCE AND GEOMETRY CUES. IEEE SigPort. http://sigport.org/2828
, 2018. PULMONARY TEXTURES CLASSIFICATION USING A DEEP NEURAL NETWORK WITH APPEARANCE AND GEOMETRY CUES. Available at: http://sigport.org/2828.
. (2018). "PULMONARY TEXTURES CLASSIFICATION USING A DEEP NEURAL NETWORK WITH APPEARANCE AND GEOMETRY CUES." Web.
1. . PULMONARY TEXTURES CLASSIFICATION USING A DEEP NEURAL NETWORK WITH APPEARANCE AND GEOMETRY CUES [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/2828

REMOTE PHOTOPLETHYSMOGRAPHY USING NONLINEAR MODE DECOMPOSITION


Remote Photoplethysmography (rPPG) is a contactless noninvasive
method for measuring physiological signals such as
the heart rate (HR) using the light reflected from the facial
tissue. Signal decomposition approaches are used to extract
the heart rate signal from the subtle changes in the skin color.
In this paper, we show that a recently proposed signal decomposition
method, namely nonlinear mode decomposition
(NMD), is quite successful in estimating the heart rate signal
from face videos in the presence of subject motion. Experimental

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Authors:
Halil Demirezen, Cigdem Eroglu Erdem
Submitted On:
13 April 2018 - 4:00pm
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NMD_Halil_PDF.pdf

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[1] Halil Demirezen, Cigdem Eroglu Erdem, "REMOTE PHOTOPLETHYSMOGRAPHY USING NONLINEAR MODE DECOMPOSITION", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/2757. Accessed: May. 25, 2018.
@article{2757-18,
url = {http://sigport.org/2757},
author = {Halil Demirezen; Cigdem Eroglu Erdem },
publisher = {IEEE SigPort},
title = {REMOTE PHOTOPLETHYSMOGRAPHY USING NONLINEAR MODE DECOMPOSITION},
year = {2018} }
TY - EJOUR
T1 - REMOTE PHOTOPLETHYSMOGRAPHY USING NONLINEAR MODE DECOMPOSITION
AU - Halil Demirezen; Cigdem Eroglu Erdem
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/2757
ER -
Halil Demirezen, Cigdem Eroglu Erdem. (2018). REMOTE PHOTOPLETHYSMOGRAPHY USING NONLINEAR MODE DECOMPOSITION. IEEE SigPort. http://sigport.org/2757
Halil Demirezen, Cigdem Eroglu Erdem, 2018. REMOTE PHOTOPLETHYSMOGRAPHY USING NONLINEAR MODE DECOMPOSITION. Available at: http://sigport.org/2757.
Halil Demirezen, Cigdem Eroglu Erdem. (2018). "REMOTE PHOTOPLETHYSMOGRAPHY USING NONLINEAR MODE DECOMPOSITION." Web.
1. Halil Demirezen, Cigdem Eroglu Erdem. REMOTE PHOTOPLETHYSMOGRAPHY USING NONLINEAR MODE DECOMPOSITION [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/2757

automatic segmentation and cardiopathy classification in cardiac MRI images based on deep neural networks


Segmentation of cardiac MRI images plays a key role in clinical diagnosis. In the traditional diagnostic process, clinical experts manually segment left ventricle (LV), right ventricle (RV) and myocardium to obtain guideline for cardiopathy diagnosis. However, manual segmentation is time-consuming and labor-intensive. In this paper, we propose automatic segmentation and cardiopathy classification in cardiac MRI images
based on deep neural networks. First, we perform object detection based on a YOLO-based network to get region of interest

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Authors:
Baoyu Song,Cheolkon Jung,Liyu Huang
Submitted On:
13 April 2018 - 7:51am
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ICASSP2018poster_Cardiac_rev_final.pdf

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[1] Baoyu Song,Cheolkon Jung,Liyu Huang, "automatic segmentation and cardiopathy classification in cardiac MRI images based on deep neural networks ", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/2689. Accessed: May. 25, 2018.
@article{2689-18,
url = {http://sigport.org/2689},
author = {Baoyu Song;Cheolkon Jung;Liyu Huang },
publisher = {IEEE SigPort},
title = {automatic segmentation and cardiopathy classification in cardiac MRI images based on deep neural networks },
year = {2018} }
TY - EJOUR
T1 - automatic segmentation and cardiopathy classification in cardiac MRI images based on deep neural networks
AU - Baoyu Song;Cheolkon Jung;Liyu Huang
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/2689
ER -
Baoyu Song,Cheolkon Jung,Liyu Huang. (2018). automatic segmentation and cardiopathy classification in cardiac MRI images based on deep neural networks . IEEE SigPort. http://sigport.org/2689
Baoyu Song,Cheolkon Jung,Liyu Huang, 2018. automatic segmentation and cardiopathy classification in cardiac MRI images based on deep neural networks . Available at: http://sigport.org/2689.
Baoyu Song,Cheolkon Jung,Liyu Huang. (2018). "automatic segmentation and cardiopathy classification in cardiac MRI images based on deep neural networks ." Web.
1. Baoyu Song,Cheolkon Jung,Liyu Huang. automatic segmentation and cardiopathy classification in cardiac MRI images based on deep neural networks [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/2689

SIMULTANEOUS ACCURATE DETECTION OF PULMONARY NODULES AND FALSE POSITIVE REDUCTION USING 3D CNNS

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12 April 2018 - 9:43pm
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ICASSP_qyl.pdf

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[1] , "SIMULTANEOUS ACCURATE DETECTION OF PULMONARY NODULES AND FALSE POSITIVE REDUCTION USING 3D CNNS", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/2549. Accessed: May. 25, 2018.
@article{2549-18,
url = {http://sigport.org/2549},
author = { },
publisher = {IEEE SigPort},
title = {SIMULTANEOUS ACCURATE DETECTION OF PULMONARY NODULES AND FALSE POSITIVE REDUCTION USING 3D CNNS},
year = {2018} }
TY - EJOUR
T1 - SIMULTANEOUS ACCURATE DETECTION OF PULMONARY NODULES AND FALSE POSITIVE REDUCTION USING 3D CNNS
AU -
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/2549
ER -
. (2018). SIMULTANEOUS ACCURATE DETECTION OF PULMONARY NODULES AND FALSE POSITIVE REDUCTION USING 3D CNNS. IEEE SigPort. http://sigport.org/2549
, 2018. SIMULTANEOUS ACCURATE DETECTION OF PULMONARY NODULES AND FALSE POSITIVE REDUCTION USING 3D CNNS. Available at: http://sigport.org/2549.
. (2018). "SIMULTANEOUS ACCURATE DETECTION OF PULMONARY NODULES AND FALSE POSITIVE REDUCTION USING 3D CNNS." Web.
1. . SIMULTANEOUS ACCURATE DETECTION OF PULMONARY NODULES AND FALSE POSITIVE REDUCTION USING 3D CNNS [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/2549

AUTOMATIC MOTION ARTIFACT DETECTION FOR WHOLE-BODY MAGNETIC RESONANCE IMAGING


Magnetic resonance (MR) plays an important role in medical imaging. It can be flexibly tuned towards different applications for deriving a meaningful diagnosis. However, its long acquisition times and flexible parametrization make it on the other hand prone to artifacts which obscure the underlying image content or can be misinterpreted as anatomy. Patient-induced motion artifacts are still one of the major extrinsic factors which degrade image quality.

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Authors:
Thomas Küstner, Marvin Jandt, Annika Liebgott, Lukas Mauch, Petros Martirosian, Fabian Bamberg, Konstantin Nikolaou, Sergios Gatidis, Fritz Schick, Bin Yang
Submitted On:
12 April 2018 - 12:45pm
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poster_icassp2018.pdf

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[1] Thomas Küstner, Marvin Jandt, Annika Liebgott, Lukas Mauch, Petros Martirosian, Fabian Bamberg, Konstantin Nikolaou, Sergios Gatidis, Fritz Schick, Bin Yang, "AUTOMATIC MOTION ARTIFACT DETECTION FOR WHOLE-BODY MAGNETIC RESONANCE IMAGING", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/2443. Accessed: May. 25, 2018.
@article{2443-18,
url = {http://sigport.org/2443},
author = {Thomas Küstner; Marvin Jandt; Annika Liebgott; Lukas Mauch; Petros Martirosian; Fabian Bamberg; Konstantin Nikolaou; Sergios Gatidis; Fritz Schick; Bin Yang },
publisher = {IEEE SigPort},
title = {AUTOMATIC MOTION ARTIFACT DETECTION FOR WHOLE-BODY MAGNETIC RESONANCE IMAGING},
year = {2018} }
TY - EJOUR
T1 - AUTOMATIC MOTION ARTIFACT DETECTION FOR WHOLE-BODY MAGNETIC RESONANCE IMAGING
AU - Thomas Küstner; Marvin Jandt; Annika Liebgott; Lukas Mauch; Petros Martirosian; Fabian Bamberg; Konstantin Nikolaou; Sergios Gatidis; Fritz Schick; Bin Yang
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/2443
ER -
Thomas Küstner, Marvin Jandt, Annika Liebgott, Lukas Mauch, Petros Martirosian, Fabian Bamberg, Konstantin Nikolaou, Sergios Gatidis, Fritz Schick, Bin Yang. (2018). AUTOMATIC MOTION ARTIFACT DETECTION FOR WHOLE-BODY MAGNETIC RESONANCE IMAGING. IEEE SigPort. http://sigport.org/2443
Thomas Küstner, Marvin Jandt, Annika Liebgott, Lukas Mauch, Petros Martirosian, Fabian Bamberg, Konstantin Nikolaou, Sergios Gatidis, Fritz Schick, Bin Yang, 2018. AUTOMATIC MOTION ARTIFACT DETECTION FOR WHOLE-BODY MAGNETIC RESONANCE IMAGING. Available at: http://sigport.org/2443.
Thomas Küstner, Marvin Jandt, Annika Liebgott, Lukas Mauch, Petros Martirosian, Fabian Bamberg, Konstantin Nikolaou, Sergios Gatidis, Fritz Schick, Bin Yang. (2018). "AUTOMATIC MOTION ARTIFACT DETECTION FOR WHOLE-BODY MAGNETIC RESONANCE IMAGING." Web.
1. Thomas Küstner, Marvin Jandt, Annika Liebgott, Lukas Mauch, Petros Martirosian, Fabian Bamberg, Konstantin Nikolaou, Sergios Gatidis, Fritz Schick, Bin Yang. AUTOMATIC MOTION ARTIFACT DETECTION FOR WHOLE-BODY MAGNETIC RESONANCE IMAGING [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/2443

AUTOMATED DETECTION OF HIGH FDG UPTAKE REGIONS IN CT IMAGES


Combined PET-CT scan is an important diagnostic tool in modern medicine, e.g. for staging or treatment planning in the field of oncology. Especially in small structures, like a tumour, textural variations visible in a PET image are not visually recognizable within a CT scan from the same region. Thus, both modalities are necessary for diagnosis. Since both techniques expose the patient to radiation, it would be desirable to get the same information about metabolic activity contained in the PET image from a CT scan only.

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Authors:
Annika Liebgott, Sergios Gatidis, Florian Liebgott, Konstantin Nikolaou, Bin Yang
Submitted On:
12 April 2018 - 11:42am
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poster_icassp2018.pdf

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[1] Annika Liebgott, Sergios Gatidis, Florian Liebgott, Konstantin Nikolaou, Bin Yang, "AUTOMATED DETECTION OF HIGH FDG UPTAKE REGIONS IN CT IMAGES", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/2414. Accessed: May. 25, 2018.
@article{2414-18,
url = {http://sigport.org/2414},
author = {Annika Liebgott; Sergios Gatidis; Florian Liebgott; Konstantin Nikolaou; Bin Yang },
publisher = {IEEE SigPort},
title = {AUTOMATED DETECTION OF HIGH FDG UPTAKE REGIONS IN CT IMAGES},
year = {2018} }
TY - EJOUR
T1 - AUTOMATED DETECTION OF HIGH FDG UPTAKE REGIONS IN CT IMAGES
AU - Annika Liebgott; Sergios Gatidis; Florian Liebgott; Konstantin Nikolaou; Bin Yang
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/2414
ER -
Annika Liebgott, Sergios Gatidis, Florian Liebgott, Konstantin Nikolaou, Bin Yang. (2018). AUTOMATED DETECTION OF HIGH FDG UPTAKE REGIONS IN CT IMAGES. IEEE SigPort. http://sigport.org/2414
Annika Liebgott, Sergios Gatidis, Florian Liebgott, Konstantin Nikolaou, Bin Yang, 2018. AUTOMATED DETECTION OF HIGH FDG UPTAKE REGIONS IN CT IMAGES. Available at: http://sigport.org/2414.
Annika Liebgott, Sergios Gatidis, Florian Liebgott, Konstantin Nikolaou, Bin Yang. (2018). "AUTOMATED DETECTION OF HIGH FDG UPTAKE REGIONS IN CT IMAGES." Web.
1. Annika Liebgott, Sergios Gatidis, Florian Liebgott, Konstantin Nikolaou, Bin Yang. AUTOMATED DETECTION OF HIGH FDG UPTAKE REGIONS IN CT IMAGES [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/2414

Classifying Pump-probe Images of Melanocytic Lesions using the Weyl Transform


Diagnosis of melanoma is fraught with uncertainty, and discordance rates among physicians remain high because of the lack of a definitive criterion. Motivated by this challenge, this paper first introduces the Patch Weyl transform (PWT), a 2-dimensional variant of the Weyl transform. It then presents a method for classifying pump-probe images of melanocytic lesions based on the PWT coefficients.

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Authors:
Qiang Qiu, Edward Bosch, Andrew Thompson, Francisco E. Robles, Guillermo Sapiro, Warren S. Warren, Robert Calderbank
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12 April 2018 - 11:47am
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ICASSP Presentations.pdf

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[1] Qiang Qiu, Edward Bosch, Andrew Thompson, Francisco E. Robles, Guillermo Sapiro, Warren S. Warren, Robert Calderbank, "Classifying Pump-probe Images of Melanocytic Lesions using the Weyl Transform", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/2406. Accessed: May. 25, 2018.
@article{2406-18,
url = {http://sigport.org/2406},
author = {Qiang Qiu; Edward Bosch; Andrew Thompson; Francisco E. Robles; Guillermo Sapiro; Warren S. Warren; Robert Calderbank },
publisher = {IEEE SigPort},
title = {Classifying Pump-probe Images of Melanocytic Lesions using the Weyl Transform},
year = {2018} }
TY - EJOUR
T1 - Classifying Pump-probe Images of Melanocytic Lesions using the Weyl Transform
AU - Qiang Qiu; Edward Bosch; Andrew Thompson; Francisco E. Robles; Guillermo Sapiro; Warren S. Warren; Robert Calderbank
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/2406
ER -
Qiang Qiu, Edward Bosch, Andrew Thompson, Francisco E. Robles, Guillermo Sapiro, Warren S. Warren, Robert Calderbank. (2018). Classifying Pump-probe Images of Melanocytic Lesions using the Weyl Transform. IEEE SigPort. http://sigport.org/2406
Qiang Qiu, Edward Bosch, Andrew Thompson, Francisco E. Robles, Guillermo Sapiro, Warren S. Warren, Robert Calderbank, 2018. Classifying Pump-probe Images of Melanocytic Lesions using the Weyl Transform. Available at: http://sigport.org/2406.
Qiang Qiu, Edward Bosch, Andrew Thompson, Francisco E. Robles, Guillermo Sapiro, Warren S. Warren, Robert Calderbank. (2018). "Classifying Pump-probe Images of Melanocytic Lesions using the Weyl Transform." Web.
1. Qiang Qiu, Edward Bosch, Andrew Thompson, Francisco E. Robles, Guillermo Sapiro, Warren S. Warren, Robert Calderbank. Classifying Pump-probe Images of Melanocytic Lesions using the Weyl Transform [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/2406

REMOVING RING ARTIFACTS IN CBCT IMAGES VIA GENERATIVE ADVERSARIAL NETWORK


Cone-beam computed tomography (CBCT) images often have some ring artifacts because of the inconsistent response of detector pixels. Removing ring artifacts in CBCT images without impairing the image quality is critical for the application of CBCT. In this paper, we explore this issue as an “adversarial problem” and propose a novel method to eliminate ring artifacts from CBCT images by using an imageto-image network based on Generative Adversarial Network (GAN).

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Authors:
Shuyang Zhao,Jianwu Li, Qirun Huo
Submitted On:
12 April 2018 - 11:27am
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ICASSP2018-2121-Poster

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[1] Shuyang Zhao,Jianwu Li, Qirun Huo, "REMOVING RING ARTIFACTS IN CBCT IMAGES VIA GENERATIVE ADVERSARIAL NETWORK", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/2386. Accessed: May. 25, 2018.
@article{2386-18,
url = {http://sigport.org/2386},
author = {Shuyang Zhao;Jianwu Li; Qirun Huo },
publisher = {IEEE SigPort},
title = {REMOVING RING ARTIFACTS IN CBCT IMAGES VIA GENERATIVE ADVERSARIAL NETWORK},
year = {2018} }
TY - EJOUR
T1 - REMOVING RING ARTIFACTS IN CBCT IMAGES VIA GENERATIVE ADVERSARIAL NETWORK
AU - Shuyang Zhao;Jianwu Li; Qirun Huo
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/2386
ER -
Shuyang Zhao,Jianwu Li, Qirun Huo. (2018). REMOVING RING ARTIFACTS IN CBCT IMAGES VIA GENERATIVE ADVERSARIAL NETWORK. IEEE SigPort. http://sigport.org/2386
Shuyang Zhao,Jianwu Li, Qirun Huo, 2018. REMOVING RING ARTIFACTS IN CBCT IMAGES VIA GENERATIVE ADVERSARIAL NETWORK. Available at: http://sigport.org/2386.
Shuyang Zhao,Jianwu Li, Qirun Huo. (2018). "REMOVING RING ARTIFACTS IN CBCT IMAGES VIA GENERATIVE ADVERSARIAL NETWORK." Web.
1. Shuyang Zhao,Jianwu Li, Qirun Huo. REMOVING RING ARTIFACTS IN CBCT IMAGES VIA GENERATIVE ADVERSARIAL NETWORK [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/2386

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