No abstract available.
*Radiographic Image Enhancement ; Mammography/*methods ; Humans

OBJECTIVE: To develop a digital breast tomosynthesis (DBT) imaging system with optimizes imaging chain. METHODS: Based on 3D tomography and DBT imaging scanning, we analyzed the methods for projection data correction, geometric correction, projection enhancement, filter modulation, and image reconstruction, and established a hardware testing platform. In the experiment, the standard ACR phantom and high-resolution phantom were used to evaluate the system stability and noise level. The patient projection data of commercial equipment was used to test the effect of the imaging algorithm. RESULTS: In the high-resolution phantom study, the line pairs were clear without confusing artifacts in the images reconstructed with the geometric correction parameters. In ACR phantom study, the calcified foci, cysts, and fibrous structures were more clearly defined in the reconstructed images after filtering and modulation. The patient data study showed a high contrast between tissues, and the lesions were more clearly displayed in the reconstructed image. CONCLUSIONS This DBT imaging system can be used for mammary tomography with an image quality comparable to that of commercial DBT systems to facilitate imaging diagnosis of breast diseases.
Algorithms ; Artifacts ; Breast ; diagnostic imaging ; Female ; Humans ; Mammography ; methods ; Phantoms, Imaging ; Radiographic Image Enhancement ; methods

OBJECTIVE: To develop a deep features-based model to classify benign and malignant breast lesions on full- filed digital mammography. METHODS: The data of full-filed digital mammography in both craniocaudal view and mediolateral oblique view from 106 patients with breast neoplasms were analyzed. Twenty-three handcrafted features (HCF) were extracted from the images of the breast tumors and a suitable feature set of HCF was selected using -test. The deep features (DF) were extracted from the 3 pre-trained deep learning models, namely AlexNet, VGG16 and GoogLeNet. With abundant breast tumor information from the craniocaudal view and mediolateral oblique view, we combined the two extracted features (DF and HCF) as the two-view features. A multi-classifier model was finally constructed based on the combined HCF and DF sets. The classification ability of different deep learning networks was evaluated. RESULTS: Quantitative evaluation results showed that the proposed HCF+DF model outperformed HCF model, and AlexNet produced the best performances among the 3 deep learning models. CONCLUSIONS The proposed model that combines DF and HCF sets of breast tumors can effectively distinguish benign and malignant breast lesions on full-filed digital mammography.
Breast Neoplasms ; classification ; diagnostic imaging ; Deep Learning ; Diagnosis, Computer-Assisted ; methods ; Female ; Humans ; Mammography ; methods

Mammography imaging is one of the most demanding imaging modalities from the point of view of the bal- ance between image quality (the visibility of small size and/or low contrast structures) and dose (screening of many asymptomatic people). Therefore, since the introduction of the first dedicated mammographic units, many efforts have been directed to seek the best possible image quality while minimizing patient dose. The performance of auto- matic exposure control (AEC) is the manifestation of this demand. The theory of AEC includes exposure detection and optimization and also involves some accomplished methodology. This review presents the development and present situa- tion of spectrum optimization, detector evolution, and the way how to accomplish and evaluate AEC methods.
Humans ; Mammography ; methods ; Phantoms, Imaging ; Radiation Dosage ; Radiographic Image Interpretation, Computer-Assisted

Adult ; Breast Neoplasms ; diagnosis ; surgery ; Calcinosis ; diagnosis ; diagnostic imaging ; Female ; Humans ; Mammography ; methods ; Mastectomy, Segmental

OBJECTIVE: The purpose of this study was to develop a new method for automated mass detection in digital mammographic images using templates. MATERIALS AND METHODS: Masses were detected using a two steps process. First, the pixels in the mammogram images were scanned in 8 directions, and regions of interest (ROI) were identified using various thresholds. Then, a mass template was used to categorize the ROI as true masses or non-masses based on their morphologies. Each pixel of a ROI was scanned with a mass template to determine whether there was a shape (part of a ROI) similar to the mass in the template. The similarity was controlled using two thresholds. If a shape was detected, then the coordinates of the shape were recorded as part of a true mass. To test the system's efficiency, we applied this process to 52 mammogram images from the Mammographic Image Analysis Society (MIAS) database. RESULTS: Three hundred and thirty-two ROI were identified using the ROI specification methods. These ROI were classified using three templates whose diameters were 10, 20 and 30 pixels. The results of this experiment showed that using the templates with these diameters achieved sensitivities of 93%, 90% and 81% with 1.3, 0.7 and 0.33 false positives per image respectively. CONCLUSION: These results indicate that the detection performance of this template based algorithm is satisfactory, and may improve the performance of computer-aided analysis of mammographic images and early diagnosis of mammographic masses.
Sensitivity and Specificity ; Radiographic Image Enhancement ; Mammography/*methods ; Humans ; False Positive Reactions ; Automation ; Algorithms

OBJECTIVE: We wanted to compare the ability of screen-film mammography (SFM) and soft-copy full-field digital mammography (s-FFDM) on two different monitors to detect and characterize microcalcifications. MATERIALS AND METHODS: The images of 40 patients with microcalcifications (three patients had malignant lesion and 37 patients had benign lesion), who underwent both SFM and FFDM at an interval of less than six months, were independently evaluated by three readers. Three reading sessions were undertaken for SFM and for FFDM on a mammography-dedicated review workstation (RWS, 2K x 2.5K), and for FFDM on a high-resolution PACS monitor (1.7K x 2.3K). The image quality, breast composition and the number and conspicuity of the microcalcifications were evaluated using a three-point rating method, and the mammographic assessment was classified into 4 categories (normal, benign, low concern and moderate to great concern). RESULTS: The image quality, the number and conspicuity of the microcalcifications by s-FFDM (on the RWS, PACS and both) were superior to those by SFM in 85.0%, 80.0% and 52.5% of the cases, respectively (p < 0.01), and those by the s-FFDM on the two different monitors were similar in 15.0%, 12.5% and 35.0% of the cases, respectively (p > 0.01). The mammographic assessment category for the microcalcifications in the three reading sessions was similar. CONCLUSION: s-FFDM gives a superior image quality to SFM and it is better at evaluating microcalcifications. In addition, s-FFDM with the PACS monitor is comparable to s-FFDM with the RWS for evaluating microcalcifications.
*Radiographic Image Enhancement ; Mammography/*methods ; Humans ; Female ; Calcinosis/*radiography ; Breast Diseases/*radiography

Mammography is the primary screening method for breast cancers. However, the sensitivity of mammographic screening is lower for dense breasts, which are an independent risk factor for breast cancers. Automated breast ultrasound (ABUS) is used as an adjunct to mammography for screening breast cancers in asymptomatic women with dense breasts. It is an effective screening modality with diagnostic accuracy comparable to that of handheld ultrasound (HHUS). Radiologists should be familiar with the unique display mode, imaging features, and artifacts in ABUS, which differ from those in HHUS. The purpose of this study was to provide a comprehensive review of the clinical significance of dense breasts and ABUS screening, describe the unique features of ABUS, and introduce the method of use and interpretation of ABUS.]]>
Artifacts ; Breast Neoplasms ; Breast ; Early Detection of Cancer ; Female ; Humans ; Mammography ; Mass Screening ; Methods ; Risk Factors ; Ultrasonography

OBJECTIVE: To improve the performance quality of mammography X-ray system, and to decrease misdiagnoses. METHODS: Quality assurance was tested and controlled from such aspect as measurement of half value layer, beam quality assessment, breast entrance exposure average glandular dose, tube tunsion accuracy and reproducibility, and radiation output. RESULTS: The image contrast, mistiness and noise were optimized. CONCLUSION With the quality assurance of the digital mammography X-ray system, the variations of the performance parameters remain in the range of permission, thus improving the quality of mammography.
Humans ; Image Enhancement ; instrumentation ; Mammography ; instrumentation ; methods ; Quality Control ; Radiographic Image Enhancement ; instrumentation

Radiographic examination of the breast has been so improved that it became a routine complement to physicalexamination. From November 1, 1983 through Sept.30, 1984, 684 patient with complaints of various breast problemwere examined by low-dose film mammography at Yong Done Hospital, Yonesei University. Among them, a comparativestudies, independently conducted physical examination, 97 cases of film mammography, 35 cases of ultrasoundmammography, 16 cases of aspiration cytology, and 3 cases of galactography were performed for our pathologicallyproven 98 cases of breast disease. Combined and complementary studies for breast diseases were analyzed in 37proven cases and authors found that specificity of those combined immediate complementary study techniques forbreast disease were 94% under colose cooperation with surgeon in Yong Dong Hospital, Yonsei University.
Breast Diseases ; Breast ; Complement System Proteins ; Humans ; Mammography ; Methods ; Physical Examination ; Sensitivity and Specificity