Semiautomatic mammographic parenchymal patterns classification using multiple statistical features.

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Serval ID
serval:BIB_B8977D155E0A
Type
Article: article from journal or magazin.
Collection
Publications
Institution
Title
Semiautomatic mammographic parenchymal patterns classification using multiple statistical features.
Journal
Academic Radiology
Author(s)
Castella C., Kinkel K., Eckstein M.P., Sottas P.E., Verdun F.R., Bochud F.O.
ISSN
1076-6332
Publication state
Published
Issued date
2007
Peer-reviewed
Oui
Volume
14
Number
12
Pages
1486-1499
Language
english
Abstract
RATIONALE AND OBJECTIVES: Our project was to investigate a complete methodology for the semiautomatic assessment of digital mammograms according to their density, an indicator known to be correlated to breast cancer risk. The BI-RADS four-grade density scale is usually employed by radiologists for reporting breast density, but it allows for a certain degree of subjective input, and an objective qualification of density has therefore often been reported hard to assess. The goal of this study was to design an objective technique for determining breast BI-RADS density. MATERIALS AND METHODS: The proposed semiautomatic method makes use of complementary pattern recognition techniques to describe manually selected regions of interest (ROIs) in the breast with 36 statistical features. Three different classifiers based on a linear discriminant analysis or Bayesian theories were designed and tested on a database consisting of 1408 ROIs from 88 patients, using a leave-one-ROI-out technique. Classifications in optimal feature subspaces with lower dimensionality and reduction to a two-class problem were studied as well. RESULTS: Comparison with a reference established by the classifications of three radiologists shows excellent performance of the classifiers, even though extremely dense breasts continue to remain more difficult to classify accurately. For the two best classifiers, the exact agreement percentages are 76% and above, and weighted kappa values are 0.78 and 0.83. Furthermore, classification in lower dimensional spaces and two-class problems give excellent results. CONCLUSION: The proposed semiautomatic classifiers method provides an objective and reproducible method for characterizing breast density, especially for the two-class case. It represents a simple and valuable tool that could be used in screening programs, training, education, or for optimizing image processing in diagnostic tasks.
Keywords
Algorithms, Bayes Theorem, Breast/pathology, Breast Neoplasms/radiography, Databases as Topic, Discriminant Analysis, Female, Humans, Image Interpretation, Computer-Assisted, Image Processing, Computer-Assisted/statistics & numerical data, Knowledge Bases, Mammography/classification, Pattern Recognition, Automated, Radiographic Image Enhancement, Radiology, Risk Factors
Pubmed
Web of science
Open Access
Yes
Create date
25/04/2008 17:11
Last modification date
20/08/2019 15:26
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