213 research outputs found
Grid Databases for Shared Image Analysis in the MammoGrid Project
The MammoGrid project aims to prove that Grid infrastructures can be used for
collaborative clinical analysis of database-resident but geographically
distributed medical images. This requires: a) the provision of a
clinician-facing front-end workstation and b) the ability to service real-world
clinician queries across a distributed and federated database. The MammoGrid
project will prove the viability of the Grid by harnessing its power to enable
radiologists from geographically dispersed hospitals to share standardized
mammograms, to compare diagnoses (with and without computer aided detection of
tumours) and to perform sophisticated epidemiological studies across national
boundaries. This paper outlines the approach taken in MammoGrid to seamlessly
connect radiologist workstations across a Grid using an "information
infrastructure" and a DICOM-compliant object model residing in multiple
distributed data stores in Italy and the UKComment: 10 pages, 5 figure
Digital mammography, cancer screening: Factors important for image compression
The use of digital mammography for breast cancer screening poses several novel problems such as development of digital sensors, computer assisted diagnosis (CAD) methods for image noise suppression, enhancement, and pattern recognition, compression algorithms for image storage, transmission, and remote diagnosis. X-ray digital mammography using novel direct digital detection schemes or film digitizers results in large data sets and, therefore, image compression methods will play a significant role in the image processing and analysis by CAD techniques. In view of the extensive compression required, the relative merit of 'virtually lossless' versus lossy methods should be determined. A brief overview is presented here of the developments of digital sensors, CAD, and compression methods currently proposed and tested for mammography. The objective of the NCI/NASA Working Group on Digital Mammography is to stimulate the interest of the image processing and compression scientific community for this medical application and identify possible dual use technologies within the NASA centers
A scalable system for microcalcification cluster automated detection in a distributed mammographic database
A computer-aided detection (CADe) system for microcalcification cluster
identification in mammograms has been developed in the framework of the
EU-founded MammoGrid project. The CADe software is mainly based on wavelet
transforms and artificial neural networks. It is able to identify
microcalcifications in different datasets of mammograms (i.e. acquired with
different machines and settings, digitized with different pitch and bit depth
or direct digital ones). The CADe can be remotely run from GRID-connected
acquisition and annotation stations, supporting clinicians from geographically
distant locations in the interpretation of mammographic data. We report and
discuss the system performances on different datasets of mammograms and the
status of the GRID-enabled CADe analysis.Comment: 6 pages, 4 figures; Proceedings of the IEEE NNS and MIC Conference,
October 23-29, 2005, Puerto Ric
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Evaluation of a multi-scale enhancement protocol for digital mammography
We have carried out a receiver operating characteristics (ROC) study for the enhancement of mammographic features in digitized mammograms. The study evaluated the benefits of multi-scale enhancement methods in terms of diagnostic performance of radiologists. The enhancement protocol relied on multi-scale expansions and non-linear enhancement functions. Dyadic spline wavelet functions were used together with a sigmoidal non-linear enhancement function. We designed a computer interface ona softcopy display and performed an ROC study with three radiologists, who specialized in mammography. Clinical cases were obtained from a national mammography database of digitized radiographs prepared by the University of South Florida and Harvard Medical School. Our study focused on dense mammograms, i.e. mammograms of density 3 and 4 on the American College of Radiology breast density rating, which are the most difficult cases in screening, were selected. To compare the performance of radiologists with an without using multi-scale enhancement, two groups of 30 cases each were diagnosed. Each group contained 15 cases of cancerous and 15 cases of normal mammograms. Conventional ROC analysis was applied, and the resulting ROC curves indicated improved diagnostic performance when radiologists used multi-scale non-linear enhancement
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Evaluation of a Multi-Scale Enhancement Protocol for Digital Mammography
We have carried out a receiver operating characteristics (ROC) study for the enhancement of mammographic features in digitized mammograms. The study evaluated the benefits of multi-scale enhancement methods in terms of diagnostic performance of radiologists. The enhancement protocol relied on multi-scale expansions and non-linear enhancement functions. Dyadic spline wavelet functions (first derivative of a cubic spline) were used together with a sigmoidal non-linear enhancement function. We designed a computer interface on a softcopy display and performed an ROC study with three radiologists, who specialized in mammography. Clinical cases were obtained from a national mammography database of digitized radiographs prepared by the University of South Florida (USF) and Harvard Medical School. Our study focused on dense mammograms, i.e. mammograms of density 3 and 4 on the American College of Radiology (ACR) breast density rating, which are the most difficult cases in screening, were selected. To compare the performance of radiologists with and without using multi-scale enhancement, two groups of 30 cases each were diagnosed. Each group contained 15 cases of cancerous and 15 cases of normal mammograms. Conventional ROC analysis was applied, and the resulting ROC curves indicated improved diagnostic performance when radiologists used multi-scale non-linear enhancement
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A wavelet based mammographic system
Mammography's role in the detection of breast cancer at early stages is well known. Although more accurate than other existing techniques, mammography still only finds 80 to 90 percent of breast cancers. It has been suggested that mammograms, as normally viewed, display only about 3% of the total information detected. The general inability to detect small tumors and other salient features within mammograms motivates our investigation of a system we call the Mammogram Display System (MDS). The core technology used for MDS image enhancement is the wavelet transform
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