Image Quality In Mammography Research Articles

Automated assessment of task-based performance of digital mammography and tomosynthesis systems using an anthropomorphic breast phantom and deep learning-based scoring.

Conventional metrics used for assessing digital mammography (DM) and digital breast tomosynthesis (DBT) image quality, including noise, spatial resolution, and detective quantum efficiency, do not necessarily predict how well the system will perform in a clinical task. A number of existing phantom-based methods have their own limitations, such as unrealistic uniform backgrounds, subjective scoring using humans, and regular signal patterns unrepresentative of common clinical findings. We attempted to address this problem with a realistic breast phantom with random hydroxyapatite microcalcifications and semi-automated deep learning-based image scoring. Our goal was to develop a methodology for objective task-based assessment of image quality for tomosynthesis and DM systems, which includes an anthropomorphic phantom, a detection task (microcalcification clusters), and automated performance evaluation using a convolutional neural network. Experimental 2D and pseudo-3D mammograms of an anthropomorphic inkjet-printed breast phantom with inserted microcalcification clusters were collected on clinical mammography systems to train a signal-present/signal-absent image classifier based on Resnet-18 architecture. In a separate validation study using simulations, this Resnet-18 classifier was shown to approach the performance of an ideal observer. Microcalcification detection performance was evaluated as a function of four dose levels using receiver operating characteristic (ROC) analysis [i.e., area under the ROC curve (AUC)]. To demonstrate the use of this evaluation approach for assessing different technologies, the method was applied to two different mammography systems, as well as to mammograms with re-binned pixels emulating a lower-resolution X-ray detector. Microcalcification detectability, as assessed by the deep learning classifier, was observed to vary with the exposure incident on the breast phantom for both DM and tomosynthesis. At full dose, experimental AUC was 0.96 (for DM) and 0.95 (for DBT), whereas at half dose, it dropped to 0.85 and 0.71, respectively. AUC performance on DM was significantly decreased with an effective larger pixel size obtained with re-binning. The task-based assessment approach also showed the superiority of a newer mammography system compared with an older system. An objective task-based methodology for assessing the image quality of mammography and tomosynthesis systems is proposed. Possible uses for this tool could be quality control, acceptance, and constancy testing, assessing the safety and effectiveness of new technology for regulatory submissions, and system optimization. The results from this study showed that the proposed evaluation method using a deep learning model observer can track differences in microcalcification signal detectability with varied exposure conditions.

Automated quality control analysis for American College of Radiology (ACR) digital mammography (DM) phantom images.

To develop and validate an automated software analysis method for mammography image quality assessment of the American College of Radiology (ACR) digital mammography (DM) phantom images. Twenty-seven DICOM images were acquired using Fuji mammography systems. All images were evaluated by three expert medical physicists using the Royal Australian and New Zealand College of Radiologists (RANZCR) mammography quality control guideline. To enhance the robustness and sensitivity assessment of our algorithm, an additional set of 12 images from a Hologic mammography system was included to test various phantom positional adjustments. The software automatically chose multiple regions of interest (ROIs) for analysis. A template matching method was primarily used for image analysis, followed by an additional method that locates and scores each target object (speck groups, fibers, and masses). The software performance shows a good to excellent agreement with the average scoring of observers (intraclass correlation coefficient [ICC] of 0.75, 0.79, 0.82 for speck groups, fibers, and masses, respectively). No significant differences were found in the scoring of target objects between human observers and the software. Both methods achieved scores meeting the pass criteria for speck groups and masses. Expert observers allocated lower scores to fiber objects, with diameters less than 0.61mm, when compared to the software. The software was able to accurately score objects when the phantom position changed by up to 25mm laterally, up to 5 degrees rotation, and overhanging the chest wall edge by up to 15mm. Automated software analysis is a feasible method that may help improve the consistency and reproducibility of mammography image quality assessment with reduced reliance on human interaction and processing time.

39 - Exploring the Association between Mammography Dose Optimization and Image Quality

39 - Exploring the Association between Mammography Dose Optimization and Image Quality

Synthesising two-dimensional mammographic images using compressed sensing-reconstructed digital breast tomosynthesis images

This study presents a method synthesizing a two-dimensional (2D) mammographic image of reasonable quality directly from a 3D digital breast tomosynthesis (DBT) image, reconstructed using an advanced compressed sensing (CS)-based algorithm. This approach aims to reduce the radiation dose required for complementary imaging technologies of digital mammography (DM) and DBT, eliminating the need for additional DM examinations. The method involves three main steps: projection data acquisition from a DBT system, CS-based DBT reconstruction, and synthesis of a 2D mammographic image from the reconstructed DBT image. To verify the efficacy of the proposed method, we conducted both a simulation and an experiment on a numerical breast and commercially available BR3D phantoms, respectively, prior to practical implementation in real-world DBT systems. Our simulation and experimental results indicated that the CS-based algorithm yielded markedly improved DBT reconstruction quality, preserving superior image homogeneity, better edge contour and sharpening, and fewer image artifacts. The measured contrast-to-noise ratio and structural similarity values of the CS-reconstructed DBT images were 10.31 and 0.78, respectively, which were approximately 2.2 and 3.1 times larger, respectively, than those of the filtered-backprojection-reconstructed DBT images in the simulation. The quality of the synthetic mammographic images using the CS-reconstructed DBT images was similar to that of conventional mammographic images obtained using a full dose, indicating the efficacy of the proposed method. Consequently, we successfully reconstructed DBT images of substantially high quality using the CS-based algorithm and synthesised 2D mammographic images of reasonable quality, potentially reducing the radiation dose to patients in the complementary imaging technologies of DM and DBT.

Pain Experience and Image Quality with Curved versus Standard Compression for Breast Cancer Screening Mammography: A Randomized Controlled Trial.

Background A curve-shaped compression paddle could reduce the pain experienced by some women at breast cancer screening. Purpose To compare curved and standard compression systems in terms of pain experience and image quality in mammography screening. Materials and Methods In this randomized controlled trial conducted between October 2021 and February 2022, participants screened at three screening sites in the Netherlands were randomized to either a curved-paddle or sham-paddle group. The sham paddle was a standard paddle that was presented as a new paddle. At a standard screening examination, one additional image was acquired with a curved or sham paddle. Pain was measured on a numerical rating scale (range, 0-10). Participants provided a pain score after compression with the standard and test paddles, resulting in two scores per participant. Differences in pain scores were compared between groups using analysis of covariance, adjusting for pain score after standard-paddle compression. Two radiographers and two radiologists performed unblinded paired comparisons of curved-paddle vs standard-paddle images, using standard image quality criteria (radiographers evaluated 1246 image pairs using 12 criteria; radiologists evaluated 320 image pairs using six criteria). The one-sample Wilcoxon signed-rank test was used to determine if there was a significant preference for either paddle. Results In total, 2499 female participants (mean age, 61.6 years ± 7.1 [SD]) were studied; 1250 in the curved-paddle group and 1249 in the sham-paddle group. The mean pain score decreased by an additional 0.19 points in the curved-paddle group compared with the sham-paddle group (95% CI: 0.09, 0.28; P < .001). In terms of image quality, the observers showed no preference or a preference for the standard paddle. Decreased image contrast (range Bonferroni-corrected P values: P < .001 to P > .99) and visibility of structures were the main concerns for curved-paddle images. Conclusion The use of the curved paddle resulted in a minimal pain reduction during mammography breast compression but image quality was reduced. © RSNA, 2024 Supplemental material is available for this article.

Optimasi Convolutional Neural Networks untuk Deteksi Kanker Payudara menggunakan Arsitektur DenseNet

Breast cancer is a disease commonly suffered by women worldwide, ranking as the second-largest disease burden. In response to the urgent need for improved detection accuracy, Convolutional Neural Networks (CNNs) promise significant advancements. The objective of this research is to optimize the use of CNNs with the DenseNet architecture for breast cancer detection. The study employs quantitative methods, leveraging Deep Learning through CNNs. Mammography data is sourced from Kaggle, specifically the “Breast Histopathology Images” dataset. This dataset comprises 90,000 digital mammography images, which are preprocessed and divided proportionally for training, validation, and model testing. Research variables encompass CNN model parameters, training techniques, and the integration of imaging modalities to enhance breast cancer detection performance. The research focuses on processed mammography data, with accuracy and image quality as key evaluation metrics for breast cancer sample identification. Our findings demonstrate that the DenseNet architecture within CNNs achieves an impressive 92% accuracy in breast cancer detection. This remarkable performance signifies success in enhancing image quality and class prediction, aligning with the DenseNet architecture’s flow diagram. Ultimately, these results contribute significantly to effective breast cancer diagnosis by optimizing CNNs with the DenseNet architecture to improve image quality during breast cancer sampling.

Computational methods applied to quality control of mammography images generated from ACR phantoms: An integrative review of current methodologies

This study aims to conduct an integrative review of research on computational methods used for mammography quality control while addressing the issue of subjectivity in existing quality control processes. We conducted an integrative search in three electronic databases to achieve our objective. Our search included studies published within the last eleven years, with a specific focus on original research that highlights the application of computational methods for assessing mammography image quality. The selected studies have been meticulously categorized based on the methodologies employed, the input variables used for image quality assessment and the overall quality of the findings. This categorization offers a holistic overview of the current state of research in this field. Our comprehensive review of these studies underscores the immense potential of automated systems designed to enhance image quality assurance in mammography. These computational methods offer a promising solution to mitigate subjectivity issues in the quality control process related to the reading of the image. By doing so, they hold the promise of improving clinical routines and ensuring the reliability of mammography diagnostics.

Phantom-based analysis of variations in automatic exposure control across three mammography systems: implications for radiation dose and image quality in mammography, DBT, and CEM

BackgroundAutomatic exposure control (AEC) plays a crucial role in mammography by determining the exposure conditions needed to achieve specific image quality based on the absorption characteristics of compressed breasts. This study aimed to characterize the behavior of AEC for digital mammography (DM), digital breast tomosynthesis (DBT), and low-energy (LE) and high-energy (HE) acquisitions used in contrast-enhanced mammography (CEM) for three mammography systems from two manufacturers.MethodsUsing phantoms simulating various breast thicknesses, 363 studies were acquired using all available AEC modes 165 DM, 132 DBT, and 66 LE-CEM and HE-CEM. AEC behaviors were compared across systems and modalities to assess the impact of different technical components and manufacturers’ strategies on the resulting mean glandular doses (MGDs) and image quality metrics such as contrast-to-noise ratio (CNR).ResultsFor all systems and modalities, AEC increased MGD for increasing phantom thicknesses and decreased CNR. The median MGD values (interquartile ranges) were 1.135 mGy (0.772–1.668) for DM, 1.257 mGy (0.971–1.863) for DBT, 1.280 mGy (0.937–1.878) for LE-CEM, and 0.630 mGy (0.397–0.713) for HE-CEM. Medians CNRs were 14.2 (7.8–20.2) for DM, 4.91 (2.58–7.20) for a single projection in DBT, 11.9 (8.0–18.2) for LE-CEM, and 5.2 (3.6–9.2) for HE-CEM. AECs showed high repeatability, with variations lower than 5% for all modes in DM, DBT, and CEM.ConclusionsThe study revealed substantial differences in AEC behavior between systems, modalities, and AEC modes, influenced by technical components and manufacturers’ strategies, with potential implications in radiation dose and image quality in clinical settings.Relevance statementThe study emphasized the central role of automatic exposure control in DM, DBT, and CEM acquisitions and the great variability in dose and image quality among manufacturers and between modalities. Caution is needed when generalizing conclusions about differences across mammography modalities.Key points• AEC plays a crucial role in DM, DBT, and CEM.• AEC determines the “optimal” exposure conditions needed to achieve specific image quality.• The study revealed substantial differences in AEC behavior, influenced by differences in technical components and strategies.Graphical

Evaluation of a Digital Mammography System with the MAM-162 phantom

Quality controls are performed to verify the performance status of X-ray equipment in relation to image quality and patient dose. Originally, the Laboratory of Radiation Physics and Metrology (LAF-RAM) in Nicaragua had only one phantom to evaluate mammography image quality subjectively; however, under a cooperation project with the International Atomic Energy Agency (IAEA), the MAM-162 phantom was acquired for the evaluation of physical image quality. In this work a CR type digital mammography system was evaluated using the MAM-162 phantom with adaptation to a single quality control protocol based on international standards such as the German Institute for Standardization (DIN) DIN 6868-162:2013-06, the IAEA technical document TECDOC protocol No. 1958 and the European Federation of Organizations for Medical Physics (EFOMP) mammo protocol. Among the main tests performed are the evaluation of the lateral image limit of the chest wall with satisfactory results, the automatic exposure control, the Signal-to-Noise difference ratio that complies with the tolerances, in the case Ghosting and dynamic range the results of both are satisfactory, the presence of artifacts was observed when applying variance mapping, a base value of the dynamic range was also obtained. For the dosimetry, the half value layer and the determination of the Mean Glandular Dose were evaluated, which does not comply with the tolerances. The final protocol document could be validated and implemented as part of the management and service documents of the Quality Control and Monitoring Laboratory (LCM) of LAF-RAM.

MLN-net: A multi-source medical image segmentation method for clustered microcalcifications using multiple layer normalization

The accurate segmentation of clustered microcalcifications in mammography is crucial for the diagnosis and treatment of breast cancer. Despite exhibiting expert-level accuracy, recent deep learning advancements in medical image segmentation provide insufficient contribution to practical applications, due to the domain shift resulting from differences in patient postures, individual gland density, and imaging modalities, etc. In this paper, a novel framework named MLN-net is proposed for clustered microcalcification segmentation. It can segment multi-source images using only single source images. Specifically, to rich domain distribution information, we introduce a source domain image augmentation for generating multi-source images. A structure of multiple layer normalization (LN) layers is then used to construct the segmentation network, which can be found efficient for clustered microcalcification segmentation in different domains. Additionally, a branch selection strategy is designed for measuring the similarity of the source domain data and the target domain data. To validate the proposed MLN-net, extensive analyses including ablation experiments are performed, comparison of 12 baseline methods. MLN-net enhances segmentation quality of full-field digital mammography (FFDM) and digital breast tomosynthe (DBT) images from the FFDM-DBT dataset, achieving the average Dice similarity coefficient (DSC) of 86.52% and the average Hausdorff distance (HD) of 20.49 mm on the source domain DBT. And it outperforms the baseline models for the task in FFDM images from both the CBIS-DDSM and the FFDM-DBT dataset, achieving the average DSC of 50.78% and the average HD of 35.12 mm on the source domain CBIS-DDSM. Extensive experiments validate the effectiveness of MLN-net in segmenting clustered microcalcifications from different domains and its segmentation accuracy surpasses state-of-the-art methods. Code will be available at https://github.com/yezanting/MLN-NET-VERSON1.

Sustaining Mammography Image Quality With a Technologist Coaching Program in the Era of the Enhancing Quality Using the Inspection Program (EQUIP).

To evaluate the ability of a long-term technologist coaching program to sustain gains in mammography quality made by a previously implemented quality improvement (QI) initiative. Mammography quality metrics from July 2014 to June 2020 were reviewed. Numbers of screening mammograms performed/audited, monthly average mammogram overall quality pass rates, changes in facilities/staffing, and technical recall rates were evaluated. Performance metrics at baseline (July 2013), during the improvement (July 2014 to January 2015), postimprovement (February 2015 to August 2015), and sustained coaching periods (after initiation of the technologist coaching model, from September 2015 to June 2020) were compared. During the postimprovement and sustained coaching periods, 93% (501/541) and 90% (8902/9929) of audited mammograms, respectively, met overall passing criteria, achieving or exceeding the QI goal of 90%, and results for both periods were significantly higher than that during the improvement period (74%, 1098/1489), at P < 0.0001 and P < 0.0001, respectively. The technical recall rates during the improvement and postimprovement periods were 2.6% (85/3321) and 1.7% (54/3236), respectively; the rate during the sustained coaching period was significantly lower than these, at 1.2% (489/40 440) (P < 0.0001 and P = 0.0232, respectively). Sustained quality passing rates and lower technical recall rates were observed despite statistically significantly increases in screening volumes. A technologist coaching program resulted in sustained high mammographic quality for almost 5 years.

Comparing eDQE and eNEQ metrics – is there an alternative approach to assessing image quality in digital mammography?

Abstract Introduction: Early detection of breast cancer requires high-quality mammographic images that have been made possible by the introduction of new technologies, such as full-field digital mammography (FFDM). In this new study, we perform extended measurements to calculate effective detective quantum efficiency (eDQE) and introduce effective noise equivalent quanta (eNEQ). Our aim was to show how these two metrics relate to the image quality of two digital mammography systems. Material and methods: Measurements were performed for a Siemens Mammomat Inspiration and a GE Senographe Pristina system. Each was equipped with an automatic exposure control (AEC) for use in a clinical setting. We used a polymethyl methacrylate (PMMA) phantom at thicknesses of 20, 30, 40 and 70 mm to cover the range of scatter conditions expected in mammography, with and without an anti-scatter grid. The Siemens system had an a-Se detector, and the GE system had an indirect-conversion detector. Measurements of Kerma were performed with Piranha Black 657 meter (RTI Electronics AB). The majority of our calculations were automated, using a modified version of our software. Results: For the two mammographic systems evaluated, we characterized physical quality parameters, such as effective modulation transfer function (eMTF), effective normalized noise power spectrum (eNNPS), eDQE and eNEQ for a wide range of exposures, phantom thicknesses, with and without an anti-scatter grid. Results are presented as a function of spatial frequency. A contrast-detail analysis was performed with a CDMAM 3.4 phantom with dedicated software (CDMAM analysis 1.5.5, NCCPM) and a set of different PMMA phantoms. Conclusions: We successfully demonstrated that the eNEQ metric can be used as a new option to evaluate image quality for images taken with and without a grid and with phantoms of different thicknesses for the Siemens and GE systems. These results were consistent with the results obtained from CDMAM.

Recommendations on screening mammography in patients with ventriculoperitoneal shunts and deep brain stimulation

Insertion of ventriculoperitoneal (VP) shunt and deep brain stimulation (DBS) are common neurosurgical procedures. Concerns have been raised regarding the safety of mammography in this patient cohort due to the risk of damaging the VP shunt tubing or DBS implantable pulse generator, and the degradation in mammography image quality secondary to the implanted devices. Based on a review of the current literature, the authors propose that mammography is safe in patients with VP shunts and DBS, and should be performed routinely as a part of population screening.

AQMI: Software for assessing the quality of mammographic images

Objective: AQMI - “Assessment of the quality of mammographic images” was developed to support the quality control (QC) of digital mammographic images. Materials and Methods: The software was implemented in the Python programming language via the Streamlit library, which involved content structuring and environmental planning. The experimental data that were selected from a public domain repository [19]. From the selected database, relevant information that was present in the DICOM file was studied to perform the image quality test. Then, from searching the literature, indicators that measure image quality were found, such as the signal-to-noise ratio, the contrast-to-noise ratio, figure of merit and image histogram. Results: AQMI assists in analyzing the image quality test established in IN 92 by the Agência Nacional de Vigilância Sanitária [8]. It also has quality addition indicators, trend graphs, and the image assessment history. Conclusion: For the functionalities of this work, the developed software is a promising tool for use in clinical practice, since it consists of a free, friendly, and easy-to-use interface.

Breast mass regions classification from mammograms using convolutional neural networks and transfer learning.

This study introduces a novel approach aimed at enhancing the quality of digital mammography images through pre-processing techniques, to improve breast cancer detection accuracy. The primary objective is to enhance image resolution, thus leading to more precise breast tissue segmentation and subsequent classification utilizing convolutional neural networks (CNNs). Three recognized public mammography databases: CBIS-DDSM, Mini-MIAS, and Inbreast were used as pre-processing data. Our statistical findings revealed that the EDSR method (PSNR = 39.05 dB/ SSIM = 0.90) consistently outperformed the visual quality of images when compared to SR-RDN (PSNR = 32.68 dB/SSIM = 0.82). Similarly, UNet demonstrated superior performance over SegNet, boasting an average Intersection over Union (IoU) of 0.862, an average Dice coefficient of 0.991, and an accuracy rate of 0.947 in Region of Interest (RoI) segmentation results. In conclusion, the ResNet model contributed to enhanced accuracy compared to conventional machine learning algorithms. However, it did not surpass state-of-the-art deep CNN-based classifiers, achieving an accuracy rate of 75%. Abbreviations: AUC: Area under curve; CAD: Computer aided system; CC: Cranio caudal; CNN: Convolutional neural network; DNN: Deep neural network; DDSM: Digital Database for Screening Mammography; DM: Digital mammography; DL: Deep learning; EDSR: Enhanced Deep Residual Network; E2E: End to End; ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks; ESPCN: Efficient sub-pixel convolutional neural network; GAN: Generative adversarial network; HR: High resolution; IoU: Intersection over Union; LR: Low resolution; MDSR: Multi-scale deep super-resolution; MLO: Mediolateral Oblique; PSNR: Peak signal to Noise Ratio; RoI: Region of interest; RDN: Residua Dense Network; RDB: Residual Dense Block; RNN: Recurrent Neural Network; ReLU: Rectified Linear Unit; SR-GAN: Super-Resolution Using a Generative Adversarial Network; SSIM: Structural Similarity Index Metric; SISR: Single image super resolution; SegNet: Segmentation Network; TP: True positive; TN: True negative; FP: False positive; FN: False negative; VGG: Visual geometric group; VDSR: Very Deep Network for SR

Automatic and standardized quality assurance of digital mammography and tomosynthesis with deep convolutional neural networks

ObjectivesThe aim of this study was to develop and validate a commercially available AI platform for the automatic determination of image quality in mammography and tomosynthesis considering a standardized set of features.Materials and methodsIn this retrospective study, 11,733 mammograms and synthetic 2D reconstructions from tomosynthesis of 4200 patients from two institutions were analyzed by assessing the presence of seven features which impact image quality in regard to breast positioning. Deep learning was applied to train five dCNN models on features detecting the presence of anatomical landmarks and three dCNN models for localization features. The validity of models was assessed by the calculation of the mean squared error in a test dataset and was compared to the reading by experienced radiologists.ResultsAccuracies of the dCNN models ranged between 93.0% for the nipple visualization and 98.5% for the depiction of the pectoralis muscle in the CC view. Calculations based on regression models allow for precise measurements of distances and angles of breast positioning on mammograms and synthetic 2D reconstructions from tomosynthesis. All models showed almost perfect agreement compared to human reading with Cohen’s kappa scores above 0.9.ConclusionsAn AI-based quality assessment system using a dCNN allows for precise, consistent and observer-independent rating of digital mammography and synthetic 2D reconstructions from tomosynthesis. Automation and standardization of quality assessment enable real-time feedback to technicians and radiologists that shall reduce a number of inadequate examinations according to PGMI (Perfect, Good, Moderate, Inadequate) criteria, reduce a number of recalls and provide a dependable training platform for inexperienced technicians.

3D directional gradient L0 norm minimization guided limited-view reconstruction in a dual-panel positron emission mammography

BackgroundDual-panel PET is often used for local organ imaging, especially breast imaging, due to its simple structure, high sensitivity, good in-plane resolution, and straightforward fusion with other imaging modalities. Nevertheless, because of data loss caused by the dual-panel structure, using conventional image reconstruction methods results in limited-view artifacts and low image quality in dual-panel positron emission mammography (PEM), which may seriously affect the diagnosis. To mitigate the limited-view artifacts in the dual-panel PEM, we propose a 3D directional gradient L0 norm minimization (3D-DL0) guided reconstruction method. MethodsThe detailed derivation and reasonable simplification of the 3D-DL0 algorithm are given first. Using this algorithm, we then obtain a prior image with edge recovery but contrast loss. To limit the solution space, the 3D-DL0 prior is introduced into the Maximum a Posteriori reconstruction. Meanwhile, a space-invariant point spread function is also implemented to restore image contrast and boundaries. Finally, the reconstructed images with limited-view artifact suppression are obtained. The proposed method was evaluated using the data acquired from physical phantoms and patients with breast tumors on a commercial dual-panel PET system. ResultsThe qualitative and quantitative studies for phantom data and the blind reader study for clinical data show that the proposed method is more effective in reaching a balance between artifact elimination and image contrast improvement compared with various limited-view reconstruction methods. In addition, the iteration process of the method is proved convergent numerically. ConclusionsThe image quality improvement confirms the potential value of the proposed reconstruction algorithm to address the limited-view problem, and thus improve diagnostic accuracy in dual-panel PEM imaging.

The impact of subjective image quality evaluation in mammography

IntroductionAccording to the guidelines, the mammograms obtained in a screening program must be evaluated to ensure that the quality of the images obtained is above 75% of score 1 (perfect and good) and less than 3% of score 3 (inadequate). This is performed by a person (usually a radiographer), so subjectivity may influence the final evaluation of the images. The aim of this study was to evaluate the impact of subjectivity on breast positioning assessments on resultant screening mammograms. Materials and methodsFive radiographers evaluated a total of 1000 mammograms. One radiographer was an expert in assessing mammography images, while the other four evaluators had varying levels of experience. All images were anonymized, and the ViewDEX software was used for visual grading analysis. The evaluators were divided into two groups, each with two evaluators. Each group evaluated 600 images, with 200 images identical between the two groups. All images had already been evaluated by the expert radiographer. All scores were compared using the Fleiss' and Cohen's kappa coefficient and accuracy score. ResultsThe results from Fleiss' kappa showed fair agreement in the mediolateral oblique (MLO) projection in the first group of evaluators whereas the other results showed poor agreement. When comparing the results from Cohen's kappa a maximum value of agreement between the evaluators was moderate 0.433 [95% CI 0.264–0.587] for the craniocaudal (CC) projection and 0.374 [95% CI 0.212–0.538] for the MLO projection. ConclusionsBased on our results, we can conclude that the agreement between all five raters was poor for both CC (κ = 0.165) and MLO (κ = 0.135) projections, based on the results of Fleiss' kappa statistic. The results show that the influence of subjectivity has a great impact on the evaluation of the quality of mammography images. Implications for practiceThus, the images are evaluated by a person, which has a high impact on subjectivity in the assessment of positioning in mammography. To achieve a more objective assessment of the images and the resulting agreement between the evaluators, we would propose to change the method of assessment. The images could be evaluated by two persons, and in the event of a discrepancy, the images would be evaluated by a third person. A computer programme could also be developed that would allow a more objective evaluation based on the geometric characteristics of the image (angle and length of the pectoral muscle, symmetry, etc.)

A Novel Medical Image Enhancement Algorithm for Breast Cancer Detection on Mammography Images Using Machine Learning.

Mammography is the most preferred method for breast cancer screening. In this study, computer-aided diagnosis (CAD) systems were used to improve the image quality of mammography images and to detect suspicious areas. The main contribution of this study is to reveal the optimal combination of various pre-processing algorithms to enable better interpretation and classification of mammography images because pre-processing algorithms significantly affect the accuracy of segmentation and classification methods. In this study, the effect of combinations of different preprocessing methods in differentiating benign and malignant breast lesions was investigated. All image processing algorithms used for lesion detection were used in the mini-MIAS database. In the first step, label information and pectoral muscle resulting from the acquisition of mammography images were removed. In the second step, median filter (MF), contrast limited adaptive histogram equalization (CLAHE), and unsharp masking (USM) algorithms with different combinations of the resolution and visibility of images are increased. In the third step, suspicious regions are extracted from the mammograms using the k-means clustering technique. Then, features were extracted from the obtained ROIs. Finally, feature datasets were classified as normal/abnormal, and benign/malign (two class classification) using Machine Learning algorithms. Test performance measures of the classification methods were examined. In both classifications made in the study, lower classification performance values were obtained when the CLAHE algorithm was used alone as a pre-processing method compared to other pre-processing combinations. When the median filter and unsharp masking algorithms are added to the CLAHE algorithm, the performance of the classification methods has increased. In terms of classification success, Support Vector Machines, Random Forest, and Neural Networks showed the best performance. It was found by comparing the performances of the classification methods that different preprocessing algorithms were effective in detecting the presence of breast lesions and distinguishing benign and malignant.

Optimization of Image Quality in Digital Mammography with the Response of a Selenium Detector by Monte Carlo Simulation

Mammography machines must meet high requirements to ensure the quality of the generated images. On the other hand, due to the use of ionizing radiation, there is a need to minimize the dose received by patients. To optimize both of these parameters (dose and image quality), the response characteristics of image detectors and, depending on the composition of the breasts, the physical contrast of the examined structures should be considered. This study aimed to determine the optimal voltage values for a given breast thickness during imaging with the use of a selenium image detector. Analysis was carried out using the Monte Carlo simulation method with the GEANT4 code. Our results reveal that the combination of Mo anode together with Mo filtration (the system recommended in analog mammography) was the least favorable combination among those used in digital mammography machines with a selenium detector. Moreover, the use of Rh filtration instead of Mo was advantageous regardless of the thickness of the breast and resulted in a significant improvement in image quality with the same dose absorbed in the breast. The most advantageous solution was found to be an X-ray tube with a W anode. The highest values of the image quality-to-dose ratio were observed for breasts with dimensions ranging from 53 mm to 60 mm in thickness. Lower image quality was observed for breasts with smaller dimensions due to high breast glandularity, resulting in the deterioration of the physical contrast.