Publications (4)1.07 Total impact
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Article: A Completely automated CAD system for mass detection in a large mammographic database
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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 A<sub>z</sub>=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. -
Article: Mammogram segmentation by contour searching and mass lesions classification with neural network
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ABSTRACT: The mammography is the most effective procedure for an early diagnosis of the breast cancer. In this paper, an algorithm for detecting masses in mammographic images will be presented. The database consists of 3762 digital images acquired in several hospitals belonging to the MAGIC-5 collaboration (Medical Applications on a Grid Infrastructure Connection). A reduction of the whole image's area under investigation is achieved through a segmentation process, by means of a ROI Hunter algorithm, without loss of meaningful information. In the following classification step, feature extraction plays a fundamental role: some features give geometrical information, other ones provide shape parameters. Once the features are computed for each ROI, they are used as inputs to a supervised neural network with momentum. The output neuron provides the probability that the ROI is pathological or not. Results are provided in terms of ROC and FROC curves: the area under the ROC curve was found to be A<sub>z</sub>=0.862plusmn0.007, and we get a 2.8 FP/Image at a sensitivity of 82%. This software is included in the CAD station actually working in the hospitals belonging to the MAGIC-5 Collaboration -
Article: The CALMA system: an artificial neural network method for detecting masses and microcalcifications in digitized mammograms
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ABSTRACT: The CALMA (Computer Assisted Library for MAmmography) project is a five years plan developed in a physics research frame in collaboration between INFN (Istituto Nazionale di Fisica Nucleare) and many Italian hospitals. At present a large database of digitized mammographic images (more than 6000) was collected and a software based on neural network algorithms for the search of suspicious breast lesions was developed. Two tools are available: a microcalcification clusters hunter, based on supervised and unsupervised feedforward neural network, and a massive lesions searcher, based on a hibrid approach. Both the algorithms analyzed preprocessed digitized images by high frequency filters. Clinical tests were performed to evaluate sensitivity and specificity of the system, considering the system as alone and as secon reader. Results show that the system is ready to be implemented by medical industry. The CALMA project, just ended, has its natural development in the GPCALMA (Grid Platform for CALMA) project, where distributed users join common resources (images, tools, statistical analysis). -
Article: A massive lesion detection algorithm in mammography.
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ABSTRACT: A new algorithm for massive lesion detection in mammography is presented. The algorithm consists in three main steps: 1) reduction of the dimension of the image to be processed through the identification of regions of interest (roi) as candidates for massive lesions; 2) characterization of the RoI by means of suitable feature extraction; 3) pattern classification through supervised neural networks. Suspect regions are detected by searching for local maxima of the pixel grey level intensity. A ring of increasing radius, centered on a maximum, is considered until the mean intensity in the ring decreases to a defined fraction of the maximum. The ROIS thus obtained are described by average, variance, skewness and kurtosis of the intensity distributions at different fractions of the radius. A neural network approach is adopted to classify suspect pathological and healthy pattern. The software has been designed in the framework of the INFN (Istituto Nazionale Fisica Nucleare) research project GPCALMA (Grid Platform for Calma) which recruits physicists and radiologists from different Italian Research Institutions and hospitals to develop software for breast cancer detection.Physica Medica 21(1):23-30. · 1.07 Impact Factor
