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Medical imaging

LESION DETECTION USING T1-WEIGHTED MRI: A NEW APPROACH BASED ON FUNCTIONAL CORTICAL ROIS

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Authors:
Dazhou Guo, Kang Zheng, Song Wang
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14 September 2017 - 1:58pm
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ICIP 2017 poster

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[1] Dazhou Guo, Kang Zheng, Song Wang, "LESION DETECTION USING T1-WEIGHTED MRI: A NEW APPROACH BASED ON FUNCTIONAL CORTICAL ROIS", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/2041. Accessed: Oct. 18, 2017.
@article{2041-17,
url = {http://sigport.org/2041},
author = {Dazhou Guo; Kang Zheng; Song Wang },
publisher = {IEEE SigPort},
title = {LESION DETECTION USING T1-WEIGHTED MRI: A NEW APPROACH BASED ON FUNCTIONAL CORTICAL ROIS},
year = {2017} }
TY - EJOUR
T1 - LESION DETECTION USING T1-WEIGHTED MRI: A NEW APPROACH BASED ON FUNCTIONAL CORTICAL ROIS
AU - Dazhou Guo; Kang Zheng; Song Wang
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/2041
ER -
Dazhou Guo, Kang Zheng, Song Wang. (2017). LESION DETECTION USING T1-WEIGHTED MRI: A NEW APPROACH BASED ON FUNCTIONAL CORTICAL ROIS. IEEE SigPort. http://sigport.org/2041
Dazhou Guo, Kang Zheng, Song Wang, 2017. LESION DETECTION USING T1-WEIGHTED MRI: A NEW APPROACH BASED ON FUNCTIONAL CORTICAL ROIS. Available at: http://sigport.org/2041.
Dazhou Guo, Kang Zheng, Song Wang. (2017). "LESION DETECTION USING T1-WEIGHTED MRI: A NEW APPROACH BASED ON FUNCTIONAL CORTICAL ROIS." Web.
1. Dazhou Guo, Kang Zheng, Song Wang. LESION DETECTION USING T1-WEIGHTED MRI: A NEW APPROACH BASED ON FUNCTIONAL CORTICAL ROIS [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/2041

COMPARISON OF OBJECTIVE FUNCTIONS IN CNN-BASED PROSTATE MAGNETIC RESONANCE IMAGE SEGMENTATION


We investigate the impacts of objective functions on the performance of deep-learning-based prostate magnetic resonance image segmentation. To this end, we first develop a baseline convolutional neural network (BCNN) for the prostate image segmentation, which consists of encoding, bridge, decoding, and classification modules. In the BCNN, we use 3D convolutional layers to consider volumetric information. Also, we adopt the residual feature forwarding and intermediate feature propagation techniques to make the BCNN reliably trainable for various objective functions.

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Authors:
Juhyeok Mun, Won-Dong Jang, Deuk Jae Sung, Chang-Su Kim
Submitted On:
13 September 2017 - 10:57pm
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ICIP_JHMUN_POSTER.pdf

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[1] Juhyeok Mun, Won-Dong Jang, Deuk Jae Sung, Chang-Su Kim, "COMPARISON OF OBJECTIVE FUNCTIONS IN CNN-BASED PROSTATE MAGNETIC RESONANCE IMAGE SEGMENTATION", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/1991. Accessed: Oct. 18, 2017.
@article{1991-17,
url = {http://sigport.org/1991},
author = {Juhyeok Mun; Won-Dong Jang; Deuk Jae Sung; Chang-Su Kim },
publisher = {IEEE SigPort},
title = {COMPARISON OF OBJECTIVE FUNCTIONS IN CNN-BASED PROSTATE MAGNETIC RESONANCE IMAGE SEGMENTATION},
year = {2017} }
TY - EJOUR
T1 - COMPARISON OF OBJECTIVE FUNCTIONS IN CNN-BASED PROSTATE MAGNETIC RESONANCE IMAGE SEGMENTATION
AU - Juhyeok Mun; Won-Dong Jang; Deuk Jae Sung; Chang-Su Kim
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/1991
ER -
Juhyeok Mun, Won-Dong Jang, Deuk Jae Sung, Chang-Su Kim. (2017). COMPARISON OF OBJECTIVE FUNCTIONS IN CNN-BASED PROSTATE MAGNETIC RESONANCE IMAGE SEGMENTATION. IEEE SigPort. http://sigport.org/1991
Juhyeok Mun, Won-Dong Jang, Deuk Jae Sung, Chang-Su Kim, 2017. COMPARISON OF OBJECTIVE FUNCTIONS IN CNN-BASED PROSTATE MAGNETIC RESONANCE IMAGE SEGMENTATION. Available at: http://sigport.org/1991.
Juhyeok Mun, Won-Dong Jang, Deuk Jae Sung, Chang-Su Kim. (2017). "COMPARISON OF OBJECTIVE FUNCTIONS IN CNN-BASED PROSTATE MAGNETIC RESONANCE IMAGE SEGMENTATION." Web.
1. Juhyeok Mun, Won-Dong Jang, Deuk Jae Sung, Chang-Su Kim. COMPARISON OF OBJECTIVE FUNCTIONS IN CNN-BASED PROSTATE MAGNETIC RESONANCE IMAGE SEGMENTATION [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/1991

Automated 3D Muscle Segmentation From MRI Data Using Convolutional Neural Network

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11 September 2017 - 12:17pm
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ICIP2017_poster.pdf

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[1] , "Automated 3D Muscle Segmentation From MRI Data Using Convolutional Neural Network", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/1913. Accessed: Oct. 18, 2017.
@article{1913-17,
url = {http://sigport.org/1913},
author = { },
publisher = {IEEE SigPort},
title = {Automated 3D Muscle Segmentation From MRI Data Using Convolutional Neural Network},
year = {2017} }
TY - EJOUR
T1 - Automated 3D Muscle Segmentation From MRI Data Using Convolutional Neural Network
AU -
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/1913
ER -
. (2017). Automated 3D Muscle Segmentation From MRI Data Using Convolutional Neural Network. IEEE SigPort. http://sigport.org/1913
, 2017. Automated 3D Muscle Segmentation From MRI Data Using Convolutional Neural Network. Available at: http://sigport.org/1913.
. (2017). "Automated 3D Muscle Segmentation From MRI Data Using Convolutional Neural Network." Web.
1. . Automated 3D Muscle Segmentation From MRI Data Using Convolutional Neural Network [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/1913

Automated 3D Muscle Segmentation From MRI Data Using Convolutional Neural Network

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11 September 2017 - 12:17pm
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[1] , "Automated 3D Muscle Segmentation From MRI Data Using Convolutional Neural Network", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/1912. Accessed: Oct. 18, 2017.
@article{1912-17,
url = {http://sigport.org/1912},
author = { },
publisher = {IEEE SigPort},
title = {Automated 3D Muscle Segmentation From MRI Data Using Convolutional Neural Network},
year = {2017} }
TY - EJOUR
T1 - Automated 3D Muscle Segmentation From MRI Data Using Convolutional Neural Network
AU -
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/1912
ER -
. (2017). Automated 3D Muscle Segmentation From MRI Data Using Convolutional Neural Network. IEEE SigPort. http://sigport.org/1912
, 2017. Automated 3D Muscle Segmentation From MRI Data Using Convolutional Neural Network. Available at: http://sigport.org/1912.
. (2017). "Automated 3D Muscle Segmentation From MRI Data Using Convolutional Neural Network." Web.
1. . Automated 3D Muscle Segmentation From MRI Data Using Convolutional Neural Network [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/1912

Automated 3D Muscle Segmentation From MRI Data Using Convolutional Neural Network

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Submitted On:
11 September 2017 - 12:17pm
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ICIP2017_poster.pdf

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[1] , "Automated 3D Muscle Segmentation From MRI Data Using Convolutional Neural Network", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/1911. Accessed: Oct. 18, 2017.
@article{1911-17,
url = {http://sigport.org/1911},
author = { },
publisher = {IEEE SigPort},
title = {Automated 3D Muscle Segmentation From MRI Data Using Convolutional Neural Network},
year = {2017} }
TY - EJOUR
T1 - Automated 3D Muscle Segmentation From MRI Data Using Convolutional Neural Network
AU -
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/1911
ER -
. (2017). Automated 3D Muscle Segmentation From MRI Data Using Convolutional Neural Network. IEEE SigPort. http://sigport.org/1911
, 2017. Automated 3D Muscle Segmentation From MRI Data Using Convolutional Neural Network. Available at: http://sigport.org/1911.
. (2017). "Automated 3D Muscle Segmentation From MRI Data Using Convolutional Neural Network." Web.
1. . Automated 3D Muscle Segmentation From MRI Data Using Convolutional Neural Network [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/1911

Automated 3D Muscle Segmentation From MRI Data Using Convolutional Neural Network

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Authors:
Submitted On:
11 September 2017 - 12:17pm
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ICIP2017_poster.pdf

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[1] , "Automated 3D Muscle Segmentation From MRI Data Using Convolutional Neural Network", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/1910. Accessed: Oct. 18, 2017.
@article{1910-17,
url = {http://sigport.org/1910},
author = { },
publisher = {IEEE SigPort},
title = {Automated 3D Muscle Segmentation From MRI Data Using Convolutional Neural Network},
year = {2017} }
TY - EJOUR
T1 - Automated 3D Muscle Segmentation From MRI Data Using Convolutional Neural Network
AU -
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/1910
ER -
. (2017). Automated 3D Muscle Segmentation From MRI Data Using Convolutional Neural Network. IEEE SigPort. http://sigport.org/1910
, 2017. Automated 3D Muscle Segmentation From MRI Data Using Convolutional Neural Network. Available at: http://sigport.org/1910.
. (2017). "Automated 3D Muscle Segmentation From MRI Data Using Convolutional Neural Network." Web.
1. . Automated 3D Muscle Segmentation From MRI Data Using Convolutional Neural Network [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/1910

LINE DETECTION IN SPECKLE IMAGES USING RADON TRANSFORM AND L1 REGULARIZATION

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Submitted On:
5 March 2017 - 12:42pm
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linerestoration.pptx

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[1] , " LINE DETECTION IN SPECKLE IMAGES USING RADON TRANSFORM AND L1 REGULARIZATION", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/1633. Accessed: Oct. 18, 2017.
@article{1633-17,
url = {http://sigport.org/1633},
author = { },
publisher = {IEEE SigPort},
title = { LINE DETECTION IN SPECKLE IMAGES USING RADON TRANSFORM AND L1 REGULARIZATION},
year = {2017} }
TY - EJOUR
T1 - LINE DETECTION IN SPECKLE IMAGES USING RADON TRANSFORM AND L1 REGULARIZATION
AU -
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/1633
ER -
. (2017). LINE DETECTION IN SPECKLE IMAGES USING RADON TRANSFORM AND L1 REGULARIZATION. IEEE SigPort. http://sigport.org/1633
, 2017. LINE DETECTION IN SPECKLE IMAGES USING RADON TRANSFORM AND L1 REGULARIZATION. Available at: http://sigport.org/1633.
. (2017). " LINE DETECTION IN SPECKLE IMAGES USING RADON TRANSFORM AND L1 REGULARIZATION." Web.
1. . LINE DETECTION IN SPECKLE IMAGES USING RADON TRANSFORM AND L1 REGULARIZATION [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/1633

Sparse Reconstruction for Fluorescence Lifetime Imaging Microscopy with Poisson Noise


We present a novel, three-stage method to solve the fluorescence lifetime imaging problem under low-photon conditions. In particular, we reconstruct the fluorophore concentration along with its support and fluorescence lifetime from the time-dependent measurements of scattered light exiting the domain. Because detectors used for these problems are photon counting devices, measurements are corrupted by Poisson noise. Consequently, we explicitly consider Poisson noise in conjunction with SPIRAL-$\ell_p$ -- a sparsity-promoting nonconvex optimization method -- to solve this problem.

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Authors:
Lasith Adhikari, Arnold D. Kim, Roummel F. Marcia
Submitted On:
7 December 2016 - 10:28am
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FLIM with Poisson

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[1] Lasith Adhikari, Arnold D. Kim, Roummel F. Marcia, "Sparse Reconstruction for Fluorescence Lifetime Imaging Microscopy with Poisson Noise", IEEE SigPort, 2016. [Online]. Available: http://sigport.org/1406. Accessed: Oct. 18, 2017.
@article{1406-16,
url = {http://sigport.org/1406},
author = {Lasith Adhikari; Arnold D. Kim; Roummel F. Marcia },
publisher = {IEEE SigPort},
title = {Sparse Reconstruction for Fluorescence Lifetime Imaging Microscopy with Poisson Noise},
year = {2016} }
TY - EJOUR
T1 - Sparse Reconstruction for Fluorescence Lifetime Imaging Microscopy with Poisson Noise
AU - Lasith Adhikari; Arnold D. Kim; Roummel F. Marcia
PY - 2016
PB - IEEE SigPort
UR - http://sigport.org/1406
ER -
Lasith Adhikari, Arnold D. Kim, Roummel F. Marcia. (2016). Sparse Reconstruction for Fluorescence Lifetime Imaging Microscopy with Poisson Noise. IEEE SigPort. http://sigport.org/1406
Lasith Adhikari, Arnold D. Kim, Roummel F. Marcia, 2016. Sparse Reconstruction for Fluorescence Lifetime Imaging Microscopy with Poisson Noise. Available at: http://sigport.org/1406.
Lasith Adhikari, Arnold D. Kim, Roummel F. Marcia. (2016). "Sparse Reconstruction for Fluorescence Lifetime Imaging Microscopy with Poisson Noise." Web.
1. Lasith Adhikari, Arnold D. Kim, Roummel F. Marcia. Sparse Reconstruction for Fluorescence Lifetime Imaging Microscopy with Poisson Noise [Internet]. IEEE SigPort; 2016. Available from : http://sigport.org/1406

Fast Dynamic MRI using Linear Dynamical System Model

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Authors:
Vimal Singh, Ahmed Tewfik
Submitted On:
20 March 2016 - 3:51pm
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ICASSP2016_draft3.pptx

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ICASSP2016_draft2.pptx

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[1] Vimal Singh, Ahmed Tewfik, "Fast Dynamic MRI using Linear Dynamical System Model", IEEE SigPort, 2016. [Online]. Available: http://sigport.org/759. Accessed: Oct. 18, 2017.
@article{759-16,
url = {http://sigport.org/759},
author = {Vimal Singh; Ahmed Tewfik },
publisher = {IEEE SigPort},
title = {Fast Dynamic MRI using Linear Dynamical System Model},
year = {2016} }
TY - EJOUR
T1 - Fast Dynamic MRI using Linear Dynamical System Model
AU - Vimal Singh; Ahmed Tewfik
PY - 2016
PB - IEEE SigPort
UR - http://sigport.org/759
ER -
Vimal Singh, Ahmed Tewfik. (2016). Fast Dynamic MRI using Linear Dynamical System Model. IEEE SigPort. http://sigport.org/759
Vimal Singh, Ahmed Tewfik, 2016. Fast Dynamic MRI using Linear Dynamical System Model. Available at: http://sigport.org/759.
Vimal Singh, Ahmed Tewfik. (2016). "Fast Dynamic MRI using Linear Dynamical System Model." Web.
1. Vimal Singh, Ahmed Tewfik. Fast Dynamic MRI using Linear Dynamical System Model [Internet]. IEEE SigPort; 2016. Available from : http://sigport.org/759

Super-resolution spectral analysis for ultrasound scatter characterization


Parametric Bayesian spectral estimation methods have been previously utilized to improve frequency resolution. Ultrasound signals have been tested in such methods resulting in higher precision frequency detection compared to common non-parametric spectral estimation methods based on the Fourier transform. Such a technique using a reversible jump Markov Chain Monte Carlo algorithm has been developed to fully characterize signals and in addition to frequency, to provide amplitude and noise estimation.

poster.pdf

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Authors:
Maruf A. Dhali, Gavin Gibson, Yan Yan, James R. Hopgood, Vassilis Sboros
Submitted On:
11 March 2016 - 7:28pm
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poster.pdf

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[1] Maruf A. Dhali, Gavin Gibson, Yan Yan, James R. Hopgood, Vassilis Sboros, "Super-resolution spectral analysis for ultrasound scatter characterization", IEEE SigPort, 2016. [Online]. Available: http://sigport.org/615. Accessed: Oct. 18, 2017.
@article{615-16,
url = {http://sigport.org/615},
author = {Maruf A. Dhali; Gavin Gibson; Yan Yan; James R. Hopgood; Vassilis Sboros },
publisher = {IEEE SigPort},
title = {Super-resolution spectral analysis for ultrasound scatter characterization},
year = {2016} }
TY - EJOUR
T1 - Super-resolution spectral analysis for ultrasound scatter characterization
AU - Maruf A. Dhali; Gavin Gibson; Yan Yan; James R. Hopgood; Vassilis Sboros
PY - 2016
PB - IEEE SigPort
UR - http://sigport.org/615
ER -
Maruf A. Dhali, Gavin Gibson, Yan Yan, James R. Hopgood, Vassilis Sboros. (2016). Super-resolution spectral analysis for ultrasound scatter characterization. IEEE SigPort. http://sigport.org/615
Maruf A. Dhali, Gavin Gibson, Yan Yan, James R. Hopgood, Vassilis Sboros, 2016. Super-resolution spectral analysis for ultrasound scatter characterization. Available at: http://sigport.org/615.
Maruf A. Dhali, Gavin Gibson, Yan Yan, James R. Hopgood, Vassilis Sboros. (2016). "Super-resolution spectral analysis for ultrasound scatter characterization." Web.
1. Maruf A. Dhali, Gavin Gibson, Yan Yan, James R. Hopgood, Vassilis Sboros. Super-resolution spectral analysis for ultrasound scatter characterization [Internet]. IEEE SigPort; 2016. Available from : http://sigport.org/615

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