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Bio Imaging and Signal Processing

Deep Tree Models for ‘Big’ Biological Data


The identification of useful temporal dependence structure in discrete time series data is an important component of algorithms applied to many tasks in statistical inference and machine learning, and used in a wide variety of problems across the spectrum of biological studies. Most of the early statistical approaches were ineffective in practice, because the amount of data required for reliable modelling grew exponentially with memory length.

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Authors:
Lambros Mertzanis, Athina Panotopoulou, Maria Skoularidou
Submitted On:
24 June 2018 - 9:36am
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Kontoyianni_slides.pdf

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[1] Lambros Mertzanis, Athina Panotopoulou, Maria Skoularidou, "Deep Tree Models for ‘Big’ Biological Data", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3320. Accessed: Sep. 20, 2018.
@article{3320-18,
url = {http://sigport.org/3320},
author = {Lambros Mertzanis; Athina Panotopoulou; Maria Skoularidou },
publisher = {IEEE SigPort},
title = {Deep Tree Models for ‘Big’ Biological Data},
year = {2018} }
TY - EJOUR
T1 - Deep Tree Models for ‘Big’ Biological Data
AU - Lambros Mertzanis; Athina Panotopoulou; Maria Skoularidou
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3320
ER -
Lambros Mertzanis, Athina Panotopoulou, Maria Skoularidou. (2018). Deep Tree Models for ‘Big’ Biological Data. IEEE SigPort. http://sigport.org/3320
Lambros Mertzanis, Athina Panotopoulou, Maria Skoularidou, 2018. Deep Tree Models for ‘Big’ Biological Data. Available at: http://sigport.org/3320.
Lambros Mertzanis, Athina Panotopoulou, Maria Skoularidou. (2018). "Deep Tree Models for ‘Big’ Biological Data." Web.
1. Lambros Mertzanis, Athina Panotopoulou, Maria Skoularidou. Deep Tree Models for ‘Big’ Biological Data [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3320

Invisible Geo-Location Signature in a Single Image


Geo-tagging images of interest is increasingly important to law enforcement, national security, and journalism. Many images today do not carry location tags that are trustworthy and resilient to tampering; and the landmark-based visual clues may not be readily present in every image, especially in those taken indoors. In this paper, we exploit an invisible signature from the power grid, the Electric Network Frequency (ENF) signal, which can be inherently recorded in a sensing stream at the time of capturing and carries useful location information.

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27 April 2018 - 6:28pm
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ICASSP18_presentation_v2.pdf

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[1] , "Invisible Geo-Location Signature in a Single Image", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3186. Accessed: Sep. 20, 2018.
@article{3186-18,
url = {http://sigport.org/3186},
author = { },
publisher = {IEEE SigPort},
title = {Invisible Geo-Location Signature in a Single Image},
year = {2018} }
TY - EJOUR
T1 - Invisible Geo-Location Signature in a Single Image
AU -
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3186
ER -
. (2018). Invisible Geo-Location Signature in a Single Image. IEEE SigPort. http://sigport.org/3186
, 2018. Invisible Geo-Location Signature in a Single Image. Available at: http://sigport.org/3186.
. (2018). "Invisible Geo-Location Signature in a Single Image." Web.
1. . Invisible Geo-Location Signature in a Single Image [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3186

Paper 3671-QUANTITATIVE LUNG NODULE ANALYSIS SYSTEM (NAS) FROM CHEST CT Poster

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Authors:
Amal Farag, Salwa Elshazly, Asem Ali, Islam Alkabbany, Albert Seow and Aly Farag
Submitted On:
15 April 2018 - 12:20am
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Paper 3671-QUANTITATIVE LUNG NODULE ANALYSIS SYSTEM (NAS) FROM CHEST CT.pdf

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[1] Amal Farag, Salwa Elshazly, Asem Ali, Islam Alkabbany, Albert Seow and Aly Farag, "Paper 3671-QUANTITATIVE LUNG NODULE ANALYSIS SYSTEM (NAS) FROM CHEST CT Poster", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/2875. Accessed: Sep. 20, 2018.
@article{2875-18,
url = {http://sigport.org/2875},
author = {Amal Farag; Salwa Elshazly; Asem Ali; Islam Alkabbany; Albert Seow and Aly Farag },
publisher = {IEEE SigPort},
title = {Paper 3671-QUANTITATIVE LUNG NODULE ANALYSIS SYSTEM (NAS) FROM CHEST CT Poster},
year = {2018} }
TY - EJOUR
T1 - Paper 3671-QUANTITATIVE LUNG NODULE ANALYSIS SYSTEM (NAS) FROM CHEST CT Poster
AU - Amal Farag; Salwa Elshazly; Asem Ali; Islam Alkabbany; Albert Seow and Aly Farag
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/2875
ER -
Amal Farag, Salwa Elshazly, Asem Ali, Islam Alkabbany, Albert Seow and Aly Farag. (2018). Paper 3671-QUANTITATIVE LUNG NODULE ANALYSIS SYSTEM (NAS) FROM CHEST CT Poster. IEEE SigPort. http://sigport.org/2875
Amal Farag, Salwa Elshazly, Asem Ali, Islam Alkabbany, Albert Seow and Aly Farag, 2018. Paper 3671-QUANTITATIVE LUNG NODULE ANALYSIS SYSTEM (NAS) FROM CHEST CT Poster. Available at: http://sigport.org/2875.
Amal Farag, Salwa Elshazly, Asem Ali, Islam Alkabbany, Albert Seow and Aly Farag. (2018). "Paper 3671-QUANTITATIVE LUNG NODULE ANALYSIS SYSTEM (NAS) FROM CHEST CT Poster." Web.
1. Amal Farag, Salwa Elshazly, Asem Ali, Islam Alkabbany, Albert Seow and Aly Farag. Paper 3671-QUANTITATIVE LUNG NODULE ANALYSIS SYSTEM (NAS) FROM CHEST CT Poster [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/2875

Fast dictionary-based approach for mass spectrometry data analysis


Mass spectrometry (MS) is a fundamental technology of analytical chemistry for measuring the structure of molecules, with many application fields such as clinical biomarker analysis or pharmacokinetics. In the context of proteomic analysis with MS, the superposition of the isotopic patterns of different proteins, in various charge-states produces MS spectra difficult to decipher. The complexity of the pattern models and the large size of the data again increase the difficulty of the analysis step.

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Authors:
Chouzenoux Emilie, Delsuc Marc André
Submitted On:
20 April 2018 - 12:12pm
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ICASSP2018_CherniAfef.pdf

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ICASSP2018_CherniAfef.pdf

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[1] Chouzenoux Emilie, Delsuc Marc André, "Fast dictionary-based approach for mass spectrometry data analysis", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/2779. Accessed: Sep. 20, 2018.
@article{2779-18,
url = {http://sigport.org/2779},
author = {Chouzenoux Emilie; Delsuc Marc André },
publisher = {IEEE SigPort},
title = {Fast dictionary-based approach for mass spectrometry data analysis},
year = {2018} }
TY - EJOUR
T1 - Fast dictionary-based approach for mass spectrometry data analysis
AU - Chouzenoux Emilie; Delsuc Marc André
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/2779
ER -
Chouzenoux Emilie, Delsuc Marc André. (2018). Fast dictionary-based approach for mass spectrometry data analysis. IEEE SigPort. http://sigport.org/2779
Chouzenoux Emilie, Delsuc Marc André, 2018. Fast dictionary-based approach for mass spectrometry data analysis. Available at: http://sigport.org/2779.
Chouzenoux Emilie, Delsuc Marc André. (2018). "Fast dictionary-based approach for mass spectrometry data analysis." Web.
1. Chouzenoux Emilie, Delsuc Marc André. Fast dictionary-based approach for mass spectrometry data analysis [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/2779

Driver estimation in non-linear autoregressive models


In non-linear autoregressive models, the time dependency of coefficients is often driven by a particular time-series which is not given and thus has to be estimated from the data. To allow model evaluation on a validation set, we describe a parametric approach for such driver estimation. After estimating the driver as a weighted sum of potential drivers, we use it in a non-linear autoregressive model with a polynomial parametrization. Using gradient descent, we optimize the linear filter extracting the driver, outperforming a typical grid-search on predefined filters.

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Authors:
Tom Dupre la Tour, Yves Grenier, Alexandre Gramfort
Submitted On:
13 April 2018 - 5:21am
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icassp2018duprelatour.pdf

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[1] Tom Dupre la Tour, Yves Grenier, Alexandre Gramfort, "Driver estimation in non-linear autoregressive models", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/2666. Accessed: Sep. 20, 2018.
@article{2666-18,
url = {http://sigport.org/2666},
author = {Tom Dupre la Tour; Yves Grenier; Alexandre Gramfort },
publisher = {IEEE SigPort},
title = {Driver estimation in non-linear autoregressive models},
year = {2018} }
TY - EJOUR
T1 - Driver estimation in non-linear autoregressive models
AU - Tom Dupre la Tour; Yves Grenier; Alexandre Gramfort
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/2666
ER -
Tom Dupre la Tour, Yves Grenier, Alexandre Gramfort. (2018). Driver estimation in non-linear autoregressive models. IEEE SigPort. http://sigport.org/2666
Tom Dupre la Tour, Yves Grenier, Alexandre Gramfort, 2018. Driver estimation in non-linear autoregressive models. Available at: http://sigport.org/2666.
Tom Dupre la Tour, Yves Grenier, Alexandre Gramfort. (2018). "Driver estimation in non-linear autoregressive models." Web.
1. Tom Dupre la Tour, Yves Grenier, Alexandre Gramfort. Driver estimation in non-linear autoregressive models [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/2666

DEEP TRANSFER LEARNING FOR EEG-BASED BRAIN COMPUTER INTERFACE


The electroencephalography classifier is the most important component of brain-computer interface based systems. There are two major problems hindering the improvement of it. First, traditional methods do not fully exploit multimodal information. Second, large-scale annotated EEG datasets are almost impossible to acquire because biological data acquisition is challenging and quality annotation is costly. Herein, we propose a novel deep transfer learning approach to solve these two problems.

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Authors:
Chuanqi Tan, Fuchun Sun, Wenchang Zhang
Submitted On:
12 April 2018 - 11:40am
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Poster Chuanqi.pdf

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[1] Chuanqi Tan, Fuchun Sun, Wenchang Zhang, "DEEP TRANSFER LEARNING FOR EEG-BASED BRAIN COMPUTER INTERFACE", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/2408. Accessed: Sep. 20, 2018.
@article{2408-18,
url = {http://sigport.org/2408},
author = {Chuanqi Tan; Fuchun Sun; Wenchang Zhang },
publisher = {IEEE SigPort},
title = {DEEP TRANSFER LEARNING FOR EEG-BASED BRAIN COMPUTER INTERFACE},
year = {2018} }
TY - EJOUR
T1 - DEEP TRANSFER LEARNING FOR EEG-BASED BRAIN COMPUTER INTERFACE
AU - Chuanqi Tan; Fuchun Sun; Wenchang Zhang
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/2408
ER -
Chuanqi Tan, Fuchun Sun, Wenchang Zhang. (2018). DEEP TRANSFER LEARNING FOR EEG-BASED BRAIN COMPUTER INTERFACE. IEEE SigPort. http://sigport.org/2408
Chuanqi Tan, Fuchun Sun, Wenchang Zhang, 2018. DEEP TRANSFER LEARNING FOR EEG-BASED BRAIN COMPUTER INTERFACE. Available at: http://sigport.org/2408.
Chuanqi Tan, Fuchun Sun, Wenchang Zhang. (2018). "DEEP TRANSFER LEARNING FOR EEG-BASED BRAIN COMPUTER INTERFACE." Web.
1. Chuanqi Tan, Fuchun Sun, Wenchang Zhang. DEEP TRANSFER LEARNING FOR EEG-BASED BRAIN COMPUTER INTERFACE [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/2408

Deriving 3D Shape Properties by Using Backward Wavelet Remesher


It is important to determine 3D shape properties of a population of 3D mesh models in biomedical imaging issues. In contrast to conventional 3D shape analysis techniques focusing on applications like shape matching and shape retrieval, we propose in this paper a strategy capable to collect statistical information of multiple triangular mesh models. Our method operates in a coarse-to-fine fashion based on wavelet synthesis. Hence, its analysis result can be invariant against the triangular tiling of the input mesh model.

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Authors:
Hao-Chiang Shao and Wen-Liang Hwang
Submitted On:
13 November 2017 - 10:30pm
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GlobalSIP2017_hcShao_upload.pdf

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[1] Hao-Chiang Shao and Wen-Liang Hwang, "Deriving 3D Shape Properties by Using Backward Wavelet Remesher", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/2348. Accessed: Sep. 20, 2018.
@article{2348-17,
url = {http://sigport.org/2348},
author = {Hao-Chiang Shao and Wen-Liang Hwang },
publisher = {IEEE SigPort},
title = {Deriving 3D Shape Properties by Using Backward Wavelet Remesher},
year = {2017} }
TY - EJOUR
T1 - Deriving 3D Shape Properties by Using Backward Wavelet Remesher
AU - Hao-Chiang Shao and Wen-Liang Hwang
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/2348
ER -
Hao-Chiang Shao and Wen-Liang Hwang. (2017). Deriving 3D Shape Properties by Using Backward Wavelet Remesher. IEEE SigPort. http://sigport.org/2348
Hao-Chiang Shao and Wen-Liang Hwang, 2017. Deriving 3D Shape Properties by Using Backward Wavelet Remesher. Available at: http://sigport.org/2348.
Hao-Chiang Shao and Wen-Liang Hwang. (2017). "Deriving 3D Shape Properties by Using Backward Wavelet Remesher." Web.
1. Hao-Chiang Shao and Wen-Liang Hwang. Deriving 3D Shape Properties by Using Backward Wavelet Remesher [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/2348

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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GlobalSIP17_Slides

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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, 2018.
@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

CIRCLET BASED FRAMEWORK FOR OPTIC DISK DETECTION


Optic Disc (OD) detection in retinal fundus images is a crucial stage for the automation of a screening system in diabetic ophthalmology. Most researches for automatic localization of OD benefit the regions of vessels. In this paper, we present a fast and novel method based on the Circlet Trans-form to detect OD in digital retinal fundus images that doesn’t utilize the location of the vessels. First, each R, G and B band is enhanced using CLAHE method. Then, the enhanced image in RGB color space is converted to L*a*b one.

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Authors:
Omid Sarrafzadeh, Hossein Rabbani
Submitted On:
6 September 2017 - 3:56am
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2017 ICIP Optic Disk 3.pdf

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[1] Omid Sarrafzadeh, Hossein Rabbani, "CIRCLET BASED FRAMEWORK FOR OPTIC DISK DETECTION", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/1838. Accessed: Sep. 20, 2018.
@article{1838-17,
url = {http://sigport.org/1838},
author = {Omid Sarrafzadeh; Hossein Rabbani },
publisher = {IEEE SigPort},
title = {CIRCLET BASED FRAMEWORK FOR OPTIC DISK DETECTION},
year = {2017} }
TY - EJOUR
T1 - CIRCLET BASED FRAMEWORK FOR OPTIC DISK DETECTION
AU - Omid Sarrafzadeh; Hossein Rabbani
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/1838
ER -
Omid Sarrafzadeh, Hossein Rabbani. (2017). CIRCLET BASED FRAMEWORK FOR OPTIC DISK DETECTION. IEEE SigPort. http://sigport.org/1838
Omid Sarrafzadeh, Hossein Rabbani, 2017. CIRCLET BASED FRAMEWORK FOR OPTIC DISK DETECTION. Available at: http://sigport.org/1838.
Omid Sarrafzadeh, Hossein Rabbani. (2017). "CIRCLET BASED FRAMEWORK FOR OPTIC DISK DETECTION." Web.
1. Omid Sarrafzadeh, Hossein Rabbani. CIRCLET BASED FRAMEWORK FOR OPTIC DISK DETECTION [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/1838

ALIGNMENT OF OPTIC NERVE HEAD OPTICAL COHERENCE TOMOGRAPHY B-SCANS IN RIGHT AND LEFT EYES


Symmetry analysis of right and left eyes can be a useful tool for early detection of eye diseases. In this study, we want to compare the Optical Coherent Tomography (OCT) images captured from optic nerve head (ONH) of right and left eyes. To do this, it is necessary to align the OCT data and com-pare equivalent B-scans in right and left eyes.

icip_7.pdf

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Authors:
Marzieh Mokhtari, Hossein Rabbani, Alireza Mehri-Dehnavi
Submitted On:
5 September 2017 - 1:33am
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icip_7.pdf

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[1] Marzieh Mokhtari, Hossein Rabbani, Alireza Mehri-Dehnavi, "ALIGNMENT OF OPTIC NERVE HEAD OPTICAL COHERENCE TOMOGRAPHY B-SCANS IN RIGHT AND LEFT EYES", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/1834. Accessed: Sep. 20, 2018.
@article{1834-17,
url = {http://sigport.org/1834},
author = {Marzieh Mokhtari; Hossein Rabbani; Alireza Mehri-Dehnavi },
publisher = {IEEE SigPort},
title = {ALIGNMENT OF OPTIC NERVE HEAD OPTICAL COHERENCE TOMOGRAPHY B-SCANS IN RIGHT AND LEFT EYES},
year = {2017} }
TY - EJOUR
T1 - ALIGNMENT OF OPTIC NERVE HEAD OPTICAL COHERENCE TOMOGRAPHY B-SCANS IN RIGHT AND LEFT EYES
AU - Marzieh Mokhtari; Hossein Rabbani; Alireza Mehri-Dehnavi
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/1834
ER -
Marzieh Mokhtari, Hossein Rabbani, Alireza Mehri-Dehnavi. (2017). ALIGNMENT OF OPTIC NERVE HEAD OPTICAL COHERENCE TOMOGRAPHY B-SCANS IN RIGHT AND LEFT EYES. IEEE SigPort. http://sigport.org/1834
Marzieh Mokhtari, Hossein Rabbani, Alireza Mehri-Dehnavi, 2017. ALIGNMENT OF OPTIC NERVE HEAD OPTICAL COHERENCE TOMOGRAPHY B-SCANS IN RIGHT AND LEFT EYES. Available at: http://sigport.org/1834.
Marzieh Mokhtari, Hossein Rabbani, Alireza Mehri-Dehnavi. (2017). "ALIGNMENT OF OPTIC NERVE HEAD OPTICAL COHERENCE TOMOGRAPHY B-SCANS IN RIGHT AND LEFT EYES." Web.
1. Marzieh Mokhtari, Hossein Rabbani, Alireza Mehri-Dehnavi. ALIGNMENT OF OPTIC NERVE HEAD OPTICAL COHERENCE TOMOGRAPHY B-SCANS IN RIGHT AND LEFT EYES [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/1834

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