Quantitative assessment of breast density from digitized mammograms into Tabar's patterns.
We describe a semi-automated technique for the quantitative assessment of breast density from digitized mammograms in comparison with patterns suggested by Tabar. It was developed using the MATLAB-based graphical user interface applications. It is based on an interactive thresholding method, after a short automated method that shows the fibroglandular tissue area, breast area and breast density each time new thresholds are placed on the image. The breast density is taken as a percentage of the fibroglandular tissue to the breast tissue areas. It was tested in four different ways, namely by examining: (i) correlation of the quantitative assessment results with subjective classification, (ii) classification performance using the quantitative assessment technique, (iii) interobserver agreement and (iv) intraobserver agreement. The results of the quantitative assessment correlated well (r2 = 0.92) with the subjective Tabar patterns classified by the radiologist (correctly classified 83% of digitized mammograms). The average kappa coefficient for the agreement between the readers was 0.63. This indicated moderate agreement between the three observers in classifying breast density using the quantitative assessment technique. The kappa coefficient of 0.75 for intraobserver agreement reflected good agreement between two sets of readings. The technique may be useful as a supplement to the radiologist's assessment in classifying mammograms into Tabar's pattern associated with breast cancer risk.
Jamal
N
N
Medical Technology Division, Malaysian Institute for Nuclear Technology Research (MINT) 43000 Kajang, Malaysia. nojamal01@yahoo.com
Ng
K-H
KH
Looi
L-M
LM
McLean
D
D
Zulfiqar
A
A
Tan
S-P
SP
Liew
W-F
WF
Shantini
A
A
Ranganathan
S
S
D015502:Absorptiometry, Photon / Q000379:methods*; D000465:Algorithms; D001185:Artificial Intelligence; D001943:Breast Neoplasms / Q000175:diagnosis*; D005260:Female; D006801:Humans; D008327:Mammography / Q000379:methods*; D010363:Pattern Recognition, Automated / Q000379:methods; D011379:Prognosis; D011856:Radiographic Image Enhancement / Q000379:methods*; D011857:Radiographic Image Interpretation, Computer-Assisted / Q000379:methods*; D015203:Reproducibility of Results; D018570:Risk Assessment / Q000379:methods*; D012307:Risk Factors; D012680:Sensitivity and Specificity; D012984:Software