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Detection of microcalcifications in mammograms using error of prediction and statistical measures

[+] Author Affiliations
Begoña Acha

Universidad de Sevilla, Escuela Superior de Ingenieros, Departamento de Teoría de la Señal y Comunicaciones, Camino de los Descubrimientos s/n, 41092-Sevilla, Spain

Carmen Serrano

Universidad de Sevilla, Escuela Superior de Ingenieros, Departamento de Teoría de la Señal y Comunicaciones, Camino de los Descubrimientos s/n, 41092-Sevilla, Spain

Rangaraj M. Rangayyan

University of Calgary, Schulich School of Engineering, Department of Electrical and Computer Engineering, 2500 University Drive Northwest, Calgary, Alberta, Canada T2N 1N4

J. E. Leo Desautels

University of Calgary, Schulich School of Engineering, Department of Electrical and Computer Engineering, 2500 University Drive Northwest, Calgary, Alberta, Canada T2N 1N4

J. Electron. Imaging. 18(1), 013011 (March 19, 2009). doi:10.1117/1.3099710
History: Received July 25, 2007; Revised January 23, 2009; Accepted February 09, 2009; Published March 19, 2009
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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-μm resolution, and the results are evaluated using a free-response 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 image.

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© 2009 SPIE and IS&T

Topics

Mammography

Citation

Begoña Acha ; Carmen Serrano ; Rangaraj M. Rangayyan and J. E. Leo Desautels
"Detection of microcalcifications in mammograms using error of prediction and statistical measures", J. Electron. Imaging. 18(1), 013011 (March 19, 2009). ; http://dx.doi.org/10.1117/1.3099710


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