Texture Classification of B-Scan Ultrasound Images : An Assessment Using Tissue Models

1982 ◽  
pp. 203-211 ◽  
Author(s):  
D. K. Nassiri ◽  
D. Nicholas ◽  
C. R. Hill
Keyword(s):  
Texture Classification ◽  
Ultrasound Images ◽  
Tissue Models

scholarly journals Variogram methods for texture classification of atherosclerotic plaque ultrasound images

2006 ◽  
Author(s):  
Oliver M. Jeromin ◽  
Marios S. Pattichis ◽  
Constantinos Pattichis ◽  
Efthyvoulos Kyriacou ◽  
Andrew Nicolaides
Keyword(s):  
Atherosclerotic Plaque ◽  
Texture Classification ◽  
Ultrasound Images

Ovarian Tumor Texture Classification Based on Sparse Auto-Encoder Network Combined with Multi-Feature Fusion and Random Forest in Ultrasound Image

2021 ◽  
Vol 11 (2) ◽  
pp. 424-431
Author(s):  
Yingxin Wang ◽  
Qianqian Zeng
Keyword(s):  
Neural Network ◽  
Feature Fusion ◽  
Texture Classification ◽  
Ultrasound Image ◽  
Texture Features ◽  
Recall Rate ◽  
Svm Classifier ◽  
Ultrasound Images ◽  
Feature Extraction Method

Texture analysis has always been active areas of ultrasound image processing research. Using texture features to classify the ultrasound images is the focus of researchers' attention. How to extract representative texture features is an important part of successful texture description. The research goal of this paper is to apply the deep neural network into the ultrasound classification of ovarian tumors, and design a novel type of ovarian cancer diagnosis system. The improved HOG feature extraction method and the gray-level concurrence matrix of LBP image are firstly adopted to extract low-level features; Then, these features are cascaded into a new feature vector, and are input into the auto-encoder neural network to learn the high-level feature. Finally, the SVM classifier is used to achieve the classification of ovarian lesion. A large number of qualitative and quantitative experiments show that the improved method has more performance than the comparisons algorithms for ovarian ultrasound lesion, and it can significantly improve the classification performance while ensuring the accuracy rate and recall rate.

scholarly journals Classification of Hepatocellular Carcinoma and Liver Abscess by Applying Neural Network to Ultrasound Images

2020 ◽  
Vol 32 (8) ◽  
pp. 2659
Author(s):  
Sendren Sheng-Dong Xu ◽  
Chun-Chao Chang ◽  
Chien-Tien Su ◽  
Pham Quoc Phu ◽  
Tifany Inne Halim ◽  
...  
Keyword(s):  
Neural Network ◽  
Hepatocellular Carcinoma ◽  
Liver Abscess ◽  
Ultrasound Images
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