Showing papers on "Digital mammography published in 1999"

Computerized detection of malignant tumors on digital mammograms

Abstract: This paper presents a tumor detection system for fully digital mammography. The processing scheme adopted in the proposed system focuses on the solution of two problems. One is how to detect tumors as suspicious regions with a very weak contrast to their background and another is how to extract features which characterize malignant tumors. For the first problem, a unique adaptive filter called the iris filter is proposed. It is very effective in enhancing approximately rounded opacities no matter what their contrasts might be. Clues for differentiation between malignant tumors and other tumors are believed to be mostly in their border areas. This paper proposes typical parameters which reflect boundary characteristics. To confirm the system performance for unknown samples, large scale experiments using 1212 CR images were performed. The results showed that the sensitivity of the proposed system was 90.5% and the average number of false positives per image was found to be only 1.3. These results show the effectiveness of the proposed system.

Combined adaptive enhancement and region-growing segmentation of breast masses on digitized mammograms.

Abstract: As an ongoing effort to develop a computer aid for detection of masses on mammograms, we recently designed an object-based region-growing technique to improve mass segmentation. This segmentation method utilizes the density-weighted contrast enhancement (DWCE) filter as a preprocessing step. The DWCE filter adaptively enhances the contrast between the breast structures and the background. Object-based region growing was then applied to each of the identified structures. The region-growing technique uses gray-scale and gradient information to adjust the initial object borders and to reduce merging between adjacent or overlapping structures. Each object is then classified as a breast mass or normal tissue based on extracted morphological and texture features. In this study we evaluated the sensitivity of this combined segmentation scheme and its ability to reduce false positive (FP) detections on a data set of 253 digitized mammograms, each of which contained a biopsy-proven breast mass. It was found that the segmentation scheme detected 98% of the 253 biopsy-proven breast masses in our data set. After final FP reduction, the detection resulted in 4.2 FP per image at a 90% true positive (TP) fraction and 2.0 FPs per image at an 80% TP fraction. The combined DWCE and object-based region growing technique increased the initial detection sensitivity, reduced merging between neighboring structures, and reduced the number of FP detections in our automated breast mass detection scheme.

Evaluating the performance of detection algorithms in digital mammography.

Abstract: The initial and relative evaluation of computer methodologies developed for assisting diagnosis in mammography is usually done by comparing the computer output to ground truth data provided by experts and/or biopsy. Reported studies, however, give little information on how the performance indices of computer assisted diagnosis(CAD) algorithms are determined in this initial stage of evaluation. Several strategies exist in the estimation of the true positive (TP) and false positive (FP) rates with respect to ground truth. Adopting one strategy over another yields different performance rates that can be over- or underestimates of the true performance. Furthermore, the estimation of pairs of TP and FP rates gives a partial picture of the performance of an algorithm. It is shown in this work that new performance indices are needed to fully describe the degree of detection (part or whole) and the type of detection (single calcification, cluster of calcifications, mass, or artifact). Several evaluation strategies were tested. The one that yielded the most realistic performances included the following criteria: The detected area should be at least 50% of the true area and no more than four times the true area in order to be considered TP. At least three true calcifications should be detected to within 1 cm 2 with nearest neighbor distances of less than √2 cm for a cluster to be considered TP. Separate detection measures should be established and used for artifacts and naturally occurring structures to maximize the benefits of the evaluation. Finally, it is critical that CAD investigators provide information on the tested image set as well as the criteria used for the evaluation of the algorithms to allow comparisons and better understanding of their methodologies.

Image feature extraction for mass detection in digital mammography: Influence of wavelet analysis

Abstract: Rationale and objectives. The objective of this work is to evaluate the importance of image preprocessing, using multiresolution and multiorientation wavelet transforms (WTs), on the performance of a previously reported computer assisted diagnostic(CAD) method for breast cancer screening, using digital mammography.Method: An analysis of the influence of WTs on image feature extraction for mass detection is achieved by comparing the discriminant ability of features extracted with and without the wavelet-based image preprocessing using computed ROC. Three indexes are proposed to assess the segmentation of the mass area with comparison to the ground truth. Data was analyzed on the region-of-interest (ROI) database that included mass and normal regions from digitized mammograms with the ground truth. Results: The metrics for the measurement of segmentation of the mass clearly demonstrated the importance of image preprocessing methods. Similarly, the relative improvement in performance was observed in feature extraction based on the evaluation of the ROC curves, where the Az values are increased, for example, from 0.71 to 0.75 for a pixel intensity feature and from 0.72 to 0.85 for a morphological feature of the Normalized Deviation of Radial Length. The improvement, therefore, depends on the feature characteristics, being large for boundary-related features while small for intensity-related features. Conclusion: The use of image preprocessing modules using wavelet transforms results in a significant improvement in feature extraction for the previously proposed CAD detection method. We are therefore exploring additional improvement in wavelet-based image preprocessing methods, including adaptive methods, to achieve a further improvement in performance and an evaluation on larger imagedatabases.

Direct conversion detector for digital mammography

Abstract: In this paper, we report measurements from a prototype 1024 X 1024 selenium-based flat panel detector suited for interventional digital mammography applications. This detector is based on an amorphous silicon TFT array, with a pixel pitch of 85 micrometer and a fill factor of 70%. A 200 micrometer layer of amorphous selenium is used to directly convert the incident x-rays into electrical charges. The detector electronics, TFT array, and selenium converter structure are designed to operate at a frame rate of 10 images per second. Experimentally, this detector yields an x-ray sensitivity of nearly 290 electrons/absorbed x-ray nearly 100% absorption of x-rays at a beam energy of 18 keV, a high spatial resolution (limited only by the pixel pitch up to the Nyquist limit), and quantum-noise limited operation down to the lowest exposures currently investigated. Images from the ACR phantom and contrast detail phantom reveal all embedded targets in the phantoms, which indicates the potential of this technology for digital mammography.

Computer-aided diagnosis in full digital mammography.

Abstract: RATIONALE AND OBJECTIVES The authors clarify the detection rates for breast cancerous tumors and clustered microcalcifications with computer-aided diagnosis (CAD) based on Fuji Computed Radiography. The authors also determine whether mammographic reading with CAD contributes to the discovery of breast cancer. METHODS Data acquired by Fuji Computed Radiography 9000, which consisted of 4148 digital mammograms including 267 cases of breast cancer, was transferred directly to an analysis workstation where an original software program determined extraction rates for breast tumors and clustered microcalcifications. Furthermore, using another 344 mammograms from 86 women, observer performance studies were conducted on five doctors for receiver operating characteristic (ROC) analysis. RESULTS Sensitivity to breast cancerous tumors and clustered microcalcifications were 89.9% and 92.8%, respectively false-positive rates were 1.35 and 0.40 per image, respectively. The observer performance studies indicate that an average Az value for the five doctors was greater with the CAD system than with a film-only reading without CAD, and that a reading with CAD was significantly superior at P < 0.022. CONCLUSIONS It has been shown that CAD based on Fuji Computed Radiography offers good detection rates for both breast cancerous tumors and clustered microcalcifications, and that the reading of mammograms with this CAD system would provide potential improvement in diagnostic accuracy for breast cancer.

Authenticity and integrity of digital mammographic images

Abstract: Image authenticity and integrity is an important issue in a telemammography system. We present an algorithm which can embed encrypted image and patient information into an image. The embedded information can be extracted and decrypted by the receiving site to verify the patient identification and confirm image authenticity and integrity. Because of the large size of mammographic images comparing to other images, data embedding in a mammogram is relatively easier to be implemented. By analyzing the noise gray level of our digital mammography system, we know that the least-significant bits of the image are noise caused by the imaging device. So these bits can be used for data embedding. The methods include: (I) Calculate the check-sum of the image and extract patient information from DICOM header, (2) Encrypt the check-sum and patient information using public-key encryption strategy, (3) Generate a set of uniformly distributed pseudo-random numbers and put the encrypted check-sum and patient information into randomly selected pixel locations. Three mammographic images are selected for our experiment. Two images are the digitized mammogram with a large breast and a small breast, and the other is the direct digital mammogram. About 500 characters of patient information and a 32 bits check-sum are embedded into each image. By comparing the original image and the embedded image from a 2k x 2k monitor, we found three embedded images of large size and small size of digitized mammogram as well as direct digital mammogram have no quality degradation. To prove the effectiveness of this method, we change the value of one pixel which is selected randomly in the embedded image. Then, we use our extraction algorithm to detect the integrity of this image. The results show we can not only extract the embedded patient information correctly, but also detect the slight difference between the original image and the altered image. Our preliminary results demonstrate that embedding extra information into an image using data hiding technology is an effective method for image integrity in telemammography.

Current imaging modalities for the diagnosis of breast cancer.

Abstract: Although mammography still remains the gold standard for breast cancer screening and diagnosis, it typically cannot differentiate benign from malignant disease and is less accurate in patients with dense glandular breasts This article is an overview of imaging modalities that have emerged to augment mammography and improve the accuracy of non-invasive breast cancer diagnosis Ultrasound is currently used to differentiate breast masses and guide aspirations and biopsies Magnetic resonance imaging has excellent sensitivity in demonstrating breast cancer but a low specificity Nuclear medicine studies have recently emerged that detect the increased metabolic rate and vascularity of breast cancers Other modalities, such as thermography and computed tomography, have a more limited utility for breast cancer diagnosis Digital mammography is among other emerging technological advancements that will continue to develop and improve the accuracy of breast cancer diagnosis in the future

Optical correlator based automated pathologic region of interest selector for integrated 3D ultrasound and digital mammography

Abstract: Ultrasonographic information about the internal structure of a subject body is combined with x-ray or other radiographic information from the same subject body by using an optical correlator to quickly find a transformation which aligns the information from both sources, even if they are acquired in different coordinate systems. Various spatial transformations are applied to the information and cross-correlations are quickly performed. The transformation which gives the best cross-correlation is used to align the two data sets, which can then be displayed visually. The resulting display can be used as an aid in medical diagnosis, for example in diagnosing suspected malignant lesions in a woman's breast.

Visual discrimination model for digital mammography

Abstract: Numerous studies have been conducted to determine experimentally the effects of image processing and display parameters on the diagnostic performance of radiologists. Comprehensive optimization of imaging systems for digital mammography based solely on measurements of reader performance is impractical, however, due to the large number of interdependent variables to be tested. A reliable, efficient alternative is needed to improve the evaluation and optimization of new imaging technologies. The Sarnoff JNDmetrixTM Visual Discrimination Model (VDM) is a computational, just-noticeable difference model of human vision that has been applied successfully to predict performance in various nonmedical detection and rating tasks. To test the applicability of the VDM to specific detection tasks in digital mammography, two observer performance studies were conducted. In the first study, effects of display tone scale and peak luminance on the detectability of microcalcifications were evaluated. The VDM successfully predicted improvements in reader performance for perceptually linearized tone scales and higher display luminances. In the second study, the detectability of JPEG and wavelet compression artifacts was evaluated, and performance ratings were again found to be highly correlated with VDM predictions. These results suggest that the VDM would be useful in the assessment and optimization of new imaging and compression technologies for digital mammography.© (1999) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.

Low contrast imaging with a GaAs pixel digital detector

Abstract: A digital mammography system based on a GaAs pixel detector has been developed by the INFN (Istituto Nazionale di Fisica Nucleare) collaboration MED46. The high atomic number makes the GaAs a very efficient material for low energy X-ray detection (10-30 keV is the typical energy range used in mammography). Low contrast details can be detected with a significant dose reduction to the patient. The system presented in this paper consists of a 4096 pixel matrix built on a 200 /spl mu/m thick semi-insulating GaAs substrate. The pixel size is 170/spl times/170 /spl mu/m/sup 2/ for a total active area of 1.18 cm/sup 2/. The detector is bump-bonded to a VLSI front-end chip which implements a single-photon counting architecture. This feature allows to enhance the radiographic contrast detection with respect to charge integrating devices. The system has been tested by using a standard mammographic tube. Images of mammographic phantoms will be presented and compared with radiographs obtained with traditional film/screen systems. Monte Carlo simulations have been also performed to evaluate the imaging capability of the system. Comparison with simulations and experimental results will be shown.

The effect of the antiscatter grid on full-field digital mammography phantom images.

Abstract: Computer Analysis of Mammography Phantom Images (CAMPI) is a method for making quantitative measurements of image quality. This article reports on a recent application of this method to a prototype full-field digital mammography (FFDM) machine. Images of a modified ACR phantom were acquired on the General Electric Diagnostic Molybdenum Rhodium (GE-DMR) FFDM machine at a number of x-ray techniques, both with and without the scatter reduction grid. The techniques were chosen so that one had sets of grid and non-grid images with matched doses (200 mrads) and matched gray-scale values (1500). A third set was acquired at constant 26 kVp and varying mAs for both grid conditions. Analyses of the images yielded signal-to-noise-ratio (SNR), contrast and noise corresponding to each target object, and a nonuniformity measure. The results showed that under conditions of equal gray-scale value the grid images were markedly superior, albeit at higher doses than the non-grid images. Under constant dose conditions, the non-grid images were slightly superior in SNR (7%) but markedly less uniform (60%). Overall, the grid images had substantially greater contrast and superior image uniformity. These conclusions applied to the whole kVp range studied for the Mo-Mo target filter combination and 4 cm of breast equivalent material of average composition. These results suggest that use of the non-grid technique in digital mammography with the GE-DMR-FFDM unit, is presently not warranted. With improved uniformity correction procedure, this conclusion would change and one should be able to realize a 14% reduction in patient dose at the same SNR by using a non-grid technique.

A model based approach to object detection in digital mammography

Abstract: Breast cancer has the highest incidence level of any cancer among women living in developed countries. Early detection of masses, and microcalcification clusters, is presently the best means whereby breast cancer mortality rates may be reduced. To this end, high resolution digital mammograms, generally at the 50 μm resolution, are used with a view to automatically detecting any potential anomalies. This paper presents an original technique for the detection of such microcalcifications. This is done by specifying a parametric model and using this to gain estimates of object positions and sizes directly.

Quantitative Modelling of Microcalcification Detection in Digital Mammography

Abstract: This article presents a simulation framework for the image acquisition on digital mammography systems. The framework is used to analyse the performance of a previously developed method for the detection of microcalcifications by a series of top-hat operators. The framework allows to determine the theoretical number of false positives and true positives for a given set of acquisition parameters and size of microcalcifications.

Near-automatic quantification of breast tissue glandularity via digitized mammograms

Abstract: Studies reported in the literature indicate that breast cancer risk is associated with mammographic densities Although, an objective, repeatable quantitative measure of risk derived from mammographic densities will be of great use in recommending alternative screening paradigms and/or preventive measures, image processing efforts toward this goal seem to very sparse in the literature, and automatic and efficient methods do not seem to exist In this paper, we describe and validate an automatic and reproducible method to segment glandular tissue regions from fat within breasts from digitized mammograms using scale-based fuzzy connectivity methods Different measures for characterizing density are computed from the segmented regions and their accuracies in terms of their linear correlation across two different projections (CC and MLO) are studied It is shown that quantization of glandularity taking into account the original intensities is more accurate than just considering the segmented areas This makes the quantification less dependent on the shape of the glandular regions and the angle of projection A simple phantom experiment is done that supports this observation© (1999) COPYRIGHT SPIE--The International Society for Optical Engineering Downloading of the abstract is permitted for personal use only

Experimental and theoretical spectral optimization for digital mammography

Abstract: The detection characteristics of digital x-ray and film-screen mammography systems are different and thus current film-screen techniques are not ideal for digital mammography. Therefore optimum technical parameters required for digital mammography are likely to be different compared with film-screen mammography. The goal of this study is to evaluate the optimum technical parameters for full-field digital mammography by experimental and computer simulation methods. A General Electric Full Field Digital Mammography (FFDM) prototype unit using Cesium Iodide (CsI) on an amorphous Silicon photodiode array was used for the experimental measurements. Using breast equivalent phantoms, images were acquired for a set of x-ray target-filters for a range of peak kilovoltage, varying breast composition and thickness, with and without an anti-scatter grid. The signal-to-noise ratio (SNR) and figure-of-merit (FOM) were determined for simulated calcification and mass targets, independently by the two methods. The results for noise, contrast, SNR and FOM were compared and agree within 5% and 6% respectively. Combined results are presented for the case of 50% glandular - 50% adipose tissue breast composition using the grid and for the calcification target. Based on the FOM approach, preliminary results suggest that a Rhodium target-filter combination will be beneficial for higher breast thickness and for denser breasts.

Simulation of digital mammogram acquisition

Abstract: This work incorporates development of a model that relates 3-D arrangement of breast tissue and its appearance in a mammogram. The primary contribution is modeling breast structures based on their anatomic properties, obtained from the literature and histologic slice images. Additionally, breast compression deformation is estimated using tissue elastic properties and a deformation model. Synthetic mammograms obtained by the proposed approach can be used in analysis of positioning and compression effects, and for testing computer algorithms for detection of abnormalities. The paper discusses three major model components: (1) modeling breast anatomic structures, (2) modeling breast tissue compression, and (3) modeling X-ray image acquisition.

Iterative method for automatic detection of masses in digital mammograms for computer-aided diagnosis

Abstract: An iterative algorithm has been developed for automatic detection of breast masses from digitalized mammograms. Theprocedure has been divided in two stages. The first one based on the histogram analysis of the input image. The second oneemploys a topological analysis from the results obtained in the first stage. The final output is a set of interest regions that aredefined as suspicious areas by the system. These suspicious regions should be harder studied in order to present a finaldiagnosis'. The developed system may be used together with any other suspicious area diagnosis algorithms. In this way acomputer assisted diagnosis (CAD) program to assist radiologists in his mammography interpretation task could be easydeveloped.Keywords: Masses, Signal Analysis, Digitalized Mammograms, Computer-Aided Diagnosis, Breast Abnormality. 1. INTRODUCTION Breast cancer is one of the most common malignancies of women, causing an estimated 44,500 deaths in the United Statesduring 199 1 .

Dual mode stereotactic localization method and application

Abstract: The invention described herein combines the structural digital X-ray image provided by conventional stereotactic core biopsy instruments with the additional functional metabolic gamma imaging obtained with a dedicated compact gamma imaging mini-camera. Before the procedure, the patient is injected with an appropriate radiopharmaceutical. The radiopharmaceutical uptake distribution within the breast under compression in a conventional examination table expressed by the intensity of gamma emissions is obtained for comparison (co-registration) with the digital mammography (X-ray) image. This dual modality mode of operation greatly increases the functionality of existing stereotactic biopsy devices by yielding a much smaller number of false positives than would be produced using X-ray images alone. The ability to obtain both the X-ray mammographic image and the nuclear-based medicine gamma image using a single device is made possible largely through the use of a novel, small and movable gamma imaging camera that permits its incorporation into the same table or system as that currently utilized to obtain X-ray based mammographic images for localization of lesions.

Image deconvolution as an aid to mammographic artifact identification: I. Basic techniques

Abstract: Digital mammography has the potential to provide radiologists with a tool which can detect tumors earlier and with greater accuracy then film based systems. Although a digital mammography system can provide much greater contrast when compared with a conventional film system, the ability to detect small artifacts associated with breast cancer is limited by a reduced spatial resolution due to screen unsharpness and scatter induced fog. In this paper we model the radiological image formation process as the convolution of a linear shift invariant point spread function (PSF) with the projected tissue density source function. We model the PSF as consisting of two components--screen unsharpness and scatter. We present results from a method designed to compensate for screen unsharpness. The screen PSF was measured and subsequently used in an iterative deconvolution algorithm which incorporated wavelet based de-noising between steps in order to reduce noise amplification. When applied to a University of Leeds TORMAX breast phantom the results show as much as a two-fold improvement in resolution at the 50 percent MTF level. Our results show that the regularized deconvolution algorithm significantly improves the signal-to-noise ratio in the restored image.

OPDIMA: large-area CCD-based x-ray image sensor for spot imaging and biopsy control in mammography

Abstract: The detection characteristics of digital x-ray and film-screen mammography systems are different and thus current film-screen techniques are not ideal for digital mammography. Therefore optimum technical parameters required for digital mammography are likely to be different compared with film-screen mammography. The goal of this study is to evaluate the optimum technical parameters for full-field digital mammography by experimental and computer simulation methods. A General Electric Full Field Digital Mammography (FFDM) prototype unit using Cesium Iodide (CsI) on an amorphous Silicon photodiode array was used for the experimental measurements. Using breast equivalent phantoms, images were acquired for a set of x-ray target-filters for a range of peak kilovoltage, varying breast composition and thickness, with and without an anti-scatter grid. The signal-to-noise ratio (SNR) and figure-of-merit (FOM) were determined for simulated calcification and mass targets, independently by the two methods. The results for noise, contrast, SNR and FOM were compared and agree within 5% and 6% respectively. Combined results are presented for the case of 50% glandular - 50% adipose tissue breast composition using the grid and for the calcification target. Based on the FOM approach, preliminary results suggest that a Rhodium target-filter combination will be beneficial for higher breast thickness and for denser breasts.

Application of image processing techniques for contrast enhancement in dense breast digital mammograms

Abstract: Dense breasts, that usually are characteristic of women less than 40 years old, difficult many times early detection of breast cancer. In this work we present the application of some image processing techniques intended to enhance the contrast in dense breast images, regarding the detection of clustered microcalcifications. The procedure was, firstly, determining in the literature the main techniques used for mammographic images contrast enhancement. The results indicate that, in general: (1) as expected, the overall performance of the CAD scheme for clusters detection decreased when applied exclusively to dense breast images, compared to the application to a set of images without this characteristic; (2) most of the techniques for contrast enhancement used successfully in generic mammography images databases are not able to enhance structures of athirst in databases formed only by dense breasts images, due to the very poor contrast between microcalcifications, for example, and other tissues. These features should stress, therefore, the need of developing a methodology specifically for this type of images in order to provide better conditions to the detection of breast suspicious structures in these group of women.

Designing and implementing a PACS-aware DICOM image object for digital x-ray, mammography, and intraoral applications

Abstract: The introduction of new digital detector technology for projection radiography created an opportunity to revisit the support for x-ray images in the DICOM standard. A new family of digital x-ray objects has been developed with greater emphasis on the productivity and workflow requirements of PACS and soft copy reading on workstations. The use of these DX objects present new design challenges for acquisition and display systems.© (1999) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.

Gaas detector optimization for different medical imaging applications

Abstract: We have investigated the detection performance of GaAs detectors made with different thickness and contact geometries. A comparison is made between these detection capabilities and the imaging requirements for the following medical applications: digital mammography, digital chest radiography and nuclear medicine. Experimental results and preliminary images are presented and discussed.