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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 μ m by the present instrumental condition. This modality could open up an x-ray pathological diagnosis.

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.

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.

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.

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.

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.

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 SFM_D 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.

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.

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.

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 μ m and 100 μ m pixel sizes ranked similarly and were inferior to the other 2 detectors.