Texture Analysis of Carotid Plaque Ultrasound Images

2014 ◽  
pp. 335-344
Author(s):  
Krishnaswamy Sumathi ◽  
Mahesh Veezhinathan
Keyword(s):  
Texture Analysis ◽  
Carotid Plaque ◽  
Ultrasound Images

scholarly journals 950-93 Automatic Identification of Calcified Regions on Intravascular Ultrasound Images of Human Coronary Arteries Using Texture Analysis

1995 ◽  
Vol 25 (2) ◽  
pp. 180A
Author(s):  
Marco Masseroli ◽  
Robert M. Cothren ◽  
E. Murat Tuzcu ◽  
Dominique S. Meier ◽  
James D. Thomas ◽  
...  
Keyword(s):  
Texture Analysis ◽  
Intravascular Ultrasound ◽  
Coronary Arteries ◽  
Automatic Identification ◽  
Ultrasound Images

scholarly journals Texture and Morphological Analysis of Ultrasound Images of the Carotid Plaque for the Assessment of Stroke

2005 ◽  
Author(s):  
Andrew Nicolaides ◽  
Marios Pattichis ◽  
Christodoulos Christodoulou ◽  
Efthyvoulos Kyriacou ◽  
Marios Pantziaris ◽  
...  
Keyword(s):  
Carotid Plaque ◽  
Morphological Analysis ◽  
Ultrasound Images

scholarly journals FORMING THE STACK OF TEXTURE FEATURES FOR LIVER ULTRASOUND IMAGES CLASSIFICATION

2020 ◽  
Vol 3 (4) ◽  
pp. 240-251
Author(s):  
Dmitro Yuriiovych Hrishko ◽  
Ievgen Arnoldovich Nastenko ◽  
Maksym Oleksandrovych Honcharuk ◽  
Volodymyr Anatoliyovich Pavlov
Keyword(s):  
Random Forest ◽  
Texture Analysis ◽  
Binary Classification ◽  
Texture Features ◽  
Normal Liver ◽  
Group Method ◽  
Ultrasound Images ◽  
Liver Ultrasound ◽  
Random Forest Models ◽  
Occurrence Matrix

This article discusses the use of texture analysis methods to obtain informative features that describe the texture of liver ultrasound images. In total, 317 liver ultrasound images were analyzed, which were provided by the Institute of Nuclear Medicine and Radiation Diagnostics of NAMS of Ukraine. The images were taken by three different sensors (convex, linear, and linear sensor in increased signal level mode). Both images of patients with a normal liver condition and patients with specific liver disease (there were diseases such as: autoimmune hepatitis, Wilson's disease, hepatitis B and C, steatosis, and cirrhosis) were present in the database. Texture analysis was used for “Feature Construction”, which resulted in more than a hundred different informative features that made up a common stack. Among them, there are such features as: three authors’ patented features derived from the grey level co-occurrence matrix; features, obtained with the help of spatial sweep method (working by the principle of group method of data handling), which was applied to ultrasound images; statistical features, calculated on the images, brought to one scale with the help of differential horizontal and vertical matrices, which are proposed by the authors; greyscale pairs ensembles (found using the genetic algorithm), which identify liver pathology on images, transformed with the help of horizontal and vertical differentiations, in the best possible way. The resulting trait stack was used to solve the problem of binary classification (“norma-pathology”) of ultrasound liver images. A Machine Learning method, namely “Random Forest”, was used for this purpose. Before the classification, in order to obtain objective results, the total samples were divided into training (70 %), testing (20 %), and examining (10 %). The result was the best three Random Forest models separately for each sensor, which gave the following recognition rates: 93.4 % for the convex sensor, 92.9 % for the linear sensor, and 92 % for the reinforced linear sensor

Application of texture analysis of b-mode ultrasound images for the quantification and prediction of intramuscular fat in living beef cattle: A methodological study

2020 ◽  
Vol 131 ◽  
pp. 254-258
Author(s):  
Enrico Fiore ◽  
Giorgia Fabbri ◽  
Luigi Gallo ◽  
Massimo Morgante ◽  
Michele Muraro ◽  
...  
Keyword(s):  
Beef Cattle ◽  
Texture Analysis ◽  
Intramuscular Fat ◽  
Ultrasound Images ◽  
Methodological Study

scholarly journals Evaluation of carotid plaque echogenicity based on the integral of the cumulative probability distribution using gray-scale ultrasound images

PLoS ONE ◽  
2017 ◽  
Vol 12 (10) ◽  
pp. e0185261 ◽  
Author(s):  
Xiaowei Huang ◽  
Yanling Zhang ◽  
Long Meng ◽  
Derek Abbott ◽  
Ming Qian ◽  
...  
Keyword(s):  
Probability Distribution ◽  
Carotid Plaque ◽  
Cumulative Probability ◽  
Ultrasound Images ◽  
Gray Scale ◽  
Cumulative Probability Distribution
Sign in / Sign up

Export Citation Format

Share Document