Semiautomatic mammographic parenchymal patterns classification using multiple statistical features.
OBJECTIVE
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.


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.


CONCLUSIONS
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.
University Institute for Radiation Physics, Centre Hospitalier Universitaire Vaudois, and University of Lausanne, Grand-Pré 1, CH-1007 Lausanne, Switzerland.
Cyril
C
Castella
Karen
K
Kinkel
Miguel P
MP
Eckstein
Pierre-Edouard
PE
Sottas
Francis R
FR
Verdun
François O
FO
Bochud
D000465:Algorithms; D001499:Bayes Theorem; D001940:Breast / Q000473:pathology*; D001943:Breast Neoplasms / Q000000981:diagnostic imaging; D019992:Databases as Topic; D016002:Discriminant Analysis; D005260:Female; D006801:Humans; D007090:Image Interpretation, Computer-Assisted; D007091:Image Processing, Computer-Assisted / Q000706:statistics & numerical data*; D051188:Knowledge Bases; D008327:Mammography / Q000145:classification*; D010363:Pattern Recognition, Automated; D011856:Radiographic Image Enhancement; D011871:Radiology; D012307:Risk Factors