Showing papers on "Digital mammography published in 2012"

Two-View and Single-View Tomosynthesis versus Full-Field Digital Mammography : High-Resolution X-Ray Imaging Observer Study

Abstract: Purpose: To compare the diagnostic accuracy of two-dimensional (2D) full-field digital mammography with that of two-view (mediolateral and craniocaudal) and single-view (mediolateral oblique) tomos ...

Dual-energy contrast-enhanced digital mammography: initial clinical results of a multireader, multicase study

Abstract: The purpose of this study was to compare the diagnostic accuracy of dual-energy contrast-enhanced digital mammography (CEDM) as an adjunct to mammography (MX) ± ultrasonography (US) with the diagnostic accuracy of MX ± US alone. One hundred ten consenting women with 148 breast lesions (84 malignant, 64 benign) underwent two-view dual-energy CEDM in addition to MX and US using a specially modified digital mammography system (Senographe DS, GE Healthcare). Reference standard was histology for 138 lesions and follow-up for 12 lesions. Six radiologists from 4 institutions interpreted the images using high-resolution softcopy workstations. Confidence of presence (5-point scale), probability of cancer (7-point scale), and BI-RADS scores were evaluated for each finding. Sensitivity, specificity and ROC curve areas were estimated for each reader and overall. Visibility of findings on MX ± CEDM and MX ± US was evaluated with a Likert scale. The average per-lesion sensitivity across all readers was significantly higher for MX ± US ± CEDM than for MX ± US (0.78 vs. 0.71 using BIRADS, p = 0.006). All readers improved their clinical performance and the average area under the ROC curve was significantly superior for MX ± US ± CEDM than for MX ± US ((0.87 vs 0.83, p = 0.045). Finding visibility was similar or better on MX ± CEDM than MX ± US in 80% of cases. Dual-energy contrast-enhanced digital mammography as an adjunct to MX ± US improves diagnostic accuracy compared to MX ± US alone. Addition of iodinated contrast agent to MX facilitates the visualization of breast lesions.

Breast tomosynthesis and digital mammography: a comparison of diagnostic accuracy.

Abstract: Objective Our aim was to compare the ability of radiologists to detect breast cancers using one-view breast tomosynthesis (BT) and two-view digital mammography (DM) in an enriched population of diseased patients and benign and/or healthy patients. Methods All participants gave informed consent. The BT and DM examinations were performed with about the same average glandular dose to the breast. The study population comprised patients with subtle signs of malignancy seen on DM and/or ultrasonography. Ground truth was established by pathology, needle biopsy and/or by 1-year follow-up by mammography, which retrospectively resulted in 89 diseased breasts (1 breast per patient) with 95 malignant lesions and 96 healthy or benign breasts. Two experienced radiologists, who were not participants in the study, determined the locations of the malignant lesions. Five radiologists, experienced in mammography, interpreted the cases independently in a free-response study. The data were analysed by the receiver operating c...

Estimation of breast percent density in raw and processed full field digital mammography images via adaptive fuzzy c-means clustering and support vector machine segmentation

Abstract: Purpose: The amount of fibroglandular tissue content in the breast as estimated mammographically, commonly referred to as breast percent density (PD%), is one of the most significant risk factors for developing breast cancer. Approaches to quantify breast density commonly focus on either semiautomated methods or visual assessment, both of which are highly subjective. Furthermore, most studies published to date investigating computer-aided assessment of breast PD% have been performed using digitized screen-film mammograms, while digital mammography is increasingly replacing screen-film mammography in breast cancer screening protocols. Digital mammography imaging generates two types of images for analysis, raw (i.e., “FOR PROCESSING”) and vendor postprocessed (i.e., “FOR PRESENTATION”), of which postprocessed images are commonly used in clinical practice. Development of an algorithm which effectively estimates breast PD% in both raw and postprocessed digital mammography images would be beneficial in terms of direct clinical application and retrospective analysis. Methods: This work proposes a new algorithm for fully automated quantification of breast PD% based on adaptive multiclass fuzzy c-means (FCM) clustering and support vector machine (SVM) classification, optimized for the imaging characteristics of both raw and processed digital mammography images as well as for individual patient and image characteristics. Our algorithm first delineates the breast region within the mammogram via an automated thresholding scheme to identify background air followed by a straight line Hough transform to extract the pectoral muscle region. The algorithm then applies adaptive FCM clustering based on an optimal number of clusters derived from image properties of the specific mammogram to subdivide the breast into regions of similar gray-level intensity. Finally, a SVM classifier is trained to identify which clusters within the breast tissue are likely fibroglandular, which are then aggregated into a final dense tissue segmentation that is used to compute breast PD%. Our method is validated on a group of 81 women for whom bilateral, mediolateral oblique, raw and processed screening digital mammograms were available, and agreement is assessed with both continuous and categorical density estimates made by a trained breast-imaging radiologist. Results: Strong association between algorithm-estimated and radiologist-provided breast PD% was detected for both raw (r = 0.82, p 0.1) due to either presentation of the image (raw vs processed) or method of PD% assessment (radiologist vs algorithm). Conclusions: The proposed fully automated algorithm was successful in estimating breast percent density from both raw and processed digital mammographic images. Accurate assessment of a woman's breast density is critical in order for the estimate to be incorporated into risk assessment models. These results show promise for the clinical application of the algorithm in quantifying breast density in a repeatable manner, both at time of imaging as well as in retrospective studies.

Clinical Digital Breast Tomosynthesis System: Dosimetric Characterization

Abstract: For the breast phantom representing the ???average??? breast (5.0 cm thick, 50% glandular fraction), the mean glandular doses (MGDs) for the full-field digital mammography and digital breast tomosynthesis acquisitions were 1.20 and 1.30 mGy, respectively, resulting in a difference of only 8% between the two modalities; a fusion two-dimensional???three-dimensional imaging study resulted in an MGD of 2.50 mGy.

Digital breast tomosynthesis (DBT): initial experience in a clinical setting.

Abstract: BackgroundDigital breast tomosynthesis (DBT) is a promising new technology. Some experimental clinical studies have shown positive results, but the future role and indications of this new technique, whether in a screening or clinical setting, need to be evaluated.PurposeTo compare digital mammography and DBT in a side-by-side feature analysis for cancer conspicuity, and to assess whether there is a potential additional value of DBT to standard state-of-the-art conventional imaging work-up with respect to detection of additional malignancies.Material and MethodsThe study had ethics committee approval. A total of 129 women underwent 2D digital mammography including supplementary cone-down and magnification views and breast ultrasonography if indicated, as well as digital breast tomosynthesis. The indication for conventional imaging in the clinical setting included a palpable lump in 30 (23%), abnormal mammographic screening findings in 54 (42%), and surveillance in 45 (35%) of the women. The women were exam...

Comparison of digital screening mammography and screen-film mammography in the early detection of clinically relevant cancers: a multicenter study.

Abstract: According to our data, there is no sign of a disproportionate increase in lowgrade ductal carcinoma in situ lesions—indicative of possible overdiagnosis— with the transition to digital mammography in breast cancer screening.

Inflammatory Breast Cancer: What We Know and What We Need to Learn

Abstract: Purpose. We review the current status of multidisciplinary care for patients with inflammatory breast cancer (IBC) and discuss what further research is needed to advance the care of patients with this disease. Design. We performed a comprehensive review of the English-language literature on IBC through computerized literature searches. Results. Significant advances in imaging, including digital mammography, high-resolution ultrasonography with Doppler capabilities, magnetic resonance imaging, and positron emission tomography–computed tomography, have improved the diagnosis and staging of IBC. There are currentlynoestablishedmolecularcriteriafordistinguishing IBC from noninflammatory breast cancer. Such crite

Breast cancer: missed interval and screening-detected cancer at full-field digital mammography and screen-film mammography-- results from a retrospective review.

Abstract: According to our study results, the implementation of full-field digital mammography (FFDM) has not reduced the challenge of missed cancers in screening mammography, and mammographic features in cancers missed at FFDM seem to differ from those in cancers missed at screen-film mammography.

Effect of image quality on calcification detection in digital mammography.

Abstract: Purpose: This study aims to investigate if microcalcification detection varies significantly when mammographicimages are acquired using different image qualities, including: different detectors,dose levels, and different image processing algorithms. An additional aim was to determine how the standard European method of measuring image quality using threshold gold thickness measured with a CDMAM phantom and the associated limits in current EU guidelines relate to calcification detection. Methods: One hundred and sixty two normal breast images were acquired on an amorphous selenium direct digital (DR) system. Microcalcification clusters extracted from magnified images of slices of mastectomies were electronically inserted into half of the images. The calcification clusters had a subtle appearance. All images were adjusted using a validated mathematical method to simulate the appearance of images from a computed radiography(CR)imagingsystem at the same dose, from both systems at half this dose, and from the DR system at quarter this dose. The original 162 images were processed with both Hologic and Agfa (Musica-2) image processing. All other image qualities were processed with Agfa (Musica-2) image processing only. Seven experienced observers marked and rated any identified suspicious regions. Free response operating characteristic (FROC) and ROC analyses were performed on the data. The lesion sensitivity at a nonlesion localization fraction (NLF) of 0.1 was also calculated. Images of the CDMAM mammographic test phantom were acquired using the automatic setting on the DR system. These images were modified to the additional image qualities used in the observer study. The images were analyzed using automated software. In order to assess the relationship between threshold gold thickness and calcification detection a power law was fitted to the data. Results: There was a significant reduction in calcification detection using CR compared with DR: the alternative FROC (AFROC) area decreased from 0.84 to 0.63 and the ROC area decreased from 0.91 to 0.79 (p 0.05). It was additionally found that lower threshold gold thickness from CDMAM analysis implied better cluster detection. The measured threshold gold thickness passed the acceptable limit set in the EU standards for all image qualities except half doseCR. However, calcification detection varied significantly between image qualities. This suggests that the current EU guidelines may need revising. Conclusions: Microcalcification detection was found to be sensitive to detector and dose used. Standard measurements of image quality were a good predictor of microcalcification cluster detection.

Diagnosing Breast Masses in Digital Mammography Using Feature Selection and Ensemble Methods

Abstract: Methods that can accurately predict breast cancer are greatly needed and good prediction techniques can help to predict breast cancer more accurately. In this study, we used two feature selection methods, forward selection (FS) and backward selection (BS), to remove irrelevant features for improving the results of breast cancer prediction. The results show that feature reduction is useful for improving the predictive accuracy and density is irrelevant feature in the dataset where the data had been identified on full field digital mammograms collected at the Institute of Radiology of the University of Erlangen-Nuremberg between 2003 and 2006. In addition, decision tree (DT), support vector machine--sequential minimal optimization (SVM-SMO) and their ensembles were applied to solve the breast cancer diagnostic problem in an attempt to predict results with better performance. The results demonstrate that ensemble classifiers are more accurate than a single classifier.

Breast Density Classification Using Multiple Feature Selection

Abstract: Mammography as an x-ray method usually gives good results for lower density breasts while higher breast tissue densities significantly reduce the overall detection sensitivity and can lead to false negative results. In automatic detection algorithms knowledge about breast density can be useful for setting an appropriate decision threshold in order to produce more accurate detection. Because the overall intensity of mammograms is not directly correlated with the breast density we have decided to observe breast density as a texture classification problem. In this paper we propose breast density classification using feature selection process for different classifiers based on grayscale features of first and second order. In feature selection process different selection methods were used and obtained results show the improvement on overall classification by choosing the appropriate method and classifier. The classification accuracy has been tested on the mini-MIAS database and KBD-FER digital mammography database with different number of categories for each database. Obtained accuracy stretches between 97.2 % and 76.4 % for different number of categories.

Average glandular dose in digital mammography and breast tomosynthesis.

Abstract: Purpose: To determine the average glandular dose (AGD) in digital full-field mammography (2 D imaging mode) and in breast tomosynthesis (3 D imaging mode). Materials and Methods: Using the method described by Boone, the AGD was calculated from the exposure parameters of 2247 conventional 2 D mammograms and 984 mammograms in 3 D imaging mode of 641 patients examined with the digital mammographic system Hologic Selenia Dimensions. The breast glandular tissue content was estimated by the Hologic R2 Quantra automated volumetric breast density measurement tool for each patient from right craniocaudal (RCC) and left craniocaudal (LCC) images in 2 D imaging mode. Results: The mean compressed breast thickness (CBT) was 52.7 mm for craniocaudal (CC) and 56.0 mm for mediolateral oblique (MLO) views. The mean percentage of breast glandular tissue content was 18.0 % and 17.4 % for RCC and LCC projections, respectively. The mean AGD values in 2 D imaging mode per exposure for the standard breast were 1.57 mGy and 1.66 mGy, while the mean AGD values after correction for real breast composition were 1.82 mGy and 1.94 mGy for CC and MLO views, respectively. The mean AGD values in 3 D imaging mode per exposure for the standard breast were 2.19 mGy and 2.29 mGy, while the mean AGD values after correction for the real breast composition were 2.53 mGy and 2.63 mGy for CC and MLO views, respectively. No significant relationship was found between the AGD and CBT in 2 D imaging mode and a good correlation coefficient of 0.98 in 3 D imaging mode. Conclusion: In this study the mean calculated AGD per exposure in 3 D imaging mode was on average 34 % higher than for 2 D imaging mode for patients examined with the same CBT.

Breast image feature learning with adaptive deconvolutional networks

Abstract: Feature extraction is a critical component of medical image analysis. Many computer-aided diagnosis approaches employ hand-designed, heuristic lesion extracted features. An alternative approach is to learn features directly from images. In this preliminary study, we explored the use of Adaptive Deconvolutional Networks (ADN) for learning high-level features in diagnostic breast mass lesion images with potential application to computer-aided diagnosis (CADx) and content-based image retrieval (CBIR). ADNs (Zeiler, et. al., 2011), are recently-proposed unsupervised, generative hierarchical models that decompose images via convolution sparse coding and max pooling. We trained the ADNs to learn multiple layers of representation for two breast image data sets on two different modalities (739 full field digital mammography (FFDM) and 2393 ultrasound images). Feature map calculations were accelerated by use of GPUs. Following Zeiler et. al., we applied the Spatial Pyramid Matching (SPM) kernel (Lazebnik, et. al., 2006) on the inferred feature maps and combined this with a linear support vector machine (SVM) classifier for the task of binary classification between cancer and non-cancer breast mass lesions. Non-linear, local structure preserving dimension reduction, Elastic Embedding (Carreira-Perpinan, 2010), was then used to visualize the SPM kernel output in 2D and qualitatively inspect image relationships learned. Performance was found to be competitive with current CADx schemes that use human-designed features, e.g., achieving a 0.632+ bootstrap AUC (by case) of 0.83 [0.78, 0.89] for an ultrasound image set (1125 cases).

Mammographic density estimation: one-to-one comparison of digital mammography and digital breast tomosynthesis using fully automated software.

Abstract: Objective To compare breast density on digital mammography and digital breast tomosynthesis using fully automated software.

Clinical and self breast examination remain important in the era of modern screening.

Abstract: Breast cancer screening recommendations are in flux. We reviewed the methods of detecting newly diagnosed breast neoplasms at our institution. A retrospective review of patients stratified by age was performed to compare mammography with self- (SBE) and clinical (CBE) breast examination methods of cancer detection from 2005 to 2009. We identified 782 patients. Patients aged <50 years were more likely to present with palpable disease (P < 0.001). Overall, 75% of patients had a mammogram within 24 months. There was a higher incidence of Tis tumors and lower incidence of T1 tumors if patients had mammography performed within 12 months versus 13–24 months (P < 0.01); tumor size, hormonal status, and lymph node (LN) status were comparable between these two groups. Patients diagnosed by SBE/CBE who had mammography performed within 12 months versus 13–24 months did not differ statistically according to tumor characteristics. In the screened cohort (mammography within 24 months), the majority of patients (64%) were diagnosed by mammography. Cancers detected by SBE/CBE were larger tumors (2.4 vs. 1.3 cm), higher grade, more frequently ER- (29 vs. 16%), triple-negative (21 vs. 10%), and lymph node-positive (39 vs. 18%; all P ≤ 0.01). There were no statistically significant differences in tumor size, T stage, or hormonal status in patients who had analog versus digital mammography. Whereas the majority of patients had image-detected breast cancer, a significant number of image-screened patients presented with palpable disease, which were more aggressive cancers. Until imaging techniques are refined, SBE and CBE remain important for breast cancer diagnosis.

Segmentation of the Breast Region in Digital Mammograms and Detection of Masses

Abstract: The mammography is the most effective procedure for an early diagnosis of the breast cancer. Finding an accurate and efficient breast region segmentation technique still remains a challenging problem in digital mammography. In this paper we explore an automated technique for mammogram segmentation. The proposed algorithm uses morphological preprocessing algorithm in order to: remove digitization noises and separate background region from the breast profile region for further edge detection and regions segmentation.

The use of a figure-of-merit (FOM) for optimisation in digital mammography: a literature review.

Abstract: The use of image quality parameters in digital mammography such as contrast-to-noise ratio (CNR), signal-to-noise ratio (SNR) and detective quantum efficiency (DQE) has been widespread, with the intention of detector evaluation and/or quantitative evaluation of the system performance. These parameters are useful in ensuring adequate system performance when tests are done against international standards or guidelines. Parameters like CNR are relative quantities that lie within a range that is manufacturer and system dependent. The use of a figure-of-merit (FOM) is a relatively new concept as a tool in digital mammography permitting quantitative assessment in terms of image quality and patient dose. This review summarises the available evidence for the use and applicability of an FOM in digital mammography.

Patient doses from screen-film and full-field digital mammography in a population-based screening programme

Abstract: This is a pre-copy-editing, author-produced PDF of an article accepted for publication in Radiation protection dosimetry following peer review. The definitive publisher-authenticated version Hauge, I. H. R., Pedersen, K., Sanderud, A., Hofvind, S. & Olerud, H. M. (2011). Patient doses from screen-film and full-field digital mammography in a population-based screening programme. Radiation protection dosimetry is available online at: http://dx.doi.org/10.1093/rpd/ncq598.

Computer-aided diagnosis of breast microcalcifications based on dual-tree complex wavelet transform.

Abstract: Background Digital mammography is the most reliable imaging modality for breast carcinoma diagnosis and breast micro-calcifications is regarded as one of the most important signs on imaging diagnosis. In this paper, a computer-aided diagnosis (CAD) system is presented for breast micro-calcifications based on dual-tree complex wavelet transform (DT-CWT) to facilitate radiologists like double reading.

Comparison of breast tissue measurements using magnetic resonance imaging, digital mammography and a mathematical algorithm.

Abstract: Women with mostly mammographically dense fibroglandular tissue (breast density, BD) have a four- to six-fold increased risk for breast cancer compared to women with little BD. BD is most frequently estimated from two-dimensional (2D) views of mammograms by a histogram segmentation approach (HSM) and more recently by a mathematical algorithm consisting of mammographic imaging parameters (MATH). Two non-invasive clinical magnetic resonance imaging (MRI) protocols: 3D gradient-echo (3DGRE) and short tau inversion recovery (STIR) were modified for 3D volumetric reconstruction of the breast for measuring fatty and fibroglandular tissue volumes by a Gaussian-distribution curve-fitting algorithm. Replicate breast exams (N = 2 to 7 replicates in six women) by 3DGRE and STIR were highly reproducible for all tissue-volume estimates (coefficients of variation 0.75 indicating that all four methods were reliable for measuring BD and that the mathematical algorithm and the two complimentary non-invasive MRI protocols could objectively and reliably estimate different types of breast tissues.

A New Unsharp Masking Algorithm for Mammography Using Non-linear Enhancement Function

Abstract: Mammography is especially valuable as an early detection tool because it can identify breast cancer at a stage when treatment may be more effective. This paper introduces a new Unsharp Masking (UM) algorithm using a non-linear enhancement function. The proposed algorithm combines the conventional UM with the non-linear enhancement function. The conventional UM algorithm is extremely sensitive to noise because of the presence of the linear high pass filter. The improved high pass filter used in the proposed work provides high frequency components of the image which are insensitive to noise which reduces the noise sensitivity of the UM algorithm. The input image is simultaneously processed using the improved high pass filter and the non-linear enhancement function; both the images are then combined to get the final enhanced image. Simulation results show that the proposed algorithm not only enhances the edges of the masses, but at the same time suppresses the background noise as well.

Dutch digital breast cancer screening: implications for breast cancer care

Abstract: Background: In comparison to other European population-based breast cancer screening programmes, the Dutch programme has a low referral rate, similar breast cancer detection and a high breast cancer mortality reduction. The referral rate in the Netherlands has increased over time and is expected to rise further, mainly following nationwide introduction of digital mammography, completed in 2010. This study explores the consequences of the introduction of digital mammography on the balance between referral rate, detection of breast cancer, diagnostic work-up and associated costs. Methods: Detailed information on diagnostic work-up (chart review) was obtained from referred women ( n = 988) in 2000–06 (100% analogue mammography) and 2007 (75% digital mammography) in Nijmegen, the Netherlands. Results: The average referral rate increased from 15 (2000–06) to 34 (2007) per 1000 women screened. The number of breast cancers detected increased from 5.5 to 7.8 per 1000 screens, whereas the positive predictive value fell from 37% to 23%. A sharp rise in diagnostic work-up procedures and total diagnostic costs was seen. On the other hand, costs of a single work-up slightly decreased, as less surgical biopsies were performed. Conclusion: Our study shows that a low referral rate in combination with the introduction of digital mammography affects the balance between referral rate and detection rate and can substantially influence breast cancer care and associated costs. Referral rates in the Netherlands are now more comparable to other countries. This effect is therefore of value in countries where implementation of digital breast cancer screening has just started or is still under discussion.

Comparing the Performance of Image Enhancement Methods to Detect Microcalcification Clusters in Digital Mammography

Abstract: Mammography is the primary imaging technique for detection and diagnosis of breast cancer; however, the contrast of a mammogram image is often poor, especially for dense and glandular tissues. In these cases the radiologist may miss some diagnostically important microcalcifications. In order to improve diagnosis of cancer correctly, image enhancement technology is often used to enhance the image and help radiologists. Methods: This paper presents a comparative study in digital mammography image enhancement based on four different algorithms: wavelet-based enhancement (Asymmetric Daubechies of order 8), Contrast-Limited Adaptive Histogram Equalization (CLAHE), morphological operators and unsharp masking. These algorithms have been tested on 114 clinical digital mammography images. The comparison for all the proposed image enhancement techniques was carried out to find out the best technique in enhancement of the mammogram images to detect microcalcifications. Results: For evaluation of performance of image enhancement algorithms, the Contrast Improvement Index (CII) and profile intensity surface area distribution curve quality assessment have been used after any enhancement. The results of this study have shown that the average of CII is about 2.61 for wavelet and for CLAHE, unsharp masking and morphology operation are about 2.047, 1.63 and 1.315 respectively. Conclusion: Experimental results strongly suggest that the wavelet transformation can be more effective and improve significantly overall detection of the Computer-Aided Diagnosis (CAD) system especially for dense breast. Compare to other studies, our method achieved a higher CII.

Mammography capacity and appointment wait times: barriers to breast cancer screening.

Abstract: To assess the impact of mammography capacity on appointment wait times. We surveyed by telephone all mammography facilities federally certified in 2008 in California, Connecticut, Georgia, Iowa, New Mexico, and New York using a simulated patient format. County-level mammography capacity, defined as the number of mammography machines per 10,000 women aged 40 and older, was estimated from FDA facility certification records and US Census data. 1,614 (86%) of 1,882 mammography facilities completed the survey. Time until next available screening mammogram appointment was 1 month at 11% of facilities. Facilities in counties with lower capacity had longer wait times, and a one-unit increase in county capacity was associated with 21% lower odds of a facility reporting a wait time >1 month (p < 0.01). There was no association between wait time and the availability of evening or weekend appointments or digital mammography. Lower mammography capacity is associated with longer wait times for screening mammograms. Enhancement of mammography resources in areas with limited capacity may reduce wait times for screening mammogram appointments, thereby increasing access to services and rates of breast cancer screening.

Non-Gaussian statistical properties of breast images

Abstract: Purpose: Several studies have shown that the power spectrum of x-ray breast images is well described by a power-law at lower frequencies where anatomical variability dominates However, an image generated from a Gaussian process with this spectrum is easily distinguished from an image of actual breast tissue by eye This demonstrates that higher order non-Gaussian statistical properties of mammograms are readily accessible to the visual system The authors’ purpose is to quantify and characterize non-Gaussian statistical properties of breast images as influenced by processing of a digital mammogram, different imaging modalities, and breast density Methods: To quantify non-Gaussian statistical properties, the authors consider histograms of filter responses from the interior of a breast image that have similar properties to receptive fields in the early visual system They quantify departure from a Gaussian distribution by the relative entropy of the histogram compared to a best-fit Gaussian distribution This entropy is normalized by the relative entropy of a best-fit Laplacian distribution into a measure they refer to as Laplacian fractional entropy (LFE) They test the LFE on a set of 26 patients recalled at screening for which they have available full-field digital mammography (FFDM), digital breast tomosynthesis (DBT), and dedicated breast CT (bCT) images as well as breast density scores and biopsy results Results: A study of LFE in FFDM comparing the raw “for-processing” transmission data from the device to log-converted density estimates and the processed “for-display” data shows that processing mammographic image data enhances the non-Gaussian content of the image A check of the methodology using a Gaussian process with a power-law power spectrum shows relatively little bias from the finite extent of the region of interests used A second study comparing LFE across FFDM, DBT, and bCT modalities shows that each maximized the non-Gaussian content of the image for different ranges of spatial frequency FFDM is optimal at high spatial frequencies (>07 mm−1), DBT is optimal at mid-range frequencies (03–07 mm−1), and bCT is optimal at low spatial frequency (<03 mm−1) A third study of breast density in FFDM and bCT shows that LFE generally rises slightly going from the low-to moderate density, and then falls considerably at higher densities Conclusions: Non-Gaussian statistical structure in breast images that is manifest in the responses of Gabor filters similar to receptive fields of the early visual system is dependent on how the image data are processed, the modality used to acquire the image, and the density of the breast tissue being imaged Higher LFE corresponds with expected improvements from image processing and 3D imaging