Feature Preserving Regularized Savitzky–Golay Filter for Ultrasonic Images

2020 ◽  
pp. 1077-1090
Author(s):  
Sonal Goyal ◽  
Navdeep Yadav ◽  
Asha Rani ◽  
Vijander Singh
Keyword(s):  
Feature Preserving ◽  
Ultrasonic Images

Discrimination of Breast Tumors in Ultrasonic Images by Classifier Ensemble Trained with AdaBoost

2009 ◽  
Vol 129 (4) ◽  
pp. 620-629
Author(s):  
Atsushi Takemura ◽  
Akinobu Shimizu ◽  
Kazuhiko Hamamoto
Keyword(s):  
Breast Tumors ◽  
Classifier Ensemble ◽  
Ultrasonic Images

Feature Preserving Mesh Simplification Algorithm Based on Square Volume Measure

2009 ◽  
Vol 32 (2) ◽  
pp. 203-212 ◽  
Author(s):  
Yuan-Feng ZHOU ◽  
Cai-Ming ZHANG ◽  
Ping HE
Keyword(s):  
Mesh Simplification ◽  
Volume Measure ◽  
Feature Preserving

Flaw Detection from Ultrasonic Images using YOLO and SSD

2019 ◽  
Author(s):  
Luka Posilovic ◽  
Duje Medak ◽  
Marko Subasic ◽  
Tomislav Petkovic ◽  
Marko Budimir ◽  
...  
Keyword(s):  
Flaw Detection ◽  
Ultrasonic Images

scholarly journals ResCaps: an improved capsule network and its application in ultrasonic image classification of thyroid papillary carcinoma

2021 ◽  
Author(s):  
Xiongzhi Ai ◽  
Jiawei Zhuang ◽  
Yonghua Wang ◽  
Pin Wan ◽  
Yu Fu
Keyword(s):  
Papillary Carcinoma ◽  
Network Model ◽  
Reliability And Validity ◽  
Thyroid Papillary Carcinoma ◽  
Data Set ◽  
Ultrasonic Image ◽  
First Choice ◽  
Thyroid Papillary ◽  
Ultrasonic Images

AbstractUltrasonic image examination is the first choice for the diagnosis of thyroid papillary carcinoma. However, there are some problems in the ultrasonic image of thyroid papillary carcinoma, such as poor definition, tissue overlap and low resolution, which make the ultrasonic image difficult to be diagnosed. Capsule network (CapsNet) can effectively address tissue overlap and other problems. This paper investigates a new network model based on capsule network, which is named as ResCaps network. ResCaps network uses residual modules and enhances the abstract expression of the model. The experimental results reveal that the characteristic classification accuracy of ResCaps3 network model for self-made data set of thyroid papillary carcinoma was $$81.06\%$$ 81.06 % . Furthermore, Fashion-MNIST data set is also tested to show the reliability and validity of ResCaps network model. Notably, the ResCaps network model not only improves the accuracy of CapsNet significantly, but also provides an effective method for the classification of lesion characteristics of thyroid papillary carcinoma ultrasonic images.

The effect of temperature on ultrasonic images in infant cadavers

1986 ◽  
Vol 59 (699) ◽  
pp. 271-272 ◽  
Author(s):  
A. C. Lamont ◽  
B. J. Cremin
Keyword(s):  
Effect Of Temperature ◽  
Ultrasonic Images

Endometrium estimation in a sequence of ultrasonic images

2001 ◽  
Vol 25 (4) ◽  
pp. 335-342 ◽  
Author(s):  
C.H. Yang ◽  
P.C. Chung ◽  
Y.C. Tsai
Keyword(s):  
Ultrasonic Images
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