Digital Mammography

Breast cancer represents the second leading cause of cancer deaths in women today and it is the most common type of cancer in women. This paper presents some experiments for tumour detection in digital mammography. We investigate the use of different data mining techniques, neural networks and association rule mining, for anomaly detection and classification. The results show that the two approaches performed well, obtaining a classification accuracy reaching over 70% percent for both techniques. Moreover, the experiments we conducted demonstrate the use and effectiveness of association rule mining in image categorization.

Breast cancer is the most frequently diagnosed malignancy and the second leading cause of mortality in women [45]-[48]. In the last decade, ultrasound along with digital mammography has come to be regarded as the gold standard for breast cancer diagnosis [9], [10], [40]

Mammographic screening of asymptomatic women has shown effectiveness in the reduction of breast cancer mortality, We are developing a computerized scheme for the detection of mammographic masses as an aid to radiologists in mammographic screening programs. Possible masses on digitized screen/film mammograms are initially identified using a nonlinear bilateral-subtraction technique, which is based on asymmetric density patterns occurring in corresponding portions of right and left mammograms. In this study, we analyze the characteristics of actual masses and nonmass detections to develop feature-analysis techniques with which to reduce the number of nonmass (ie, false-positive) detections. These featureanalysis techniques involve (1) the extraction of various features (such as area, contrast, circularity and border-distance based on the density and geometric information of masses in both processed, and original breast images), and (2) tests of the extracted features to reduce nonmass detections. Cumulative histograms of both actual-mass detections and nonmass detections are used to characterize extracted features and to determine the cutoff values used in the feature tests. The effectiveness of the feature-analysis techniques is evaluated in combination with the computerized detection scheme that uses the nonlinear bilateralsubtraction technique using free-response receiver operating characteristic analysis and 77 patient cases (308 mammograms). Results show that the featureanalysis techniques effectively improve the performance of the computerized detection scheme: about 35% false-positive detections were eliminated without 1055 in sensitivity when the feature-analysis techniques were used.

In mammography diagnosis systems, high False Negative Rate (FNR) has always been a significant problem since a false negative answer may lead to a patient's death. This paper is directed towards the development of a novel Computer-aided Diagnosis (CADx) system for the diagnosis of breast masses. It aims at intensifying the performance of CADx algorithms as well as reducing the FNR by utilizing Zernike moments as descriptors of shape and margin characteristics. The input Regions of Interest (ROIs) are segmented manually and further subjected to a number of preprocessing stages. The outcomes of preprocessing stage are two processed images containing co-scaled translated masses. Besides, one of these images represents the shape characteristics of the mass, while the other describes the margin characteristics. Two groups of Zernike moments have been extracted from the preprocessed images and applied to the feature selection stage. Each group includes 32 moments with different orders and iterations. Considering the performance of the overall CADx system, the most effective moments have been chosen and applied to a Multi-layer Perceptron (MLP) classifier, employing both generic Back Propagation (BP) and Opposition-based Learning (OBL) algorithms. The Receiver Operational Characteristics (ROC) curve and the performance of resulting CADx systems are analyzed for each group of features. The designed systems yield Az ¼ 0.976, representing fair sensitivity, and Az ¼ 0.975 demonstrating fair specificity. The best achieved FNR and FPR are 0.0% and 5.5%, respectively.

Architectural distortion is an important sign of breast cancer, but because of its subtlety, it is a common cause of false-negative findings on screening mammograms. This paper presents methods for the detection of architectural distortion in mammograms of interval cancer cases taken prior to the detection of breast cancer using Gabor filters, phase portrait analysis, fractal analysis, and texture analysis. The methods were used to detect initial candidates for sites of architectural distortion in prior mammograms of interval cancer and also normal control cases. A total of 4,224 regions of interest (ROIs) were automatically obtained from 106 prior mammograms of 56 interval cancer cases, including 301 ROIs related to architectural distortion, and from 52 prior mammograms of 13 normal cases. For each ROI, the fractal dimension and Haralick’s texture features were computed. Feature selection was performed separately using stepwise logistic regression and stepwise regression. The best results achieved, in terms of the area under the receiver operating characteristics curve, with the features selected by stepwise logistic regression are 0.76 with the Bayesian classifier, 0.73 with Fisher linear discriminant analysis, 0.77 with an artificial neural network based on radial basis functions, and 0.77 with a support vector machine. Analysis of the performance of the methods with free-response receiver operating characteristics indicated a sensitivity of 0.80 at 7.6 false positives per image. The methods have good potential in detecting architectural distortion in mammograms of interval cancer cases.

We review some of the most recent advances in the area of wavelet applications in medical imaging. We first review key concepts in the processing of medical images with wavelet transforms and multiscale analysis, including timefrequency tiling, overcomplete representations, higher dimensional bases, symmetry, boundary effects, translational invariance, orientation selectivity, and best-basis selection. We next describe some applications in magnetic resonance imaging, including activation detection and denoising of functional magnetic resonance imaging and encoding schemes. We then present an overview in the area of ultrasound, including computational anatomy with three-dimensional cardiac ultrasound. Next, wavelets in tomography are reviewed, including their relationship to the radon transform and applications in position emission tomography imaging. Finally, wavelet applications in digital mammography are reviewed, including computer-assisted diagnostic systems that support the detection and classification of small masses and methods of contrast enhancement.

Background: High mammographic density is associated with increased breast cancer risk. Previous studies have shown that estrogens increase breast density on mammograms, but the effect on mammographic density of selective estrogen receptor modulators, such as raloxifene, is unknown. We assessed changes in mammographic density among women receiving placebo, raloxifene, or conjugated equine estrogens in an osteoporosis prevention trial. Methods: In a 5-year multicenter, double-blind, randomized, placebocontrolled osteoporosis prevention trial, healthy postmenopausal women who had undergone hysterectomy less than 15 years before the study and had no history of breast cancer received placebo, raloxifene (at one of two doses), or conjugated estrogens (ERT). Women from English-speaking investigative sites who had baseline and 2-year craniocaudal mammograms with comparable positioning (n = 168) were eligible for this analysis. Changes in mammographic density were determined by digital scanning and computer-assisted segmentation of mammograms and were analyzed with the use of analysis of variance. All statistical tests were two-sided. Results: Among the four treatment groups after 2 years on study, the mean breast density (craniocaudal view) was statistically significantly greater in the ERT group than it was in the other three groups (P<.01 for all three comparisons). Within treatment groups, the mean breast density from baseline to 2 years decreased statistically significantly in women receiving the placebo or either the higher or lower raloxifene dose (P = .003, P = .002, and P<.001, respectively) and

This paper presents a fully automated scheme for breast density estimation and asymmetry detection on mammographic images. Image preprocessing and segmentation techniques are first applied to the image, in order to extract the features for the breast density categorization. Also a new fractalrelated feature is proposed for the classification. The classification to 3 classes is realized according to classification and regression trees (CARTs). The same segmentation result is used to extract a set of new statistical features for each breast; the difference of these feature values, between the two images of each pair of mammograms, are estimated and the asymmetric pairs are detected according to a modified version of k-nearest neighbor classifier. This composite method has been implemented and applied to miniMIAS database, consisting of 322 mediolateral oblique (MLO) view mammograms, obtained via a digitization procedure. The results are very promising, showing equal or higher success rates compared to other related algorithms in the literature, despite the fact that some of them use only small portions of the specific database. In contrast our methodology is applied to the complete datatabase.

A prototype breast tomosynthesis system ** has been developed, allowing a total angular view of ±25 o . The detector used in this system is an amorphous selenium direct-conversion digital flat-panel detector suitable for digital tomosynthesis. The system is equipped with various readout sequences to allow the investigation of different tomosynthetic data acquisition modes. In this paper, we will present basic physical properties -such as MTF , NPS, and DQE -measured for the full resolution mode and a binned readout mode of the detector. From the measured projections, slices are reconstructed employing a special version of filtered backprojection algorithm. In a phantom study, we compare binned and full resolution acquisition modes with respect to image quality. Under the condition of same dose, we investigate the impact of the number of views on artifacts. Finally, we show tomosynthesis images reconstructed from first clinical data.

Purpose: The objective of this study was to evaluate white matter tissue damage in patients with Alzheimer's disease (AD) and in patients with mild cognitive impairment (MCI) using diffusion tensor imaging (DTI). Materials and Methods: Forty-seven subjects were evaluated: 14 patients with AD, 15 with MCI, and 18 healthy volunteers. All subjects were studied using conventional magnetic resonance imaging and DTI (32 directions) with a 1.5-T magnet. Fractional anisotropy (FA) was measured in the following regions: frontal, occipital, parietal, and temporal white matter and in the genu and splenium of the corpus callosum. The results were compared between the different groups and correlated with the Mini-Mental State Evaluation (MMSE) scores. Results: A statistically significant difference was obtained between controls and MCI patients (Pb.007) and between controls and AD patients (Pb.05) with regard to FA of the white matter in the splenium. A statistically significant difference was obtained between controls and AD patients with regard to FA in the genu (Pb.016). Moreover, there was a statistically significant difference between controls and AD patients considering the genu (Pb.016) and the frontal white matter on the right side (Pb.024). The MMSE scores correlated with the FA values measured in the genu, the splenium, and frontal white matter on the right side. No significant differences were identified between patients with AD and those with MCI. Conclusions: DTI could be of value in the early detection of white-matter damage in patients with MCI and AD. The DTI values correlate with the neuropsychological tests. Tissue characterization of head and neck lesions using diffusion-weighted MR imaging with SPLICE

Transfer learning in deep convolutional neural networks (DCNNs) is an important step in its application to medical imaging tasks. We propose a multi-task transfer learning DCNN with the aim of translating the 'knowledge' learned from non-medical images to medical diagnostic tasks through supervised training and increasing the generalization capabilities of DCNNs by simultaneously learning auxiliary tasks. We studied this approach in an important application: classification of malignant and benign breast masses. With Institutional Review Board (IRB) approval, digitized screen-film mammograms (SFMs) and digital mammograms (DMs) were collected from our patient files and additional SFMs were obtained from the Digital Database for Screening Mammography. The data set consisted of 2242 views with 2454 masses (1057 malignant, 1397 benign). In single-task transfer learning, the DCNN was trained and tested on SFMs. In multi-task transfer learning, SFMs and DMs were used to train the DCNN, which was then tested on SFMs. N-fold cross-validation with the training set was used for training and parameter optimization. On the independent test set, the multi-task transfer learning DCNN was found to have significantly (p = 0.007) higher performance compared to the single-task transfer learning DCNN. This study demonstrates that multi-task transfer learning may be an effective approach for training DCNN in medical imaging applications when training samples from a single modality are limited.

The purpose of this study was to perform a complete evaluation of three pieces of clinical digital mammography equipment. Image quality was assessed by performing physical characterization and contrast-detail ͑CD͒ analysis. We considered three different FFDM systems: a computed radiography unit ͑Fuji "FCR 5000 MA"͒ and two flat-panel units, the indirect conversion a-Si based GE "Senographe 2000D" and the direct conversion a-Se based IMS "Giotto Image MD." The physical characterization was estimated by measuring the MTF, NNPS, and DQE of the detectors with no antiscatter grid and over the clinical range of exposures. The CD analysis was performed using a CDMAM 3.4 phantom and custom software designed for automatic computation of the contrastdetail curves. The physical characterization of the three digital systems confirms the excellent MTF properties of the direct conversion flat-panel detector ͑FPD͒. We performed a relative standard deviation ͑RSD͒ analysis, for investigating the different components of the noise presented by the three systems. It turned out that the two FPDs show a significant additive component, whereas for the CR system the statistical noise is dominant. The multiplicative factor is a minor constituent for all the systems. The two FPDs demonstrate better DQE, with respect to the CR system, for exposures higher than 70 Gy. The CD analysis indicated that the three systems are not statistically different for detail objects with a diameter greater than 0.3 mm. However, the IMS system showed a statistically significant different response for details smaller than 0.3 mm. In this case, the poor response of the a-Se detector could be attributed to its high-frequency noise characteristics, since its MTF, NEQ, and DQE are not inferior to those of the other systems. The CD results were independent of exposure level, within the investigated clinical range. We observed slight variations in the CD results, due to the changes in the visualization parameters ͑window/level and magnification factor͒. This suggests that radiologists would benefit from viewing images using varied window/ level and magnification.

Breast papilloma is a term that describes an intraductal papillary configuration of the mammary epithelium on macroscopic or microscopic examination. It includes solitary intraductal papillomas, multiple papillomas, papillomatosis, and juvenile papillomatosis (JP).Recent advances in mammary ductoscopy (MD) have raised new possibilities in the diagnosis and treatment of breast papillomas. This technique represents an important diagnostic adjunct in patients with pathological nipple discharge (PND) by allowing direct visualisation and biopsy of intraductal lesions and guiding duct excision surgery. Treatment of breast papillomas often entails surgical duct excision for symptomatic relief and histopathological examination. Recently, more conservative approach has been adapted. MD-assisted microdochectomy should be considered the procedure of choice for a papilloma-related single duct discharge. Furthermore, there is increasing evidence that MD has the potential to reduce the number of ...

The paper evaluates a previous uni-axial creep testpiece design which has slitted extensometer ridges to relieve the constraint to axial deformation provided by conventional, or unslitted, ridges. The results of experiments are reported on slitted and unslitted ridged testpieees; and they are used together with theoretical studies to evaluate the effectiveness of the slitted ridged testpiece design for different specimen gauge lengths. Firstly, the test results are used to derive true constitutive equations (i.e. those equations for testpieces without ridges), these are then used to predict the response of the majority of the testpieces, using the creep Continuum Damage Mechanics (CDM) Finite Element solver DAMAGE XX. The creep rupture mechanisms, lifetimes and accuracy of the measured creep strains have been determined theoretically for testpieces with unslitted and slitted extensometer ridges and gauge lengths of 50, 30 and 10 ram. Close agreement has been obtained between the experimental and theoretical results for the creep failure mode and lifetimes. It has been shown how the accuracy of the creep behaviour measured using short gauge length testpieces (10 mm), of the type used in tension-compression testing, may be greatly improved by using slitted extensometer ridges.

European Guidelines for quality control in digital mammography specify minimum and achievable standards of image quality in terms of threshold contrast, based on readings of images of the CDMAM test object by human observers. However this is time-consuming and has large inter-and intra-observer error. To overcome these problems a software program (CDCOM) is available to automatically read CDMAM images. After some further analysis the automated measurements can be used to predict the threshold contrast for a typical observer. The results of threshold contrast determination by human observers at three different centres were compared against automated readings. These data provide a means of predicting average human performance using the automated reading software. The coefficient of variation in automatically determined threshold gold thickness was about 4% for detail sizes from 0.2 to 1.0mm when 8 images were analysed. The coefficient of variation was about 10% at a detail size of 0.1mm. Using larger numbers of images improved reproducibility for all detail sizes. A change in phantom design could greatly improve reproducibility for the smallest detail sizes. Greater consistency of phantom construction would also be desirable as one of the four phantoms tested was significantly different from the other three. Despite some limitations automated reading of CDMAM images can provide a reproducible means of assessing digital mammography systems against European Guidelines.

The substitution of digital representations for analog images provides access to methods for digital storage and transmission and enables the use of a variety of digital image processing techniques, including enhancement and computer assisted screening and diagnosis. Lossy compression can further improve the efficiency of transmission and storage and can facilitate subsequent image processing. Both digitization (or digital acquisition) and lossy compression alter an image from its traditional form, and hence it becomes important that any such alteration be shown to improve or at least not damage the utility of the image in a screening or diagnostic application. One approach to demonstrating in a quantifiable manner that a specific image mode is at least equal to another is by clinical experiment simulating ordinary practice and suitable statistical analysis. In this paper we describe a general protocol for performing such a verification and present preliminary results of a specific experiment designed to show that 12 bpp digital mammograms compressed in a lossy fashion to .15 bpp using an embedded P. C. Cosman is with the

An experimental method of determining the optimal beam quality for digital mammography systems was applied to two systems (Fuji Profect and GE Senographe 2000D). The mean glandular dose (MGD) and contrast-to-noise ratio (CNR) were measured using Perspex breast phantoms simulating breasts from 20 mm to 90 mm thick. For each thickness, four combinations of tube voltage and target/filter were tested. Optimal beam quality was defined as giving a target CNR for the lowest MGD and was similar for the two systems. For breasts with a thickness of 21 mm or 32 mm, a tube voltage of either 25 kV or 28 kV and a Mo/Mo target/filter combination was optimal. For breast thicknesses of 45 mm and greater, the combination that had the highest X-ray energy (34 kV Rh/Rh) was optimal. Optimization using the higher energy beam quality required greater detector dose to compensate for the lower contrast. Thus for a 75 mm thick breast the 34 kV Rh/Rh combination required about a 90% greater detector dose than 28 kV Mo/Mo to achieve the same CNR because of the 25% reduction in contrast. Nonetheless, the MGD was reduced by 32% by choosing the higher energy spectra and achieving the same CNR. Current automatic exposure control (AEC) designs that aim for a fixed detector dose are not optimal and greater use of higher energy spectra should be accompanied by higher detector doses at all breast thicknesses which are average or above. This may result in slightly higher doses, but better image quality for these breasts.

In this paper, methods are presented for automatic detection of the nipple and the pectoral muscle edge in mammograms via image processing in the Radon domain. Radon-domain information was used for the detection of straight-line candidates with high gradient. The longest straight-line candidate was used to identify the pectoral muscle edge. The nipple was detected as the convergence point of breast tissue components, indicated by the largest response in the Radon domain. Percentages of false-positive (FP) and false-negative (FN) areas were determined by comparing the areas of the pectoral muscle regions delimited manually by a radiologist and by the proposed method applied to 540 mediolateral-oblique (MLO) mammographic images. The average FP and FN were 8.99% and 9.13%, respectively. In the detection of the nipple, an average error of 7.4 mm was obtained with reference to the nipple as identified by a radiologist on 1,080 mammographic images (540 MLO and 540 craniocaudal views).

Objetivo. Revisar la evidencia científica relativa al uso de la mamografía digital (MD) comparándola con la mamografía analógica (MA) en el contexto clínico. Material y método. Se realizó una búsqueda en Medline y EMbase de los estudios publicados entre 1989 y 2005, que comparaban resultados de MD y MA en el mismo grupo o en dos grupos de pacientes, valorando la certeza diagnóstica (sensibilidad, especificidad, área bajo la curva ROC), tasa de rellamadas, tasa de biopsias y exposición a la radiación. Se revisaron y compararon metodológicamente los trabajos presentados. Resultados. Se encontraron 8 publicaciones y 8 presentaciones a congresos. No se han observado diferencias estadísticamente significativas en la detección de cáncer de mama entre MA y MD. Hasta la publicación de los estudios Oslo II y DMIST (Digital Mammographic Imaging Screening Trial) las series eran cortas, lo que no permitía explotar las pequeñas diferencias de certeza diagnóstica entre ambas técnicas y con gran disparidad metodológica, por lo que no se podían agrupar los resultados, en ocasiones contradictorios. No se incluían seguimientos. Conclusión. No hay diferencias estadísticamente significativas en la certeza diagnóstica entre MA y MD, excepto en mujeres con mamas densas o heterogéneamente densas, menores de 50 años y peri o premenopáusicas, donde la MD tiene mejores resultados en un rango significativo. Estos datos deben ser confirmados en estudios a más largo plazo para valorar específicamente los efectos en la mortalidad por cáncer de mama. Son importantes los estudios de coste-efectividad si se pretende una sustitución de la técnica. Palabras clave: mamografía, mamografía analógica, mamografía digital, cribado mamográfico, cáncer de mama. A review of the scientific evidence concerning the clinical application of digital mammography Objective. To review the scientific evidence with respect to the use of digital mammography and compare it with analogical mammography in the clinical context. Material and method. We searched Medline and EMbase for studies published between 1989 and 2005 that compared the results of digital and analogical mammography in the same group of patients or in two different groups of patients to evaluate their respective diagnostic accuracy (sensitivity, specificity, area under the ROC curve), recall rate, biopsy rate, and exposure to radiation. We reviewed and compared the different methodologies of the studies published. Results. Eight articles and eight presentations at congresses were found. No statistically significant differences were observed between digital and analogical mammography for the detection of breast cancer. Until the publication of the Oslo II (2004) and Digital Mammographic Imaging Screening Trial (2005) studies, the series presented were small and used a variety of methodologies. This made it impossible to appreciate small differences in diagnostic accuracy between the two techniques and to grup the, sometimes, contradictory results. Furthermore, these first series did not include follow-up. Conclusion. There are no statistically significant differences between the two techniques for diagnostic accuracy, except in women with dense or heterogeneously dense breasts, in those under 50 years of age, and in peri-or pre-menopausal women, in which cases digital mammography is significantly better. These data should be confirmed in longer term studies to enable the effects on the breast cancer mortality rate to be specifically evaluated. Cost-effectiveness studies are important when considering changing techniques.

Objectives: To investigate the referral pattern after the transition to full-field digital mammography (FFDM) in a population-based breast cancer screening programme. Methods: Preceding the nationwide digitalisation of the Dutch screening programme, an FFDM feasibility study was conducted. Detection and referral rates for FFDM and screenfilm mammography (SFM) were compared for first and subsequent screens. Furthermore, radiological characteristics of referrals in digital screening were assessed. Results: A total of 312,414 screening mammograms were performed (43,913 digital and 268,501 conventional), with 4,473 consecutive referrals (966 following FFDM). Initially the FFDM referral rate peaked, and many falsepositive results were noted as a consequence of pseudolesions and increased detection of (benign) microcalcifications. A higher overall referral rate was observed in FFDM screening in both first and subsequent examinations (p<.001), with a significant increase in cancer detection (p=.010). Conclusion: As a result of initial inexperience with digital screening images implementing FFDM in a population-based breast cancer screening programme may lead to a strong, but temporary increase in referral. Dedicated training in digital screening for radiographers and screening radiologists is therefore recommended. Referral rates decrease and stabilise (learning curve effect) at a higher level than in conventional screening, yet with significantly enhanced cancer detection.

With IRB approval, digital breast tomosynthesis (DBT) images of human subjects were collected using a GE GEN2 DBT prototype system. Corresponding digital mammograms (DMs) of the same subjects were collected retrospectively from patient files. The data set contained a total of 237 views of DBT and equal number of DM views from 120 human subjects, each included 163 views with microcalcification clusters (MCs) and 74 views without MCs. The data set was separated into training and independent test sets. The pre-processing, object prescreening and segmentation, false positive reduction and clustering strategies for MC detection by three computer-aided detection (CADe) systems designed for DM, DBT, and a planar projection image generated from DBT were analyzed. Receiver operating characteristic (ROC) curves based on features extracted from microcalcifications and free-response ROC (FROC) curves based on scores from MCs were used to quantify the performance of the systems. Jackknife FROC (JAFROC) and non-parametric analysis methods were used to determine the statistical difference between the FROC curves. The difference between the CAD DM and CAD DBT systems when the false positive rate was estimated from cases without MCs did not reach statistical significance. The study indicates that the large search space in DBT may not be a limiting factor for CADe to achieve similar performance as that observed in DM.

Mammography is at present one of the available method for early detection of masses or abnormalities which is related to breast cancer. The most common abnormalities that may indicate breast cancer are masses and calcifications. The challenge lies in early and accurate detection to overcome the development of breast cancer that affects more and more women throughout the world. Masses appear in a mammogram as fine, granular clusters, which are often difficult to identify in a raw mammogram. Digital mammogram is one of the best technologies currently being used for diagnosing breast cancer. Breast cancer is diagnosed at advanced stages with the help of the digital mammogram image. In this paper, a method has been developed to make a supporting tool. This will make it easy and less time consuming for identification of abnormal masses in digital mammography images. The identification technique is divided into two distinct parts i.e. Formation of Homogeneous Blocks and Color Quantization after preprocessing. The type of masses, orientation of masses, shape and distribution of masses, size of masses, position of masses, density of masses, symmetry between two pair etc. are clearly sited after proposed method is executed on raw mammogram, for easy and early detection of abnormalities.

We have devised a mammogram modeling system which greatly simplifies the development of, and can improve the accuracy and consistency of, computer-aided display and analysis algorithms for digital mammography. Our system segments the five major components of a mammogram: background, uncompressed-fat, fat, dense, and muscle. Differences in the amount and distribution of these components account for much of the variation between mammograms. Via segmentation, the corresponding variations are isolated; automated algorithms can consider the components independently or adapt their parameters based on component-specific statistics. In this paper, we present our system and demonstrate its versatility. Our system is able to segment a wide variety of digital mammograms because of its combined use of geometric (i.e., gradient magnitude ridge traversal) and statistical (i.e., Gaussian mixture modeling) techniques. Using images from Fischer, General Electric, and Trex digital mammography units, we define and evaluate automated, component-based algorithms for (1) "general" intensity windowing, i.e., displaying a digital mammogram such that it resembles a screen-film mammogram for breast cancer screening; (2) componentspecific intensity windowing for breast lesion characterization; and (3) breast density estimation for breast cancer risk assessment.

The aim of the present study was to investigate the imaging transfer characteristics and the luminescence efficiency (XLE) of powder scintillator for use in X-ray mammography detectors. emits in the red part of the visible spectrum, having very good spectral compatibility with optical sensors employed in digital imaging systems. Three powder scintillating screens, with coating thicknesses 33.1, 46.4 and 63.1 , were prepared in our laboratory. The imaging performance of these screens was assessed by experimental determination of the modulation transfer function (MTF), the noise transfer function (NTF) and the detective quantum efficiency (DQE) as well as a single index image quality parameter such as the information capacity (IC). A theoretical model, describing radiation and light transfer, was used to fit experimental MTF data. This has allowed the estimation of optical attenuation coefficients of the scintillator. In addition, a previously validated Monte Carlo code, based on the X-ray attenuation properties and on the Mie light scattering theory, was used to estimate the X-ray detection efficiency, the Swank factor and the zero frequency DQE of the scintillator. Results showed that exhibits high MTF and DQE values, which are comparable to those of a commercially employed screen. In addition shows high compatibility (effective gain) to CCDs and to recently introduced CMOS based detectors. Considering our image quality parameters and luminescence efficiency results, this material can potentially be considered for use in digital X-ray mammography detectors.

The purpose of this study was to investigate physical characteristics of two full field digital mammography ͑FFDM͒ systems ͑GE Senographe Essential and DS͒. Both are indirect conversion ͑x ray to light͒ a-Si flat panels coupled with a CsI͑Tl͒ scintillator. The examined systems have the same pixel size ͑100 m͒ but a different field of view: a conventional size 23ϫ 19.2 cm 2 and a large field 24ϫ 30.7 cm 2 , specifically designed to image large breasts. In the GE Senographe Essential model relevant improvements in flat panel design were implemented and new deposition tools for metal, a-Si, and CsI͑Tl͒ were introduced by GE. These changes in detector design are expected to be beneficial for advanced applications such as breast tomosynthesis. The presampling modulation transfer function ͑MTF͒, normalized noise power spectrum ͑NNPS͒, and detective quantum efficiency ͑DQE͒ were measured for a wide range of exposure ͑25-240 Gy͒ with a RQA-M2 technique ͑28 kVp with a Mo/Mo target/filter combination and 2 mm of additional aluminum filtration͒. At 1, 2, and at 4 lp/mm MTF is equal to 0.9, 0.76, and 0.46 for the conventional field detector and to 0.85, 0.59, and 0.24 for the large field detector. The latter detector exhibits an improved NNPS due to a lower electronic noise and a better DQE that reaches 60%. In addition a contrast-detail analysis was performed with CDMAM 3.4 phantom and CDCOM software: GE Senographe DS showed statistically significant poorer detection ability in comparison with the GE Senographe Essential. These results could have been expected, at least qualitatively, considering the relative DQE of the two systems.

Admitting that mammographic breast density is an important independent risk factor for breast cancer in the general population, has a crucial economical health care impact, since it might lead to increasing screening frequency or reinforcing additional modalities. Thus, the impact of density as a risk factor has to be carefully investigated and might be debated. Some authors suggested that high density would be either a weak factor or confused with a masking effect. Others concluded that most of the studies have methodological biases in basic physics to quantify percentage of breast density, as well as in mammographic acquisition parameters. The purpose of this review is to evaluate mammographic procedures and density assessments in published studies regarding density as a breast cancer risk. No standardization was found in breast density assessments and compared density categories. High density definitions varied widely from 25 to 75% of dense tissues on mammograms. Some studies showed an insufficient follow-up to reveal masking effect related to mammographic false negatives. Evaluating breast density impact needs thorough studies with consensual mammographic procedures, methods of density measurement, breast density classification as well as a standardized definition of high breast density. Digital mammography, more effective in dense breasts, should help to re-evaluate the issue of density as a risk factor for breast cancer.

This project evaluated human observer performance and that of a human visual system model (JNDmetrix) to assess whether the veiling glare of a digital display influences observer performance during soft-copy interpretation of mammographic images for the detection of masses.

The last decade witnessed a growing interest in research on content-based image retrieval (CBIR) and related areas. Several systems for managing and retrieving images have been proposed, each one tailored to a specific application. Functionalities commonly available in CBIR systems include: storage and management of complex data, development of feature extractors to support similarity queries, development of index structures to speed up image retrieval, and design and implementation of an intuitive graphical user interface tailored to each application. To facilitate the development of new CBIR systems, we propose an image-handling extension to the relational database management system (RDBMS) PostgreSQL. This extension, called PostgreSQL-IE, is independent of the application and provides the advantage of being open source and portable. The proposed system extends the functionalities of the structured query language SQL with new functions that are able to create new feature extraction procedures, new feature vectors as combinations of previously defined features, and new access methods, as well as to compose similarity queries. PostgreSQL-IE makes available a new image data type, which permits the association of various images with a given unique image attribute. This resource makes it possible to combine visual features of different images in the same feature vector. To validate the concepts and resources available in the proposed extended RDBMS, we propose a CBIR system applied to the analysis of mammograms using PostgreSQL-IE.