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

Bellotti, R., De Carlo, F., Tangaro, S., Gargano, G., Maggipinto, G., Castellano, M., Massafra, R., Cascio, D., Fauci, F., Magro, R., and others. (2006) A completely automated CAD system for mass detection in a large mammographic database. Medical Physics, 33 (8). pp. 3066-3075. ISSN 0094-2405. (doi:10.1118/1.2214177) (The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided) (KAR id:92336)

The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided.
Official URL: https://doi.org/10.1118/1.2214177

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.

Item Type:	Article
DOI/Identification number:	10.1118/1.2214177
Uncontrolled keywords:	breast cancer; mammographic mass detection; image processing; computer-aided detection (CAD); textural features; neural network
Subjects:	Q Science > QA Mathematics (inc Computing science) > QA 75 Electronic computers. Computer science
Divisions:	Divisions > Division of Computing, Engineering and Mathematical Sciences > School of Computing
Depositing User:	Amy Boaler
Date Deposited:	14 Dec 2021 14:36 UTC
Last Modified:	05 Nov 2024 12:57 UTC
Resource URI:	https://kar.kent.ac.uk/id/eprint/92336 (The current URI for this page, for reference purposes)

University of Kent Author Information

Masala, Giovanni Luca.

Creator's ORCID:	https://orcid.org/0000-0001-6734-9424
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