Computer-aided detection of architectural distortion in prior mammograms of interval cancer

Abstract

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.

Fig. 1

a Prior mammogram of an interval cancer case. The rectangle is of size 65 × 39 mm and indicates the region of architectural distortion identified by a radiologist. The size of the full image is 1,372 × 675 pixels at 200 μm per pixel. b Magnitude response obtained using a bank of 180 real Gabor filters. c Orientation field angle superimposed on the mammographic image; needles are drawn for every 12th pixel. d Zoomed view of the rectangular area shown in a. e Magnitude response zoomed. f Orientation field zoomed; needles are drawn for every sixth pixel for clarity.

Fig. 2

a Node map at 800 μm/pixel for the mammogram in Figure 1a. Each asterisk mark corresponds to a peak position detected automatically in the node map. The numbers next to the asterisk marks indicate the peaks in descending order of magnitude. b The 29 ROIs obtained automatically using the peaks detected in the node map. The size of each ROI is 128 × 128 pixels at 200 μm per pixel (except at the edges).

Fig. 3

Examples of true-positive (a–j) and false-positive (k–t) ROIs detected. Each ROI is of size 128 × 128 pixels at 200 μm/pixel, or 25.6 × 25.6 mm.

Fig. 4

Flowchart of the procedures used to detect architectural distortion in prior mammograms. The steps in the dashed box labeled in a are shown in detail in b. The connecting lines and the boxes in dotted lines indicate options to be selected. CLS curvilinear structure, ROI region of interest, FLDA Fisher linear discriminant analysis, ANN artificial neural network, SLFF single-layer feedforward network, MLP multilayer perceptron, RBF radial basis function, SVM support vector machine.

Fig. 5

FROC curves for the dataset of only the prior mammograms of the interval cancer cases with the selected features using the ANN-RBF classifier and the leave-one-image-out method. The FROC curve generated using the node value only is also shown for reference. Sensitivity=true-positive fraction.

Fig. 6

FROC curves: a using four classification techniques with the leave-one image-out method and the features obtained by stepwise logistic regression, b using four classification techniques with the leave-one-image-out method and the features selected by stepwise regression analysis. The dataset includes the prior mammograms of the interval cancer cases as well as the normal control cases.

Fig. 7

FROC curves for the full dataset including the prior mammograms of the interval cancer cases and normal control cases. The curves are shown for the ANN-RBF classifier and the leave-one-image-out method using the features obtained by stepwise logistic regression and stepwise regression. The FROC curve generated using the node value only is also shown for reference. Sensitivity=true-positive fraction.