A Computer Aided Detection system for mammographic images implemented on a GRID infrastructure

The use of an automatic system for the analysis of mammographic images has proven to be very useful to radiologists in the investigation of breast cancer, especially in the framework of mammographic-screening programs. A breast neoplasia is often marked by the presence of microcalcification clusters and massive lesions in the mammogram: hence the need for tools able to recognize such lesions at an early stage. In the framework of the GPCALMA (GRID Platform for Computer Assisted Library for MAmmography) project, the co-working of italian physicists and radiologists built a large distributed database of digitized mammographic images (about 5500 images corresponding to 1650 patients) and developed a CAD (Computer Aided Detection) system, able to make an automatic search of massive lesions and microcalcification clusters. The CAD is implemented in the GPCALMA integrated station, which can be used also for digitization, as archive and to perform statistical analyses. Some GPCALMA integrated stations have already been implemented and are currently on clinical trial in some italian hospitals. The emerging GRID technology can been used to connect the GPCALMA integrated stations operating in different medical centers. The GRID approach will support an effective tele- and co-working between radiologists, cancer specialists and epidemiology experts by allowing remote image analysis and interactive online diagnosis.Comment: 5 pages, 5 figures, to appear in the Proceedings of the 13th IEEE-NPSS Real Time Conference 2003, Montreal, Canada, May 18-23 200

Grid Computing: Concepts and Applications

The challenge of CERN experiments at the Large Hadron Collider (LHC), which will collect data at rates in the range of PBs/year, requires the development of GRID technologies to optimize the exploitation of distributed computing power and the automatic access to distributed data storage. Several projects are addressing the problem of setting up the hardware infrastructure of a GRID, as well as the development of the middleware required to manage it: a working GRID should look like a set of services, accessible to registered applications, which will help cooperate the different computing and storage resources. As it happened for the World Wide Web, GRID concepts are in principle important not only for High Energy Physics (HEP): for this reason, GRID developers, while keeping in mind the needs of HEP experiments, are trying to design GRID services in the most general way. As examples, two applications are described: the CERN/ALICE experiment at the LHC and a recently approved INFN project (GPCALMA) which will set up a GRID prototype between several mammographic centres in Italy

GPCALMA: A Tool For Mammography With A GRID-Connected Distributed Database

The GPCALMA (Grid Platform for Computer Assisted Library for MAmmography) collaboration involves several departments of physics, INFN sections, and italian hospitals. The aim of this collaboration is developing a tool that can help radiologists in early detection of breast cancer. GPCALMA has built a large distributed database of digitised mammographic images (about 5500 images corresponding to 1650 patients) and developed a CAD (Computer Aided Detection) software which is integrated in a station that can also be used for acquire new images, as archive and to perform statistical analysis. The images are completely described: pathological ones have a consistent characterization with radiologist's diagnosis and histological data, non pathological ones correspond to patients with a follow up at least three years. The distributed database is realized throught the connection of all the hospitals and research centers in GRID tecnology. In each hospital local patients digital images are stored in the local database. Using GRID connection, GPCALMA will allow each node to work on distributed database data as well as local database data. Using its database the GPCALMA tools perform several analysis. A texture analysis, i.e. an automated classification on adipose, dense or glandular texture, can be provided by the system. GPCALMA software also allows classification of pathological features, in particular massive lesions analysis and microcalcification clusters analysis. The performance of the GPCALMA system will be presented in terms of the ROC (Receiver Operating Characteristic) curves. The results of GPCALMA system as "second reader" will also be presented.Comment: 6 pages, Proceedings of the Seventh Mexican Symposium on Medical Physics 2003, Vol. 682/1, pp. 67-72, Mexico City, Mexic

GPCALMA, a mammographic CAD in a GRID connection

6 pages, 4 figures, to appear in CARS 2003 Proceedings, Computer Assisted Radiology and Surgery 17th International Congress and Exhibition, London, June 25-28, 2003Purpose of this work is the development of an automatic system which could be useful for radiologists in the investigation of breast cancer. A breast neoplasia is often marked by the presence of microcalcifications and massive lesions in the mammogram: hence the need for tools able to recognize such lesions at an early stage. GPCALMA (Grid Platform Computer Assisted Library for MAmmography), a collaboration among italian physicists and radiologists, has built a large distributed database of digitized mammographic images (at this moment about 5500 images corresponding to 1650 patients). This collaboration has developed a CAD (Computer Aided Detection) system which, installed in an integrated station, can also be used for digitization, as archive and to perform statistical analysis. With a GRID configuration it would be possible for the clinicians tele- and co-working in new and innovative groupings ('virtual organisations') and, using the whole database, by the GPCALMA tools several analysis can be performed. Furthermore the GPCALMA system allows to be abreast of the CAD technical progressing into several hospital locations always with remote working by GRID connection. We report in this work the results obtained by the GPCALMA CAD software implemented with a GRID connection

A Computer Aided Detection system for mammographic images implemented on a GRID infrastructure

5 pages, 5 figures, to appear in the Proceedings of the 13th IEEE-NPSS Real Time Conference 2003, Montreal, Canada, May 18-23 2003The use of an automatic system for the analysis of mammographic images has proven to be very useful to radiologists in the investigation of breast cancer, especially in the framework of mammographic-screening programs. A breast neoplasia is often marked by the presence of microcalcification clusters and massive lesions in the mammogram: hence the need for tools able to recognize such lesions at an early stage. In the framework of the GPCALMA (GRID Platform for Computer Assisted Library for MAmmography) project, the co-working of italian physicists and radiologists built a large distributed database of digitized mammographic images (about 5500 images corresponding to 1650 patients) and developed a CAD (Computer Aided Detection) system, able to make an automatic search of massive lesions and microcalcification clusters. The CAD is implemented in the GPCALMA integrated station, which can be used also for digitization, as archive and to perform statistical analyses. Some GPCALMA integrated stations have already been implemented and are currently on clinical trial in some italian hospitals. The emerging GRID technology can been used to connect the GPCALMA integrated stations operating in different medical centers. The GRID approach will support an effective tele- and co-working between radiologists, cancer specialists and epidemiology experts by allowing remote image analysis and interactive online diagnosis

CADe tools for early detection of breast cancer

A breast neoplasia is often marked by the presence of microcalcifications and massive lesions in the mammogram: hence the need for tools able to recognize such lesions at an early stage. Our collaboration, among italian physicists and radiologists, has built a large distributed database of digitized mammographic images and has developed a Computer Aided Detection (CADe) system for the automatic analysis of mammographic images and installed it in some Italian hospitals by a GRID connection. Regarding microcalcifications, in our CADe digital mammogram is divided into wide windows which are processed by a convolution filter; after a self-organizing map analyzes each window and produces 8 principal components which are used as input of a neural network (FFNN) able to classify the windows matched to a threshold. Regarding massive lesions we select all important maximum intensity position and define the ROI radius. From each ROI found we extract the parameters which are used as input in a FFNN to distinguish between pathological and non-pathological ROI. We present here a test of our CADe system, used as a second reader and a comparison with another (commercial) CADe system.Comment: 4 pages, Proceedings of the 4th International Symposium on Nuclear and Related Techniques 2003, Vol. unico, pp. d10/1-d10/4 Havana, Cub

Multi-scale analysis of lung computed tomography images

A computer-aided detection (CAD) system for the identification of lung internal nodules in low-dose multi-detector helical Computed Tomography (CT) images was developed in the framework of the MAGIC-5 project. The three modules of our lung CAD system, a segmentation algorithm for lung internal region identification, a multi-scale dot-enhancement filter for nodule candidate selection and a multi-scale neural technique for false positive finding reduction, are described. The results obtained on a dataset of low-dose and thin-slice CT scans are shown in terms of free response receiver operating characteristic (FROC) curves and discussed.Comment: 18 pages, 12 low-resolution figure

Aplicação de técnicas de data mining para suporte ao diagnóstico de cancro da mama

More than ever, there is an increase of the number of decision support methods and computer aided diagnostic systems applied to various areas of medicine. In breast cancer research, many works have been done in order to reduce false-positives when used as a double reading method. In this study, we aimed to present a set of data mining techniques that were applied to approach a decision support system in the area of breast cancer diagnosis. This method is geared to assist clinical practice in identifying mammographic findings such as microcalcifications, masses and even normal tissues, in order to avoid misdiagnosis. In this work a reliable database was used, with 410 images from about 115 patients, containing previous reviews performed by radiologists as microcalcifications, masses and also normal tissue findings. Throughout this work, two feature extraction techniques were used: the gray level co-occurrence matrix and the gray level run length matrix. For classification purposes, we considered various scenarios according to different distinct patterns of injuries and several classifiers in order to distinguish the best performance in each case described. The many classifiers used were Naïve Bayes, Support Vector Machines, k-nearest Neighbors and Decision Trees (J48 and Random Forests). The results in distinguishing mammographic findings revealed great percentages of PPV and very good accuracy values. Furthermore, it also presented other related results of classification of breast density and BI-RADS® scale. The best predictive method found for all tested groups was the Random Forest classifier, and the best performance has been achieved through the distinction of microcalcifications. The conclusions based on the several tested scenarios represent a new perspective in breast cancer diagnosis using data mining techniques.Cada vez mais assistimos a um aumento global do número de métodos de apoio a decisão e diagnóstico assistido por computador, aplicados a diversas áreas da medicina. Na área de investigação do cancro da mama muitos são os trabalhos que têm sido desenvolvidos como segunda leitura de modo a reduzir o número de falsos positivos no diagnóstico. Neste estudo é apresentado um conjunto de técnicas de data mining que poderão ser aplicadas a um sistema de apoio à decisão na área do diagnóstico de cancro da mama. Esta abordagem tem por objetivo ajudar os clínicos na identificação de achados mamográficos como microcalcificações, massas e mesmo tecidos normais, de forma a evitar diagnósticos errados. Para isso, neste trabalho é usada uma base de dados fidedigna, de 410 imagens correspondentes a 115 pacientes, contendo análises prévias, realizadas por radiologistas, de microcalcificações, massas e tecidos considerados normais. Ao longo deste trabalho são utilizadas duas técnicas de extração de características, a matriz de coocorrência de níveis de cinza e a matriz de comprimento da linha de níveis de cinza. Para a classificação foram considerados diferentes cenários de acordo com diferentes padrões de distinção de lesões e ainda vários classificadores de forma a distinguir as melhores performances em cada caso descrito. Os vários classificadores usados foram Naïve Bayes, Support Vector Machines, k-nearest Neighbors e Decision Trees (J48 e Random Forests). Os resultados obtidos na distinção dos achados mamográficos revelaram percentagens de valor preditivo positivo e de precisão bastante boas. São ainda apresentados outros resultados relacionados com sistemas de classificação de densidade mamária e escala BI-RADS®. O melhor método de previsão encontrado, perante todos os grupos testados, foi o classificador Random Forest e o melhor desempenho foi conseguido através da distinção de microcalcificações. As conclusões feitas ao longo dos vários cenários testados foram interessantes em termos que representam uma nova perspetiva no diagnóstico do cancro da mama, utilizando técnicas de data mining

MRI analysis for Hippocampus segmentation on a distributed infrastructure

Medical image computing raises new challenges due to the scale and the complexity of the required analyses. Medical image databases are currently available to supply clinical diagnosis. For instance, it is possible to provide diagnostic information based on an imaging biomarker comparing a single case to the reference group (controls or patients with disease). At the same time many sophisticated and computationally intensive algorithms have been implemented to extract useful information from medical images. Many applications would take great advantage by using scientific workflow technology due to its design, rapid implementation and reuse. However this technology requires a distributed computing infrastructure (such as Grid or Cloud) to be executed efficiently. One of the most used workflow manager for medical image processing is the LONI pipeline (LP), a graphical workbench developed by the Laboratory of Neuro Imaging (http://pipeline.loni.usc.edu). In this article we present a general approach to submit and monitor workflows on distributed infrastructures using LONI Pipeline, including European Grid Infrastructure (EGI) and Torque-based batch farm. In this paper we implemented a complete segmentation pipeline in brain magnetic resonance imaging (MRI). It requires time-consuming and data-intensive processing and for which reducing the computing time is crucial to meet clinical practice constraints. The developed approach is based on web services and can be used for any medical imaging application

GPCALMA, a mammographic CAD in a GRID connection

The purpose of this work is the development of an automatic system which could be useful for radiologists in the investigation of breast cancer. A breast neoplasia is often marked by the presence of microcalcifications and massive lesions in the mammogram: hence the need for tools able to recognize such lesions at an early stage. Grid Platform Computer-Assisted Library for MAmmography (GPCALMA), a collaboration among Italian physicists and radiologists, has built a large distributed database of digitized mammographic images (at this moment about 5500 images corresponding to 1650 patients). This collaboration has developed a Computer Aided Detection (CAD) system which, installed in an integrated station, can also be used for digitization, as archive, and to perform statistical analysis. With a GRID configuration, it would be possible for clinicians tele- and co-working in new and innovative groupings ('virtual organizations'), and using the whole database, by the GPCALMA tools, several analysis can be performed. Furthermore, GPCALMA allows to be abreast of the CAD technical progressing into several hospital locations always with remote working by GRID connection. We report the results obtained by the GPCALMA CAD software implemented with a GRID connection. (C) 2003 Published by Elsevier Science B.V