scholarly journals Ultrasound image-based comanipulation for enhanced perception of the contacts with a distal soft organ

2011 ◽  
Author(s):  
Cecile Poquet Torterotot ◽  
Marie-Aude Vitrani ◽  
Pierre Mozer ◽  
Guillaume Morel
Keyword(s):  
Ultrasound Image

Combining Global Probability Density Difference and Local Gray Level Fitting for Ultrasound Image Segmentation

2010 ◽  
Vol 36 (7) ◽  
pp. 951-959 ◽  
Author(s):  
Bo LIU ◽  
Jian-Hua HUANG ◽  
Xiang-Long TANG ◽  
Jia-Feng LIU ◽  
Ying-Tao ZHANG
Keyword(s):  
Image Segmentation ◽  
Probability Density ◽  
Ultrasound Image ◽  
Density Difference ◽  
Gray Level ◽  
Ultrasound Image Segmentation

Improved anisotropic diffusion ultrasound image denoising method based on logarithmic compression

2013 ◽  
Vol 32 (11) ◽  
pp. 3218-3220
Author(s):  
Jin YANG ◽  
Zhi-qin LIU ◽  
Yao-bin WANG ◽  
Xiao-ming GAO
Keyword(s):  
Image Denoising ◽  
Anisotropic Diffusion ◽  
Ultrasound Image ◽  
Denoising Method

A simple technique for alignment of perineal needle template to ultrasound image grid for permanent prostate implants

2000 ◽  
Vol 27 (1) ◽  
pp. 141-143 ◽  
Author(s):  
Sasa Mutic ◽  
Daniel A. Low ◽  
Gilbert H. Nussbaum ◽  
Jeffrey F. Williamson ◽  
Delena Haefner
Keyword(s):  
Simple Technique ◽  
Ultrasound Image ◽  
Prostate Implants

scholarly journals Automatic Recognition of Fetal Facial Standard Plane in Ultrasound Image via Fisher Vector

PLoS ONE ◽  
2015 ◽  
Vol 10 (5) ◽  
pp. e0121838 ◽  
Author(s):  
Baiying Lei ◽  
Ee-Leng Tan ◽  
Siping Chen ◽  
Liu Zhuo ◽  
Shengli Li ◽  
...  
Keyword(s):  
Ultrasound Image ◽  
Automatic Recognition ◽  
Fisher Vector

scholarly journals Ultrasound Image Classification of Thyroid Nodules Using Machine Learning Techniques

Medicina ◽  
2021 ◽  
Vol 57 (6) ◽  
pp. 527
Author(s):  
Vijay Vyas Vadhiraj ◽  
Andrew Simpkin ◽  
James O’Connell ◽  
Naykky Singh Singh Ospina ◽  
Spyridoula Maraka ◽  
...  
Keyword(s):  
Decision Making ◽  
Sensitivity And Specificity ◽  
Thyroid Nodules ◽  
Median Filter ◽  
Ultrasound Image ◽  
Multiple Instance Learning ◽  
Image Features ◽  
Classification Model ◽  
Support Vector ◽  
Accuracy Score

Background and Objectives: Thyroid nodules are lumps of solid or liquid-filled tumors that form inside the thyroid gland, which can be malignant or benign. Our aim was to test whether the described features of the Thyroid Imaging Reporting and Data System (TI-RADS) could improve radiologists’ decision making when integrated into a computer system. In this study, we developed a computer-aided diagnosis system integrated into multiple-instance learning (MIL) that would focus on benign–malignant classification. Data were available from the Universidad Nacional de Colombia. Materials and Methods: There were 99 cases (33 Benign and 66 malignant). In this study, the median filter and image binarization were used for image pre-processing and segmentation. The grey level co-occurrence matrix (GLCM) was used to extract seven ultrasound image features. These data were divided into 87% training and 13% validation sets. We compared the support vector machine (SVM) and artificial neural network (ANN) classification algorithms based on their accuracy score, sensitivity, and specificity. The outcome measure was whether the thyroid nodule was benign or malignant. We also developed a graphic user interface (GUI) to display the image features that would help radiologists with decision making. Results: ANN and SVM achieved an accuracy of 75% and 96% respectively. SVM outperformed all the other models on all performance metrics, achieving higher accuracy, sensitivity, and specificity score. Conclusions: Our study suggests promising results from MIL in thyroid cancer detection. Further testing with external data is required before our classification model can be employed in practice.

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