Literature DB >> 11382947

Texture detection of simulated microcalcification susceptibility effects in magnetic resonance imaging of breasts.

D James1, B D Clymer, P Schmalbrock.   

Abstract

The presence, size, structure and clustering characteristics of microcalcifications can indicate breast cancer. The magnetic susceptibility of microcalcifications differs from soft biological tissues, leading to directional blurring effects that can be detected by statistical image processing methods. A study of the ability of statistical texture analysis to detect simulated localized blurring in magnetic resonance imaging (MRI) of dense breast is presented. This method can detect localized blurring with sensitivity of 88.89% to 94.44%, specificity of 99.72% to 100%, positive predictive value of 73.91% to 100% and negative predictive value of 99.91% to 99.95%. J. Magn. Reson. Imaging 2001;13:876-881. Copyright 2001 Wiley-Liss, Inc.

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Year:  2001        PMID: 11382947     DOI: 10.1002/jmri.1125

Source DB:  PubMed          Journal:  J Magn Reson Imaging        ISSN: 1053-1807            Impact factor:   4.813


  7 in total

1.  3-T breast magnetic resonance imaging in patients with suspicious microcalcifications on mammography.

Authors:  B L Stehouwer; L G Merckel; H M Verkooijen; N H G M Peters; R M Mann; K M Duvivier; W P Th M Mali; P H M Peeters; W B Veldhuis; M A A J van den Bosch
Journal:  Eur Radiol       Date:  2014-03       Impact factor: 5.315

2.  Computer-Assisted Diagnosis System for Breast Cancer in Computed Tomography Laser Mammography (CTLM).

Authors:  Afsaneh Jalalian; Syamsiah Mashohor; Rozi Mahmud; Babak Karasfi; M Iqbal Saripan; Abdul Rahman Ramli
Journal:  J Digit Imaging       Date:  2017-12       Impact factor: 4.056

3.  Real-time texture analysis for identifying optimum microbubble concentration in 2-D ultrasonic particle image velocimetry.

Authors:  Lili Niu; Ming Qian; Liang Yan; Wentao Yu; Bo Jiang; Qiaofeng Jin; Yanping Wang; Robin Shandas; Xin Liu; Hairong Zheng
Journal:  Ultrasound Med Biol       Date:  2011-06-17       Impact factor: 2.998

4.  Classification of MR-Detected Additional Lesions in Patients With Breast Cancer Using a Combination of Radiomics Analysis and Machine Learning.

Authors:  Hyo-Jae Lee; Anh-Tien Nguyen; So Yeon Ki; Jong Eun Lee; Luu-Ngoc Do; Min Ho Park; Ji Shin Lee; Hye Jung Kim; Ilwoo Park; Hyo Soon Lim
Journal:  Front Oncol       Date:  2021-12-02       Impact factor: 6.244

5.  Assessment of tumor heterogeneity: an emerging imaging tool for clinical practice?

Authors:  Fergus Davnall; Connie S P Yip; Gunnar Ljungqvist; Mariyah Selmi; Francesca Ng; Bal Sanghera; Balaji Ganeshan; Kenneth A Miles; Gary J Cook; Vicky Goh
Journal:  Insights Imaging       Date:  2012-10-24

Review 6.  Foundation and methodologies in computer-aided diagnosis systems for breast cancer detection.

Authors:  Afsaneh Jalalian; Syamsiah Mashohor; Rozi Mahmud; Babak Karasfi; M Iqbal B Saripan; Abdul Rahman B Ramli
Journal:  EXCLI J       Date:  2017-02-20       Impact factor: 4.068

7.  Assessment of Invasive Breast Cancer Heterogeneity Using Whole-Tumor Magnetic Resonance Imaging Texture Analysis: Correlations With Detailed Pathological Findings.

Authors:  Eun Sook Ko; Jae-Hun Kim; Yaeji Lim; Boo-Kyung Han; Eun Yoon Cho; Seok Jin Nam
Journal:  Medicine (Baltimore)       Date:  2016-01       Impact factor: 1.817

  7 in total

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