Literature DB >> 16481695

A study on the computerized fractal analysis of architectural distortion in screening mammograms.

Georgia D Tourassi1, David M Delong, Carey E Floyd.   

Abstract

Architectural distortion (AD) is a sign of malignancy often missed during mammographic interpretation. The purpose of this study was to explore the application of fractal analysis to the investigation of AD in screening mammograms. The study was performed using mammograms from the Digital Database for Screening Mammography (DDSM). The fractal dimension (FD) of mammographic regions of interest (ROIs) was calculated using the circular average power spectrum technique. Initially, the variability of the FD estimates depending on ROI location, mammographic view and breast side was studied on normal mammograms. Then, the estimated FD was evaluated using receiver operating characteristics (ROC) analysis to determine if it can discriminate ROIs depicting AD from those depicting normal breast parenchyma. The effect of several factors such as ROI size, image subsampling and breast density was studied in detail. Overall, the average FD of the normal ROIs was statistically significantly higher than that of the ROIs with AD. This result was consistent across all factors studied. For the studied set of implementation parameters, the best ROC performance achieved was 0.89 +/- 0.02. The generalizability of these conclusions across different digitizers was also demonstrated.

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Year:  2006        PMID: 16481695     DOI: 10.1088/0031-9155/51/5/018

Source DB:  PubMed          Journal:  Phys Med Biol        ISSN: 0031-9155            Impact factor:   3.609


  14 in total

1.  Measures of angular spread and entropy for the detection of architectural distortion in prior mammograms.

Authors:  Shantanu Banik; Rangaraj M Rangayyan; J E Leo Desautels
Journal:  Int J Comput Assist Radiol Surg       Date:  2012-03-30       Impact factor: 2.924

2.  Evaluation of an improved algorithm for producing realistic 3D breast software phantoms: application for mammography.

Authors:  K Bliznakova; S Suryanarayanan; A Karellas; N Pallikarakis
Journal:  Med Phys       Date:  2010-11       Impact factor: 4.071

3.  Detection of architectural distortion in prior mammograms via analysis of oriented patterns.

Authors:  Rangaraj M Rangayyan; Shantanu Banik; J E Leo Desautels
Journal:  J Vis Exp       Date:  2013-08-30       Impact factor: 1.355

4.  Radiomics: a new application from established techniques.

Authors:  Vishwa Parekh; Michael A Jacobs
Journal:  Expert Rev Precis Med Drug Dev       Date:  2016-03-31

5.  A pilot study of architectural distortion detection in mammograms based on characteristics of line shadows.

Authors:  Mitsutaka Nemoto; Soshi Honmura; Akinobu Shimizu; Daisuke Furukawa; Hidefumi Kobatake; Shigeru Nawano
Journal:  Int J Comput Assist Radiol Surg       Date:  2008-10-28       Impact factor: 2.924

6.  Characterization and classification of tumor lesions using computerized fractal-based texture analysis and support vector machines in digital mammograms.

Authors:  Qi Guo; Jiaqing Shao; Virginie F Ruiz
Journal:  Int J Comput Assist Radiol Surg       Date:  2008-10-28       Impact factor: 2.924

7.  Methodology for generating a 3D computerized breast phantom from empirical data.

Authors:  Christina M Li; W Paul Segars; Georgia D Tourassi; John M Boone; James T Dobbins
Journal:  Med Phys       Date:  2009-07       Impact factor: 4.071

8.  Characterization of Architectural Distortion in Mammograms Based on Texture Analysis Using Support Vector Machine Classifier with Clinical Evaluation.

Authors:  Amit Kamra; V K Jain; Sukhwinder Singh; Sunil Mittal
Journal:  J Digit Imaging       Date:  2016-02       Impact factor: 4.056

9.  Characterizing Architectural Distortion in Mammograms by Linear Saliency.

Authors:  Fabián Narváez; Jorge Alvarez; Juan D Garcia-Arteaga; Jonathan Tarquino; Eduardo Romero
Journal:  J Med Syst       Date:  2016-12-22       Impact factor: 4.460

10.  An efficient fractal method for detection and diagnosis of breast masses in mammograms.

Authors:  S M A Beheshti; H AhmadiNoubari; E Fatemizadeh; M Khalili
Journal:  J Digit Imaging       Date:  2014-10       Impact factor: 4.056

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