Tangaro, Sonia and De Carlo, Francesco and Gargano, Gianfranco and Bellotti, Roberto and Bottigli, Ubaldo and Masala, Giovanni Luca Christian and Cerello, Piergiorgio and Cheran, Sorin Cristian and Cataldo, Rossella (2007) Mass lesion detection in mammographic images using Haralik textural features. In: Computational modelling of objects represented in images : fundamentals, methods and applications : proceedings of the International Symposium CompIMAGE 2006, 20-21 October 2006, Coimbra, Portugal. London, Taylor & Francis. ISBN 9780415433495. Conference or Workshop Item.
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In this article we present a classification system for an automatic detection of masses in digitized mammographic images. The systems consists in three main processing levels: a) image segmentation for the localization of regions of interest (ROIs); 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 grey level intensity; c) ROI classification by means of a neural network, with supervision provided by the radiologist’s diagnosis. The CAD system was developed and evaluated using a database of NI = 3369 mammographic images: the breakdown of the cases was NIn = 2307 negative images, and NIp = 1062 pathological (or positive) images, containing at least one confirmed mass, as diagnosed by an expert radiologist. To examine the performance of the overall CAD system, receiver operating characteristic (ROC) and free-response ROC (FROC) analysis were employed. The area under the ROC curve was found to be Az = 0.78 ± 0.008 for ROI-based classification. When evaluating the accuracy of the CAD against the radiologist-drawn boundaries, 4.23 false positive per image (FPpI) are found at 80% mass sensitivity.
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