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Automatic and optimal segmentation of the left ventricle in cardiac magnetic resonance images independent of the training sets
期刊论文
IET IMAGE PROCESSING, 2019, 卷号: 13, 期号: 10, 页码: 1725-1735
作者:
Wang, Zhenzhou
收藏
  |  
浏览/下载:11/0
  |  
提交时间:2019/12/11
biomedical MRI
learning (artificial intelligence)
image segmentation
medical image processing
cardiology
deep learning-based methods
significant break-throughs
complete training sets
training
sets-independent method
segment LV optimally
available
state-of-the-art training-sets-independent image segmentation methods
segmentation method
poor quality images
optimal solution
state-of-the-art training-sets-independent segmentation methods
optimal
segmentation
left ventricle
cardiac magnetic resonance images
cardiac
imaging
Employment of the appropriate range of sawtooth-shaped-function illumination intensity to improve the image quality
期刊论文
OPTIK, 2018, 卷号: 175, 页码: 189-196
作者:
Yu, Jianping
;
Li, Gang
;
Wang, Shaohui
;
Lin, Ling
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浏览/下载:3/0
  |  
提交时间:2019/12/11
Light intensity range
Sawtooth-shaped-Function
Gray-scale resolution
Medical image quality
Signal-to-noise ratio
Fundus Image Quality-Guided Diabetic Retinopathy Grading
期刊论文
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2018, 卷号: 11039 LNCS, 页码: 245-252
作者:
Zhou, K.
;
Gu, Z.
;
Li, A.
;
Cheng, J.
;
Gao, S.
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  |  
浏览/下载:9/0
  |  
提交时间:2019/12/30
Deep learning
Diagnosis
Eye protection
Grading
Image quality
Medical imaging
Pathology
Diabetic retinopathy
Fundus camera
Fundus image
Grading methods
Screening system
Unbalanced data
Unbalanced distribution
Uneven illuminations
Image analysis
Ultrasonic imaging contrast enhancement using modified dehaze image model
期刊论文
ELECTRONICS LETTERS, 2013, 卷号: Vol.49 No.19, 页码: 1209
作者:
Guo, Dequan
;
Liu, Paul
;
She, Yue
;
Yang, Hongyu
;
Liu, Dong
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  |  
浏览/下载:7/0
  |  
提交时间:2019/03/01
acoustic
noise
biomedical
ultrasonics
image
enhancement
image
resolution
medical
image
processing
ultrasonic
imaging
ultrasonic
imaging
contrast
enhancement
modified
dehaze
image
model
modified
dark
channel
model
smoothed
out
electronic
noise
layer
dehaze
photographs
ultrasonic
image
quality
The application of adaptive enhancement algorithm based on gray entropy in mammary gland CR image (EI CONFERENCE)
会议论文
2012 2nd International Conference on Consumer Electronics, Communications and Networks, CECNet 2012, April 21, 2012 - April 23, 2012, Three Gorges, China
Zhang M.-H.
;
Zhang Y.-Y.
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浏览/下载:26/0
  |  
提交时间:2013/03/25
Mammary gland is composed entirely of soft tissue with approximate density
therefore mammary gland CR medicine radiation image presents a low contrast
and slight difference changes may be a manifestation of tumor
so it is necessary to enhance mammary gland CR image to improve its visual quality in order to meet the demands of doctor's clinical diagnosis. However the general enhancement algorithms over enhance the contrast and noise
due to image details lost
aiming at the defects
a mammary gland CR medicine image adaptive enhancement arithmetic based on image gray entropy is put forward. The arithmetic adapts dizzy image to magnify selected spatial frequency response in order to enhance the edge details of mammary gland CR images. It can adjust weighted factor K according to image gray characteristics namely pixel gray entropy. Experiments results demonstrate that mammary gland CR image enhanced by the algorithm has abundant details and high signal-to-noise ratio
moreover
CR image enhanced has good visual effect. So the method is effective and fit for enhancing CR medical radiation image edge details. 2012 IEEE.
Application of adaptive enhancement means in head and neck CR image (EI CONFERENCE)
会议论文
2012 3rd International Conference on Information Technology for Manufacturing Systems, ITMS 2012, September 8, 2012 - September 9, 2012, Qingdao, China
Zhang M.-H.
;
Zhang Y.-Y.
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浏览/下载:26/0
  |  
提交时间:2013/03/25
Digital CR of head and neck overcomes the disadvantage of regular X-ray radiography
which can not reveal bone and soft tissue position deficiency in one exposing
and reduces the Xray radiation dose. Meanwhile
various factors cause the decline of image quality
and images must be enhanced in order to meet demands of doctor's clinical diagnosis. The general enhancement algorithms don't consider body's structure differences and density characteristics. A new adaptive CR enhancement algorithm was proposed in this article
and head and neck CR images were processed with this method and compared with linear unsharp masking method. The experiment proves that the details of CR image enhanced were abundant and enhanced CR image had good visual effect
SNR was high
as well as detail variance /background variance (DV/BV) indicating that this algorithm is suitable for head and neck CR medical images. (2012) Trans Tech Publications
Switzerland.
The research of digltal CR medicine image adapitive enhancement method (EI CONFERENCE)
会议论文
4th International Conference on Mechanical and Electrical Technology, ICMET 2012, July 24, 2012 - July 26, 2012, Kuala Lumpur, Malaysia
Ming-Hui Z.
;
Yao-Yu Z.
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浏览/下载:55/0
  |  
提交时间:2013/03/25
Digital CR medicine radiation image is in doctor's favor and has became medicine imaging technology new hot spot because of its high gray contrast
powerful computer disposal function
little radiation dosage
non-film diagnosis
different area consultation. But degradation of digital X-ray medical image such as low contrast and blurring during radiographic imaging
caused by complexity of body tissue and effects of X-ray scattering and electrical noise etc.
can worsen the results of analysis and diagnosis. So it is usually needed that CR medicine image is enhanced to improve its vision quality
and easy to doctor's more accurate diagnosis. The general enhancement algorithms over enhancing the contrast and lose image details
aiming at the defects
an enhancement algorithm for CR image is proposed based on the ratio of deviation to mean of domain. The arithmetic enhance CR image edge details by adjusting factor K based on the ratio of deviation to mean of domain of CR image. Experiment results demonstrate that the algorithm enhances CR image detail and CR image enhanced has good visual effect
the adaptive enhancement method is fit for CR medicine image. (2012) Trans Tech Publications
Switzerland.
Multi-focus image fusion algorithm based on adaptive PCNN and wavelet transform (EI CONFERENCE)
会议论文
International Symposium on Photoelectronic Detection and Imaging 2011: Advances in Imaging Detectors and Applications, May 24, 2011 - May 26, 2011, Beijing, China
Wu Z.-G.
;
Wang M.-J.
;
Han G.-L.
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浏览/下载:34/0
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提交时间:2013/03/25
Being an efficient method of information fusion
image fusion has been used in many fields such as machine vision
medical diagnosis
military applications and remote sensing.In this paper
Pulse Coupled Neural Network (PCNN) is introduced in this research field for its interesting properties in image processing
including segmentation
target recognition et al.
and a novel algorithm based on PCNN and Wavelet Transform for Multi-focus image fusion is proposed. First
the two original images are decomposed by wavelet transform. Then
based on the PCNN
a fusion rule in the Wavelet domain is given. This algorithm uses the wavelet coefficient in each frequency domain as the linking strength
so that its value can be chosen adaptively. Wavelet coefficients map to the range of image gray-scale. The output threshold function attenuates to minimum gray over time. Then all pixels of image get the ignition. So
the output of PCNN in each iteration time is ignition wavelet coefficients of threshold strength in different time. At this moment
the sequences of ignition of wavelet coefficients represent ignition timing of each neuron. The ignition timing of PCNN in each neuron is mapped to corresponding image gray-scale range
which is a picture of ignition timing mapping. Then it can judge the targets in the neuron are obvious features or not obvious. The fusion coefficients are decided by the compare-selection operator with the firing time gradient maps and the fusion image is reconstructed by wavelet inverse transform. Furthermore
by this algorithm
the threshold adjusting constant is estimated by appointed iteration number. Furthermore
In order to sufficient reflect order of the firing time
the threshold adjusting constant is estimated by appointed iteration number. So after the iteration achieved
each of the wavelet coefficient is activated. In order to verify the effectiveness of proposed rules
the experiments upon Multi-focus image are done. Moreover
comparative results of evaluating fusion quality are listed. The experimental results show that the method can effectively enhance the edge details and improve the spatial resolution of the image. 2011 SPIE.
Segmentation short scan FDK reconstruction algorithm for cone-beam CT
期刊论文
2010, 2010
Chen Lian
;
Wu Zhifang
;
Wang Qinya
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浏览/下载:5/0
A vector quantization based approach for CFA data compression in wireless endoscopy capsule
期刊论文
2010, 2010
Xiaowen Li
;
Guolin Li
;
Xiang Xie
;
Zhihua Wang
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  |  
浏览/下载:3/0
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