Literature DB >> 15556588

Texture analysis of medical images.

G Castellano1, L Bonilha, L M Li, F Cendes.   

Abstract

The analysis of texture parameters is a useful way of increasing the information obtainable from medical images. It is an ongoing field of research, with applications ranging from the segmentation of specific anatomical structures and the detection of lesions, to differentiation between pathological and healthy tissue in different organs. Texture analysis uses radiological images obtained in routine diagnostic practice, but involves an ensemble of mathematical computations performed with the data contained within the images. In this article we clarify the principles of texture analysis and give examples of its applications, reviewing studies of the technique.

Mesh:

Year:  2004        PMID: 15556588     DOI: 10.1016/j.crad.2004.07.008

Source DB:  PubMed          Journal:  Clin Radiol        ISSN: 0009-9260            Impact factor:   2.350


  246 in total

1.  Prostate cancer: The applicability of textural analysis of MRI for grading.

Authors:  Frederick Kelcz; David F Jarrard
Journal:  Nat Rev Urol       Date:  2016-02-16       Impact factor: 14.432

2.  Early detection of Alzheimer's disease using MRI hippocampal texture.

Authors:  Lauge Sørensen; Christian Igel; Naja Liv Hansen; Merete Osler; Martin Lauritzen; Egill Rostrup; Mads Nielsen
Journal:  Hum Brain Mapp       Date:  2015-12-21       Impact factor: 5.038

3.  Texture analysis improves level set segmentation of the anterior abdominal wall.

Authors:  Zhoubing Xu; Wade M Allen; Rebeccah B Baucom; Benjamin K Poulose; Bennett A Landman
Journal:  Med Phys       Date:  2013-12       Impact factor: 4.071

4.  Texture analysis of MR images to identify the differentiated degree in hepatocellular carcinoma: a retrospective study.

Authors:  Mengmeng Feng; Mengchao Zhang; Yuanqing Liu; Nan Jiang; Qian Meng; Jia Wang; Ziyun Yao; Wenjuan Gan; Hui Dai
Journal:  BMC Cancer       Date:  2020-06-30       Impact factor: 4.430

5.  Texture features and pharmacokinetic parameters in differentiating benign and malignant breast lesions by dynamic contrast enhanced magnetic resonance imaging.

Authors:  Qingliang Niu; Xiaomei Jiang; Qin Li; Zhaolong Zheng; Hanwang Du; Shasha Wu; Xuexi Zhang
Journal:  Oncol Lett       Date:  2018-07-23       Impact factor: 2.967

6.  Electrical stimulation during gait promotes increase of muscle cross-sectional area in quadriplegics: a preliminary study.

Authors:  Daniela Cristina Carvalho de Abreu; Alberto Cliquet; Jane Maryan Rondina; Fernando Cendes
Journal:  Clin Orthop Relat Res       Date:  2008-09-13       Impact factor: 4.176

7.  Exploring Variability in CT Characterization of Tumors: A Preliminary Phantom Study.

Authors:  Binsheng Zhao; Yongqiang Tan; Wei Yann Tsai; Lawrence H Schwartz; Lin Lu
Journal:  Transl Oncol       Date:  2014-02-01       Impact factor: 4.243

8.  Efficient Data Mining for Local Binary Pattern in Texture Image Analysis.

Authors:  Jin Tae Kwak; Sheng Xu; Bradford J Wood
Journal:  Expert Syst Appl       Date:  2015-06-01       Impact factor: 6.954

9.  CT reconstruction algorithms affect histogram and texture analysis: evidence for liver parenchyma, focal solid liver lesions, and renal cysts.

Authors:  Su Joa Ahn; Jung Hoon Kim; Sang Min Lee; Sang Joon Park; Joon Koo Han
Journal:  Eur Radiol       Date:  2018-11-19       Impact factor: 5.315

Review 10.  Quantifying tumour heterogeneity in 18F-FDG PET/CT imaging by texture analysis.

Authors:  Sugama Chicklore; Vicky Goh; Musib Siddique; Arunabha Roy; Paul K Marsden; Gary J R Cook
Journal:  Eur J Nucl Med Mol Imaging       Date:  2012-10-13       Impact factor: 9.236

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