Full-field Digital Mammograms Research Articles

Volumetric Breast Density Estimation from Full-Field Digital Mammograms: A Validation Study

ObjectivesTo objectively evaluate automatic volumetric breast density assessment in Full-Field Digital Mammograms (FFDM) using measurements obtained from breast Magnetic Resonance Imaging (MRI).Material and MethodsA commercially available method for volumetric breast density estimation on FFDM is evaluated by comparing volume estimates obtained from 186 FFDM exams including mediolateral oblique (MLO) and cranial-caudal (CC) views to objective reference standard measurements obtained from MRI.ResultsVolumetric measurements obtained from FFDM show high correlation with MRI data. Pearson’s correlation coefficients of 0.93, 0.97 and 0.85 were obtained for volumetric breast density, breast volume and fibroglandular tissue volume, respectively.ConclusionsAccurate volumetric breast density assessment is feasible in Full-Field Digital Mammograms and has potential to be used in objective breast cancer risk models and personalized screening.

Use of volumetric features for temporal comparison of mass lesions in full field digital mammograms

Temporal comparison of lesions might improve classification between benign and malignant lesions in full-field digital mammograms (FFDM). The authors compare the use of volumetric features for lesion classification, which are computed from dense tissue thickness maps, to the use of mammographic lesion area. Use of dense tissue thickness maps for lesion characterization is advantageous, since it results in lesion features that are invariant to acquisition parameters. The dataset used in the analysis consisted of 60 temporal mammogram pairs comprising 120 mediolateral oblique or craniocaudal views with a total of 65 lesions, of which 41 were benign and 24 malignant. The authors analyzed the performance of four volumetric features, area, and four other commonly used features obtained from temporal mammogram pairs, current mammograms, and prior mammograms. The authors evaluated the individual performance of all features and of different feature sets. The authors used linear discriminant analysis with leave-one-out cross validation to classify different feature sets. Volumetric features from temporal mammogram pairs achieved the best individual performance, as measured by the area under the receiver operating characteristic curve (Az value). Volume change (Az = 0.88) achieved higher Az value than projected lesion area change (Az = 0.78) in the temporal comparison of lesions. Best performance was achieved with a set that consisted of a set of features extracted from the current exam combined with four volumetric features representing changes with respect to the prior mammogram (Az = 0.90). This was significantly better (p = 0.005) than the performance obtained using features from the current exam only (Az = 0.77). Volumetric features from temporal mammogram pairs combined with features from the single exam significantly improve discrimination of benign and malignant lesions in FFDM mammograms compared to using only single exam features. In the comparison with prior mammograms, use of volumetric change may lead to better performance than use of lesion area change.

Mass type-specific sparse representation for mass classification in computer-aided detection on mammograms

BackgroundBreast cancer is the leading cause of both incidence and mortality in women population. For this reason, much research effort has been devoted to develop Computer-Aided Detection (CAD) systems for early detection of the breast cancers on mammograms. In this paper, we propose a new and novel dictionary configuration underpinning sparse representation based classification (SRC). The key idea of the proposed algorithm is to improve the sparsity in terms of mass margins for the purpose of improving classification performance in CAD systems.MethodsThe aim of the proposed SRC framework is to construct separate dictionaries according to the types of mass margins. The underlying idea behind our method is that the separated dictionaries can enhance the sparsity of mass class (true-positive), leading to an improved performance for differentiating mammographic masses from normal tissues (false-positive). When a mass sample is given for classification, the sparse solutions based on corresponding dictionaries are separately solved and combined at score level. Experiments have been performed on both database (DB) named as Digital Database for Screening Mammography (DDSM) and clinical Full Field Digital Mammogram (FFDM) DBs. In our experiments, sparsity concentration in the true class (SCTC) and area under the Receiver operating characteristic (ROC) curve (AUC) were measured for the comparison between the proposed method and a conventional single dictionary based approach. In addition, a support vector machine (SVM) was used for comparing our method with state-of-the-arts classifier extensively used for mass classification.ResultsComparing with the conventional single dictionary configuration, the proposed approach is able to improve SCTC of up to 13.9% and 23.6% on DDSM and FFDM DBs, respectively. Moreover, the proposed method is able to improve AUC with 8.2% and 22.1% on DDSM and FFDM DBs, respectively. Comparing to SVM classifier, the proposed method improves AUC with 2.9% and 11.6% on DDSM and FFDM DBs, respectively.ConclusionsThe proposed dictionary configuration is found to well improve the sparsity of dictionaries, resulting in an enhanced classification performance. Moreover, the results show that the proposed method is better than conventional SVM classifier for classifying breast masses subject to various margins from normal tissues.

Learning from unbalanced data: A cascade-based approach for detecting clustered microcalcifications

Finding abnormalities in diagnostic images is a difficult task even for expert radiologists because the normal tissue locations largely outnumber those with suspicious signs which may thus be missed or incorrectly interpreted. For the same reason the design of a Computer-Aided Detection (CADe) system is very complex because the large predominance of normal samples in the training data may hamper the ability of the classifier to recognize the abnormalities on the images. In this paper we present a novel approach for computer-aided detection which faces the class imbalance with a cascade of boosting classifiers where each node is trained by a learning algorithm based on ranking instead of classification error. Such approach is used to design a system (CasCADe) for the automated detection of clustered microcalcifications (μCs), which is a severely unbalanced classification problem because of the vast majority of image locations where no μC is present. The proposed approach was evaluated with a dataset of 1599 full-field digital mammograms from 560 cases and compared favorably with the Hologic R2CAD ImageChecker, one of the most widespread commercial CADe systems. In particular, at the same lesion sensitivity of R2CAD (90%) on biopsy proven malignant cases, CasCADe and R2CAD detected 0.13 and 0.21 false positives per image (FPpi), respectively (p-value=0.09), whereas at the same FPpi of R2CAD (0.21), CasCADe and R2CAD detected 93% and 90% of true lesions respectively (p-value=0.11) thus showing that CasCADe can compete with high-end CADe commercial systems.

PB.24: Is digital breast tomosynthesis helpful within a symptomatic one-stop breast clinic for characterisation of subtle findings?

The study investigated the accuracy of two-view full-field digital (2D) mammograms (FFDM) by comparing its performance with two-view digital breast tomosynthesis (3D) plus FFDM combination in a symptomatic setting.

Breast density classification to reduce false positives in CADe systems

This paper describes a novel weighted voting tree classification scheme for breast density classification. Breast parenchymal density is an important risk factor in breast cancer. Moreover, it is known that mammogram interpretation is more difficult when dense tissue is involved. Therefore, automated breast density classification may aid in breast lesion detection and analysis. Several classification methods have been compared and a novel hierarchical classification procedure of combined classifiers with linear discriminant analysis (LDA) is proposed as the best solution to classify the mammograms into the four BIRADS tissue classes. The classification scheme is based on 298 texture features. Statistical analysis to test the normality and homoscedasticity of the data was carried out for feature selection. Thus, only features that are influenced by the tissue type were considered. The novel classification techniques have been incorporated into a CADe system to drive the detection algorithms and tested with 1459 images. The results obtained on the 322 screen-film mammograms (SFM) of the mini-MIAS dataset show that 99.75% of samples were correctly classified. On the 1137 full-field digital mammograms (FFDM) dataset results show 91.58% agreement. The results of the lesion detection algorithms were obtained from modules integrated within the CADe system developed by the authors and show that using breast tissue classification prior to lesion detection leads to an improvement of the detection results. The tools enhance the detectability of lesions and they are able to distinguish their local attenuation without local tissue density constraints.

Estimation of the breast skin-line in mammograms using multidirectional Gabor filters

Segmentation of the breast region is a fundamental step in any system for computerized analysis of mammograms. In this work, we propose a novel procedure for the estimation of the breast skin-line based upon multidirectional Gabor filtering. The method includes an adaptive values-of-interest (VOI) transformation, extraction of the skin–air ribbon by Otsu's thresholding method and the Euclidean distance transform, Gabor filtering with 18 real kernels, and a step for suppression of false edge points using the magnitude and phase responses of the filters. On a test set of 361 images from different acquisition modalities (screen-film and full-field digital mammograms), the average Hausdorff and polyline distances obtained were 2.85mm and 0.84mm, respectively, with reference to the ground-truth boundaries provided by an expert radiologist. When compared with the results obtained by other state-of-the-art methods on the same set of images and with respect to the same ground-truth boundaries, our method mostly outperformed the other approaches. The results demonstrate the effectiveness and robustness of the proposed algorithm.

Validation of DM-Scan, a computer-assisted tool to assess mammographic density in full-field digital mammograms

We developed a semi-automated tool to assess mammographic density (MD), a phenotype risk marker for breast cancer (BC), in full-field digital images and evaluated its performance testing its reproducibility, comparing our MD estimates with those obtained by visual inspection and using Cumulus, verifying their association with factors that influence MD, and studying the association between MD measures and subsequent BC risk.Three radiologists assessed MD using DM-Scan, the new tool, on 655 processed images (craniocaudal view) obtained in two screening centers. Reproducibility was explored computing pair-wise concordance correlation coefficients (CCC). The agreement between DM-Scan estimates and visual assessment (semi-quantitative scale, 6 categories) was quantified computing weighted kappa statistics (quadratic weights). DM-Scan and Cumulus readings were compared using CCC. Variation of DM-Scan measures by age, body mass index (BMI) and other MD modifiers was tested in regression mixed models with mammographic device as a random-effect term.The association between DM-Scan measures and subsequent BC was estimated in a case–control study. All BC cases in screening attendants (2007–2010) at a center with full-field digital mammography were matched by age and screening year with healthy controls (127 pairs). DM-Scan was used to blindly assess MD in available mammograms (112 cases/119 controls). Unconditional logistic models were fitted, including age, menopausal status and BMI as confounders.DM-Scan estimates were very reliable (pairwise CCC: 0.921, 0.928 and 0.916). They showed a reasonable agreement with visual MD assessment (weighted kappa ranging 0.79-0.81). DM-Scan and Cumulus measures were highly concordant (CCC ranging 0.80-0.84), but ours tended to be higher (4%-5% on average). As expected, DM-Scan estimates varied with age, BMI, parity and family history of BC. Finally, DM-Scan measures were significantly associated with BC (p-trend=0.005). Taking MD<7% as reference, OR per categories of MD were: OR7%-17%=1.32 (95% CI=0.59-2.99), OR17%-28%=2.28 (95% CI=1.03-5.04) and OR>=29%=3.10 (95% CI=1.35-7.14). Our results confirm that DM-Scan is a reliable tool to assess MD in full-field digital mammograms.Electronic supplementary materialThe online version of this article (doi:10.1186/2193-1801-2-242) contains supplementary material, which is available to authorized users.

Automatic Detection of the Nipple in Screen-Film and Full-field Digital Mammograms Using a Novel Hessian-Based Method

Automatic detection of the nipple in mammograms is an important step in computerized systems that combine multiview information for accurate detection and diagnosis of breast cancer. Locating the nipple is a difficult task owing to variations in image quality, presence of noise, and distortion and displacement of the breast tissue due to compression. In this work, we propose a novel Hessian-based method to locate automatically the nipple in screen-film and full-field digital mammograms (FFDMs). The method includes detection of a plausible nipple/retroareolar area in a mammogram using geometrical constraints, analysis of the gradient vector field by mean and Gaussian curvature measurements, and local shape-based conditions. The proposed procedure was tested on 566 mammographic images consisting of 372 randomly selected scanned films from two public databases (mini-MIAS and DDSM), and 194 digital mammograms acquired with a GE Senographe 2000D FFDM system. A radiologist independently marked the centers of the nipples for evaluation of the results. The average error obtained was 6.7 mm (22 pixels) with reference to the center of the nipple as identified by the radiologist. Only two out of the 566 detected nipples (0.35 %) had an error larger than 50 mm. The method was also directly compared with two other techniques for the detection of the nipple. The results indicate that the proposed method outperforms other algorithms presented in the literature and can be used to identify accurately the nipple on various types of mammographic images.

Erratum to: Women’s features and inter-/intra-rater agreement on mammographic density assessment in full-field digital mammograms (DDM-SPAIN)

Erratum to: Women’s features and inter-/intra-rater agreement on mammographic density assessment in full-field digital mammograms (DDM-SPAIN)

Abstract A116: Inter-scan reproducibility of mammographic density measurements

Abstract Introduction: A decrease in mammographic density is increasingly used as an intermediate marker to evaluate the effect of interventions for reducing breast cancer risk. Technical aspects, such as a different amount of compression, could influence density measurements. We investigated the inter-scan reproducibility of mammographic density measurements to see whether this may influence measurements of true changes in density. Methods: We included women with two mammograms made closely after one another (median 12 days), with no true changes in breast density expected to happen: 106 had two Full-Field Digital Mammograms (FFDM), 12 had two Film-Screen Mammograms (FSM) and 52 had one FFDM and one FSM. We estimated intraclass correlation coefficients, and mean absolute density differences between both mammograms. Results: The intraclass correlation between percent density measurements on two FFDMs was 0.93 (95%CI 0.91-0.95), between two FSMs 0.94 (95%CI 0.84-0.98), and between FFDM and FSM 0.72 (95%CI 0.61-0.80). The mean absolute difference in percent density between two FFDMs was 4.3% (95%CI 3.8-4.9) and between two FSMs 6.5% (95%CI 3.4-9.6). On average, percent density in the same woman was higher on FFDM than on FSM (difference 12.9% (95%CI 11.0-14.8)). Conclusion: When studying changes in mammographic density, it is important to compare two mammograms of the same type (FSM or FFDM). Even then, although intraclass correlation coefficients are high, the absolute differences in percent density are still of a magnitude that may influence detectability of true changes in mammographic density. Citation Format: Mariëtte Lokate, Marleen Emaus, Michiel Kallenberg, Marije Bakker, Nico Karssemeijer, Wouter Veldhuis, Petra Peeters, Carla van Gils. Inter-scan reproducibility of mammographic density measurements. [abstract]. In: Proceedings of the Eleventh Annual AACR International Conference on Frontiers in Cancer Prevention Research; 2012 Oct 16-19; Anaheim, CA. Philadelphia (PA): AACR; Cancer Prev Res 2012;5(11 Suppl):Abstract nr A116.

A comparison of reading times in full-field digital mammography and digital breast tomosynthesis

Currently, breast screening is implemented with X-ray mammography, but new technology such as digital breast tomosynthesis (DBT) may provide significant advantages because it produces three-dimensional breast images with high spatial resolution. To aid assessment of the cost-effectiveness of DBT, we have compared reading times for DBT images and full-field digital mammograms (FFDM) of the same women.

Computer-aided Detection of Masses at Mammography: Interactive Decision Support versus Prompts

To compare effectiveness of an interactive computer-aided detection (CAD) system, in which CAD marks and their associated suspiciousness scores remain hidden unless their location is queried by the reader, with the effect of traditional CAD prompts used in current clinical practice for the detection of malignant masses on full-field digital mammograms. The requirement for institutional review board approval was waived for this retrospective observer study. Nine certified screening radiologists and three residents who were trained in breast imaging read 200 studies (63 studies containing at least one screen-detected mass, 17 false-negative studies, 20 false-positive studies, and 100 normal studies) twice, once with CAD prompts and once with interactive CAD. Localized findings were reported and scored by the readers. In the prompted mode, findings were recorded before and after activation of CAD. The partial area under the location receiver operating characteristic (ROC) curve for an interval of low false-positive fractions typical for screening, from 0 to 0.2, was computed for each reader and each mode. Differences in reader performance were analyzed by using software. The average partial area under the location ROC curve with unaided reading was 0.57, and it increased to 0.62 with interactive CAD, while it remained unaffected by prompts. The difference in reader performance for unaided reading versus interactive CAD was statistically significant (P = .009). When used as decision support, interactive use of CAD for malignant masses on mammograms may be more effective than the current use of CAD, which is aimed at the prevention of perceptual oversights.

Malignant lesions on mammography: accuracy of two different computer-aided detection systems

We retrospectively compared the accuracy of two computer-aided detection (CAD) systems for the detection of malignant breast lesions on full-field digital mammograms. Mammograms of 326 patients were analyzed (117 patients with breast cancer, 209 negative cases), and each set of cases was read by two CAD systems (Second Look versus AccuDetect Galileo). True-positive fractions per image and case for soft densities, microcalcifications, and total cancers were assessed. Study results showed better overall performance of AccuDetect Galileo (when compared to Second Look) in detecting masses, microcalcifications, and all cancer types, especially in extremely dense breast parenchyma.

Computerized Analysis of Mammographic Parenchymal Patterns on a Large Clinical Dataset of Full-Field Digital Mammograms: Robustness Study with Two High-Risk Datasets

The purpose of this study was to demonstrate the robustness of our prior computerized texture analysis method for breast cancer risk assessment, which was developed initially on a limited dataset of screen-film mammograms. This current study investigated the robustness by (1) evaluating on a large clinical dataset, (2) using full-field digital mammograms (FFDM) as opposed to screen-film mammography, and (3) incorporating analyses over two types of high-risk patient sets, as well as patients at low risk for breast cancer. The evaluation included the analyses on the parenchymal patterns of women at high risk of developing of breast cancer, including both BRCA1/2 gene mutation carriers and unilateral cancer patients, and of women at low risk of developing breast cancer. A total of 456 cases, including 53 women with BRCA1/2 gene mutations, 75 women with unilateral cancer, and 328 low-risk women, were retrospectively collected under an institutional review board approved protocol. Regions-of-interest (ROIs), were manually selected from the central breast region immediately behind the nipple. These ROIs were subsequently used in computerized feature extraction to characterize the mammographic parenchymal patterns in the images. Receiver operating characteristic analysis was used to assess the performance of the computerized texture features in the task of distinguishing between high-risk and low-risk subjects. In a round robin evaluation on the FFDM dataset with Bayesian artificial neural network analysis, AUC values of 0.82 (95% confidence interval [0.75, 0.88]) and 0.73 (95% confidence interval [0.67, 0.78]) were obtained between BRCA1/2 gene mutation carriers and low-risk women, and between unilateral cancer and low-risk women, respectively. These results from computerized texture analysis on digital mammograms demonstrated that high-risk and low-risk women have different mammographic parenchymal patterns. On this large clinical dataset, we validated our methods for quantitative analyses of mammographic patterns on FFDM, statistically demonstrating again that women at high risk tend to have dense breasts with coarse and low-contrast texture patterns.

Compression paddle tilt correction in full-field digital mammograms

During the acquisition of a mammogram the breast is compressed between the compression paddle and the support table. When compression is applied, the upper plate is tilted which results in variation in breast thickness from the chest wall to the breast margin. Variation in breast thickness influences the grey-level values of the image and hampers image analysis, such as volumetric breast density estimation. In this paper, we present and compare two methods that estimate and correct image tilt. The first method estimates tilt from fatty tissue regions. The second method is based on the entropy of the grey-level distribution of the image. Both methods use a classifier that distinguishes fatty areas from dense tissue based on texture features independent of tilt. The tilt correction methods are evaluated by assessing their accuracies in estimating artificial tilts that are added to images that are known to have only a small tilt. On average, both methods are able to estimate the artificial tilt. To the best of our knowledge, this is the first paper that presents and validates tilt correction methods on individual mammograms.

P5-08-03: How Reader's Training, Software, and Image Formats Impact Percent Dense Area Measures.

Abstract BACKGROUND: Mammographic percent dense area, the percent ratio of dense to total breast area in a mammogram, is one of the strongest measures of a woman's risk of breast cancer. However, systematic differences have been observed between readers and mammography technologies (film and digital) that could cause clinically inconsistent associations with risk. The purpose of this study was to evaluate inter- and intra-reproducibility of percent dense area between readers and between film and digital technologies. METHODS: One hundred digitized film mammograms were randomly selected with 25 films in each of quartile of percent density and read by two readers at two different sites (Mayo Clinic and UCSF). The readers had extensive experience and were also jointly trained at university of Toronto using Cumulus software. After training, all films were read twice with at least one year between duplicate readings. The Mayo clinic reading used Cumulus while UCSF used custom semiautomatic software to estimate total and dense tissue area. In addition, digitized films and unprocessed full field digital mammograms of the same women were assessed by one reader. The time between the film and digital acquisitions ranged from nine to twenty-four months. Interclass correlation coefficient (ICC) was calculated for each comparison. RESULTS: The intra- and inter-observer ICCs, consistency for film images, were 0.96 (UCSF) and 0.97 (Mayo), and 0.96 (UCSF vs. Mayo). We found ICC between film and digital mammograms for percent dense area was 0.88. The digital mammogram had 9% significantly higher total breast area and 5% significantly lower percent density area compared to film. CONCLUSIONS: Similarly trained readers had a high reproducibility regardless of the software used. Our results suggest centralized reader training should enable pooling of film breast density results from different clinics. However, pooling film and digital results would need careful calibration due to lower measured percent dense areas than on film. Citation Information: Cancer Res 2011;71(24 Suppl):Abstract nr P5-08-03.

Improving Performance of Computer-aided Detection of Masses by Incorporating Bilateral Mammographic Density Asymmetry: An Assessment

Bilateral mammographic density asymmetry is a promising indicator in assessing risk of having or developing breast cancer. This study aims to assess the performance improvement of a computer-aided detection (CAD) scheme in detecting masses by incorporating bilateral mammographic density asymmetrical information. A testing dataset containing 2400 full-field digital mammograms (FFDM) acquired from 600 examination cases was established. Among them, 300 were positive cases with verified cancer associated with malignant masses and 300 were negative cases. Two computerized schemes were applied to process images of each case. The first single-image based CAD scheme detected suspicious mass regions and the second scheme computed average and difference of mammographic tissue density depicted between the left and right breast. A fusion method based on rotation of the CAD scoring projection reference axis was then applied to combine CAD-generated mass detection scores and either the computed average or difference (asymmetry) of bilateral mammographic density scores. The CAD performance levels with and without incorporating mammographic density information were evaluated and compared using a free-response receiver operating characteristic type data analysis method. CAD achieved a case-based mass detection sensitivity of 0.74 and a region-based sensitivity of 0.56 at a false-positive rate of 0.25 per image. By fusing the CAD and bilateral mammographic density asymmetry scores, the case-based and region-based sensitivity levels of the CAD scheme were increased to 0.84 and 0.69, respectively, at the same false-positive rate. Fusion with average mammographic density only slightly increased CAD sensitivity to 0.75 (case-based) and 0.57 (region-based). This study indicated that 1) bilateral mammographic density asymmetry was a stronger indicator of the case depicting suspicious masses than the average density computed from two breasts and 2) fusion between the conventional CAD scores and bilateral mammographic density asymmetry information could substantially increase CAD performance in mass detection.

Dose Reduction in Digital Breast Tomosynthesis (DBT) Screening using Synthetically Reconstructed Projection Images: An Observer Performance Study

The aim of this study was to retrospectively compare the interpretive performance of synthetically reconstructed two-dimensional images in combination with digital breast tomosynthesis (DBT) versus full-field digital mammography (FFDM) plus DBT. Ten radiologists trained in reading tomosynthesis examinations interpreted retrospectively, under two modes, 114 mammograms. One mode included the directly acquired full-field digital mammograms combined with DBT, and the other included synthetically reconstructed projection images combined with DBT. The reconstructed images do not require additional radiation exposure. The two modes were compared with respect to sensitivity, namely, recommendation to recall a breast with either a pathology-proven cancer (n = 48) or a high-risk lesion (n = 6), and specificity, namely, no recommendation to recall a breast not depicting an abnormality (n = 144) or depicting only benign abnormalities (n = 30). The average sensitivity for FFDM with DBT was 0.826, compared to 0.772 for synthetic FFDM with DBT (difference, 0.054; P = .017 and P = .053 for fixed and random reader effects, respectively). The proportions of breasts with no or benign abnormalities recommended to be recalled were virtually the same: 0.298 and 0.297 for the two modalities, respectively (95% confidence intervals for the difference, -0.028 to 0.036 and -0.070 to 0.066 for fixed and random reader effects, respectively). Sixteen additional clusters of microcalcifications ("positive" breasts) were missed by all readers combined when interpreting the mode with synthesized images versus FFDM. Lower sensitivity with comparable specificity was observed with the tested version of synthetically generated images compared to FFDM, both combined with DBT. Improved synthesized images with experimentally verified acceptable diagnostic quality will be needed to eliminate double exposure during DBT-based screening.

INbreast: Toward a Full-field Digital Mammographic Database

Computer-aided detection and diagnosis (CAD) systems have been developed in the past two decades to assist radiologists in the detection and diagnosis of lesions seen on breast imaging exams, thus providing a second opinion. Mammographic databases play an important role in the development of algorithms aiming at the detection and diagnosis of mammary lesions. However, available databases often do not take into consideration all the requirements needed for research and study purposes. This article aims to present and detail a new mammographic database. Images were acquired at a breast center located in a university hospital (Centro Hospitalar de S. João [CHSJ], Breast Centre, Porto) with the permission of the Portuguese National Committee of Data Protection and Hospital's Ethics Committee. MammoNovation Siemens full-field digital mammography, with a solid-state detector of amorphous selenium was used. The new database-INbreast-has a total of 115 cases (410 images) from which 90 cases are from women with both breasts affected (four images per case) and 25 cases are from mastectomy patients (two images per case). Several types of lesions (masses, calcifications, asymmetries, and distortions) were included. Accurate contours made by specialists are also provided in XML format. The strengths of the actually presented database-INbreast-relies on the fact that it was built with full-field digital mammograms (in opposition to digitized mammograms), it presents a wide variability of cases, and is made publicly available together with precise annotations. We believe that this database can be a reference for future works centered or related to breast cancer imaging.