Network


Latest external collaboration on country level. Dive into details by clicking on the dots.

Hotspot


Dive into the research topics where G. Forni is active.

Publication


Featured researches published by G. Forni.


IEEE Symposium Conference Record Nuclear Science 2004. | 2004

Mammogram segmentation by contour searching and massive lesion classification with neural network

F. Fauci; S. Bagnasco; R. Bellotti; D. Cascio; S.C. Cheran; F. De Carlo; G. De Nunzio; M.E. Fantacci; G. Forni; A. Lauria; Ernesto Lopez Torres; R. Magro; Giovanni Luca Christian Masala; P. Oliva; Maurizio Quarta; G. Raso; Alessandra Retico; S. Tangaro

The mammography is the most effective procedure for an early diagnosis of the breast cancer. In this paper, an algorithm for detecting massive lesions in mammographic images will be presented. The database consists of 3762 digital images acquired in several hospitals belonging to the MAGIC-5 collaboration. A reduction of the surface under investigation is achieved, without loss of meaningful information, through segmentation of the whole image, by means of a ROI Hunter algorithm. 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 Az=(85.6plusmn0.8)%. This software is included in the CAD station actually working in the hospitals belonging to the MAGIC-5 Collaboration


Medical Physics | 2006

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

Roberto Bellotti; F. De Carlo; S. Tangaro; Gianfranco Gargano; G. Maggipinto; M. Castellano; R. Massafra; D. Cascio; F. Fauci; R. Magro; G. Raso; A. Lauria; G. Forni; S. Bagnasco; P. Cerello; Zanon E; S. C. Cheran; E. Lopez Torres; U. Bottigli; Giovanni Luca Christian Masala; P. Oliva; A. Retico; Maria Evelina Fantacci; Rosella Cataldo; I. De Mitri; G. De Nunzio

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 radiologists 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.


ieee nuclear science symposium | 2006

Mammogram Segmentation by Contour Searching and Mass Lesions Classification With Neural Network

D. Cascio; F. Fauci; R. Magro; G. Raso; R. Bellotti; F. De Carlo; Sonia Tangaro; G. De Nunzio; G. Forni; A. Lauria; M.E. Fantacci; A. Retico; G.L. Masala; P. Oliva; S. Bagnasco; S.C. Cheran; E.L. Torres

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 images 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 AZ=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


Physica Medica | 2005

A massive lesion detection algorithm in mammography

F. Fauci; G. De Nunzio; R. Magro; G. Forni; A. Lauria; S. Bagnasco; P. Cerello; S. C. Cheran; E. Lopez Torres; Roberto Bellotti; F. De Carlo; Gianfranco Gargano; S. Tangaro; I. De Mitri; Raso

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.


nuclear science symposium and medical imaging conference | 2004

The MAGIC-5 Project: medical applications on a GRID infrastructure connection

R. Bellotti; S. Bagnasco; U. Bottigli; Marcello Castellano; Rosella Cataldo; Ezio Catanzariti; P. Cerello; Sc Cheran; F. De Carlo; P. Delogu; I. De Mitri; G. De Nunzio; Me Fantacci; F. Fauci; G. Forni; G. Gargano; Bruno Golosio; Pl Indovina; A. Lauria; El Torres; R. Magro; D. Martello; Giovanni Luca Christian Masala; R. Massafra; P. Oliva; Rosa Palmiero; Ap Martinez; R Prevete; L. Ramello; G. Raso

The MAGIC-5 Project aims at developing computer aided detection (CAD) software for medical applications on distributed databases by means of a GRID infrastructure connection. The use of automatic systems for analyzing medical images is of paramount importance in the screening programs, due to the huge amount of data to check. Examples are: mammographies for breast cancer detection, computed-tomography (CT) images for lung cancer analysis, and the positron emission tomography (PET) imaging for the early diagnosis of the Alzheimer disease. The need for acquiring and analyzing data stored in different locations requires a GRID approach of distributed computing system and associated data management. The GRID technologies allow remote image analysis and interactive online diagnosis, with a relevant reduction of the delays actually associated to the screening programs. From this point of view, the MAGIC-5 Collaboration can be seen as a group of distributed users sharing their resources for implementing different virtual organizations (VO), each one aiming at developing screening programs, tele-training, tele-diagnosis and epidemiologic studies for a particular pathology.


Nuclear Instruments & Methods in Physics Research Section A-accelerators Spectrometers Detectors and Associated Equipment | 2004

The CALMA system: an artificial neural network method for detecting masses and microcalcifications in digitized mammograms

A. Lauria; Rosa Palmiero; G. Forni; P. Cerello; Bruno Golosio; F. Fauci; R. Magro; G. Raso; S. Tangaro; Pier Luigi Indovina

Abstract The Computer Assisted Library for MAmmography (CALMA) project is a 5 years plan developed in a physics research frame in collaboration between 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 lesion 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 second 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 Grid Platform for CALMA project, where distributed users join common resources (images, tools, and statistical analysis).


Methods of Information in Medicine | 2005

GPCALMA: a Grid-based Tool for Mammographic Screening

P. Cerello; S. Bagnasco; U. Bottigli; S. C. Cheran; Pasquale Delogu; Maria Evelina Fantacci; F. Fauci; G. Forni; A. Lauria; E. Lopez Torres; R. Magro; Giovanni Luca Christian Masala; P. Oliva; Rosa Palmiero; L. Ramello; G. Raso; A. Retico; M. Sitta; S. Stumbo; S. Tangaro; Zanon E


European Journal of Radiology | 2005

A study on two different CAD systems for mammography as an aid to radiological diagnosis in the search of microcalcification clusters

A. Lauria; Rosa Palmiero; G. Forni; Maria Evelina Fantacci; M Imbriaco; A Sodano; Pl Indovina


Archive | 2006

Mammogram segmentation by contour searchingand mass lesions classification with neural network

Donato Cascio; F. Fauci; R. Magro; G. Raso; Roberto Bellotti; Francesco De Carlo; Sonia Tangaro; Giorgio De Nunzio; Maurizio Quarta; G. Forni; A. Lauria; Maria Evelina Fantacci; Alessandra Retico; Giovanni Luca Christian Masala; P. Oliva; S. Bagnasco; S.C. Cheran; Ernesto Lopez Torres


International Congress Series | 2005

Implementation of a computer aided detection system for mammographic lesions with GRID applications

A. Lauria; G. Forni

Collaboration


Dive into the G. Forni's collaboration.

Top Co-Authors

Avatar

A. Lauria

University of Sassari

View shared research outputs
Top Co-Authors

Avatar

F. Fauci

University of Palermo

View shared research outputs
Top Co-Authors

Avatar

R. Magro

University of Palermo

View shared research outputs
Top Co-Authors

Avatar

G. Raso

University of Palermo

View shared research outputs
Top Co-Authors

Avatar

S. Bagnasco

Istituto Nazionale di Fisica Nucleare

View shared research outputs
Top Co-Authors

Avatar

P. Oliva

University of Sassari

View shared research outputs
Top Co-Authors

Avatar
Top Co-Authors

Avatar
Top Co-Authors

Avatar
Top Co-Authors

Avatar

G. De Nunzio

Istituto Nazionale di Fisica Nucleare

View shared research outputs
Researchain Logo
Decentralizing Knowledge