253 research outputs found
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
A scalable Computer-Aided Detection system for microcalcification cluster identification in a pan-European distributed database of mammograms
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 kinds 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 the
FROC analyses of the CADe system performances on three different dataset of
mammograms, i.e. images of the CALMA INFN-founded database collected in the
Italian National screening program, the MIAS database and the so-far collected
MammoGrid images. The sensitivity values of 88% at a rate of 2.15 false
positive findings per image (FP/im), 88% with 2.18 FP/im and 87% with 5.7 FP/im
have been obtained on the CALMA, MIAS and MammoGrid database respectively.Comment: 6 pages, 5 figures; Proceedings of the ITBS 2005, 3rd International
Conference on Imaging Technologies in Biomedical Sciences, 25-28 September
2005, Milos Island, Greec
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
Convolutional Neural Networks for the segmentation of microcalcification in Mammography Imaging
Cluster of microcalcifications can be an early sign of breast cancer. In this
paper we propose a novel approach based on convolutional neural networks for
the detection and segmentation of microcalcification clusters. In this work we
used 283 mammograms to train and validate our model, obtaining an accuracy of
98.22% in the detection of preliminary suspect regions and of 97.47% in the
segmentation task. Our results show how deep learning could be an effective
tool to effectively support radiologists during mammograms examination.Comment: 13 pages, 7 figure
Application of Fractal and Wavelets in Microcalcification Detection
Breast cancer has been recognized as one or the most frequent, malignant tumors in women, clustered microcalcifications in mammogram images has been widely recognized as an early sign of breast cancer. This work is devote to review the application of Fractal and Wavelets in microcalcifications detection
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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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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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