scholarly journals Group-wise feature-based registration of CT and ultrasound images of spine

2010 ◽  
Author(s):  
Abtin Rasoulian ◽  
Parvin Mousavi ◽  
Mehdi Hedjazi Moghari ◽  
Pezhman Foroughi ◽  
Purang Abolmaesumi
Keyword(s):  
Ultrasound Images ◽  
Feature Based

Feature-based multibody rigid registration of CT and ultrasound images of lumbar spine

2012 ◽  
Vol 39 (6Part1) ◽  
pp. 3154-3166 ◽  
Author(s):  
Abtin Rasoulian ◽  
Purang Abolmaesumi ◽  
Parvin Mousavi
Keyword(s):  
Lumbar Spine ◽  
Ultrasound Images ◽  
Rigid Registration ◽  
Feature Based

Hybrid Feature-Based Diffeomorphic Registration for Tumor Tracking in 2-D Liver Ultrasound Images

2013 ◽  
Vol 32 (9) ◽  
pp. 1647-1656 ◽  
Author(s):  
Amalia Cifor ◽  
Laurent Risser ◽  
Daniel Chung ◽  
Ewan M. Anderson ◽  
Julia A. Schnabel
Keyword(s):  
Ultrasound Images ◽  
Diffeomorphic Registration ◽  
Liver Ultrasound ◽  
Tumor Tracking ◽  
Feature Based

scholarly journals RiIG Modeled WCP Image-Based CNN Architecture and Feature-Based Approach in Breast Tumor Classification from B-Mode Ultrasound

2021 ◽  
Vol 11 (24) ◽  
pp. 12138
Author(s):  
Shahriar Mahmud Kabir ◽  
Mohammed I. H. Bhuiyan ◽  
Md Sayed Tanveer ◽  
ASM Shihavuddin
Keyword(s):  
Breast Tumor ◽  
Texture Features ◽  
Classification Performance ◽  
Tumor Classification ◽  
P Value ◽  
Ultrasound Images ◽  
Custom Made ◽  
Feature Based ◽  
Sensitivity Specificity ◽  
Breast Tumor Classification

This study presents two new approaches based on Weighted Contourlet Parametric (WCP) images for the classification of breast tumors from B-mode ultrasound images. The Rician Inverse Gaussian (RiIG) distribution is considered for modeling the statistics of ultrasound images in the Contourlet transform domain. The WCP images are obtained by weighting the RiIG modeled Contourlet sub-band coefficient images. In the feature-based approach, various geometrical, statistical, and texture features are shown to have low ANOVA p-value, thus indicating a good capacity for class discrimination. Using three publicly available datasets (Mendeley, UDIAT, and BUSI), it is shown that the classical feature-based approach can yield more than 97% accuracy across the datasets for breast tumor classification using WCP images while the custom-made convolutional neural network (CNN) can deliver more than 98% accuracy, sensitivity, specificity, NPV, and PPV values utilizing the same WCP images. Both methods provide superior classification performance, better than those of several existing techniques on the same datasets.

scholarly journals Feature-based fuzzy connectedness segmentation of ultrasound images with an object completion step

2015 ◽  
Vol 26 (1) ◽  
pp. 30-46 ◽  
Author(s):  
Sylvia Rueda ◽  
Caroline L. Knight ◽  
Aris T. Papageorghiou ◽  
J. Alison Noble
Keyword(s):  
Ultrasound Images ◽  
Fuzzy Connectedness ◽  
Feature Based ◽  
Completion Step
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