Computer-aided detection of architectural distortion in prior mammograms of interval cancer.
Architectural distortion is an important sign of breast cancer, but because of its subtlety, it is a common cause of false-negative findings on screening mammograms. This paper presents methods for the detection of architectural distortion in mammograms of interval cancer cases taken prior to the detection of breast cancer using Gabor filters, phase portrait analysis, fractal analysis, and texture analysis. The methods were used to detect initial candidates for sites of architectural distortion in prior mammograms of interval cancer and also normal control cases. A total of 4,224 regions of interest (ROIs) were automatically obtained from 106 prior mammograms of 56 interval cancer cases, including 301 ROIs related to architectural distortion, and from 52 prior mammograms of 13 normal cases. For each ROI, the fractal dimension and Haralick's texture features were computed. Feature selection was performed separately using stepwise logistic regression and stepwise regression. The best results achieved, in terms of the area under the receiver operating characteristics curve, with the features selected by stepwise logistic regression are 0.76 with the Bayesian classifier, 0.73 with Fisher linear discriminant analysis, 0.77 with an artificial neural network based on radial basis functions, and 0.77 with a support vector machine. Analysis of the performance of the methods with free-response receiver operating characteristics indicated a sensitivity of 0.80 at 7.6 false positives per image. The methods have good potential in detecting architectural distortion in mammograms of interval cancer cases.
Rangayyan
Rangaraj M
RM
Department of Electrical and Computer Engineering, Schulich School of Engineering, Calgary, AB T2N1N4, Canada. ranga@ucalgary.ca
Banik
Shantanu
S
Desautels
J E Leo
JE
D000465:Algorithms; D001943:Breast Neoplasms / Q000000981:diagnostic imaging* / Q000473:pathology; D016022:Case-Control Studies; D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D016002:Discriminant Analysis; D005189:False Positive Reactions; D005260:Female; D005583:Fourier Analysis; D017709:Fractals; D006801:Humans; D008327:Mammography; D016571:Neural Networks, Computer; D010363:Pattern Recognition, Automated / Q000379:methods*; D011237:Predictive Value of Tests; D012372:ROC Curve; D011857:Radiographic Image Interpretation, Computer-Assisted / Q000379:methods*; D012680:Sensitivity and Specificity