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Medical image analysis

Microvasculature Segmentation of Arterioles Using Deep CNN


Segmenting microvascular structures is an important requirement in understanding angioadaptation by which vascular networks remodel their morphological structures. Accurate segmentation for separating microvasculature structures is important in quantifying remodeling process. In this work, we utilize a deep convolutional neural network (CNN) framework for obtaining robust segmentations of microvasculature from epifluorescence microscopy imagery of mice dura mater.

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Authors:
Yasmin M. Kassim, V. B. Surya Prasath, Olga V. Glinskii, Vladislav V. Glinsky, Virginia H. Huxley, Kannappan Palaniappan
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10 September 2017 - 10:05pm
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ICIP_17_yasmin - sigport.pdf

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[1] Yasmin M. Kassim, V. B. Surya Prasath, Olga V. Glinskii, Vladislav V. Glinsky, Virginia H. Huxley, Kannappan Palaniappan, "Microvasculature Segmentation of Arterioles Using Deep CNN", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/1891. Accessed: Sep. 20, 2020.
@article{1891-17,
url = {http://sigport.org/1891},
author = {Yasmin M. Kassim; V. B. Surya Prasath; Olga V. Glinskii; Vladislav V. Glinsky; Virginia H. Huxley; Kannappan Palaniappan },
publisher = {IEEE SigPort},
title = {Microvasculature Segmentation of Arterioles Using Deep CNN},
year = {2017} }
TY - EJOUR
T1 - Microvasculature Segmentation of Arterioles Using Deep CNN
AU - Yasmin M. Kassim; V. B. Surya Prasath; Olga V. Glinskii; Vladislav V. Glinsky; Virginia H. Huxley; Kannappan Palaniappan
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/1891
ER -
Yasmin M. Kassim, V. B. Surya Prasath, Olga V. Glinskii, Vladislav V. Glinsky, Virginia H. Huxley, Kannappan Palaniappan. (2017). Microvasculature Segmentation of Arterioles Using Deep CNN. IEEE SigPort. http://sigport.org/1891
Yasmin M. Kassim, V. B. Surya Prasath, Olga V. Glinskii, Vladislav V. Glinsky, Virginia H. Huxley, Kannappan Palaniappan, 2017. Microvasculature Segmentation of Arterioles Using Deep CNN. Available at: http://sigport.org/1891.
Yasmin M. Kassim, V. B. Surya Prasath, Olga V. Glinskii, Vladislav V. Glinsky, Virginia H. Huxley, Kannappan Palaniappan. (2017). "Microvasculature Segmentation of Arterioles Using Deep CNN." Web.
1. Yasmin M. Kassim, V. B. Surya Prasath, Olga V. Glinskii, Vladislav V. Glinsky, Virginia H. Huxley, Kannappan Palaniappan. Microvasculature Segmentation of Arterioles Using Deep CNN [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/1891

ADABOOST-BASED DETECTION AND SEGMENTATION OF BIORESORBABLE VASCULAR SCAFFOLDS STRUTS IN IVOCT IMAGES


In this paper, we presented an automatic method for BVS struts detection in IVOCT images based on Haar-like features and Adaboost algorithm. Then, DP algorithm was used for struts segmentation. Based on the detection and segmentation results, apposed and malapposed struts were distinguished automatically. The qualitative and quantitative evaluation shows that our method is effective and robust for BVS struts detection and segmentation, and is capable of malapposition analysis.

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Authors:
Yifeng Lu, Yihui Cao, Qinhua Jin, Yundai Chen, Qinye Yin, Jianan Li, Rui Zhu, Wei Zhao
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4 September 2017 - 11:30pm
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poster

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[1] Yifeng Lu, Yihui Cao, Qinhua Jin, Yundai Chen, Qinye Yin, Jianan Li, Rui Zhu, Wei Zhao, "ADABOOST-BASED DETECTION AND SEGMENTATION OF BIORESORBABLE VASCULAR SCAFFOLDS STRUTS IN IVOCT IMAGES", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/1829. Accessed: Sep. 20, 2020.
@article{1829-17,
url = {http://sigport.org/1829},
author = {Yifeng Lu; Yihui Cao; Qinhua Jin; Yundai Chen; Qinye Yin; Jianan Li; Rui Zhu; Wei Zhao },
publisher = {IEEE SigPort},
title = {ADABOOST-BASED DETECTION AND SEGMENTATION OF BIORESORBABLE VASCULAR SCAFFOLDS STRUTS IN IVOCT IMAGES},
year = {2017} }
TY - EJOUR
T1 - ADABOOST-BASED DETECTION AND SEGMENTATION OF BIORESORBABLE VASCULAR SCAFFOLDS STRUTS IN IVOCT IMAGES
AU - Yifeng Lu; Yihui Cao; Qinhua Jin; Yundai Chen; Qinye Yin; Jianan Li; Rui Zhu; Wei Zhao
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/1829
ER -
Yifeng Lu, Yihui Cao, Qinhua Jin, Yundai Chen, Qinye Yin, Jianan Li, Rui Zhu, Wei Zhao. (2017). ADABOOST-BASED DETECTION AND SEGMENTATION OF BIORESORBABLE VASCULAR SCAFFOLDS STRUTS IN IVOCT IMAGES. IEEE SigPort. http://sigport.org/1829
Yifeng Lu, Yihui Cao, Qinhua Jin, Yundai Chen, Qinye Yin, Jianan Li, Rui Zhu, Wei Zhao, 2017. ADABOOST-BASED DETECTION AND SEGMENTATION OF BIORESORBABLE VASCULAR SCAFFOLDS STRUTS IN IVOCT IMAGES. Available at: http://sigport.org/1829.
Yifeng Lu, Yihui Cao, Qinhua Jin, Yundai Chen, Qinye Yin, Jianan Li, Rui Zhu, Wei Zhao. (2017). "ADABOOST-BASED DETECTION AND SEGMENTATION OF BIORESORBABLE VASCULAR SCAFFOLDS STRUTS IN IVOCT IMAGES." Web.
1. Yifeng Lu, Yihui Cao, Qinhua Jin, Yundai Chen, Qinye Yin, Jianan Li, Rui Zhu, Wei Zhao. ADABOOST-BASED DETECTION AND SEGMENTATION OF BIORESORBABLE VASCULAR SCAFFOLDS STRUTS IN IVOCT IMAGES [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/1829

Identifying fMRI Dynamic Connectivity States Using Affinity Propagation Clustering Method: Application to Schizophrenia


Numerous studies have shown that brain functional connectivity patterns can be time-varying over periods of tens of seconds. K-means has been widely used to extract the connectivity states from dynamic functional connectivity. However, K-means is dependent on initialization and can be exponentially slow in converging due to extensive noise in dynamic functional connectivity. In this work, we propose to use an affinity propagation clustering method to estimate the connectivity states. The new approach found more meaningful group differences than K-means. Our finding supports that our method is promising in exploring biomarkers of mental disorders.

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Authors:
Salman M. S., Du Y., Calhoun V. D.
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10 March 2017 - 9:19pm
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[1] Salman M. S., Du Y., Calhoun V. D., "Identifying fMRI Dynamic Connectivity States Using Affinity Propagation Clustering Method: Application to Schizophrenia", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/1740. Accessed: Sep. 20, 2020.
@article{1740-17,
url = {http://sigport.org/1740},
author = {Salman M. S.; Du Y.; Calhoun V. D. },
publisher = {IEEE SigPort},
title = {Identifying fMRI Dynamic Connectivity States Using Affinity Propagation Clustering Method: Application to Schizophrenia},
year = {2017} }
TY - EJOUR
T1 - Identifying fMRI Dynamic Connectivity States Using Affinity Propagation Clustering Method: Application to Schizophrenia
AU - Salman M. S.; Du Y.; Calhoun V. D.
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/1740
ER -
Salman M. S., Du Y., Calhoun V. D.. (2017). Identifying fMRI Dynamic Connectivity States Using Affinity Propagation Clustering Method: Application to Schizophrenia. IEEE SigPort. http://sigport.org/1740
Salman M. S., Du Y., Calhoun V. D., 2017. Identifying fMRI Dynamic Connectivity States Using Affinity Propagation Clustering Method: Application to Schizophrenia. Available at: http://sigport.org/1740.
Salman M. S., Du Y., Calhoun V. D.. (2017). "Identifying fMRI Dynamic Connectivity States Using Affinity Propagation Clustering Method: Application to Schizophrenia." Web.
1. Salman M. S., Du Y., Calhoun V. D.. Identifying fMRI Dynamic Connectivity States Using Affinity Propagation Clustering Method: Application to Schizophrenia [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/1740

Classification of Thyroid Nodules in Ultrasound Images Using Deep Model Based Transfer Learning and Hybrid Features


Ultrasonography is a valuable diagnosis method for thyroid nodules. Automatically discriminating benign and malignant nodules in the ultrasound images can provide aided diagnosis suggestions, or increase the diagnosis accuracy when lack of experts. The core problem in this issue is how to capture appropriate features for this specific task. Here, we propose a feature extraction method for ultrasound images based on the convolution neural networks (CNNs), try to introduce more meaningful semantic features to the classification.

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Authors:
Tianjiao Liu, Shuaining Xie, Jing Yu, Lijuan Niu, Weidong Sun
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8 March 2017 - 3:35am
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Classification of Thyroid Nodules in Ultrasound Images Using Deep Model Based Transfer Learning and Hybrid Features

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[1] Tianjiao Liu, Shuaining Xie, Jing Yu, Lijuan Niu, Weidong Sun, "Classification of Thyroid Nodules in Ultrasound Images Using Deep Model Based Transfer Learning and Hybrid Features", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/1701. Accessed: Sep. 20, 2020.
@article{1701-17,
url = {http://sigport.org/1701},
author = {Tianjiao Liu; Shuaining Xie; Jing Yu; Lijuan Niu; Weidong Sun },
publisher = {IEEE SigPort},
title = {Classification of Thyroid Nodules in Ultrasound Images Using Deep Model Based Transfer Learning and Hybrid Features},
year = {2017} }
TY - EJOUR
T1 - Classification of Thyroid Nodules in Ultrasound Images Using Deep Model Based Transfer Learning and Hybrid Features
AU - Tianjiao Liu; Shuaining Xie; Jing Yu; Lijuan Niu; Weidong Sun
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/1701
ER -
Tianjiao Liu, Shuaining Xie, Jing Yu, Lijuan Niu, Weidong Sun. (2017). Classification of Thyroid Nodules in Ultrasound Images Using Deep Model Based Transfer Learning and Hybrid Features. IEEE SigPort. http://sigport.org/1701
Tianjiao Liu, Shuaining Xie, Jing Yu, Lijuan Niu, Weidong Sun, 2017. Classification of Thyroid Nodules in Ultrasound Images Using Deep Model Based Transfer Learning and Hybrid Features. Available at: http://sigport.org/1701.
Tianjiao Liu, Shuaining Xie, Jing Yu, Lijuan Niu, Weidong Sun. (2017). "Classification of Thyroid Nodules in Ultrasound Images Using Deep Model Based Transfer Learning and Hybrid Features." Web.
1. Tianjiao Liu, Shuaining Xie, Jing Yu, Lijuan Niu, Weidong Sun. Classification of Thyroid Nodules in Ultrasound Images Using Deep Model Based Transfer Learning and Hybrid Features [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/1701

FREQUENCY-TUNED ACM FOR BIOMEDICAL IMAGE SEGMENTATION


Biomedical images are usually corrupted by strong noise and
intensity inhomogeneity simultaneously. Existing regionbased active contour models (RACMs) easily fail when segmenting such images. In the frequency domain, we propose a
generalized RACM that presents a new way to understand the
essence of classical RACMs whose segmentation results are
determined by a frequency filter to extract the proposed frequency boundary energy. Then, we introduce the difference
of Gaussians as the optimal filter to exclude strong noise and

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4 March 2017 - 10:08am
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FBEACM_presentation.pptx

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[1] , "FREQUENCY-TUNED ACM FOR BIOMEDICAL IMAGE SEGMENTATION", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/1625. Accessed: Sep. 20, 2020.
@article{1625-17,
url = {http://sigport.org/1625},
author = { },
publisher = {IEEE SigPort},
title = {FREQUENCY-TUNED ACM FOR BIOMEDICAL IMAGE SEGMENTATION},
year = {2017} }
TY - EJOUR
T1 - FREQUENCY-TUNED ACM FOR BIOMEDICAL IMAGE SEGMENTATION
AU -
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/1625
ER -
. (2017). FREQUENCY-TUNED ACM FOR BIOMEDICAL IMAGE SEGMENTATION. IEEE SigPort. http://sigport.org/1625
, 2017. FREQUENCY-TUNED ACM FOR BIOMEDICAL IMAGE SEGMENTATION. Available at: http://sigport.org/1625.
. (2017). "FREQUENCY-TUNED ACM FOR BIOMEDICAL IMAGE SEGMENTATION." Web.
1. . FREQUENCY-TUNED ACM FOR BIOMEDICAL IMAGE SEGMENTATION [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/1625

Automatic segmentation of retinal vasculature

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Authors:
Renoh Johnson Chalakkal, Waleed Abdulla
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4 March 2017 - 2:37am
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Retinal Imaging

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[1] Renoh Johnson Chalakkal, Waleed Abdulla, "Automatic segmentation of retinal vasculature", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/1623. Accessed: Sep. 20, 2020.
@article{1623-17,
url = {http://sigport.org/1623},
author = {Renoh Johnson Chalakkal; Waleed Abdulla },
publisher = {IEEE SigPort},
title = {Automatic segmentation of retinal vasculature},
year = {2017} }
TY - EJOUR
T1 - Automatic segmentation of retinal vasculature
AU - Renoh Johnson Chalakkal; Waleed Abdulla
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/1623
ER -
Renoh Johnson Chalakkal, Waleed Abdulla. (2017). Automatic segmentation of retinal vasculature. IEEE SigPort. http://sigport.org/1623
Renoh Johnson Chalakkal, Waleed Abdulla, 2017. Automatic segmentation of retinal vasculature. Available at: http://sigport.org/1623.
Renoh Johnson Chalakkal, Waleed Abdulla. (2017). "Automatic segmentation of retinal vasculature." Web.
1. Renoh Johnson Chalakkal, Waleed Abdulla. Automatic segmentation of retinal vasculature [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/1623

A SEMI-SUPERVISED METHOD FOR MULTI-SUBJECT FMRI FUNCTIONAL ALIGNMENT


Practical limitations on the duration of individual fMRI scans have led neuroscientist to consider the aggregation of data from multiple subjects. Differences in anatomical structures and functional topographies of brains require aligning data across subjects. Existing functional alignment methods serve as a preprocessing step that allows subsequent statistical methods to learn from the aggregated multi-subject data. Despite their success, current alignment methods do not leverage the labeled data used in the subsequent methods.

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Authors:
Javier S. Turek, Theodore L. Willke, Po-Hsuan Chen, Peter J. Ramadge
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2 March 2017 - 12:56pm
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Semi-Supervised fMRI Functional Alignment

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[1] Javier S. Turek, Theodore L. Willke, Po-Hsuan Chen, Peter J. Ramadge, "A SEMI-SUPERVISED METHOD FOR MULTI-SUBJECT FMRI FUNCTIONAL ALIGNMENT", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/1587. Accessed: Sep. 20, 2020.
@article{1587-17,
url = {http://sigport.org/1587},
author = {Javier S. Turek; Theodore L. Willke; Po-Hsuan Chen; Peter J. Ramadge },
publisher = {IEEE SigPort},
title = {A SEMI-SUPERVISED METHOD FOR MULTI-SUBJECT FMRI FUNCTIONAL ALIGNMENT},
year = {2017} }
TY - EJOUR
T1 - A SEMI-SUPERVISED METHOD FOR MULTI-SUBJECT FMRI FUNCTIONAL ALIGNMENT
AU - Javier S. Turek; Theodore L. Willke; Po-Hsuan Chen; Peter J. Ramadge
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/1587
ER -
Javier S. Turek, Theodore L. Willke, Po-Hsuan Chen, Peter J. Ramadge. (2017). A SEMI-SUPERVISED METHOD FOR MULTI-SUBJECT FMRI FUNCTIONAL ALIGNMENT. IEEE SigPort. http://sigport.org/1587
Javier S. Turek, Theodore L. Willke, Po-Hsuan Chen, Peter J. Ramadge, 2017. A SEMI-SUPERVISED METHOD FOR MULTI-SUBJECT FMRI FUNCTIONAL ALIGNMENT. Available at: http://sigport.org/1587.
Javier S. Turek, Theodore L. Willke, Po-Hsuan Chen, Peter J. Ramadge. (2017). "A SEMI-SUPERVISED METHOD FOR MULTI-SUBJECT FMRI FUNCTIONAL ALIGNMENT." Web.
1. Javier S. Turek, Theodore L. Willke, Po-Hsuan Chen, Peter J. Ramadge. A SEMI-SUPERVISED METHOD FOR MULTI-SUBJECT FMRI FUNCTIONAL ALIGNMENT [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/1587

Active Learning for Magnetic Resonance Image Quality Assessment


In medical imaging, the acquired images are usually analyzed by a human observer and rated with respect to a diagnostic question. However, this procedure is time-demanding and expensive. Furthermore, the lack of a reference image makes this task challenging. In order to support the human observer in assessing image quality and to ensure an objective evaluation, we extend in this paper our previous no-reference magnetic resonance (MR) image quality assessment system with an active learning loop to reduce the amount of necessary labeled training data.

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Authors:
Annika Liebgott, Thomas Küstner, Sergios Gatidis, Fritz Schick, Bin Yang
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21 March 2016 - 9:02am
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Kuestner_ICASSP_poster_FINAL.pdf

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[1] Annika Liebgott, Thomas Küstner, Sergios Gatidis, Fritz Schick, Bin Yang, "Active Learning for Magnetic Resonance Image Quality Assessment", IEEE SigPort, 2016. [Online]. Available: http://sigport.org/924. Accessed: Sep. 20, 2020.
@article{924-16,
url = {http://sigport.org/924},
author = {Annika Liebgott; Thomas Küstner; Sergios Gatidis; Fritz Schick; Bin Yang },
publisher = {IEEE SigPort},
title = {Active Learning for Magnetic Resonance Image Quality Assessment},
year = {2016} }
TY - EJOUR
T1 - Active Learning for Magnetic Resonance Image Quality Assessment
AU - Annika Liebgott; Thomas Küstner; Sergios Gatidis; Fritz Schick; Bin Yang
PY - 2016
PB - IEEE SigPort
UR - http://sigport.org/924
ER -
Annika Liebgott, Thomas Küstner, Sergios Gatidis, Fritz Schick, Bin Yang. (2016). Active Learning for Magnetic Resonance Image Quality Assessment. IEEE SigPort. http://sigport.org/924
Annika Liebgott, Thomas Küstner, Sergios Gatidis, Fritz Schick, Bin Yang, 2016. Active Learning for Magnetic Resonance Image Quality Assessment. Available at: http://sigport.org/924.
Annika Liebgott, Thomas Küstner, Sergios Gatidis, Fritz Schick, Bin Yang. (2016). "Active Learning for Magnetic Resonance Image Quality Assessment." Web.
1. Annika Liebgott, Thomas Küstner, Sergios Gatidis, Fritz Schick, Bin Yang. Active Learning for Magnetic Resonance Image Quality Assessment [Internet]. IEEE SigPort; 2016. Available from : http://sigport.org/924

Implicit kernel presentation aware object segmentation framework


Given a set of training shapes and an input image with a shape similar to some of the elements in the training set, this poster introduces a new implicit kernel sparse model with a twofold goal. First, to obtain an implicit kernel sparse neighbor based combination that best represents the object. Second, to accurately segment the object taking into accounts both the high-level implicit kernel presentation and the low-level image information.

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Authors:
Jincao Yao, Huimin Yu, Roland Hu
Submitted On:
20 March 2016 - 9:06am
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Implicit_kernel_presentation_aware_object_segmentation_framework.pdf

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[1] Jincao Yao, Huimin Yu, Roland Hu, "Implicit kernel presentation aware object segmentation framework", IEEE SigPort, 2016. [Online]. Available: http://sigport.org/874. Accessed: Sep. 20, 2020.
@article{874-16,
url = {http://sigport.org/874},
author = {Jincao Yao; Huimin Yu; Roland Hu },
publisher = {IEEE SigPort},
title = {Implicit kernel presentation aware object segmentation framework},
year = {2016} }
TY - EJOUR
T1 - Implicit kernel presentation aware object segmentation framework
AU - Jincao Yao; Huimin Yu; Roland Hu
PY - 2016
PB - IEEE SigPort
UR - http://sigport.org/874
ER -
Jincao Yao, Huimin Yu, Roland Hu. (2016). Implicit kernel presentation aware object segmentation framework. IEEE SigPort. http://sigport.org/874
Jincao Yao, Huimin Yu, Roland Hu, 2016. Implicit kernel presentation aware object segmentation framework. Available at: http://sigport.org/874.
Jincao Yao, Huimin Yu, Roland Hu. (2016). "Implicit kernel presentation aware object segmentation framework." Web.
1. Jincao Yao, Huimin Yu, Roland Hu. Implicit kernel presentation aware object segmentation framework [Internet]. IEEE SigPort; 2016. Available from : http://sigport.org/874

A novel array processing method for precise depth detection of ultrasound point scatter


A signal based algorithm resulting in increased depth resolution is presented for medical ultrasound. It relies on multiple foci beamforming that is enabled by current ultrasound imaging systems. The concept stems from optical microscopy and is translated here into ultrasound using the Field II simulation software. A 7 MHz linear transducer is used to scan a single point scatterer phantom that can move in the axial direction.

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Authors:
Paul A. Dalgarno, Alan H. Greenaway, Tom Anderson, Jorgen A. Jensen, Vassilis Sboros
Submitted On:
11 March 2016 - 7:19pm
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ICASSP_2016_Diamantis.pdf

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[1] Paul A. Dalgarno, Alan H. Greenaway, Tom Anderson, Jorgen A. Jensen, Vassilis Sboros, "A novel array processing method for precise depth detection of ultrasound point scatter", IEEE SigPort, 2016. [Online]. Available: http://sigport.org/616. Accessed: Sep. 20, 2020.
@article{616-16,
url = {http://sigport.org/616},
author = {Paul A. Dalgarno; Alan H. Greenaway; Tom Anderson; Jorgen A. Jensen; Vassilis Sboros },
publisher = {IEEE SigPort},
title = {A novel array processing method for precise depth detection of ultrasound point scatter},
year = {2016} }
TY - EJOUR
T1 - A novel array processing method for precise depth detection of ultrasound point scatter
AU - Paul A. Dalgarno; Alan H. Greenaway; Tom Anderson; Jorgen A. Jensen; Vassilis Sboros
PY - 2016
PB - IEEE SigPort
UR - http://sigport.org/616
ER -
Paul A. Dalgarno, Alan H. Greenaway, Tom Anderson, Jorgen A. Jensen, Vassilis Sboros. (2016). A novel array processing method for precise depth detection of ultrasound point scatter. IEEE SigPort. http://sigport.org/616
Paul A. Dalgarno, Alan H. Greenaway, Tom Anderson, Jorgen A. Jensen, Vassilis Sboros, 2016. A novel array processing method for precise depth detection of ultrasound point scatter. Available at: http://sigport.org/616.
Paul A. Dalgarno, Alan H. Greenaway, Tom Anderson, Jorgen A. Jensen, Vassilis Sboros. (2016). "A novel array processing method for precise depth detection of ultrasound point scatter." Web.
1. Paul A. Dalgarno, Alan H. Greenaway, Tom Anderson, Jorgen A. Jensen, Vassilis Sboros. A novel array processing method for precise depth detection of ultrasound point scatter [Internet]. IEEE SigPort; 2016. Available from : http://sigport.org/616

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