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The estimation and subsequent use of tissue T1(x) parameters at each image location x can potentially lead to a more reliable classification of breast tissues. T1 values can be estimated using multiple (typically 3) MRI images of different flip angles. However, breathing and other slight movements can render the highly non-linear estimation procedure error-prone. In this paper, a simultaneous multiple image registration method is proposed to solve this problem. The registration method is built upon the idea of conserving inverse consistency and transitivity among the multiple image transformations. The algorithm is applied to both simulated data and real breast MRI images. The performance is compared with existing pairwise image registration method. The results clearly indicate that the simultaneous multiple image registration algorithm leads to much more accurate T1 estimation. [ABSTRACT FROM AUTHOR]

We compared three conspicuity tests applied to four full field digital mammography (FFDM) systems. The tests included: 1) the calculation of noise equivalent quanta (NEQ); 2) contrast-detail analysis with the CDMAM 3.4 phantom and 3) evaluation of the detectability of (simulated) microcal-cifications with specific well-known dimensions in mastectomy images. For each contrast-resolution test method, the exposure, processing and viewing conditions were identical. As a result, the only variable for a given test was the physical performance of the detector. The three test methods each rank the detectors in the same order. The flat-panel detector ranked the best overall, the dual-sided read-out storage phosphor detector ranked second and the single-sided-read-out storage phosphor detectors with 50 μm and 100 μm pixel sizes ranked similarly and were inferior to the other 2 detectors. [ABSTRACT FROM AUTHOR]

Two graph theoretic methods are used in conjunction with active contours to segment the pectoral muscle in 82 screening mammograms. To validate the method, the boundaries are also marked by four radiologists with different levels of experience in mammography. The simultaneous truth and performance level estimation (STAPLE) method is used to estimate the true boundary and to estimate the sensitivity and specificity of the segmentation schemes. The performance of one of the two algorithms is found not differ significantly from radiologists. [ABSTRACT FROM AUTHOR]

Topological properties of the breast ductal network have shown the potential for classifying clinical breast images with and without radiological findings. In this paper, we review three methods for the description and classification of breast ductal topology. The methods are based on ramification matrices and symbolic representation via string encoding signatures. The performance of these methods has been compared using clinical x-ray and MR images of breast ductal networks. We observed the accuracy of the classification between the ductal trees segmented from the x-ray galactograms with radiological findings and normal cases in the range of 0.86-0.91%. The accuracy of the classification of the ductal trees segmented from the MR autogalactograms was observed in the range of 0.5-0.89%. [ABSTRACT FROM AUTHOR]

Several studies have showed that increased mammographic density is an important risk factor for breast cancer. Dense tissue often appears as textured regions in mammograms, so density and texture estimation are inextricably linked. It has been demonstrated that texture classes can be learned, and that subsequently textures can be classified using the joint distribution of intensity values over extremely compact neighbourhoods. Motivated by the success of texture classification, we propose an fully automated scheme for mammogram texture classification and segmentation. The classification method first has a training step to model the joint distribution for each breast density class. Subsequently, a statistical comparison is used to determine the class label for new images. Inspired by the classification, we combine the so-called image patch method with a HMRF(Hidden Markov Random Field) to achieve mammogram segmentation. [ABSTRACT FROM AUTHOR]

This paper describes an algorithm to segment mammo- graphic images into regions corresponding to different densities. The breast parenchymal segmentation uses information extracted for statistical texture based classification which is in turn incorporated in multi-vector Markov Random Fields. Such segmentation is key to developing quantitative mammographic analysis. The algorithm's performance is evaluated quantitatively and qualitatively and the results show the feasibility of segmenting different mammographic densities. [ABSTRACT FROM AUTHOR]

Purpose: To evaluate efficacy and diagnostic accuracy of BARCO LCD 5Mpixel displays, compared to BARCO CRT 5Mpixel displays in full-field-digital-mammography (FFDM) interpretation. Material and Methods: FFDM mammograms obtained by 100 patients, were analyzed by three independent radiologists experienced in breast imaging, using two different CRT and LCD displays. All cases were selected by a fourth radiologist in order to cover several possible ages and types of breast. Half of cases were negative and half were positive for malignancy, proven by percutaneous biopsy. Readers were blinded to history of patients, ultrasound examination and biopsy results. To minimize recall bias, an interval of at least 30 days between interpretations of each case on two different monitors was chosen. Each reader evaluated cases classifying them according the ACR BIRADS categories. Moreover, they assigned a rate (0-100) corresponding to the Probability of Malignancy (POM) of each case classified into BIRADS categories 3 to 5. Finally, they assigned a rate (0-100) corresponding to reading confidence. Analysis included ROC curves of POM for each doctor and for pooled data, sensitivity and specificity for the BIRADS≥3 and BIRADS≥4 thresholds for each doctor and for pooled data, and finally main results of "Multireader- Multicase ROC Analysis Of Variance". For each analysis a comparison was made between the two monitors. Results: No statistical significance was seen between the two displays regarding POM, sensitivity and specificity, nor for single reader either for pooled data. Conclusions: This study provides a reasonable assurance that the examined CRT and LCD display systems are comparable for FFDM interpretation. [ABSTRACT FROM AUTHOR]

Mammographic risk assessment is concerned with the probability of a woman developing breast cancer. Recently, it has been suggested that the density of linear structures is related to risk. For 321 images from the MIAS database, a measure of line strength was obtained for each pixel using the Line Operator method. The proportion of pixels with line strength above a threshold level was calculated for each image and the results categorised by Tabar pattern, Boyd SCC class and BIRADS class. The results indicated a significant difference between Boyd classes 1-3 (low risk) and classes 4-6 (high risk), and between most Tabar patterns and BIRADS classes. [ABSTRACT FROM AUTHOR]

Digital mammography is rapidly becoming a mature imaging modality. To maintain high quality in mammography, a routine quality control program is necessary to detect drifting or degradation of system performance over time. The American College of Radiology is developing a quality control program which will apply to all types of full-field digital mammography equipment, and provide effective and more efficient validation of performance. In the DMIST trial, there were no failures for many of the QC tests during the 24 months imaging was performed. When systems failed, they generally did so suddenly, rather than through gradual deterioration of performance. A recommended set of tests is presented, which can be used to ensure that full-field digital mammography (FFDM) systems are functioning correctly, and consistently producing mammograms of excellent image quality. Keywords: Digital mammography, quality control, image quality. [ABSTRACT FROM AUTHOR]

A method of extracting salient image features in mammograms at multiple scales using the monogenic signal is presented. The derived local phase provides structure information (such as edge, ridge etc.) while the local amplitude encodes the local brightness and contrast information. Together with the simultaneously computed orientation, these three pieces of information can be used for mammogram segmentation including locating the inner breast edge which is important for quantitative breast density assessment. Due to the contrast invariant property of the local phase, the algorithm proves to be very reliable on an extensive datasets of images obtained from various sources and digitized by different scanners. [ABSTRACT FROM AUTHOR]

We have developed computerized methods for the analysis of mammographic lesions in order to aid in the diagnosis of breast cancer. Our automatic methods include the extraction of the lesion from the breast parenchyma, the characterization of the lesion features in terms of mathematical descriptors, and the merging of these lesion features into an estimate of the probability of malignancy. Our initial development was performed on digitized screen film mammograms. We report our progress here in converting our methods for use with images from full-field digital mammography (FFDM). It is apparent from our initial comparisons on CAD for SFMD and FFDM that the overall concepts and image analysis techniques are similar, however reoptimization for a particular lesion segmentation or a particular mammographic imaging system are warranted. [ABSTRACT FROM AUTHOR]

The availability of the large dataset of screen/film and full-field digital mammograms acquired through the Digital Mammography Imaging Screening Trial (DMIST) presents an extraordinary opportunity for the assessment of CAD devices. The National Cancer Institute and the National Institute of Biomedical Imaging and Bioengineering at the U.S. National Institutes of Health have engaged FDA scientists in the development of a plan to leverage this imaging resource to benchmark the performance of current CAD systems. In this talk, we will present an initial proposal for utilizing the DMIST data to quantitatively assess current CAD systems. It is our goal to engage the IWDM community and other interested groups in the development of a consensus on acceptable study designs for this purpose. [ABSTRACT FROM AUTHOR]

Automated detection of masses on mammograms is challenged by the presence of dense breast parenchyma. The aim of this study is to investigate the feasibility of wavelet-based feature analysis in identifying spiculated and circumscribed masses in dense breast parenchyma. The method includes an edge detection step for breast border identification and employs Gaussian mixture modeling for dense parenchyma labeling. Subsequently, wavelet decomposition is performed and intensity as well as orientation features are extracted from approximation and detail subimages, respectively. Logistic regression analysis (LRA) is employed to differentiate spiculated and circumscribed masses from normal dense parenchyma. The proposed method is tested in 90 dense mammograms containing spiculated masses (30), circumscribed masses (30) and normal parenchyma (30). Free-response receiver operating characteristic (FROC) analysis is used to evaluate the performance of the method, achieving 83.3% sensitivity at 1.5 and 1.8 false positives per image for identifying spiculated and circumscribed masses, respectively. [ABSTRACT FROM AUTHOR]

A method that performs multiresolution enhancement, adaptive to breast components, for optimal visualization of the entire breast area is presented. The method includes an edge detection step to distinguish breast area from mammogram background and employs Gaussian mixture modeling to segment breast components (uncompressed fat, fat and dense). The original image is decomposed using a redundant discrete wavelet transform and magnitude coefficients corresponding to each breast component are linearly mapped for contrast enhancement. Coefficient mapping is controlled by a gain factor provided by the parameters of the modeled breast components. The processed image is derived by reconstruction of the modified wavelet coefficients. The algorithm is compared with two enhancement methods proposed for soft-copy display, in a dataset of 68 mammograms containing lesions. The proposed method demonstrates increased performance in accentuating lesions embedded in fatty or dense parenchyma, as well as in visualization of anatomical features in the entire breast area. [ABSTRACT FROM AUTHOR]

First 3D X-ray internal observation of DCIS (ductal carcinoma in-situ) is reported. Its rod shaped specimen with 3.6 mm in diameter and 4.7 mm in height was punched out to have successfully observed by using a newly made algorithm due to refraction for x-ray CT. Its data was acquired by the x-ray optics DEI (diffraction-enhanced imaging). Data of 900 projections with interval of 0.2 degrees was used at Photon Factory, KEK in Tsukuba. A reconstructed CT image may include clearly revealed ductus lactiferi, microcalcification and other structure. The voxel resolution is approximately 50 μm by the present instrumental condition. This modality could open up an x-ray pathological diagnosis. Keywords: X-ray refraction, X-ray dark-field imaging (XDFI), DEI, breast cancer, DCIS, pathological diagnosis, clinical diagnosis, ductus lactiferi. [ABSTRACT FROM AUTHOR]

The file size of images generated using digital mammography systems varies between 8 MB and 50 MB. The amount of data to be stored in digital screening programs is huge. Image compression may be helpful. In this study 8491 digital and digitised mammography images are compressed using 14 lossless compression schemes. The results show that using lossless image compression, the total amount of data to be stored can be reduced by a factor of 1.3 to 6.9 without loss of image quality. The actual data reduction depends strongly on the selected compression algorithm and the systems used to acquire and process the mammograms. The JPEG-LS and JPEG 2000 algorithms, both included in the DICOM standard, prove to be promising algorithms for screening programs because of the high compression ratios. [ABSTRACT FROM AUTHOR]

A method is proposed based on the combination of wavelet analysis and principal component analysis (PCA). Microcalcification (MC) candidate regions are initially labeled using area and contrast criteria. Mallat's redundant dyadic wavelet transform is used to analyze the frequency content of image patterns at horizontal and vertical directions. PCA is used to efficiently encode MC patterns in wavelet-decomposed images. Feature weights are computed from the projection of each candidate MC pattern at the wavelet-based principal components. To assess the effectiveness of the proposed method, the same analysis is carried out in original images. Candidate MC patterns are classified by means of Linear Discriminant Analysis (LDA). Free-response Receiver Operating Characteristic (FROC) curves are produced for identifying MC clusters. The highest performance is obtained when PCA is applied in wavelet decomposed images achieving 80% sensitivity at 0.5 false positives per image in a dataset with 50 subtle MC clusters in dense parenchyma. [ABSTRACT FROM AUTHOR]

In general, the use of more projections results in fewer tomosynthesis reconstruction artifacts. However, under a fixed dose, an excess number of projections will make the detector noise more pronounced in each of the x-ray shots and thus degrade image quality. Even in the absence of detector noise the advantages of higher projection numbers eventually have diminishing returns, making more projections unnecessary. In this study, we explore the dependence of tomosynthesis imaging performance on the number of projections, while keeping other factors fixed. We take the contrast-to-noise ratio as the figure of merit to search for the range of optimal projection number. The study is carried out through both simulations and experiments, with phantoms consisting of micro-calcification and mass objects, and a cadaver breast. The goal of this paper is to describe our methodology in general, and use a prototype tomosynthesis system as an example. The knowledge learned will help the design of future generation clinical tomosynthesis systems. [ABSTRACT FROM AUTHOR]

Most of the CAD Systems for Mammograms are composed of algorithms analysing the four X-ray images individually. It is a general experience, that algorithms in search of microcalcification clusters can obtain high sensitivity only if specificity is low. To overcome efficiency problem this paper proposes a simple algorithm to combine information of the two views (MLO/CC) of the breast. The procedure is based upon the experiences of radiologists: masses and calcifications should emerge on both views, so if no matching is found, the given object is a false positive hit. A positioning system is evolved to find corresponding regions on the two images. Calcification clusters obtained in individual images are matched in "2.5-D" provided by the positioning system. The credibility value of the hit is reassessed by the matching. The proposed approach can significantly reduce the number of false positive hits in calcification. [ABSTRACT FROM AUTHOR]

The experience of clinical use of breast CAD system at the hospital of Gifu University School of Medicine was reported. The CAD system was Image Checker M1000-DM available for Senographe 2000D.During February 4, 2005 to May 16, 125 cases was examined by this device and 22cases of breast cancers were found. A case was misdiagnosed by radiologist before CAD, and CAD detected the lesion. Another case was correctly detected by radiologist before CAD, but CAD could not point out the lesion. 20 remain cases were detected correctly by both radiologist and CAD. CAD was supposed to be useful for the mammographic diagnosis of breast cancer. [ABSTRACT FROM AUTHOR]

A new breast imaging approach is proposed and implemented that combines Magnetic Resonance Mammography and Electrical Impedance Scanning. In this paper, we report the results of a pilot study that demonstrated the feasibility of this new breast imaging approach. We also discuss our initial experience with the MR imaging parameters and sequences that are critical in observing the desired signal. [ABSTRACT FROM AUTHOR]

Breast tomosynthesis has the potential to improve lesion visibility and localization compared to conventional mammography. To be clinically useful, tomosynthesis must be able to achieve high image quality at acceptable radiation doses. Tomosynthesis data sets of simple low-contrast phantoms were acquired at varying dose levels. Image quality in the reconstructed volumes was analyzed by evaluating the voxel-to-voxel signal difference to noise ratio between a simulated lesion and the surrounding "tissue". Preliminary results indicate that image quality of small lesions is limited by scatter and reconstruction artifacts. In uniform backgrounds image quality appears to be quantum-noise limited, while in more complex backgrounds the structural noise tends to dominate. [ABSTRACT FROM AUTHOR]

Two observer experiments were performed to evaluate the performance of wavelet enhancement and compression methodologies for digitized mammography. One experiment was based on the localization response operating characteristic (LROC) model. The other estimated detection and localization accuracy rates. The results of both studies showed that the two algorithms consistently improved radiologists' performance although not always in a statistically significant way. An important outcome of this work was that lossy wavelet compression was as successful in improving the quality of digitized mammograms as the wavelet enhancement technique. The compression algorithm not only did not degrade the readers' performance but it improved it consistently while achieving compression rates in the range of 14 to 2051:1. The proposed wavelet algorithms yielded superior results for digitized mammography relative to conventional processing methodologies. Wavelets are valuable and diverse tools that could make digitized screen/film mammography equivalent to its direct digital counterpart leading to a filmless mammography clinic with full inter- and intra-system integration and real-time telemammography. [ABSTRACT FROM AUTHOR]

The purpose of this work was to investigate whether extra flat field corrections should be applied prior to the calculations of quality control quantities and whether there are necessary precautions regarding flat fielding in digital mammography. Effects from using one standard flat field correction for all imaging situations or absence of correction procedures were examined using homogenous PMMA slabs. Differences in field profiles for various exposure geometries and breast thicknesses were quantified. For three systems the maximal deviation of averaged pixel values (along a profile of ROIs parallel to the chest wall) varied from 1.3 % to 6.8 % over the whole image and from 0.6 % to 2.6 % if the analysis is limited to the central part. Extra flat field corrections are not necessary for most applications. If required, the corrections should be performed from images acquired with the same manual exposure and not after a time gap. [ABSTRACT FROM AUTHOR]

Digital breast tomosynthesis is becoming a clinically attractive modality based on its potential to combine the high resolution and high contrast images, and affordability of digital mammography, with the advantages of 3D image acquisition. In order to facilitate comparison of tomosynthesis images with previous mammographic exams of the same women, there is a need for a method of registering a mammogram with a tomosynthetic image of the same breast; this is the focus of this paper. We have chosen to approach this multimodality registration problem, starting from the simpler problem of registering a mammogram and the central tomosynthesis source image. Such a registration pair represents the most similar breast images obtained from different clinical modalities. In this study of 15 pairs of mammograms and central tomosynthesis projections of the same breast, on average we were able to compensate 94 percent of the per-pixel intensity differences that existed between the two images before the registration. [ABSTRACT FROM AUTHOR]

CDMAM phantom scoring is widely used to assess the detectability performance of mammography systems. We propose to study the impact of structured background on this performance assessment, using simulated CDMAM phantom images with flat and textured backgrounds. Three dose levels have been investigated, ranging from -50% to +50% around the reference dose computed by the acquisition system. For textured backgrounds, the simulated projected breast corresponds to a 50mm thick, 60% glandular breast, with a texture generated by a power-law filtered noise model. Images have been scored by four image quality experts. For the smaller insert sizes, Image Quality Factor (IQF) scores obtained in textured backgrounds are lower than and well correlated with those obtained in flat backgrounds. IQF values increased with dose. For the larger insert sizes, detectability performance in textured background is even more degraded and is not as dose dependent as it is in flat backgrounds. [ABSTRACT FROM AUTHOR]

The purpose of this study was to review the dose and image characteristics of three different FFDM systems; Sectra MDM, GE SenoDS, Lorad Selenia. The dose and image quality characteristics were assessed in terms of both physics and clinical performance. A phantom study was carried out to look at the mean glandular dose delivered to breasts of varying thickness. The dose was also investigated by carrying out a clinical dose survey of a random sample of women on each of the three units. The CDMAM 3.4 was imaged on each unit with varying thicknesses of PMMA and the results correlated with the dose results at each equivalent thickness. The CNR of each unit at varying breast thicknesses was also calculated. [ABSTRACT FROM AUTHOR]

Numerous studies have investigated the relation between mammographic density and breast cancer risk. These studies indicate that women with high breast density have a four to six fold risk increase. An investigation of whether or not this relation is causal is important for, e.g., hormone replacement therapy (HRT), which has been shown to actually increase the density. No gold standard for automatic assessment of mammographic density exists. Manual methods such as Wolfe patterns and BI-RADS are helpful for communication of diagnostic sensitivity, but they are both time consuming and crude. For serial, temporal analysis it is necessary to be able to detect more subtle changes. In previous work, a method for measuring the effect of HRT w.r.t. changes in biological density in the breast is described. The method provides structural information orthogonal to intensity-based methods. Hessian-based features and a clustering of these is employed to divide a mammogram into four structurally different areas. Subsequently, based on the relative size of the areas, a density score is determined. We have previously shown that this method can separate patients receiving HRT from patients receiving placebo. In this work, the focus is on deeper understanding of the methodology using tests on sets of artificial images of regular elongated structures. [ABSTRACT FROM AUTHOR]

To compare image quality and dose of a photon-counting multi-slit scanner (PC) and a system based on amorphous silicon (aSi), images of the CDMAM 3.4 were taken in standard mode. For 3cm PMMA, the PC used 29kV/11.7mAs, aSi used 27kV/ 50mAs. For 5cm PMMA, PC used 35kV /14.8mAs, aSi used 31kV/ 50 mAs. Exposure was manually increased for PC and lowered for aSi-system. Average glandular dose and an image quality index (IQI) were calculated over the diameter ranges 0.06 - 2.0mm and 0.1-1. In standard mode with 3cm PMMA, IQI for PC was 35% lower than for aSi at 80% lower dose. Increased dose of PC resulted in 13% lower IQI at 57% lower dose. With 5cm PMMA IQI in standard mode was 18 % lower with PC at a 69% lower dose. Increasing the dose of PC resulted a 7% lower IQI at 54% lower dose. In conclusion the PC-system might reduce dose by up to 54% at equivalent image quality, although maximal quality of aSi could not be reached. [ABSTRACT FROM AUTHOR]

The ability of human observers to discriminate between textures is related to the contrast between key structural elements and their repeating patterns. Here we have developed an automatic texture classification approach based on this principle. Local contrast information is modelled and a hybrid metric, based on probability density distributions and transportation estimation, are used to classify unseen samples. Quantitative and qualitative evaluation, based on mammographic images and Wolfe classification, is presented and shows segmentation results in line with the various classes. [ABSTRACT FROM AUTHOR]

To establish a practical CAD (Computer-Aided Diagnosis) system to facilitate the diagnosis of microcalcifications, we propose a filter-based technique to detect microcalcifications. Via examination of an existing optimal filter-based technique, it is found that its performance on highlighting the energy of mammograms is seriously affected by artefacts and the background of breast. As a result, four methods in pre and post-processing are described in this paper to improve the optimal filtering, leading to an adaptive selection of thresholds for input mammograms. These methods have been tested by using 30 mammograms (with 25 microcalcifications) from the MIAS database and 23 mammograms from DDSM database. Comparing with the original optimal filter-based technique, our technique reduces the false detections (FD), eliminates the influence of the background in mammograms and is able to adaptively select the threshold for the detection of microcalcifications. [ABSTRACT FROM AUTHOR]

In digital mammography it is of utmost importance that the quality of screen devices is checked on a regularly basis. The EUREF guidelines propose to do this daily using the AAPMtg18-QC pattern. In this paper we report our initial results with the use of an alternative, recently developed, dynamic pattern ("MoniQA") and a scoring scheme. As soon as the observers are familiar with the procedure, the measurements are very stable and we could not observe big variations in the quality of the monitor. In order to control the intrinsic quality of the monitor, the number of quality control checks could thus be reduced. The global working condition (such as the ambient light level) is controlled as well with the proposed procedure and this may be of great interest, especially during the start-up of digital mammography (screening) units: it is very informative to trace the influences of different light sources (such as (occasional) viewing boxes). [ABSTRACT FROM AUTHOR]

Mammographic risk assessment provides an indication of the likelihood of women developing breast cancer. A number of mammographic image based classification methods have been developed, such as Wolfe, Boyd, BI-RADS and Tabár based assessment. We provide a comparative study of these four approaches. Results on the full MIAS database are presented, which indicate strong correlation (Spearman's > 0.9) between Wolfe, Boyd and BI-RADS based classification, whilst the correlation with Tabár based classification is less straight forward (Spearman's < 0.5, but low correlations mainly caused by one of the classes). [ABSTRACT FROM AUTHOR]

The clustered microcalcifications and mass are the important findings in interpreting breast cancer, architectural distortion on mammograms as well. We have developed the detection algorithm for distorted area based on concentration of mammary gland. The purpose of this study is to suggest the improvement of extraction method of mammary gland in order to achieve higher sensitivity. The mean curvature, and the combination of shape index and curvedness were performed for extracting of mammary gland in our previous methods. In our new method, the dynamic-range compression was added as the pre-processing before extracting mammary gland by mean curvature. The detection rate at initial pick-up stage was improved by this improvement. It was concluded that our detection method would be effective. [ABSTRACT FROM AUTHOR]

Mass screening of breast cancer utilizing mammography (MMG) has been widely carried out. However, MMG might not be able to depict small impalpable masses in dense breast tissue clearly. We have developed a computer-aided detection (CAD) scheme in whole breast ultrasound (US) system for mass screening which has been developed by ALOKA CO., LTD., Japan. Our CAD scheme and image processing techniques have the following three benefits. 1Indication of mass candidates by our CAD scheme. 2Visualization of breast US images in two views of B-planes (CC View and ML View) and C-plane. 3Comparison of left and right breast images as in MMG. The performance of the CAD scheme in detecting malignant masses on an initial study has a true positive fraction of 0.91 (10/11) at a 0.69 (633/924) false positive per image. Although mass screening utilizing US was not appropriate because images acquired by conventional hand probe were poor in reproduction, the problem could be solved in our system. [ABSTRACT FROM AUTHOR]

In this work the implementation of a database of digitized mammograms is described. The digitized images were collected since 1999 by a community of physicists in collaboration with radiologists in several Italian hospitals, as a first step in order to develop and implement a Computer Aided Detection (CAD) system. 3369 mammograms were collected from 967 patients; they were classified according to the type and the morphology of the lesions, the type of the breast tissue and the type of pathologies. A dedicated Graphical User Interface was developed for mammography visualization and processing, in order to support the medical diagnosis directly on a high-resolution screen. The database has been the starting point for the development of other medical imaging applications such as a breast CAD, currently being upgraded and optimized for the use in conjunction of the GRID technology in the framework of the INFN-funded MAGIC-5 project. [ABSTRACT FROM AUTHOR]

The Digital Database for Screening Mammography (DDSM) is an invaluable resource for digital mammography research. However, there are two particular shortcomings that can pose a significant barrier to many of those who may want to use the resource: 1) the actual mammographic image data is encoded using a non-standard lossless variant of the JPEG image format; 2) although detailed metadata is provided, it is not in a form that permits it to be searched, manipulated or reasoned over by standard tools. This paper describes web services that will allow both humans and computers to query for, and obtain, mammograms from the DDSM in a standard and well-supported image file format. Further, this paper describes how these and other services can be used within grid-based workflows, allowing digital mammography researchers to make use of distributed computing facilities. [ABSTRACT FROM AUTHOR]

Medical ontologies are being developed with some of these specifically for mammographic computer aided diagnosis (CAD) systems. However, to provide full functionality for such mammographic CAD systems it is essential that the ontology information is fully linked to the image information. This linking can be through problem specific image attributes. However, such an approach tends to be non-generic. Here, we propose a framework that will use generic image structures and the topology that links the image structures. In the process we describe a comparison approach which takes the classes, attributes and semantics into account. [ABSTRACT FROM AUTHOR]

An experimental method of determining the optimal beam qualities and doses for digital mammography systems is described, and applied to a CR system. The mean glandular dose (MGD) and contrast-to-noise ratio (CNR) were measured using phantoms. For each thickness of phantom a range of kV and target/filter combinations were tested. Optimal beam quality was defined as that giving a target CNR for the lowest MGD. The target CNR was that necessary to achieve at least the minimum standard of image quality defined in European Guidance. An inverse relationship between CNR and threshold contrast was confirmed over a range of thicknesses of PMMA and different beam qualities and doses. Optimisation indicated that relatively high energy beam qualities (e.g. 31 kV Rh/Rh) should be used with a greater detector dose to compensate for the lower contrast when compared to using lower energy X-rays. The results also indicate that current AEC designs that aim for a fixed detector dose are not optimal. [ABSTRACT FROM AUTHOR]

A link between increased breast density, as visualised in mammograms, and increased risk of developing breast cancer has been established. Recently, a number of objective, quantitative methods for measuring breast density have been described. One such method requires a calibration object to be imaged alongside the breast. However, it is important that this should not interfere with the routine imaging process. In this paper, we investigate the amount of space in mammographic images which is not currently occupied by the breast or existing patient labels and markers, and which would therefore be available for imaging an additional calibration device. We do this with a view to estimating the likelihood of failure of the method, and also to determining whether, without detriment to the imaging process, a device could be permanently fixed to the breast support platform. We also examine the impact of markers attached to the compression plate on the visibility of breast tissue. The results show that our existing calibration device may be used successfully without interfering with the routine imaging process, although permanently fixing such a device may present problems in a small minority of cases, and we demonstrate that the number of cases which would fail can be reduced by using a smaller stepwedge. [ABSTRACT FROM AUTHOR]

Breast cancer is one of the main leading causes to women mortality in the world especially in the western countries. Since the causes are still unknown, breast cancer cannot be prevented completely even till now. Microcalcification clusters are primary indicators of malignant types of breast cancer, the detection is important to prevent and treat the disease. The microcalcifications appear in the small clusters of a few pixels with relatively high intensity and closed contours compared with their neighboring pixels. However, it is a challenge to detect all the microcalcifications since they appear as spots which are slightly brighter than their backgrounds. This paper presents an approach for detecting microcalcifications in digital mammograms employing a dual-threshold method. These microcalcifications can be located by our new method which is developed from LoG edge detection method. Two thresholds are proposed in our method based on two additional criterions. Experimental results show that the proposed method can locate the microcalcifications exactly in mammogram as well as restrain the contours produced by the noises. [ABSTRACT FROM AUTHOR]

A novel approach to the detection of masses and clustered microcalcification is presented. Lesion detection is considered as a two-class pattern recognition problem. In order to get an effective and stable representation, the detection scheme codifies the image by using a ranklet transform. The vectors of ranklet coefficients obtained are classified by means of an SVM classifier. Our approach has two main advantages. First it does not need any feature selected by the trainer. Second, it is quite stable, with respect to the image histogram. That allows us to tune the detection parameters in one database and use the trained CAD on other databases without needing any adjustment. In this paper, training is accomplished on images coming from different databases (both digitized and digital). Test results are calculated on images coming from a few FFDM Giotto Image MD clinical units. The sensitivity of our CAD system is about 85% with a false-positive rate of 0.5 marks per image. [ABSTRACT FROM AUTHOR]

In recent years a large number of digital mammograms have been generated in hospitals and breast screening centers. To assist diagnosis through indexing those mammogram databases, we proposed a content-based image retrieval framework along with a novel feature extraction technique for describing the degree of calcification phenomenon revealed in the mammograms and six relevance feedback learning algorithms, which fall in the category of query point movement, for improving system performance. The results show that the proposed system can reach a precision rate of 0.716 after five rounds of relevance feedback have been performed. [ABSTRACT FROM AUTHOR]

Classifier plays an important role in a system detecting abnormal shadows from mammograms. In this paper, we propose the novel classification system that cascades four weak classifiers and a classifier ensemble to improve both computational cost and classification accuracy. The first several weak classifiers eliminate a large number of false positives in a short time which are easy to distinguish from abnormal regions, and the final classifier ensemble focuses on the remaining candidate regions difficult to classify, which results in high accuracy. We also show the experimental results using 2,564 mammograms. [ABSTRACT FROM AUTHOR]

We evaluated three combinations of filters (Mo/Mo, Mo/ Rh, and W /Rh) in direct a-Se FFDM system to optimize radiation dose clinically. We measured CNR (Contrast to Noise Ratio) as physical characteristics changing radiation dose and phantom thickness in clinical range. In 20, 30, 40, and 50mm PMMA phantoms, Mo / Mo showed the best performances. On the other hand, in 60 and 70 mm, W/Rh 30kV showed best performance. In addition, in 40 and 50mm PMMA phantoms, W/Rh 30kV showed the second best performance. In direct a-Se FFDM system, W/Rh was valuable in minimizing radiation dose. [ABSTRACT FROM AUTHOR]

In this paper, co-occurrence matrix based texture features are used to classify masses as benign or malignant. As (digitized) mammograms have high depth resolution (4096 gray levels in this study) and the size of a co-occurrence matrix depends on Q, the number of gray levels used for image intensity (depth) quantization, computation using co-occurrence matrices derived from mammograms can be expensive. Re-quantization using a lower value of Q is routinely performed but the effect of such procedure has not been sufficiently investigated. This paper investigates the effect of re-quantization using different Q. Four feature pools are formed with features measured on co-occurrence matrices with Q ∈{400}, Q ∈{100}, Q ∈{50} and Q ∈{400, 100, 50}. Classification results are obtained from each pool separately with the use of a genetic algorithm and the Fisher's linear discriminant classifier. For Q ∈{400, 100, 50}, the best feature subsets selected by the genetic algorithm and of size k=6,7,8 have a leave-one-out area under the receiver operating characteristic (ROC) curve of 0.92, 0.93 and 0.94, respectively. Pairwise comparisons of the area index show that the differences in classification results for Q ∈{400, 100, 50} and Q ∈{50} are significant (p<0.06) for all k while that for Q ∈{400, 100, 50} and Q ∈{400} or Q ∈{100} are not significant. [ABSTRACT FROM AUTHOR]

Radiographic signs indicating the presence of a malignancy are a result of the morphology and composition of the lesion. Assessment of the size, distribution, extent and location of disease are crucial in guiding patient management. Often mammographic estimates of size and extent are underestimated. Radiologic/pathologic correlation between features is often by indirect classification methods rather than a direct, whole-volume, one-to-one spatial correlation between radiologic and pathologic images. As an initial step toward understanding how tumour morphology and composition yields a mammographic sign, we have begun work on correlating whole-mount histology sections to cone-beam computed tomography (CBCT) images of the same specimen. Preliminary results for a lumpectomy sample containing a 3.5 cm invasive ductal carcinoma qualitatively show a remarkable correspondence between CBCT slices and histology sections. Ultimately, the 3D CBCT data could be used to predict mammographic features, which could then be correlated precisely to the anatomy of the tumour. [ABSTRACT FROM AUTHOR]

The Multiple Image Radiography (MIR) method is new imaging modality that extends the capability of conventional absorption based radiography by adding the additional contrast mechanisms of x-ray refraction and ultra-small angle scatter. In order to design a clinically based MIR system, the MIR specific x-ray properties in breast tissue must be analyzed to determine which are diagnostically useful. Developing MIR as an imaging modality also requires developing new phantoms that incorporate x-ray refraction and ultra-small angle scatter in addition to traditional x-ray absorption. Three breast cancer specimens were imaged using MIR to demonstrate its MIR specific x-ray properties. An uncompressed anthropomorphic breast phantom with an imbedded low absorption contrast acrylic sphere was imaged to provide a physical model of how the unique properties of MIR can be utilized to improve upon conventional mammography and illustrate how these can be used to design a clinically useful imaging system. [ABSTRACT FROM AUTHOR]