Digital Mammography

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.

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 0.015 bpp using an embedded wavelet coding scheme result in no significant differences from the analog or digital originals. 0 1997 Elsevier Science B.V. R&sum& La substitution d'images analogiques par des reprCsentations numCriques donne accts g des mCthodes de stockage et de transmission numkriques, et permet l'utilisation d'une grande variCtC de techniques de traitement d'images, incluant le rehaussement, les tests de dCpistage assist6 ordinateur et le diagnostic. La compression avec pertes peut encore amCliorer l'efficacitk de la transmission et du stockage, et peut faciliter le traitement ultCrieur des images. La numirisation et la compression avec pertes alttrant toutes deux une image par rapport $ sa forme traditionnelle, il devient important de montrer qu'une telle alttration amkliore, ou du moins ne rtduit pas, I'utilitC de l'image dans un screening ou une application de diagnostic. Une approche pour dt?montrer d'une man&e quantifiable qu'un mode d'image specifique est au moins Cgal 5 un autre est l'exptrimentation clinique simulant la pratique ordinaire jointe $ une analyse statistique adapt&e. Dans cet article, nous dttcrivons un protocole g&n&al pour effectuer une telle vCrification et prtsentons les rCsultats prCliminaires d'une expkrience faite pour montrer que des mamogrammes numCrisis g 12 bpp et comprimts avec pertes $ 0.15 bpp d l'aide d'une technique de codage par ondelettes incluses ne presentent pas de diffkrences significatives par rapport aux versions originales analogique ou numCrique. 0 1997 Elsevier Science B.V.

Breast Cancer is one of the significant reasons for death among ladies. Many research has been done on the diagnosis and detection of breast cancer using various image processing and classification techniques. Nonetheless, the disease remains as one of the deadliest disease. Having conceive one out of six women in her lifetime. Since the cause of breast cancer stays obscure, prevention becomes impossible. Thus, early detection of tumour in breast is the only way to cure breast cancer. Using CAD (Computer Aided Diagnosis) on mammographic image is the most efficient and easiest way to diagnosis for breast cancer. Accurate discovery can effectively reduce the mortality rate brought about by using mamma cancer. Masses and microcalcifications clusters are an important early symptoms of possible breast cancers. They can help predict breast cancer at it's infant state. The image for this work is being used from the DDSM Database (Digital Database for Screening Mammography) which contains approximately 3000 cases and is being used worldwide for cancer research. This paper quantitatively depicts the analysis methods used for texture features for detection of cancer. These texture featuresare extracted from the ROI of the mammogram to characterize the microcalcifications into harmless, ordinary or threatening. These features are further decreased using Principle Component Analysis(PCA) for better identification of Masses. These features are further compared and passed through Back Propagation algorithm (Neural Network) for better understanding of the cancer pattern in the mammography image.

The early detection of breast cancer increases the survival rate in women. Today, film-screen mammography is the most common and effective technique for the detection of breast cancer. However, the film-screen image recording system of current mammography has several technical limitations that can reduce the breast cancer diagnostic accuracy. A state-of-theart technology, full-field direct digital mammography (FFDDM) has the potential to increase the sensitivity of clinical and screening examinations and emerge as a film-screen mammography replacement. It is believed that efficiently delivering the superior image quality of FFDDM to expert mammographers will significantly increase the breast cancer diagnostic accuracy. This concept motivates the studies of FFDDM telemammography.

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

A dynamic-contrast-enhanced magnetic resonance imaging (DCE-MRI) dataset consists of many imaging frames, often acquired both before and after contrast injection. Due to the length of time spent acquiring images, patient motion is likely and image re-alignment or registration is required before further analysis such as pharmacokinetic model fitting. Non-rigid image registration procedures may be used to correct motion artefacts; however, a careful choice of registration strategy is required to reduce misregistration artefacts associated with enhancing features. This work investigates the effect of registration on the results of model-fitting algorithms for 52 DCE-MR mammography cases for 14 patients. Results are divided into two sections: a comparison of registration strategies in which a DCE-MRI-specific algorithm is preferred in 50% of cases, followed by an investigation of parameter changes with known applied deformations, inspecting the effect of magnitude and timing of motion artefacts. Increased motion magnitude correlates with increased model-fit residual and is seen to have a strong influence on the visibility of strongly enhancing features. Motion artefacts in images close to the contrast agent arrival have a disproportionate effect on discrepancies in parameter estimation. The choice of algorithm, magnitude of motion and timing of the motion are each shown to influence estimated pharmacokinetic parameters even when motion magnitude is small.

A significant metric in federal mammography quality standards is the phantom image quality assessment. The present work seeks to demonstrate that automated image analyses for American College of Radiology ͑ACR͒ mammographic accreditation phantom ͑MAP͒ images may be performed by a computer with objectivity, once a human acceptance level has been established. Twelve MAP images were generated with different x-ray techniques and digitized. Nineteen medical physicists in diagnostic roles ͑five of which were specially trained in mammography͒ viewed the original film images under similar conditions and provided individual scores for each test object ͑fibrils, microcalcifications, and nodules͒. Fourier domain template matching, used for low-level processing, combined with derivative filters, for intermediate-level processing, provided translation and rotation-independent localization of the test objects in the MAP images. The visibility classification decision was modeled by a Bayesian classifer using threshold contrast. The 50% visibility contrast thresholds established by the trained observers' responses were: fibrils 1.010, microcalcifications 1.156, and nodules 1.016. Using these values as an estimate of human observer performance and given the automated localization of test objects, six images were graded with the computer algorithm. In all but one instance, the algorithm scored the images the same as the diagnostic physicists. In the case where it did not, the margin of disagreement was 10% due to the fact that the human scoring did not allow for half-visible fibrils ͑agreement occurred for the other test objects͒. The implication from this is that an operator-independent, machine-based scoring of MAP images is feasible and could be used as a tool to help eliminate the effect of observer variability within the current system, given proper, consistent digitization is performed. © 1997 American Association of Physicists in Medicine. ͓S0094-2405͑97͒00205-8͔

Nowadays, the utilization of the rapidly growing novel results and possibilities to be produced by the non-invasive digital imaging systems in clinical application and the related research and developing works are serious challenges. The technological background is provided by the electronic archival system-PACS-with communication structures for multi-modality images. Due to the last 6 years research and developing activities in the department (supported by 3 domestic companies and research grants) a PACS system with communication architecture have been created and then integrated into the daily clinical applications as well as the research, development and education. The on-line telecommunication consists of both ISDN technology and Internet based tools. Three modalities-CT, NM, VIDAR X-ray film scanner (mainly for mammography)-are connected to the PACS by on-line DICOM 3.0 communication (PracticeBuilder 1-2-3 R and MEDISA), while the other two modalities (Howtek Multi-RAD 850 film scanner and ACUSON ultrasound) by DICOM conversion with FTP protocol (off-line way). Simultaneous presentations of multi-modality images, tele-conference and the electronic archival are playing significant role in the routine clinical work. Several external institutes are WEB based connected to the central archives and can access the necessary and/or available modalities according to the access rights. PACS based multi-modality became an essential condition for the following research activities: digital image processing, small animal experiments, quantitative evaluation of digitized mammography and construction of advisory level database as well as pattern recognition. Fusion between various image modalities is one of the main powers of the future non-invasive diagnostics. A developing project is in progress in order to work out the necessary technological conditions for clinical introduction of a 0531-5131/ D 2005 CARS & Elsevier B.V. All rights reserved.

Silicon microstrip detectors in the edge on geometry, i.e. oriented with the strips parallel to the incoming beam, allow a high absorption efficiency for X-rays in the 10–100 keV energy range. In medical imaging, this results in a significant reduction of the dose given to the patient with respect to conventional screen-film systems. This geometrical configuration was first proposed in the early 90s and since then it has been applied to several medical imaging projects. In this short review, an overview of some medical imaging applications using edge on detectors will be presented, focusing the attention on the main characteristics of the detection systems. The studies performed in Trieste in order to optimize such detectors for digital mammography with synchrotron radiation will be described in detail. In particular, novel imaging techniques (phase contrast, refraction and scatter imaging, computed tomography) implemented and tested at the Trieste synchrotron light source will be shortly discussed.

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.

A computer aided neural network classification of regions of suspicion (ROS) on digitized mammograms is presented in this paper which employs features extracted by a new technique based on independent component analysis. Our approach is concentrated in finding a set of independent source regions that generate the observed mammograms. The coefficients of the linear transformation of the source regions are used as features that describe effectively any normal and abnormal region in digital mammograms as well as benign and malignant ROS in the latter case. Extensive experiments in the MIAS Database have shown a recognition accuracy of 88.23% in the detection of all kinds of abnormalities and 79.31% in the task of distinguishing between benign and malignant regions, outperforming in both cases standard textural features, widely used for cancer detection in mammograms.

Analysis of complex imaging tasks requires a phantom that simulates the patient anatomy. We have developed a technique to fabricate 3D physical anthropomorphic breast phantoms for image quality assessment of 2D and 3D breast x-ray imaging systems. The phantom design is based on an existing computer model that can generate breast voxel phantoms of varying size, shape, glandularity, and internal composition. The physical phantom is produced in two steps. First, the computer model of the glandular tissue, skin and Coopers' ligaments is separated into sections. These sections are fabricated by high-resolution rapid prototype printing using a single tissue equivalent material. The adipose tissue regions in the sections are filled using an epoxy-based resin combined with phenolic microspheres. The phantom sections are then stacked. The phantom is provided with an extra section modified to include iodine-enhanced masses. We fabricated a prototype phantom corresponding to a 450 ml breast with 45% dense tissue deformed to represent a 5 cm compressed thickness. The rapid prototype and epoxy based resin phantom materials attenuate x rays similar to 50% glandular tissue and 100% adipose tissue, respectively. The iodinated masses are between 4.0 and 9.6 mm thick and contain 2.5 mg/ml and 5 mg/ml iodine. Digital mammography and digital breast tomosynthesis images of the phantom are qualitatively similar in appearance to clinical images. In summary, a method to fabricate a 3D physical anthropomorphic breast phantom has been developed with known ground truth in the form of a companion voxel phantom. This combined system of physical and computational phantoms allows for both qualitative and quantitative image quality assessment.

Digital imaging systems for medical applications use amorphous silicon thin-film transistor (TFT) technology due to its ability to be manufactured over large areas. However, TFT technology is far inferior to crystalline silicon CMOS technology in terms of the speed, stability, noise susceptibility, and feature size. This work investigates the feasibility of integrating an imaging array fabricated in CMOS technology with an a-Se detector. The design of a CMOS passive pixel sensor (PPS) array is presented, in addition to how an 8×8 PPS array is integrated with the 75 micron thick stabilized amorphous selenium detector. A non-linear increase in the dark current of 200 pA, 500 pA and 2 nA is observed with 0.27, 0.67 and 1.33 V/micron electric field respectively, which shows a successful integration of selenium layer with the CMOS array. Results also show that the integrated Selenium-CMOS PPS array has good responsivity to optical light and X-rays, leaving the door open for further research on implementing CMOS imaging architectures going forward. Demonstrating that the PPS chips using CMOS technology can use a-Se as a detector is thus the first step in a promising path of research, which should yield substantial and exciting results for the field. Though area may still prove challenging, larger CMOS wafers can be manufactured and tiled to allow for a large enough size for certain diagnostic imaging applications and potentially even large area applications like digital mammography.

This paper on digital mammography image storage, retrieval, and transmission is 1 of 3 papers written as part of an intersociety effort to establish image quality standards for digital mammography. The information included in this paper is intended to support the development of an American College of Radiology (ACR) guideline on image quality for digital mammography. The topics of the other 2 papers are digital mammography image acquisition and digital mammography image display. The societies that were represented in compiling this document were the Radiological Society of North America, the ACR, the American Association of Physicists in Medicine, and the Society for Computer Applications in Radiology. These papers describe in detail what is known to improve image quality for digital mammography and make recommendations about how digital mammography should be performed to optimize the visualization of breast cancers using this imaging tool. Through the publication of these papers, the ACR is seeking input from industry, radiologists, and other interested parties on their contents so that the final ACR guideline for digital mammography will represent the consensus of the broader community interested in these topics.

Introduction Digital breast tomosynthesis (DBT) may improve the accuracy of mammography by enabling visual separation of overlapping tissues (Andersson et al. 2008, Poplack et al. 2007). Methods Following local research ethics approval, all women attending the assessment clinic for evaluation of a mammographic abnormality found on routine screening (film-screen) were invited to take part in the study subject to informed consent. Participants underwent bilateral two-view two-dimensional (2D) digital mammography and bilateral twoview DBT. Mammography scores using the RCR Breast Group classification were sequentially obtained for the screening mammogram, 2D digital and DBT, and these were each compared with the final assessment outcome. Results Ninety-one percent of eligible women participated. Results from the first 300 participants are presented in below. Kappa coefficients for agreement of each imaging method with final assessment outcome were calculated. Screening mammograms had the lowest agreement with the final outcome (Kappa = 0.02; P = 0.22), 2D digital mammography was better (Kappa = 0.26; P = 0.0000) and DBT had the highest score (Kappa = 0.37; P = 0.0000). Conclusion The preliminary results of this ongoing study show that DBT increases the diagnostic confidence of the radiologist. This supports the need for a larger multicentre study.

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.

— we present a new approach to classify breast tissue density which is widely accepted to be an important risk indicator for the development of breast cancer. The computer aided diagnosis (CAD) framework developed, first segments the breast area then represents each image using the bag-of-features (BoF) approach. To represent the images, we first extract local binary patterns (LBP) features, which are then quantized to create a codebook. Second, we encode the features using the obtained codebook and a sparse coding algorithm to obtain a final image representation. Finally, we use support vector machines (SVM) classifier to carry out the classification task. In order to evaluate the efficiency of the proposed approach, we tested the framework using the digital database of screening mammograms (DDSM). The results showed that 91.25% of the samples were correctly classified. We also investigated the codebook size and selected the one that enhanced the results. Our proposal showed better performance compared to previous methods that classified the same dataset. Keywords— breast tissue density classification; computer-aided diagnostic systems; bag of features; local binary patterns; sparse coding; LLC;max-pooling .

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 employing text mining techniques.. 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%.

Breast Cancer is the most common Cancer among women. Currently there are no methods to prevent breast cancer that is the reason why early detection represents a very important factor in cancer treatment. Mammography is currently the best available radiological technique for early detection of breast cancer. Mass detection using Computer Aided Design and Detection systems serve as a second decision tool to radiologists for discovering masses in the mammograms. This work presents a methodology for segmentation of masses in Mammogram Images. The proposed algorithm uses Median filtering, Optimal Global thresholding using Otsu's method and morphological processing in order to: (1) enhance quality of mammography image, (2) Segmentation of masses based on threshold resulting Region of Interest (ROI)(3) extracting segmented masses from image. The proposed techniques are implemented and tested under MATLAB environment.

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.

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.

We analyzed breast percent density (PD) estimated from 35 women with existing or recently detected abnormalities. Analyzed were digital mammograms (DM) and the central digital breast tomosynthesis (DBT) reconstructed slices. PD was estimated from the breast contralateral to cancer. We have examined the effect of modifying the DBT reconstruction filter on the accuracy of dense tissue segmentation; we selected the filter that effectively reduced image intensity overshoot near the breast edge, which interfered with segmentation. The Pearson correlation coefficient between PD estimates from DM and the central reconstructed DBT slices was r = 0.90. The corresponding quadratic-weighted kappa coefficient was κ = 0.78, indicating substantial agreement. The observed results are comparable with the agreement between PD estimates from DM and the central DBT projection images (r = 0.89 and κ = 0.74). This suggests that PD is robust to variations in acquisition conditions.

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.

This paper presents a neural network based technique that may be applied to data compression. The proposed technique breaks down large images into smaller windows and eliminates redundant information. Finally, the technique uses a neural network trained by direct solution methods. Conventional techniques such as Huffman coding and the Shannon Fano method are discussed as well as more recent methods for the compression of data and images. Intelligent methods for data compression are reviewed including the use of Backpropagation and Kohonen neural networks. The proposed technique has been implemented in C on the SP2 and tested on digital mammograms and other images. The results obtained are presented in this paper.

This paper presents a fully automated scheme for breast den- sity estimation and asymmetry detection on mammographic images. Image preprocessing and segmentation techniques are first applied to the image, in order to extract the feature s for the breast density categorization. Also a new fractal- related feature is proposed for the classification. The clas - sification to 3 classes is

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 ...

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 ...

Digital breast tomosynthesis (DBT) is a tomographic technique in which individual slices through the breast are reconstructed from x-ray projection images acquired over a limited angular range. In contrast-enhanced DBT (CE-DBT) functional information is observed by administration of an radiographic contrast agent. The uptake of iodine in the breast is very small and causes changes in x-ray transmission that are smaller than 5%. This presents significant technical challenges if quantitative assessment of contrast agent concentration in tissue is desired. We modeled CE-DBT acquisition by simulating x-ray spectra from 40 to 49 kV. Comparison of attenuation data of our simulated and measured spectra were found to agree well. We investigated the effect of patient motion and scatter on iodine uptake. These parameters were evaluated by means of experiments and theoretical modeling.

Reducing the classification process time, for identifying male tissues in medical
images, has always been a challenge. In this paper, an efficient approach is
presented that improves the classification time of breast cancer mammogram
images. First, a preprocessing phase is applied for background correction and
noise removal using Wave Atom transform and a universal cascaded filter,
respectively. Then, the segmentation process is carried out through Particle
Swarm Optimization, Hidden Markov Random Field Model with its Expectation Maximization algorithm, and Kirsch's templates. Finally, features extraction and
classification are employed. The proposed method is used to efficiently reduce
the amount of data that represent the statistically based textural features of the
image. Experimental results show that the employment of the selected
preprocessing algorithms leads to a better classification accuracy than that of the
commonly known digital filters. Moreover, the proposed method improves the
classification time by using only six, out of twenty-two, features that mostly
contains the image information. Finally, classification procedures using Naive Bayes (NB) is applied depending
on the reduced features. The accuracy of the obtained results is compared with literature and good results are
achieved.

— Breast cancer is the most harmful of all types of cancers that leads to the death of women. The initial screening test for breast cancer is made using Mammography. Masses, Calcification and Architectural distortion are the three major signs notable as causes for cancer recognized using mammogram images. Soft Computing methods provide solutions to biologically inspired problem of medical domain like breast cancer. Neural Networks, Fuzzy Logic and Genetic Algorithms contribute novel algorithms to deal with breast cancer. Breast cancer can be diagnosed using soft computing methods. The effective diagnosis of breast cancer can be achieved by using feature reduction and classification methods. The feature reduction method applied is Principal Component Analysis (PCA) and the classification method includes Support Vector Machines (SVM). The objective of this study is to improve the breast cancer early detection with the application of soft-computing techniques.

Mammography x-rays uses to demonstrate different lesions among women by using ionizing radiation. This irradiation exposes the patient to slight high radiation amounts. This study conducted in amongst mammography patients in Khartoum, Sudan. The sample magnitude was 100 patients whose different types of diseases. All patients were underwent mammography. The factors were documented in this study were patient's characteristics and radiation exposure related factors. The mean MGD values calculated were 3.6 + 0.29 (p<0.05). Those results were suggestively more than the standard dose determined by international agencies (IAEA, NCRP and ACR).

En 2004, le dépistage du cancer du sein sera national en France. Cette évolution fait rentrer l'exercice de la mammographie dans une mission de santé publique et bouleverse profondément notre exercice professionnel. La mammographie numérique plein-champ a des résultats équivalents à l'analogique dans la détection des cancers, l'application la plus pertinente étant représentée par les systèmes experts d'aide à la détection. La mise en place d'un contrôle qualité permettra de l'intégrer dans le dépistage organisé courant 2004. La sénologie interventionnelle s'est définitivement imposée comme outil de diagnostic des lésions infracliniques, permettant de sélectionner les patientes pour une chirurgie. Elle a rendu évidente et incontournable la collaboration multidisciplinaire dans la prise en charge des patientes. Les indications de l'IRM ne se sont pas modifiées du fait d'une spécificité modérée et de l'absence de développement de l'interventionnel guidé par cette technique. Le travail d'homogénéisation concernant le descriptif et le classement des lésions infracliniques se poursuit. Déjà appliqué pour la mammographie, la 4 e édition du BI-RADS de l'American College of Radiology (disponible en 2003) intégrera les classifications pour les lésions détectées en échographie et en IRM.

c o m p u t e r m e t h o d s a n d p r o g r a m s i n b i o m e d i c i n e 1 0 2 ( 2 0 1 1 ) 47-63 Automated mammogram segmentation Breast boundary Pectoral muscle Breast density Breast asymmetry Nipple detection a b s t r a c t This paper presents a fully automated segmentation and classification scheme for mammograms, based on breast density estimation and detection of asymmetry. First, image preprocessing and segmentation techniques are applied, including a breast boundary extraction algorithm and an improved version of a pectoral muscle segmentation scheme. Features for breast density categorization are extracted, including a new fractal dimensionrelated feature, and support vector machines (SVMs) are employed for classification, achieving accuracy of up to 85.7%. Most of these properties are used to extract a new set of statistical features for each breast; the differences among these feature values from the two images of each pair of mammograms are used to detect breast asymmetry, using an one-class SVM classifier, which resulted in a success rate of 84.47%. This composite methodology has been applied to the miniMIAS database, consisting of 322 (MLO) mammograms -including 15 asymmetric pairs of images-, obtained via a (noisy) digitization procedure.

— In this paper a robust regions-of-suspicion (ROS) diagnosis system on mammograms, recognizing all types of abnormalities is presented and evaluated. A new type of de-scriptors, based on Shapelet decomposition, estimate the source images that generate the observed ROS in mammo-grams. The Shapelet decomposition coefficients can be used to efficiently detect ROS areas using a new classifier base on quaternionic representation. Extensive experiments using the Mammographic Image Analysis Society (MIAS) database have shown high recognition accuracy over 86% for all kinds of breast, with less computational cost.

To report our experience with targeted-ultrasound in assessing 142 cases with clustered microcalcifications of intermediate concern detected on digital mammography. All cases had histopathologically-proven microcalcifications within the biopsied or surgical specimens. There were 30%[43/142] breast cancers and 70%[99/142] benign lesions. Only 26%[37/142] of clustered microcalcifications were identified on targeted-ultrasound and other findings including negative study (n = 33), anechoic ducts or cysts

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.

The performance of an automatic software package was evaluated with phantom images acquired by a full-field digital mammography unit. After the validation, the software was used, together with a Leeds TORMAS test object, to model the image acquisition process. Process modelling results were used to evaluate the sensitivity of the method in detecting changes of exposure parameters from routine image quality measurements in digital mammography, which is the ultimate purpose of long-term reproducibility tests. Image quality indices measured by the software included the mean pixel value and standard deviation of circular details and surrounding background, contrast-to-noise ratio and relative contrast; detail counts were also collected. The validation procedure demonstrated that the software localizes the phantom details correctly and the difference between automatic and manual measurements was within few grey levels. Quantitative analysis showed sufficient sensitivity to relate fluctuations in exposure parameters (kV p or mAs) to variations in image quality indices. In comparison, detail counts were found less sensitive in detecting image quality changes, even when limitations due to observer subjectivity were overcome by automatic analysis. In conclusion, longterm reproducibility tests provided by the Leeds TORMAS phantom with quantitative analysis of multiple IQ indices have been demonstrated to be effective in predicting causes of deviation from standard operating conditions and can be used to monitor stability in full-field digital mammography.