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

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.

[1]  S. Bagnasco,et al.  Mammogram segmentation by contour searching and massive lesion classification with neural network , 2004, IEEE Symposium Conference Record Nuclear Science 2004..

[2]  Francesco Fauci,et al.  A massive lesion detection algorithm in mammography. , 2005, Physica medica : PM : an international journal devoted to the applications of physics to medicine and biology : official journal of the Italian Association of Biomedical Physics.

[3]  Rene Vargas-Voracek,et al.  Computer-assisted detection of mammographic masses: a template matching scheme based on mutual information. , 2003, Medical physics.

[4]  Geoffrey Cox,et al.  Experiments in Lung Cancer Nodule Detection Using Texture Analysis and Neural Network Classifiers , 1992 .

[5]  L. Tabár,et al.  REDUCTION IN MORTALITY FROM BREAST CANCER AFTER MASS SCREENING WITH MAMMOGRAPHY Randomised Trial from the Breast Cancer Screening Working Group of the Swedish National Board of Health and Welfare , 1985, The Lancet.

[6]  R. M. Haralick,et al.  Textural features for image classification. IEEE Transaction on Systems, Man, and Cybernetics , 1973 .

[7]  Francesco Fauci,et al.  Search of microcalcification clusters with the CALMA CAD station , 2002, SPIE Medical Imaging.

[8]  G. Simonetti,et al.  Mammography: Guide to Interpreting, Reporting and Auditing Mammographic Images - Re.Co.R.M. , 2005 .

[9]  R. Bird,et al.  Analysis of cancers missed at screening mammography. , 1992, Radiology.

[10]  J A Swets,et al.  Measuring the accuracy of diagnostic systems. , 1988, Science.

[11]  J. Hanley,et al.  The meaning and use of the area under a receiver operating characteristic (ROC) curve. , 1982, Radiology.

[12]  Richard W. Conners,et al.  A Theoretical Comparison of Texture Algorithms , 1980, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[13]  N. Karssemeijer,et al.  Segmentation of suspicious densities in digital mammograms. , 2001, Medical physics.

[14]  R A Clark,et al.  False-positive reduction in CAD mass detection using a competitive classification strategy. , 2001, Medical physics.

[15]  J. Wolfe.  Risk for breast cancer development determined by mammographic parenchymal pattern , 1976, Cancer.

[16]  Y H Chang,et al.  Performance gain in computer-assisted detection schemes by averaging scores generated from artificial neural networks with adaptive filtering. , 2001, Medical physics.

[17]  U Bottigli,et al.  [Application of a computer-aided detection (CAD) system to digitalized mammograms for identifying microcalcifications]. , 2001, La Radiologia medica.

[18]  Y. Wu,et al.  Artificial neural networks in mammography: application to decision making in the diagnosis of breast cancer. , 1993, Radiology.

[19]  N. Karssemeijer,et al.  A new 2D segmentation method based on dynamic programming applied to computer aided detection in mammography. , 2004, Medical physics.

[20]  N. Petrick,et al.  Computer-aided classification of mammographic masses and normal tissue: linear discriminant analysis in texture feature space. , 1995, Physics in medicine and biology.

[21]  R. Prevete,et al.  The MAGIC-5 Project: medical applications on a GRID infrastructure connection , 2004, IEEE Symposium Conference Record Nuclear Science 2004..

[22]  B Sahiner,et al.  False-positive reduction technique for detection of masses on digital mammograms: global and local multiresolution texture analysis. , 1997, Medical physics.

[23]  Nico Karssemeijer,et al.  Computer-aided detection versus independent double reading of masses on mammograms. , 2003, Radiology.

[24]  N. Petrick,et al.  Improvement of radiologists' characterization of mammographic masses by using computer-aided diagnosis: an ROC study. , 1999, Radiology.

[25]  Y H Chang,et al.  Computerized detection of masses in digitized mammograms using single-image segmentation and a multilayer topographic feature analysis. , 1995, Academic radiology.

[26]  James L. McClelland,et al.  Parallel distributed processing: explorations in the microstructure of cognition, vol. 1: foundations , 1986 .

[27]  Craig K. Abbey,et al.  A mammographic mass CAD system incorporating features from shape, fractal, and channelized Hotelling observer measurements: preliminary results , 2003, SPIE Medical Imaging.

[28]  A. Jones,et al.  Reduction in mortality from breast cancer , 2005, BMJ : British Medical Journal.

[29]  Azriel Rosenfeld,et al.  A Comparative Study of Texture Measures for Terrain Classification , 1975, IEEE Transactions on Systems, Man, and Cybernetics.

[30]  Craig K Abbey,et al.  Computer aided detection of masses in mammography using subregion Hotelling observers. , 2003, Medical physics.

[31]  David M Catarious,et al.  Incorporation of an iterative, linear segmentation routine into a mammographic mass CAD system. , 2004, Medical physics.

[32]  Mohan M. Trivedi,et al.  Segmentation of a high-resolution urban scene using texture operators , 1984, Comput. Vis. Graph. Image Process..

[33]  Piernicola Oliva,et al.  Comparison of imaging properties of several digital radiographic systems , 2001 .