PANDA- T1ρ: Integrating principal component analysis and dictionary learning for fast T1ρ mapping

2014 ◽  
Vol 73 (1) ◽  
pp. 263-272 ◽  
Author(s):  
Yanjie Zhu ◽  
Qinwei Zhang ◽  
Qiegen Liu ◽  
Yi-Xiang J. Wang ◽  
Xin Liu ◽  
...  
Keyword(s):  
Principal Component Analysis ◽  
Dictionary Learning ◽  
Principal Component ◽  
Component Analysis ◽  
T1ρ Mapping

scholarly journals Image Denoising Algorithm Combined with SGK Dictionary Learning and Principal Component Analysis Noise Estimation

2018 ◽  
Vol 2018 ◽  
pp. 1-10
Author(s):  
Wenjing Zhao ◽  
Yue Chi ◽  
Yatong Zhou ◽  
Cheng Zhang
Keyword(s):  
Principal Component Analysis ◽  
Standard Deviation ◽  
Dictionary Learning ◽  
Learning Algorithm ◽  
Principal Component ◽  
Estimation Algorithm ◽  
Component Analysis ◽  
Noise Estimation ◽  
Noise Standard Deviation ◽  
Estimation Algorithms

SGK (sequential generalization of K-means) dictionary learning denoising algorithm has the characteristics of fast denoising speed and excellent denoising performance. However, the noise standard deviation must be known in advance when using SGK algorithm to process the image. This paper presents a denoising algorithm combined with SGK dictionary learning and the principal component analysis (PCA) noise estimation. At first, the noise standard deviation of the image is estimated by using the PCA noise estimation algorithm. And then it is used for SGK dictionary learning algorithm. Experimental results show the following: (1) The SGK algorithm has the best denoising performance compared with the other three dictionary learning algorithms. (2) The SGK algorithm combined with PCA is superior to the SGK algorithm combined with other noise estimation algorithms. (3) Compared with the original SGK algorithm, the proposed algorithm has higher PSNR and better denoising performance.

scholarly journals Dictionary-Based Image Denoising by Fused-Lasso Atom Selection

2014 ◽  
Vol 2014 ◽  
pp. 1-10 ◽  
Author(s):  
Ao Li ◽  
Hayaru Shouno
Keyword(s):  
Principal Component Analysis ◽  
Image Denoising ◽  
Dictionary Learning ◽  
Geometric Structure ◽  
Principal Component ◽  
Component Analysis ◽  
Fused Lasso ◽  
Split Bregman ◽  
Learned Dictionary ◽  
Local Geometric Structure

We proposed an efficient image denoising scheme by fused lasso with dictionary learning. The scheme has two important contributions. The first one is that we learned the patch-based adaptive dictionary by principal component analysis (PCA) with clustering the image into many subsets, which can better preserve the local geometric structure. The second one is that we coded the patches in each subset by fused lasso with the clustering learned dictionary and proposed an iterative Split Bregman to solve it rapidly. We present the capabilities with several experiments. The results show that the proposed scheme is competitive to some excellent denoising algorithms.

A German version of the Intermittent Claudication Questionnaire (ICQ): cultural adaptation and validation

VASA ◽  
2012 ◽  
Vol 41 (5) ◽  
pp. 333-342 ◽  
Author(s):  
Kirchberger ◽  
Finger ◽  
Müller-Bühl
Keyword(s):  
Principal Component Analysis ◽  
Intermittent Claudication ◽  
Completion Time ◽  
Short Form ◽  
Principal Component ◽  
Component Analysis ◽  
German Version ◽  
Average Completion Time ◽  
Sf 36 ◽  
Related Quality

Background: The Intermittent Claudication Questionnaire (ICQ) is a short questionnaire for the assessment of health-related quality of life (HRQOL) in patients with intermittent claudication (IC). The objective of this study was to translate the ICQ into German and to investigate the psychometric properties of the German ICQ version in patients with IC. Patients and methods: The original English version was translated using a forward-backward method. The resulting German version was reviewed by the author of the original version and an experienced clinician. Finally, it was tested for clarity with 5 German patients with IC. A sample of 81 patients were administered the German ICQ. The sample consisted of 58.0 % male patients with a median age of 71 years and a median IC duration of 36 months. Test of feasibility included completeness of questionnaires, completion time, and ratings of clarity, length and relevance. Reliability was assessed through a retest in 13 patients at 14 days, and analysis of Cronbach’s alpha for internal consistency. Construct validity was investigated using principal component analysis. Concurrent validity was assessed by correlating the ICQ scores with the Short Form 36 Health Survey (SF-36) as well as clinical measures. Results: The ICQ was completely filled in by 73 subjects (90.1 %) with an average completion time of 6.3 minutes. Cronbach’s alpha coefficient reached 0.75. Intra-class correlation for test-retest reliability was r = 0.88. Principal component analysis resulted in a 3 factor solution. The first factor explained 51.5 of the total variation and all items had loadings of at least 0.65 on it. The ICQ was significantly associated with the SF-36 and treadmill-walking distances whereas no association was found for resting ABPI. Conclusions: The German version of the ICQ demonstrated good feasibility, satisfactory reliability and good validity. Responsiveness should be investigated in further validation studies.

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