An Adaptive Edge Enhancement Method Based on Histogram Matching for Ultrasound Images

Author(s):  
Xianfeng Yang ◽  
Jing Zhang ◽  
Bo Peng ◽  
Shutao You
2013 ◽  
Vol 785-786 ◽  
pp. 1391-1394
Author(s):  
Chun Ying Pang ◽  
Qi Yu Jiao

To make B ultrasound images clear, a new image enhancement method was studied.The study used an improved fuzzy algorithm based on gray-level to process B ultrasound images. And the process is largely simple by adding threshold selection which could meet different clinical demand. Several kinds of enhancement algorithms for B ultrasound images are evaluated and compared by using MATLAB to process the same image. The results after contrast show that the improved fuzzy algorithm is effective and achieved in clinical practice.


2021 ◽  
Author(s):  
Xiaochen Zhao ◽  
Xiaoduo Jiang ◽  
AJ Han ◽  
Tianyi Mao ◽  
Weiji He ◽  
...  

2016 ◽  
Vol 39 (2) ◽  
pp. 96-107 ◽  
Author(s):  
Deepti Mittal

This work is presented with the objective to assess quantitatively the impact of modified anisotropic diffusion–based enhancement method of Mittal et al. in computer-aided classification of focal liver lesions. This assessment was made before and after enhancement of clinically acquired ultrasound images with the comparison of (a) discrimination capability of radiologically important texture contrast feature using box plot and p-value statistics and (b) test results of designed computer-aided classification schemes to detect/classify focal liver tissues using receiver operating characteristic curves. The results reveal that the application of enhancement method on clinically acquired ultrasound image may effectively improve the confidence of clinicians/radiologists in computer-aided diagnostic solutions to detect and classify focal liver lesions.


1996 ◽  
Vol 32 (1) ◽  
pp. 45
Author(s):  
G. Goyins ◽  
D. Frederiksen ◽  
J.H. Papke ◽  
I.D. Boise

2021 ◽  
Vol 2021 ◽  
pp. 1-6
Author(s):  
Yiqun Zhang ◽  
Lu Xue ◽  
Chunlian Zhang ◽  
Jianying Zhou

In this paper, the effect of ultrasound images based on a 3-dimensional image fusion algorithm in the diagnosis of adenomyosis was evaluated. 88 patients with adenomyosis who were treated in the hospital from February 2019 to May 2020 were selected as the research subjects. They were rolled into localized type (Group A), with 40 cases, and diffuse type (Group B), with 48 cases. At the same time, 45 women of normal childbearing age who underwent physical examination in the outpatient clinic were rolled into the control group (Group C). Three-dimensional ultrasound scans of the uterus were performed on all patients, to observe the enhancement methods and characteristics. Then, the image characteristics of adenomyosis were studied through the time-intensity curve (TIC). Arise time ATs of three groups of patients were not different greatly ( P > 0.05 ). It was found that the enhancement method, enhancement uniformity, and enhancement level of ultrasound scan in Groups A and B were significantly different from Group C ( P < 0.05 ). In contrast with Group C, the rise time (RT) of the ultrasound scan of the two groups was less, the time-to-peak (TTP) was faster, and the image maximum (IMAX) was higher ( P < 0.05 ). What is more, contrast-enhanced ultrasonic (CEUS) detection in patients with adenomyosis showed centrality and nonuniform high enhancement. Besides, less RT, faster TTP, and higher IMAX than the normal population can be the key evidence for the clinical diagnosis of adenomyosis. In conclusion, according to the blood supply characteristics of adenomyosis and other gynecological diseases, the enhancement method and enhancement time of ultrasound images are significantly different. TIC can reflect the hemodynamic difference between the lesion and the normal ones. Therefore, the CEUS based on the three-dimensional image fusion algorithm can be applied to the image diagnosis of adenomyosis.


2006 ◽  
Vol 25 (3) ◽  
pp. 297-311 ◽  
Author(s):  
Yong Yue ◽  
M.M. Croitoru ◽  
A. Bidani ◽  
J.B. Zwischenberger ◽  
J.W. Clark

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