9 research outputs found
Detection of microcalcifications in mammograms using error of prediction and statistical measures
A two-stage method for detecting microcalcifications in
mammograms is presented. In the first stage, the determination of
the candidates for microcalcifications is performed. For this purpose,
a 2-D linear prediction error filter is applied, and for those pixels
where the prediction error is larger than a threshold, a statistical
measure is calculated to determine whether they are candidates for
microcalcifications or not. In the second stage, a feature vector is
derived for each candidate, and after a classification step using a
support vector machine, the final detection is performed. The algorithm
is tested with 40 mammographic images, from Screen Test:
The Alberta Program for the Early Detection of Breast Cancer with
50- m resolution, and the results are evaluated using a freeresponse
receiver operating characteristics curve. Two different
analyses are performed: an individual microcalcification detection
analysis and a cluster analysis. In the analysis of individual microcalcifications,
detection sensitivity values of 0.75 and 0.81 are obtained
at 2.6 and 6.2 false positives per image, on the average,
respectively. The best performance is characterized by a sensitivity
of 0.89, a specificity of 0.99, and a positive predictive value of 0.79.
In cluster analysis, a sensitivity value of 0.97 is obtained at 1.77
false positives per image, and a value of 0.90 is achieved at 0.94
false positive per imag
Detection of microcalcifications in mammograms using error of prediction and statistical measures
A two-stage method for detecting microcalcifications in
mammograms is presented. In the first stage, the determination of
the candidates for microcalcifications is performed. For this purpose,
a 2-D linear prediction error filter is applied, and for those pixels
where the prediction error is larger than a threshold, a statistical
measure is calculated to determine whether they are candidates for
microcalcifications or not. In the second stage, a feature vector is
derived for each candidate, and after a classification step using a
support vector machine, the final detection is performed. The algorithm is tested with 40 mammographic images, from Screen Test:
The Alberta Program for the Early Detection of Breast Cancer with
50- m resolution, and the results are evaluated using a freeresponse receiver operating characteristics curve. Two different
analyses are performed: an individual microcalcification detection
analysis and a cluster analysis. In the analysis of individual microcalcifications, detection sensitivity values of 0.75 and 0.81 are obtained at 2.6 and 6.2 false positives per image, on the average,
respectively. The best performance is characterized by a sensitivity
of 0.89, a specificity of 0.99, and a positive predictive value of 0.79.
In cluster analysis, a sensitivity value of 0.97 is obtained at 1.77
false positives per image, and a value of 0.90 is achieved at 0.94
false positive per imageMinisterio de Sanidad FIS05-202
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