Literature DB >> 24462151

Modeling of errors in Nakagami imaging: illustration on breast mass characterization.

Aymeric Larrue1, J Alison Noble2.   

Abstract

Nakagami imaging is an attractive tissue characterization method, as the parameter estimated at each location is related to properties of the tissues. The application to clinical ultrasound images is problematic, as the estimation of the parameters is disturbed by the presence of complex structures. We propose to consider separately the different aspects potentially affecting the value of the Nakagami parameters and quantify their effects on the estimation. This framework is applied to the classification of breast masses. Quantitative parameters are computed on two groups of ultrasound images of benign and malignant tumors. A statistical analysis of the result indicated that the previously observed difference between average values of the Nakagami parameters is explained mostly by estimation errors. In the future, new methods for reliable computation of Nakagami parameters need to be developed, and factors of error should be considered in studies using Nakagami parameters.
Copyright © 2014 World Federation for Ultrasound in Medicine & Biology. Published by Elsevier Inc. All rights reserved.

Entities:  

Keywords:  Breast mass classification; Nakagami imaging; Ultrasound tissue characterization

Mesh:

Year:  2014        PMID: 24462151     DOI: 10.1016/j.ultrasmedbio.2013.11.018

Source DB:  PubMed          Journal:  Ultrasound Med Biol        ISSN: 0301-5629            Impact factor:   2.998


  5 in total

1.  A Computer-Aided Diagnosis Scheme For Detection Of Fatty Liver In Vivo Based On Ultrasound Kurtosis Imaging.

Authors:  Hsiang-Yang Ma; Zhuhuang Zhou; Shuicai Wu; Yung-Liang Wan; Po-Hsiang Tsui
Journal:  J Med Syst       Date:  2015-11-12       Impact factor: 4.460

2.  Quantification of ultrasonic texture intra-heterogeneity via volumetric stochastic modeling for tissue characterization.

Authors:  Omar S Al-Kadi; Daniel Y F Chung; Robert C Carlisle; Constantin C Coussios; J Alison Noble
Journal:  Med Image Anal       Date:  2014-12-27       Impact factor: 8.545

3.  Feature-based fuzzy connectedness segmentation of ultrasound images with an object completion step.

Authors:  Sylvia Rueda; Caroline L Knight; Aris T Papageorghiou; J Alison Noble
Journal:  Med Image Anal       Date:  2015-07-17       Impact factor: 8.545

4.  Small-window parametric imaging based on information entropy for ultrasound tissue characterization.

Authors:  Po-Hsiang Tsui; Chin-Kuo Chen; Wen-Hung Kuo; King-Jen Chang; Jui Fang; Hsiang-Yang Ma; Dean Chou
Journal:  Sci Rep       Date:  2017-01-20       Impact factor: 4.379

5.  Characterization of limb lymphedema using the statistical analysis of ultrasound backscattering.

Authors:  Ya-Lun Lee; Yen-Ling Huang; Sung-Yu Chu; Wen-Hui Chan; Ming-Huei Cheng; Ying-Hsiu Lin; Tu-Yung Chang; Chih-Kuang Yeh; Po-Hsiang Tsui
Journal:  Quant Imaging Med Surg       Date:  2020-01
  5 in total

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