scholarly journals Automatic image quality assessment and measurement of fetal head in two-dimensional ultrasound image

2017 ◽  
Vol 4 (2) ◽  
pp. 024001 ◽  
Author(s):  
Lei Zhang ◽  
Nicholas J. Dudley ◽  
Tryphon Lambrou ◽  
Nigel Allinson ◽  
Xujiong Ye
Keyword(s):  
Image Quality ◽  
Quality Assessment ◽  
Image Quality Assessment ◽  
Ultrasound Image ◽  
Two Dimensional ◽  
Fetal Head

scholarly journals Ultrasound image quality assessment: a framework for evaluation of clinical image quality

2010 ◽  
Author(s):  
Martin Christian Hemmsen ◽  
Mads Møller Petersen ◽  
Svetoslav Ivanov Nikolov ◽  
Michael Backmann Nielsen ◽  
Jørgen Arendt Jensen
Keyword(s):  
Image Quality ◽  
Quality Assessment ◽  
Image Quality Assessment ◽  
Ultrasound Image ◽  
Clinical Image

scholarly journals Evaluations of UltraiQ software for objective ultrasound image quality assessment using images from a commercial scanner

2018 ◽  
Vol 19 (2) ◽  
pp. 298-304 ◽  
Author(s):  
Zaiyang Long ◽  
Donald J Tradup ◽  
Scott F Stekel ◽  
Krzysztof R Gorny ◽  
Nicholas J Hangiandreou
Keyword(s):  
Image Quality ◽  
Quality Assessment ◽  
Image Quality Assessment ◽  
Ultrasound Image

FUIQA: Fetal Ultrasound Image Quality Assessment With Deep Convolutional Networks

2017 ◽  
Vol 47 (5) ◽  
pp. 1336-1349 ◽  
Author(s):  
Lingyun Wu ◽  
Jie-Zhi Cheng ◽  
Shengli Li ◽  
Baiying Lei ◽  
Tianfu Wang ◽  
...  
Keyword(s):  
Image Quality ◽  
Quality Assessment ◽  
Image Quality Assessment ◽  
Ultrasound Image ◽  
Fetal Ultrasound ◽  
Convolutional Networks

Image Quality Assessment Method Based on Fractional Fourier Transform

2013 ◽  
Vol 433-435 ◽  
pp. 372-375
Author(s):  
Bin Wang
Keyword(s):  
Fourier Transform ◽  
Image Quality ◽  
Quality Assessment ◽  
Image Quality Assessment ◽  
Fractional Fourier Transform ◽  
Quality Score ◽  
Reference Image ◽  
Support Vector ◽  
Two Dimensional ◽  
Assessment Approach

Image quality assessment is an important issue in the area of image processing, and the no-reference image quality assessment tries to evaluate the quality of image without the reference image. The present no-reference image quality assessment approach can not predict the quality score accurately. This paper proposes a new image quality assessment approach based on two-dimensional discrete fractional Fourier transform (FRFT). After the image is processed by two dimensional discrete fourier transform, the histogram of FRFT coefficients in different order are modeled by generalized Gaussian distribution (GGD). The parameters of GGD are estimated and the feature vector is formed by parameters of GGD. After that, the image is classified into five distortion type by the trained support vector machine. At last, the quality score is predicted by the trained support vector regression machine. The experiment results show that the performance of proposed method is better than the traditional method.

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