Mass localization plays a crucial role in computer-aided detection CAD systems for the classification of suspicious regions in mammograms. In this article we present a completely automated classification system for the detection of masses in digitized mammographic images. The tool system we discuss consists in three processing levels: a Image segmentation for the localization of regions of interest ROIs. This step relies on an iterative dynamical threshold algorithm able to select iso-intensity closed contours around gray level maxima of the mammogram. b ROI characterization by means of textural features computed from the gray tone spatial dependence matrix GTSDM, containing second-order spatial statistics information on the pixel gray level intensity. As the images under study were recorded in different centers and with different machine settings, eight GTSDM features were selected so as to be invariant under monotonic transformation. In this way, the images do not need to be normalized, as the adopted features depend on the texture only, rather than on the gray tone levels, too. c ROI classification by means of a neural network, with supervision provided by the radiologist’s diagnosis. The CAD system was evaluated on a large database of 3369 mammographic images 2307 negative, 1062 pathological or positive, containing at least one confirmed mass, as diagnosed by an expert radiologist. To assess the performance of the system, receiver operating characteristic ROC and free-response ROC analysis were employed. The area under the ROC curve was found to be Az=0.783±0.008 for the ROI-based classification. When evaluating the accuracy of the CAD against the radiologist-drawn boundaries, 4.23 false positives per image are found at 80% of mass sensitivity.

A completely automated CAD system for mass detection in a large mammographic database

LAURIA, ADELE;
2006

Abstract

Mass localization plays a crucial role in computer-aided detection CAD systems for the classification of suspicious regions in mammograms. In this article we present a completely automated classification system for the detection of masses in digitized mammographic images. The tool system we discuss consists in three processing levels: a Image segmentation for the localization of regions of interest ROIs. This step relies on an iterative dynamical threshold algorithm able to select iso-intensity closed contours around gray level maxima of the mammogram. b ROI characterization by means of textural features computed from the gray tone spatial dependence matrix GTSDM, containing second-order spatial statistics information on the pixel gray level intensity. As the images under study were recorded in different centers and with different machine settings, eight GTSDM features were selected so as to be invariant under monotonic transformation. In this way, the images do not need to be normalized, as the adopted features depend on the texture only, rather than on the gray tone levels, too. c ROI classification by means of a neural network, with supervision provided by the radiologist’s diagnosis. The CAD system was evaluated on a large database of 3369 mammographic images 2307 negative, 1062 pathological or positive, containing at least one confirmed mass, as diagnosed by an expert radiologist. To assess the performance of the system, receiver operating characteristic ROC and free-response ROC analysis were employed. The area under the ROC curve was found to be Az=0.783±0.008 for the ROI-based classification. When evaluating the accuracy of the CAD against the radiologist-drawn boundaries, 4.23 false positives per image are found at 80% of mass sensitivity.
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11588/105812
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