Literature DB >> 17022213

Finding corresponding regions of interest in mediolateral oblique and craniocaudal mammographic views.

Saskia van Engeland1, Sheila Timp, Nico Karssemeijer.   

Abstract

In this paper we present a method to link potentially suspicious mass regions detected by a Computer-Aided Detection (CAD) scheme in mediolateral oblique (MLO) and craniocaudal (CC) mammographic views of the breast. For all possible combinations of mass candidate regions, a number of features are determined. These features include the difference in the radial distance from the candidate regions to the nipple, the gray scale correlation between both regions, and the mass likelihood of the regions determined by the single view CAD scheme. Linear Discriminant Analysis (LDA) is used to discriminate between correct and incorrect links. The method was tested on a set of 412 cancer cases. In each case a malignant mass, architectural distortion, or asymmetry was annotated. In 92% of these cases the candidate mass detections by CAD included the cancer regions in both views. It was found that in 82% of the cases a correct link between the true positive regions in both views could be established by our method. Possible applications of the method may be found in multiple view analysis to improve CAD results, and for the presentation of CAD results to the radiologist on a mammography workstation.

Entities:  

Mesh:

Year:  2006        PMID: 17022213     DOI: 10.1118/1.2230359

Source DB:  PubMed          Journal:  Med Phys        ISSN: 0094-2405            Impact factor:   4.071


  7 in total

1.  Bilateral analysis based false positive reduction for computer-aided mass detection.

Authors:  Yi-Ta Wu; Jun Wei; Lubomir M Hadjiiski; Berkman Sahiner; Chuan Zhou; Jun Ge; Jiazheng Shi; Yiheng Zhang; Heang-Ping Chan
Journal:  Med Phys       Date:  2007-08       Impact factor: 4.071

2.  An ellipse-fitting based method for efficient registration of breast masses on two mammographic views.

Authors:  Jiantao Pu; Bin Zheng; Joseph Ken Leader; David Gur
Journal:  Med Phys       Date:  2008-02       Impact factor: 4.071

3.  Automatic detection of the nipple in screen-film and full-field digital mammograms using a novel Hessian-based method.

Authors:  Paola Casti; Arianna Mencattini; Marcello Salmeri; Antonietta Ancona; Fabio Felice Mangieri; Maria Luisa Pepe; Rangaraj Mandayam Rangayyan
Journal:  J Digit Imaging       Date:  2013-10       Impact factor: 4.056

4.  CT colonography computer-aided polyp detection: Effect on radiologist observers of polyp identification by CAD on both the supine and prone scans.

Authors:  Ronald M Summers; Jiamin Liu; Bhavya Rehani; Phillip Stafford; Linda Brown; Adeline Louie; Duncan S Barlow; Donald W Jensen; Brooks Cash; J Richard Choi; Perry J Pickhardt; Nicholas Petrick
Journal:  Acad Radiol       Date:  2010-06-12       Impact factor: 3.173

5.  Prediction of Short-Term Breast Cancer Risk with Fusion of CC- and MLO-Based Risk Models in Four-View Mammograms.

Authors:  Yane Li; Wei Yuan; Ming Fan; Bin Zheng; Lihua Li
Journal:  J Digit Imaging       Date:  2022-08       Impact factor: 4.903

6.  Automated medical image segmentation techniques.

Authors:  Neeraj Sharma; Lalit M Aggarwal
Journal:  J Med Phys       Date:  2010-01

7.  Matching breast masses depicted on different views a comparison of three methods.

Authors:  Bin Zheng; Jun Tan; Marie A Ganott; Denise M Chough; David Gur
Journal:  Acad Radiol       Date:  2009-07-25       Impact factor: 3.173

  7 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.