64 research outputs found
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
Breast Cancer: Modelling and Detection
This paper reviews a number of the mathematical models used in cancer modelling and then chooses a specific cancer, breast carcinoma, to illustrate how the modelling can be used in aiding detection. We then discuss mathematical models that underpin mammographic image analysis, which complements models of tumour growth and facilitates diagnosis and treatment of cancer. Mammographic images are notoriously difficult to interpret, and we give an overview of the primary image enhancement technologies that have been introduced, before focusing on a more detailed description of some of our own recent work on the use of physics-based modelling in mammography. This theoretical approach to image analysis yields a wealth of information that could be incorporated into the mathematical models, and we conclude by describing how current mathematical models might be enhanced by use of this information, and how these models in turn will help to meet some of the major challenges in cancer detection
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
Abnormality Detection in Mammography using Deep Convolutional Neural Networks
Breast cancer is the most common cancer in women worldwide. The most common
screening technology is mammography. To reduce the cost and workload of
radiologists, we propose a computer aided detection approach for classifying
and localizing calcifications and masses in mammogram images. To improve on
conventional approaches, we apply deep convolutional neural networks (CNN) for
automatic feature learning and classifier building. In computer-aided
mammography, deep CNN classifiers cannot be trained directly on full mammogram
images because of the loss of image details from resizing at input layers.
Instead, our classifiers are trained on labelled image patches and then adapted
to work on full mammogram images for localizing the abnormalities.
State-of-the-art deep convolutional neural networks are compared on their
performance of classifying the abnormalities. Experimental results indicate
that VGGNet receives the best overall accuracy at 92.53\% in classifications.
For localizing abnormalities, ResNet is selected for computing class activation
maps because it is ready to be deployed without structural change or further
training. Our approach demonstrates that deep convolutional neural network
classifiers have remarkable localization capabilities despite no supervision on
the location of abnormalities is provided.Comment: 6 page
Modelling mammographic microcalcification clusters using persistent mereotopology
AbstractIn mammographic imaging, the presence of microcalcifications, small deposits of calcium in the breast, is a primary indicator of breast cancer. However, not all microcalcifications are malignant and their distribution within the breast can be used to indicate whether clusters of microcalcifications are benign or malignant. Computer-aided diagnosis (CAD) systems can be employed to help classify such microcalcification clusters. In this paper a novel method for classifying microcalcification clusters is presented by representing discrete mereotopological relations between the individual microcalcifications over a range of scales in the form of a mereotopological barcode. This barcode based representation is able to model complex relations between multiple regions and the results on mammographic microcalcification data shows the effectiveness of this approach. Classification accuracies of 95% and 80% are achieved on the MIAS and DDSM datasets, respectively. These results are comparable to existing state-of-the art methods. This work also demonstrates that mereotopological barcodes could be used to help trained clinicians in their diagnosis by providing a clinical interpretation of barcodes that represent both benign and malignant cases
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