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In this study, we determined the average glandular dose (AGD) from the craniocaudal (CC) and mediolateral oblique (MLO) views of 496 breasts (247 women) at eight clinics in Sudan. The incident air kerma from the X-ray tube output values and typical patient-specific breast exposure factors were measured. AGD values were inferred from the measured incident air kerma and breast-specific dose conversion coefficients. The AGD per CC and MLO projection and per woman ranged from 0.34–5.3 mGy (average: 2.46), 0.29–3.39 mGy (average: 1.50), and 0.6–7.4 mGy (average: 3.95). The proposed national diagnostic reference levels (mGy) are 3.48, 2.03, and 6.44 mGy for CC, MLO, and per woman, respectively. Establishing the proposed diagnostic reference levels is an essential step in ensuring patient protection from radiation and will help promote dose optimization for X-ray mammography at the national levels and beyond. The results provide important baseline data that can be used to formulate the national diagnostic reference levels.

The article discusses the normative documents in force in our country of the Ministry of Health that regulate the timing, order and age criteria for examining the mammary glands using x-ray mammography. A sufficient number of disagreements were revealed among the Orders, Letters, and Methodological and Clinical Recommendations that have not lost their legal force, which significantly complicate the daily work of clinicians and radiologists of mammography rooms. The lack of a unified approach to the standard of mammographic examination among the medical community inevitably leads to medical errors and increases the number of untimely diagnoses. In order to improve indicators of early diagnosis of breast cancer and minimize the amount of subsequent treatment, it is necessary to develop a single normative document that defines the algorithm for mammographic examination of the population.

PURPOSE To explore the radiomics features of triple negative breast cancer (TNBC) and non-triple negative breast cancer (non-TNBC) based on X-ray mammography, and to differentiate the two groups of cases. MATERIALS AND METHODS Preoperative mammograms of 120 patients with breast ductal carcinoma confirmed by surgical pathology were retrospectively analyzed, which include 30 TNBC and 90 non-TNBC patients. The manual segmentation of breast lesions was performed by ITK-SNAP software and 12 radiomics features were extracted by Omni-Kinetics software. The differences of these radiomics features between TNBC and non-TNBC groups were compared, and the receiver operating characteristic (ROC) curve was used to determine the optimal cutoff value of each radiomics parameter for differentiating TNBC from non-TNBC, and the corresponding area under the curve (AUC), sensitivity and specificity were obtained. RESULTS There were statistically significant differences for 4 radiomics features between TNBC and non-TNBC datasets (P

Background Detection of breast cancer in women with high breast densities is a clinical challenge. Purpose To study the influence of different degrees of breast density on the sensitivity of contrast-enhanced breast magnetic resonance imaging (CE-BMRI) versus X-ray mammography (XRM). Material and Methods We performed an additional analysis of two large Phase III clinical trials (G1; G2) which included women with histologically proven breast cancers, called “index cancers.” Additional cancers were detected during image reading. We compared the sensitivity of CE-BMRI and XRM in women with different breast densities (ACR A→D; Version 5). For each study, six blinded readers evaluated the images. Results are given as the “Median Reader.” Results A total of 774 patients were included, 169 had additional cancers. While sensitivity of CE-BMRI for detecting all index cancers was independent of breast density (ACR A→D) (G1: 83%→83%; G2: 91%→91%) the sensitivity of XRM declined (ACR A→D) (G1: 79%→62%; G2: 82%→64%). Thus, the sensitivity difference between both imaging modalities in ACR A breasts of 3% (G1) and 9% (G2) increased to 21% (G1) and 26% (G2) in ACR D breasts. Sensitivity of CE-BMRI for detecting at least one additional cancer increased with increasing breast density (ACR A→D) (G1: 50%→73%, G2: 57%→81%). XRM’s sensitivity decreased (G1: 34%→20%) or remained stable (G2: 24%→25%). Conclusion CE-BMRI showed significantly higher sensitivity compared to XRM.

X-ray mammography is considered one of the primary diagnostic methods for malignant breast tumors. Since the conclusion is mainly based on the visual analysis of analog or digital X-ray images of the breast, the objectivity of the method is highly dependent on the professional experience of the radiologist. Therefore, the automation of the process of analyzing X-ray mammograms is a relevant task. The present study aims to develop a method for the automatic classification of the types of tumors on x-ray mammograms. To this end, the boundaries of breast tissue densities were described analytically using the mathematical apparatus of contour analysis. It has been found that malignant tumors are characterized by rough contours, which enables the determination of the tumor type by calculating the straightness coefficient of its contour. The straightness coefficient values for malignant and benign tumors have been found. Based on a representative sample from the patients with a previously known diagnosis, consistent classification results have been obtained which is an indication of the feasibility of the proposed method.

Objective: to improve breast cancer diagnosis using an independent mammographic teleassessment by two radiologists. Material and methods. The results of a dual independent assessment of 3440 X-ray mammographic examinations were analyzed. The first assessment was made by a radiologist from a therapeutic-and-prophylactic establishment who had less than one year of practical experience in a mammography screening room. The second assessment was carried out by a radiologist with more than 5 years of experience from a specialized cancer institution. Results. According to the developed specialized interpretation software, recommendations and statistics for mammographic breast cancer screening, the investigators identified a group of 31 women who were recommended to be additionally examined at an oncology dispensary to verify the diagnosis of breast cancer. Eight of 17 (47%) women with morphologically confirmed cancers after dual assessments of X-ray mammographs were called again and referred to the oncology dispensary. Conclusion. Dual independent assessment of X-ray mammographs improved the diagnosis of BC with a maximum size of 1.5 cm or less, as well as that of malignant tumors manifesting as microcalcifications or imitating a benign process.

X-ray Mammography has been a common technique of breast cancer identification. A single X-ray mammogram will not be able to convey full information about cancer to the radiologist. In this, an image fusion using Weighted Average SWT is proposed and histogram equalization is performed to enhance the quality of the fused X-ray mammogram. The resultant X-ray mammogram is same as conventional X-ray mammogram but with appreciably superior detail and is then reconstructed by using its inverse transform. This fused X-ray mammogram is well-suited for clinical settings and equips the radiologist to use lifetime diagnosis experience in X-ray mammography.

Introduction: Breast USG is an established and accurate tool for the primary evaluation of breast lumps and pathology. It also compliments X-ray mammography in further evaluation and characterization of breast masses and thus avoids surgeries in benign breast diseases and pathology. Method: For USG examination of the breast lumps, a linear-array transducer of 5-7 MHz frequency is required with a good resolution machine. Results: We present a pictorial essay on the role of USG in evaluation and characterization of various breast lumps and pathology. Conclusion: Breast sonography considerably improves the visualization and evaluation of lumps in mammographically radiodense breasts and helpful in the characterization of it, either as solid or cystic lesion. It also improves the specificity of X-ray mammography when used as an adjunct to it. It is also helpful in guiding FNAC/biopsy from the breast masses.

Mammographic image analysis is an important tool for the detection and assessment of breast cancer. Previous studies have shown that the performance of image analysis algorithms can be improved by applying them in the retroareolar (RA) region of the breast. However, previous works have relied on subjective, manual segmentation of the RA region. This paper presents a method for the fully-automated detection of the RA region in x-ray mammography images. The method is based on a curvilinear coordinate system that automatically adapts to the breast shape and size. Experiments using logistic regression analysis on images from a publicly available dataset show that the proposed method outperforms the traditional approach in the task of cancer detection.

This chapter will serve as an introduction to the relevant imaging diagnostics of the breast – i.e. X-ray mammography, sonography, and magnetic resonance (MR) mammography – so that the reader gets a first, basic understanding of the topic. Furthermore, a large number of pathognomonic findings will contribute to enabling deeper insight into the topic.

Computer aided detection CADe of breast cancer is mainly focused on monomodal applications. We propose an automated multimodal CADe approach, which uses patient-specific image registration of MRI and X-ray mammography to estimate the spatial correspondence of tissue structures. Then, based on the spatial correspondence, features are extracted from both MRI and X-ray mammography. As proof of principle, distinct regions of interest ROI were classified into normal and suspect tissue. We investigated the performance of different classifiers, compare our combined approach against a classification with MRI features only and evaluate the influence of the registration error. Using the multimodal information, the sensitivity for detecting suspect ROIs improved by 7i¾?% compared to MRI-only detection. The registration error influences the results: using only datasets with a registration error below $$10\,mm$$, the sensitivity for the multimodal detection increases by 10i¾?% to a maximum of 88i¾?%, while the specificity remains constant. We conclude that automatically combining MRI and X-ray can enhance the result of a CADe system.

Breast density is an important cue to detect both the presence of suspicious cancerous masses and to predict future possibility for cancer development. A fast breast density classification method is presented and successfully tested on two state-of-the-art mammogram databases. The X-ray digital mammogram tissue texture is locally represented by the two-dimensional adaptive causal autoregressive spatial model and its parameters are used as the classification features.

PURPOSE To compare the image quality of digital X-ray mammographies obtained with wet imagers with that of standard dry imaging technology. MATERIAL AND METHODS Beginning 03/08/2003, 200 X-ray mammographies with a digital fullfield mammography system (Lorad Selenia, Lorad/Hologic) were prospectively and consecutively documented with a wet laser imager (Scopix LR 5200, Agfa), a dry infrared laser imager (DryView 8610, Kodak) and a dry imager using the principle of direct thermography (Drystar 4500M, Agfa, N = 166). One X-ray exposure was systematically chosen from each examination and was presented in an anonymous and randomized form to three radiologists who evaluated the films using a structured questionnaire. RESULTS The visualization of normal anatomic structures was considered being good to excellent for all imagers with the mean assessments 1.0 - 2.4 for the Drystar 4500M, 1.0 - 2.1 for the DryView 8610 and 1.1 - 2.0 for the Scopix LR 5200. The mean assessments were 0.1 - 0.6 points lower in dense than in normal parenchyma, thus, the parenchymal density is the predominant factor for image quality. CONCLUSION In view of the comparable image quality obtained with the different imagers used in the study, individual decisions to purchase a specific imager will be based on economics rather than on diagnostic points of view.

aDepartment of Neuroradiology, University of Erlangen-Nurnberg, Schwabachanlage 6, D-91054, Germany bDepartment of Radiology, Division of Molecular and Gender Imaging, Medical University Vienna, Wahringer Gurtel 18–20, A-1090, Vienna, Austria c Karlsruhe Institute of Technology (KIT), Institute for Data Processing and Electronics, Postfach 3640, D-76021 Karlsruhe, Germany d Institute of Diagnostic and Interventional Radiology I, Friedrich-Schiller-University Jena, Erlanger Allee 101, D-07740 Jena, Germany

Purpose. To investigate the patient radiation dose saving of grid-less x-ray mammography acquisitions compared with conventional full-field digital mammography (FFDMG). Methods and materials. The Siemens Inspiration MAMMOMAT PRIME system with software based scatter correction was used to investigate the dose saving in grid-less acquisition compared with conventional full-field digital mammography (FFDMG) acquisitions. A Piranha 657 solid-state detector was used to measure the entrance exposure. The entrance exposure was directly measured on different PMMA thicknesses of 20-70 mm in steps of 10 mm. The PMMA block thicknesses were then converted to an equivalent compressed breast tissue thicknesses. The average glandular dose (AGD) is calculated. Results. Dose reduction in both the directly measured entrance exposure and the calculated AGD is between 13% and 32% in the grid-less mammography acquisition. The contrast to noise ratio calculated in the gridless and the FFDMG acquisition also was the same. Conclusion. It was possible to save patient dose (13%-32%) in grid-less mammography acquisition without affecting the image quality.

Breast biphasic compression (22.5 degrees angled paddle, followed by progressive angle reduction) was compared with standard monophasic compression in x-ray mammography. The presence of the pectoral muscle was recorded for the craniocaudal (CC) view and the presence of the inframammary fold for the mediolateral oblique (MLO) view. The amount of breast in each study and image quality were assessed for both views. For all parameters, biphasic compression performed better than monophasic compression in both CC (P:.006) and MLO (P:.04) views.

A method for segmenting MR images of the breast was applied to determine fatty and fibroglandular tissue volumes in breasts of women in different age groups. The results were compared with subjective assessments of breast density from X-ray mammograms in the same patients.Two experienced mammographers assessed the percentage of fat in the breasts of 40 women who were 20-83 years old. MR images were obtained on a 1.0-T scanner equipped with a bilateral receive-only breast coil. Images were acquired using a three-dimensional T1-weighted gradient-echo sequence with a 1.25 x 1.4 x 2.5 mm resolution. On average, breast parenchyma appeared in 30 images in each breast. Image segmentation was based on a semiautomated, two-compartmental (fatty and fibroglandular tissue) model that accounts for partial volume effects. To validate the accuracy of the MR imaging segmentation technique, we performed a phantom study using an identical imaging sequence.The accuracy of the MR imaging segmentation of the phantom was of the order of 2%. In our subjects, fat content was 42.5% +/- 30.3% (mean +/- SD) on mammography versus 66.5% +/- 18% on MR images. Although we found a significant correlation (r = .63) between the two techniques, mammography poorly differentiated breasts containing less than 45% fat. When our analysis included only dense breasts (i.e., those containing less than 75% fat on MR images), the correlation coefficient decreased to .34. The largest discrepancies between mammography and MR imaging occurred in breasts that had 60-80% fat as measured on MR imaging.Fatty and fibroglandular tissue can be differentiated on MR images of the breast with high precision and accuracy, therefore allowing assessment of breast density. The conclusions of researchers who used mammographic density patterns should be reassessed.

Microcalcifications can be indicative in the diagnosis of early breast cancer. Here we report a non-invasive diagnostic method that may potentially distinguish between different types of microcalcifications using X-ray phase-contrast imaging. Our approach exploits the complementary nature of the absorption and small-angle scattering signals of microcalcifications, obtained simultaneously with an X-ray grating interferometer on a conventional X-ray tube. We demonstrate that the new approach has 100% sensitivity and specificity when applied to phantom data, and we provide evidence of the solidity of the technique by showing its discrimination power when applied to fixed biopsies, to non-fixed tissue specimens and to fresh, whole-breast samples. The proposed method might be further developed to improve early breast cancer diagnosis and has the potential to increase the diagnostic accuracy and reduce the number of uncomfortable breast biopsies, or, in case of widespread microcalcifications, to select the biopsy site before intervention.

We have previously demonstrated the utilization of spatially co-registered diffuse optical tomography (DOT) and digital breast tomosynthesis (DBT) for joint breast cancer diagnosis. However, clinical implementation of such a multi-modality approach may require development of integrated DOT/DBT imaging scanners, which can be costly and time-consuming. Exploring effective image registration methods that combine the diagnostic information from a standalone DOT measurement and a separate mammogram can be a cost-effective solution, which may eventually enable adding functional optical assessment to all previously installed digital mammography systems. In this study, we investigate a contour-based image registration method to convert independent optical and x-ray scans into co-registered datasets that can benefit from a joint image analysis. The breast surface used in 3D optical DOT reconstruction is registered with the breast contour line extracted from an x-ray mammogram acquired separately. This allows us to map the 2D mammogram to the optical measurement space and build structural constraints for optical image reconstruction. A non-linear reconstruction utilizing structure-priors is then performed to produce hemoglobin maps with improved resolution. To validate this approach, we used a set of tumor patient measurements with simultaneous DOT/DBT and separate 2D mammographic scans. The images recovered from the registration procedure derived from DOT and 2D mammogram present similar image quality compared to those recovered from co-registered DOT/DBT measurements.

To maintain high quality in mammography, a routine quality control program is necessary to detect drifting or degradation of system performance overtime and it is becoming more important especially when the machine is used for population screening, a large number of asymptomatic women will receive a large amount of dose. Therefore, the importance of optimization of image quality and minimization of radiation dose is essential to accomplish the goals of mammography screening, and this require to have and make sure that the Mammography X-ray machine working properly and stable. Due to advances in technology, the regulation and testing of digital mammography systems has become complicated. Not only does each mammographic X-ray unit model have its own test protocol, peripheral equipment must also be tested according to manufacturer specifications. And this program must be followed in detail by both physicists testing the device and other clinical users performing routine QC functions on a daily basis. In this lecture the Mammography QC testing program will be discussed and recent advances in the automation of QC measurements by some manufacturers will be highlighted.

Purpose: Determination of image quality of a new digital mammography system with an a-Se-detector. Material and Method: Radiograms of the Wisconsin Mammographic Random Phantom, Modell 152 A (Radiation Measurements Inc., Wisconsin) were acquired using a conventional film screen and a digital system. Results: With the conventional film screen system there were detected 191/a 38.2 and with the digital mammography 219/a 43.8 of 225 possible details. Conclusion: Based on these results there is the possibility to replace the conventional film screen system by the digital mammography with a-Se-detector - this has to be confirmed in further clinical trials.

Breast cancer is the most common cancer among women. The established screening method to detect breast cancer in an early state is X-ray mammography. However, X-ray frequently provides limited contrast of tumors located within glandular tissue. A new imaging approach is Ultrasound Computer Tomography generating threedimensional volumes of the breast. Three different images are available: reflectivity, attenuation and speed of sound. The correlation of USCT volumes with X-ray mammograms is of interest for evaluation of the new imaging modality as well as for a multimodal diagnosis. Yet, both modalities differ in image dimensionality, patient positioning and deformation state of the breast. In earlier work we proposed a methodology based on Finite Element Method to register speed of sound images with the according mammogram. In this work, we enhanced the methodology to register all three image types provided by USCT. Furthermore, the methodology is now completely automated using image similarity measures to estimate rotations in datasets. A fusion methodology is proposed which combines the information of the three USCT image types with the X-ray mammogram via semitransparent overlay images. The evaluation was done using 13 datasets from a clinical study. The registration accuracy was measured by the displacement of the center of a lesion marked in both modalities. Using the automated rotation estimation, a mean displacement of 10.4 mm was achieved. Due to the clinically relevant registration accuracy, the methodology provides a basis for evaluation of the new imaging device USCT as well as for multimodal diagnosis.

A novel method of skin segmentation is presented aimed to obtain as many pixels belonging to the real skin as possible. This method is validated by experts in radiology. In addition, a biomechanical model of the breast, which considers the skin segmented in this way, is constructed to study the influence of considering real skin in the simulation of the breast compression during an X-ray mammography. The reaction forces of the plates are obtained and compared with the reaction forces obtained using classical methods that model the skin as a 2D membranes that cover all the breast. The results of this work show that, in most of the cases, the method of skin segmentation is accurate and that real skin should be considered in the simulation of the breast compression during the X-ray mammographies. Copyright © 2012 J. A. Solves Llorens et al., This project has been partially funded by the Regional Valencian Government through IMPIVA with FEDER funding (reference IMIDTF/2010/111), by CDTI (reference IDI-20101153), and by MICINN (reference TIN2010-20999-C04-01). The authors would like to express their gratitude to the personnel from the Hospitals HCB and La Fe.

Electrical Impedance Tomography (EIT) has shown great potential to be used as a method for breast cancer screening. Combining EIT with x-ray mammography allows for both sets of data to be used in conjunction to make a more accurate diagnosis. Previous efforts to implement EIT in the mammography geometry have used analytical solutions to the forward model. This paper presents a three dimensional numerical forward model which uses a finite element method (FEM) to implement the complete electrode model (CEM) in EIT. We show that the numerical solution achieves greater accuracy than analytic models, and opens the way to implement an iterative inversion algorithm.

This paper presents a novel approach to X-ray mammography - MRI registration. The proposed method uses an intensity-based technique and an affine transformation matrix to approximate the 3D deformation of the breast resulting from the compression applied during mammogram acquisition. The registration is driven by a similarity measure that is calculated at each iteration of the algorithm between the target X-ray mammogram and a simulated X-ray image, created from the MR volume. Although the similarity measure is calculated in 2D, we compute a 3D transformation that is updated at each iteration. We have performed two types of experiments. In the first set, we used simulated X-ray target data, for which the ground truth deformation of the volume was known and thus the results could be validated. For this case, we examined the performance of 4 different similarity measures and we show that Normalized Cross Correlation and Gradient Difference perform best. The calculated mean reprojection error was for both similarity measures 4mm, for an initial misregistration of 14mm. In the second set of experiments, we present the initial results of registering real X-ray mammograms with MR volumes. The results indicate that the breast boundaries were registered well and the volume was deformed in 3D in a similar way to the deformation of the breast during X-ray mammogram acquisition. The experiments were carried out on five patients.

Breast cancer is one of the most common cancers among woman of the developing countries in the world, and it has also become a major cause of death. Treatment of breast cancer is effective only if it is detected at an early stage. X-ray mammography is the most effective method for early detection but the mammography images are complex. Thus nowadays, image processing and image analysis techniques are use to assist radiologist for detecting tumors in mammography images. In this paper we specify and determined the important and significant Breast Cancer Feature Extraction. After that, we analyze Breast cancer mammography images using these significant features. However, the aim of this study is to determine the features extraction ranges values for Breast Cancer mammography image.

Between January 1987 and May 1988 a prospective study was carried out on 232 women with a palpable breast mass. They underwent physical examination, x-ray mammography and ultrasound mammography. The results of each study were interpreted independently by separate observers, and consensus was achieved. From all solid tumours histology was available. Compared to x-ray mammography, ultrasound mammography proved to be significantly (p

Image fusion, or combination of two images obtained by different techniques, has been widely used in medical imaging. Vibro-acoustography (VA) is a new imaging modality that has been applied to both medical and industrial imaging. Combining unique diagnostic information of VA with other medical imaging is one of our research interests. In this work, we studied the VA and X-ray image pairs and adopted two methods for fusing the registered VA and X-ray image. In the first method, the fused image is defined as a linear combination of the VA and X-ray images. Moreover, a color-based fusion technique was employed to combine the images for better visualization of structural information. By combining the information from X-ray mammogram and VA modalities into a single image, the accuracy of mammography interpretation which is important for women's health can be increased.

Recent years have seen significant effort s deployed to apply optical imaging tec hniques in clinical indications. Optical mammography as an adjunct to X-ray mammography is one such application. 3D optical mammography relies on the sensitivity of near-infrared light to endogenous breast chromopho res in order to generate in vivo functional views of the breast. This work presents prospective tissue characterization results from a multi-site clinical study targeting optical tomography as an adjunct to conventional mammography. A 2 nd -generation multi-wavelength time-domain acquisition system was used to scan a wide population of women presenting normal or suspicious X-ray mammograms. Application specific algorithms based on a diffusive model of light trans port were used to quantify the breast’s optical properties and derive 3D images of physiological indices. Using histopathological findings as a gold standard, results confirm that optically derived parameters provide statistically significant discrimination between malignant and benign tissue in wide population of subjects. The methodology developed for case reviews, lesion delineation and characterization allows for better translation of the optical data to the more traditional x-ray paradigm while maintaining efficacy. They also point to the need for guidelines that facilitate correlation of optical data if those results are to be confirmed in a clinical setti ng. Key words: optical tomography breast cancer, x-ray mammography, time domain

The purpose of this study was to investigate approaches for combining information from the MLO and CC mammographic views for Computer-aided Diagnosis (CADx) algorithms. Feature level and classifier output level combinations were explored. Linear discriminant analysis (LDA) with step-wise feature selection from a set of Haralick's texture features was used to develop classifiers for distinguishing between benign and malignant mammographic lesions. The effect of correlation between features from the two views on the performance of classifiers was investigated. The single view models included: (a) an LDA model with stepwise selection based on the MLO view only (MLO-Only) and similarly (b) a CC-Only LDA model. The feature-level combination models included: (a) LDA based on concatenation of feature sets selected independently from the two views (FEAT_CON), (b) LDA based on the concatenated feature sets along with the corresponding value of each feature from the opposite view (FEAT_COR_CON) if the correlation was below a threshold, (c) LDA based on the average of the MLO and CC feature values (FEAT_AVG). The classifier output level combination models investigated included: (a) average of the outputs of the MLO-Only and CC-Only classifiers (OUTPUT_AVG), (b) maximum of the outputs of the MLO-Only and CC-Only classifiers (OUTPUT_MAX), (c) minimum of the outputs of the MLO-Only and CC-Only classifiers (OUTPUT_MIN), (d) a second level LDA classifier on the outputs of the MLO-Only and CC-Only classifiers (OUTPUT_LDA), (e) product of the output values of the two classifiers (OUTPUT_PROD). The performance of the models was assessed and compared using the ROC methodology to determine if combination models performed better than the single-view models.

A system for the automated detection of massive lesions in mammograms is presented. The approach we adopted is a pixel-based and multi-level one. Each pixel in a mammogram is flagged with the appropriate class membership, e.g. massive lesions or normal breast tissue., Comment: 1 page, Computer Assisted Radiology and Surgery, Proceedings of the 19th International Congress and Exhibition, Berlin, June 22 - 25, 2005