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Journal articles on the topic 'Mammogram'

Author: Grafiati

Published: 4 June 2021

Last updated: 25 July 2025

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1

Hegar, Veronica, Kristin Oliveira, Bharat Kakarala, Alicia Mangram, and Ernest Dunn. "Annual Mammography Screening: Is it Necessary?" American Surgeon 78, no. 1 (2012): 104–6. http://dx.doi.org/10.1177/000313481207800145.

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Recent recommendations from the U.S. Preventative Services Task Force suggest that screening mammography for women should be biennial starting at age 50 years and continue to age 74 years. With these recommendations in mind, we proposed a study to evaluate women at our institution in whom breast cancer is diagnosed within 1 year of a previously benign mammogram. A retrospective chart review was performed over a 4-year period. Only patients who had both diagnostic mammograms and previous mammograms performed at our institution and a pathologic diagnosis of breast cancer were included. Benign ma

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Wen, Xuesong, Jianjun Li, and Liyuan Yang. "Breast Cancer Diagnosis Method Based on Cross-Mammogram Four-View Interactive Learning." Tomography 10, no. 6 (2024): 848–68. http://dx.doi.org/10.3390/tomography10060065.

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Computer-aided diagnosis systems play a crucial role in the diagnosis and early detection of breast cancer. However, most current methods focus primarily on the dual-view analysis of a single breast, thereby neglecting the potentially valuable information between bilateral mammograms. In this paper, we propose a Four-View Correlation and Contrastive Joint Learning Network (FV-Net) for the classification of bilateral mammogram images. Specifically, FV-Net focuses on extracting and matching features across the four views of bilateral mammograms while maximizing both their similarities and dissim

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Oza, Parita Rajiv, Paawan Sharma, and Samir Patel. "Transfer Learning Assisted Classification of Artefacts Removed and Contrast Improved Digital Mammograms." Scalable Computing: Practice and Experience 23, no. 3 (2022): 115–27. http://dx.doi.org/10.12694/scpe.v23i3.1992.

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Mammograms are essential radiological images used to diagnose breast cancer well in advance. However, an accurate diagnosis also depends on the quality of mammogram images. Therefore, removal of artefacts and mammogram enhancement are necessary pre-processing steps. Artefact removal helps exclude unsolicited regions in the mammograms and limits the search for suspicious regions without excessive impact from the background. Mammogram enhancements improve apparent visual details and improve some features of an image. In this paper, we propose a method for mammogram pre-processing. These pre-proc

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Manik, Ritika, Connor B. Grady, Sara Ginzberg, et al. "Racial disparities in diagnostic follow-up following BIRADS 0 mammogram." Journal of Clinical Oncology 40, no. 16_suppl (2022): 6559. http://dx.doi.org/10.1200/jco.2022.40.16_suppl.6559.

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6559 Background: Delays in follow-up after abnormal mammograms can lead to worse outcomes and may contribute to health disparities. BIRADS 0 mammograms necessitate additional diagnostic imaging, and BIRADS 4 or 5 mammograms should be followed by biopsy. The goal of this study was to investigate racial disparities in rates and timeliness of (1) diagnostic follow-up after a BIRADS 0 screening mammogram and (2) biopsy following a subsequent BIRADS 4 or 5 diagnostic mammogram. Methods: We included women ≥18 years old who underwent a screening mammogram at the Hospital of the University of Pennsylv

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Zhong, Yang Jun, and Qian Cai. "A Novel Registration Approach for Mammograms Based on SIFT and Graph Transformation." Applied Mechanics and Materials 157-158 (February 2012): 1313–19. http://dx.doi.org/10.4028/www.scientific.net/amm.157-158.1313.

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Mammogram registration is an important step in the processing of automatic detection of breast cancer. It provides aid to better visualization correspondence on temporal pairs of mammograms. This paper presents a novel algorithm based on SIFT feature and Graph Transformation methods for mammogram registration. First, features are extracted from the mammogram images by scale invariant feature transform (SIFT) method. Second, we use graph transformation matching (GTM) approach to obtain more accurate image information. At last, we registered a pair of mammograms using Thin-Plate spline (TPS) int

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Biggs, MJP, and D. Ravichandran. "Mammography in Symptomatic Women Attending a Rapid Diagnosis Breast Clinic: A Prospective Study." Annals of The Royal College of Surgeons of England 88, no. 3 (2006): 306–8. http://dx.doi.org/10.1308/003588406x98603.

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INTRODUCTION We determined whether it is safe to avoid mammograms in a group of symptomatic women with a non-suspicious history and clinical examination. PATIENTS AND METHODS Symptomatic women aged 35 years or over newly referred to a rapid-diagnosis breast clinic underwent mammography on arrival in the clinic. A breast radiologist reported on the mammograms. An experienced clinician who was unaware of the mammogram findings examined patients and decided whether a mammogram was indicated or not. If not, a management plan was formulated. Mammogram findings were then provided to the clinician an

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Abdullah, Nasturah, Noorhida Baharudin, Mariam Mohamad, and Mohamed-Syarif Mohamed-Yassin. "Factors Associated with Screening Mammogram Uptake among Women Attending an Urban University Primary Care Clinic in Malaysia." International Journal of Environmental Research and Public Health 19, no. 10 (2022): 6103. http://dx.doi.org/10.3390/ijerph19106103.

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Screening mammograms have resulted in a reduction in breast cancer mortality, yet the uptake in Malaysia was low. This study aimed to determine the prevalence and factors associated with screening mammogram uptake among women attending a Malaysian primary care clinic. A cross-sectional study was conducted among 200 women aged 40 to 74 attending the clinic. The data was collected using questionnaires assessing sociodemographic, clinical characteristics, knowledge and health beliefs. Multiple logistic regression was used to identify factors associated with mammogram uptake. The prevalence of scr

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Howard, Daniel, Simon C. Roberts, Conor Ryan, and Adrian Brezulianu. "Textural Classification of Mammographic Parenchymal Patterns with the SONNET Selforganizing Neural Network." Journal of Biomedicine and Biotechnology 2008 (2008): 1–11. http://dx.doi.org/10.1155/2008/526343.

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In nationwide mammography screening, thousands of mammography examinations must be processed. Each consists of two standard views of each breast, and each mammogram must be visually examined by an experienced radiologist to assess it for any anomalies. The ability to detect an anomaly in mammographic texture is important to successful outcomes in mammography screening and, in this study, a large number of mammograms were digitized with a highly accurate scanner; and textural features were derived from the mammograms as input data to a SONNET selforganizing neural network. The paper discusses h

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Palos, Guadalupe R., Katherine Gilmore, Patricia Chapman, Weiqi Bi, Paula Lewis-Patterson, and Alma Maria Rodriguez. "HSR19-104: Patterns of Providers’ Mammogram Referrals for Asymptomatic Breast Cancer Survivors." Journal of the National Comprehensive Cancer Network 17, no. 3.5 (2019): HSR19–104. http://dx.doi.org/10.6004/jnccn.2018.7227.

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Background: NCCN Guidelines recommend annual mammography for surveillance in asymptomatic women diagnosed with breast cancer. There is limited evidence to support which type of mammogram—screening or diagnostic—should be ordered for asymptomatic breast cancer survivors. Our objective was to assess the referral patterns for mammograms in 2 breast clinics: survivorship (SC) and oncology (OC). Methods: A retrospective analysis of institutional databases was conducted to identify a convenience sample of women with limited invasive breast cancer who were (1) alive 5 years post-treatment and (2) see

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Eberl, M., G. Broffman, J. Pomerantz, et al. "Linked claims and medical records for case management of breast cancer diagnosis." Journal of Clinical Oncology 24, no. 18_suppl (2006): 6000. http://dx.doi.org/10.1200/jco.2006.24.18_suppl.6000.

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6000 Background: Real time linkage of medical records and administrative claims may assist physicians in managing cancer care. Failure to obtain requisite follow-up (f/u) of abnormal mammograms may delay cancer diagnosis and impact outcome with studies showing that up to 20% of women do not get recommened f/u. We tested the accuracy of claims data linked to medical records in identifying women who did not receive f/u for an abnormal mammogram (BI-RADS 0,3,4,5). Methods: Electronic medical records in a staff model practice affiliated with a single payer were scanned to identify the BI-RADS code

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Hermawan, Edy. "Active Contour Lankton untuk Segmentasi Kanker Payudara pada Citra Mammogram." Eksplora Informatika 9, no. 1 (2019): 28–37. http://dx.doi.org/10.30864/eksplora.v9i1.258.

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Mammografi merupakan salah satu alat terbaik sampai saat ini untuk melakukan deteksi dini terhadap keberadaan kanker payudara. Penggunaan mammografi efektif menurunkan tingkat kematian akibat kanker payudara sebesar 30% sampai 70%. Akan tetapi, terdapat kesulitan melakukan interpretasi terhadap mammogram sebagai hasil luaran dari mammografi karena sangat bergantung pada kualitas mammogram dan pengalaman dari ahli radiologi dalam mendeteksi lesi kanker payudara. Computer Aided Diagnosis (CAD) sebagai pembaca ganda mammogram dapat dipergunakan untuk meningkatkan akurasi deteksi dan segmentasi da

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Jen, Chun-Chu, and Shyr-Shen Yu. "AUTOMATIC NIPPLE DETECTION IN MAMMOGRAMS USING LOCAL MAXIMUM FEATURES ALONG BREAST CONTOUR." Biomedical Engineering: Applications, Basis and Communications 27, no. 04 (2015): 1550035. http://dx.doi.org/10.4015/s1016237215500350.

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Mammogram registration is an important preprocessing technique, which helps in finding asymmetrical regions in left and right breast. However, correct nipple position is the crucial key point of mammogram registration since it is the only consistent and stable landmark upon a mammogram. To locate the nipple coordinates accurately in mammogram images, this work improves previous algorithms such as maximum height of the breast border (MHBB) and proposes a novel method consisting of local spatial-maximum mean intensity (LSMMI), local maximum zero-crossing (LMZC) based on the second-order derivati

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Estrella, F., C. del Frate, T. Hauer, et al. "Resolving Clinicians’ Queries Across a Grid’s Infrastructure." Methods of Information in Medicine 44, no. 02 (2005): 149–53. http://dx.doi.org/10.1055/s-0038-1633936.

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Summary Objectives: The past decade has witnessed order of magnitude increases in computing power, data storage capacity and network speed, giving birth to applications which may handle large data volumes of increased complexity, distributed over the internet. Methods: Medical image analysis is one of the areas for which this unique opportunity likely brings revolutionary advances both for the scientist’s research study and the clinician’s everyday work. Grids [1] computing promises to resolve many of the difficulties in facilitating medical image analysis to allow radiologists to collaborate

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Lee, Aaron M., and John Temple. "Getting mammograms shouldn’t be so bumpy: Improving mammogram workflow in VA community care." JCO Oncology Practice 20, no. 10_suppl (2024): 345. http://dx.doi.org/10.1200/op.2024.20.10_suppl.345.

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345 Background: The interplay between VA-based care and Community Care (CC) has long been important for continuity of patient care, though transitioning care between the two is often fraught with problems. Mammograms ordered at VA San Diego (VASD) are often deferred to Community Care; while this process should be smooth and seamless, it is instead challenging to navigate, with inconsistent rates of completion and results often not relayed to patients leading to delayed/missed diagnoses. We applied extensive QI methodology towards overhauling the VASD Community Care Mammogram workflow to improv

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Mullai, N., N. Murugesan, L. Burton, V. Goodin, and A. Stout. "Risk of noncompliance due to patient discomfort during screening mammogram." Journal of Clinical Oncology 27, no. 15_suppl (2009): 1522. http://dx.doi.org/10.1200/jco.2009.27.15_suppl.1522.

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1522 Background: There is enough evidence supporting the benefits of screening mammograms than any other screening procedure for the early detection of malignancy. Compliance to the screening method is very important to achieve this purpose. There is some concern about future trends in breast cancer mortality as a decline in rates of mammogram screening has been noticed. Pain during mammography has been recognized as a significant deterrent to breast screening. Hence this study was done to evaluate compliance in screening mammogram and to analyze major reasons for noncompliance. Methods: Rando

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Ma, Fei, Limin Yu, Gang Liu, and Qiang Niu. "Computer aided mass detection in mammography with temporal change analysis." Computer Science and Information Systems 12, no. 4 (2015): 1255–72. http://dx.doi.org/10.2298/csis141230049m.

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This paper presents a method to extract change information from temporal mammogram pairs and to incorporate the temporal change information in the malignant mass classification. In this method, a temporal mammogram registration framework which is based on spatial relations between regions of interest and graph matching was used to create correspondences between regions of current mammogram and regions of previous mammogram. 18 image features were then used to capture the differences (temporal changes) between the matched regions. To assess the contribution of temporal change information to the

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Johnson, Kimberly J., Caitlin P. O’Connell, R. J. Waken, and Justin M. Barnes. "Impact of COVID-19 pandemic on breast cancer screening in a large midwestern United States academic medical center." PLOS ONE 19, no. 5 (2024): e0303280. http://dx.doi.org/10.1371/journal.pone.0303280.

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Background Access to breast screening mammogram services decreased during the COVID-19 pandemic. Our objectives were to estimate: 1) the COVID-19 affected period, 2) the proportion of pandemic-associated missed or delayed screening encounters, and 3) pandemic-associated patient attrition in screening encounters overall and by sociodemographic subgroup. Methods We included screening mammogram encounter EPIC data from 1-1-2019 to 12-31-2022 for females ≥40 years old. We used Bayesian State Space models to describe weekly screening mammogram counts, modeling an interruption that phased in and out

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Chiou, Yih-Chih, Chern-Sheng Lin, and Cheng-Yu Lin. "HYBRID REGISTRATION OF CORRESPONDING MAMMOGRAM IMAGES FOR AUTOMATIC DETECTION OF BREAST CANCER." Biomedical Engineering: Applications, Basis and Communications 19, no. 06 (2007): 359–74. http://dx.doi.org/10.4015/s101623720700046x.

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Mammogram registration is a critical step in automatic detection of breast cancer. Much research has been devoted to registering mammograms using either feature-matching or similarity measure. However, a few studies have been done on combining these two methods. In this research, a hybrid mammogram registration method for the early detection of breast cancer is developed by combining feature-based and intensity-based image registration techniques. Besides, internal and external features were used simultaneously during the registration to obtain a global spatial transformation. The experimental

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Alkhaifi, Sarah, and Hanan Badr. "Association between Breast Cancer Knowledge and Mammogram Utilization among Immigrant Muslim Arab Women in California: Cross-Sectional Design." Healthcare 10, no. 12 (2022): 2526. http://dx.doi.org/10.3390/healthcare10122526.

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Background: Regular mammogram screenings have contributed to early breast cancer (BC) diagnoses and lowered the mortality rate by 40% in the United States of America (USA). Nonetheless, ethnic women living in developed countries, such as immigrant Muslim Arab women (IMAW), are less likely to get mammograms. Aim of the study: In our study, we aimed to understand health behaviors among IMAWs as understudied populations in the USA. Methods: We conducted a cross-sectional study on a convenience sample of IMAW living in southern California. We used logistic regression and multivariate logistic regr

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North, Frederick, Elissa M. Nelson, Rebecca J. Buss, Rebecca J. Majerus, Matthew C. Thompson, and Brian A. Crum. "The Effect of Automated Mammogram Orders Paired With Electronic Invitations to Self-schedule on Mammogram Scheduling Outcomes: Observational Cohort Comparison." JMIR Medical Informatics 9, no. 12 (2021): e27072. http://dx.doi.org/10.2196/27072.

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Background Screening mammography is recommended for the early detection of breast cancer. The processes for ordering screening mammography often rely on a health care provider order and a scheduler to arrange the time and location of breast imaging. Self-scheduling after automated ordering of screening mammograms may offer a more efficient and convenient way to schedule screening mammograms. Objective The aim of this study was to determine the use, outcomes, and efficiency of an automated mammogram ordering and invitation process paired with self-scheduling. Methods We examined appointment dat

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Sakaida, Miu, Takaaki Yoshimura, Minghui Tang, et al. "The Effectiveness of Semi-Supervised Learning Techniques in Identifying Calcifications in X-ray Mammography and the Impact of Different Classification Probabilities." Applied Sciences 14, no. 14 (2024): 5968. http://dx.doi.org/10.3390/app14145968.

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Identifying calcifications in mammograms is crucial for early breast cancer detection, and semi-supervised learning, which utilizes a small dataset for supervised learning combined with deep learning, is anticipated to be an effective approach for automating this identification process. This study explored the impact of semi-supervised learning on identifying mammographic calcifications by including 712 mammographic images from 252 patients in public datasets. Initially, 212 mammogram images were segmented into patches and classified visually for calcification presence. A subset of these patch

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Picton, M. E., B. Ramirez, D. Liles, T. R. Sastry, and M. Petruzziello. "Barriers to screening and treatment of breast cancer: Data analysis from Edgecombe County." Journal of Clinical Oncology 29, no. 27_suppl (2011): 232. http://dx.doi.org/10.1200/jco.2011.29.27_suppl.232.

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232 Background: Edgecombe County in NC was described as the 3rd highest location breast cancer mortality according to the Susan G. Komen report (2007). The major issues detected were low education, lack of healthcare providers, and large numbers of uninsured individuals. Our analysis sought to further characterize the circumstances unique to this region and measures to improve mortality. Methods: Between October 2008 and January 2009, 493 surveys were conducted throughout the county. The surveyors randomly approached female residents of Edgecombe County who completed a questionnaire, which was

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Southwell, Brian G., Jonathan S. Slater, Christina L. Nelson, and Alexander J. Rothman. "Does It Pay to Pay People to Share Information? Using Financial Incentives to Promote Peer Referral for Mammography among the Underinsured." American Journal of Health Promotion 26, no. 6 (2012): 348–51. http://dx.doi.org/10.4278/ajhp.110120-arb-34.

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Purpose. Efforts to screen underinsured women for breast cancer face challenges in reaching desired audiences. One option is viral marketing through peer referral. We sought the optimal way to solicit nominations of peers. Design. An experiment (N = 2968) compared impact of incentives on peer nomination. Women were offered a $20 incentive each time someone they referred was screened, a $5 incentive for each name and valid address or phone number (regardless of screening completion), or no financial incentive for nomination. Setting. Study sample was drawn from free mammography program particip

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Sundaram, M., K. Ramar, N. Arumugam, and G. Prabin. "EFFICIENT EDGE EMPHASIZED MAMMOGRAM IMAGE ENHANCEMENT FOR DETECTION OF MICROCALCIFICATION." Biomedical Engineering: Applications, Basis and Communications 26, no. 05 (2014): 1450056. http://dx.doi.org/10.4015/s1016237214500562.

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An efficient detection of microcalcification based on edge enhancement using discrete wavelet transform (DWT) is presented in this paper. The proposed method is implemented by separating the wavelet coefficients into weak and strong edge coefficients for effective detection of microcalcification. Identification of strong and weak edge locations corresponding to microcalcification is obtained by allowing the input image through appropriate filters before wavelet decomposition. Before reconstructing the output image, the strong and weak edge coefficients are modified based on the energy of the c

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Stojic, Tomislav. "Visual enhancement of microcalcifications and masses in digital mammograms using modified multifractal analysis." Nuclear Technology and Radiation Protection 30, no. 1 (2015): 61–69. http://dx.doi.org/10.2298/ntrp1501061s.

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Microcalcifications and masses, as breast tissue anomalies (deviations from observed background regularity), may be viewed as statistically rare occurrences in a mammogram image. After recognizing their principal common features - bright image parts not belonging to the surrounding tissue, with significant local contrast just around the edges - several modifications to multifractal image analysis have been introduced. Starting from a mammogram image, the proposed method creates corresponding multifractal images. Additional post-processing, based on mathematical morphology, refines the procedur

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Hsu, John, Jie Huang, and Bruce Fireman. "Mammography screening among women 40-49 years old, before and after USPSTF guideline changes." Journal of Clinical Oncology 31, no. 26_suppl (2013): 113. http://dx.doi.org/10.1200/jco.2013.31.26_suppl.113.

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113 Background: The age at which to start breast cancer screening is controversial. The US Preventive Task Force (USPSTF) recommended against routine mammography screening in women 40-49 years old in November 2009 (the 2002 report had recommended screening), and instead suggested that women consult their physicians. We examined the frequency of screening among 40-49yo, and characteristics of the screened. Methods: We examined mammography screening among 85,650 women who were continuously enrolled in a prepaid integrated delivery system and who were between 40-45yo in 2006. We excluded women wi

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Allison, Kimberly H., Linn A. Abraham, Donald L. Weaver, et al. "Tissue sampling frequency and breast pathology diagnoses following mammography: Time trends and age group analysis from the Breast Cancer Surveillance Consortium (BCSC)." Journal of Clinical Oncology 31, no. 15_suppl (2013): 559. http://dx.doi.org/10.1200/jco.2013.31.15_suppl.559.

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559 Background: Pathology diagnoses in a well-characterized population of women can be used to identify tissue sampling and diagnosis trends following mammography. Methods: Screening and diagnostic mammography, patient characteristics, and pathology reports from the BCSC performed from 1996-2008 were identified. Diagnosis was based on the most severe pathology interpretation in the same breast within 60 days of a post-mammogram tissue sample. Age, mammogram year and type, breast density, and family history of breast cancer were evaluated for associations with tissue sampling and most severe pa

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Bajcsi, Adél, Camelia Chira, and Anca Andreica. "Extended Mammogram Classification From Textural Features." Studia Universitatis Babeș-Bolyai Informatica 67, no. 2 (2023): 5–20. http://dx.doi.org/10.24193/subbi.2022.2.01.

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"The efficient analysis of digital mammograms has an important role in the early detection of breast cancer and can lead to a higher percentage of recovery. This paper presents an extended computer-aided diagnosis system for the classification of mammograms into three classes (normal, benign and malignant). The performance of the system is evaluated for two different mammogram databases (MIAS and DDSM) in order to assess its robustness. We discuss the changes required in the system, particularly at the level of the image preprocessing and feature extraction. Computational experiments are perfo

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Gopal, Annapoorani, Lathaselvi Gandhimaruthian, and Javid Ali. "Role of General Adversarial Networks in Mammogram Analysis: A Review." Current Medical Imaging Formerly Current Medical Imaging Reviews 16, no. 7 (2020): 863–77. http://dx.doi.org/10.2174/1573405614666191115102318.

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The Deep Neural Networks have gained prominence in the biomedical domain, becoming the most commonly used networks after machine learning technology. Mammograms can be used to detect breast cancers with high precision with the help of Convolutional Neural Network (CNN) which is deep learning technology. An exhaustive labeled data is required to train the CNN from scratch. This can be overcome by deploying Generative Adversarial Network (GAN) which comparatively needs lesser training data during a mammogram screening. In the proposed study, the application of GANs in estimating breast density,

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Rehman, Khalil ur, Jianqiang Li, Yan Pei, Anaa Yasin, Saqib Ali, and Yousaf Saeed. "Architectural Distortion-Based Digital Mammograms Classification Using Depth Wise Convolutional Neural Network." Biology 11, no. 1 (2021): 15. http://dx.doi.org/10.3390/biology11010015.

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Architectural distortion is the third most suspicious appearance on a mammogram representing abnormal regions. Architectural distortion (AD) detection from mammograms is challenging due to its subtle and varying asymmetry on breast mass and small size. Automatic detection of abnormal ADs regions in mammograms using computer algorithms at initial stages could help radiologists and doctors. The architectural distortion star shapes ROIs detection, noise removal, and object location, affecting the classification performance, reducing accuracy. The computer vision-based technique automatically remo

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Alkhenizan, Abdullah, Aneela Hussain, and Adher Alsayed. "The sensitivity and specificity of screening mammography in primary care setting in Saudi Arabia." Journal of Clinical Oncology 31, no. 15_suppl (2013): e12551-e12551. http://dx.doi.org/10.1200/jco.2013.31.15_suppl.e12551.

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e12551 Background: Breast cancer is the leading cancer diagnosed in women in Saudi Arabia, accounting for 25% of all cancers diagnosed in women. The mammogram screening program at King Faisal Specialist Hospital and Research Center (KFSHRC) is the only structured screening program in the country. KFSHRC provides primary care services for a catchment population of 30,000 patients. This program covers all women above the age of 40 within this catchment population. Methods: A retrospective review of electronic and paper records were reviewed for mammograms done between January 2002- January 2012.

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Sivakumar, V., and P. Shanmugavadivu. "Mammogram Mass Segmentation Using Fractals." Journal of Computational and Theoretical Nanoscience 16, no. 8 (2019): 3437–42. http://dx.doi.org/10.1166/jctn.2019.8305.

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A new fractal-based technique for the segmentation of masses from digital mammograms is shown in this paper. This proposed fractal-based approach aims to detect and segment the masses from mammograms after successful suppression of pectoral muscle. This paper identifies and suppresses the pectoral muscle efficiently from the input mammogram and further segments the masses precisely with the help of fractal dimensions and fractal hurst based thresholding oriented computation. This approach is proved to be efficient in getting accurate segmentation results that guarantees the virtue of the proje

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Stojic, Tomislav. "A fast and simple method for the visual enhancement of microcalcifications in digital mammograms based on mathematical morphology." Nuclear Technology and Radiation Protection 29, no. 2 (2014): 108–15. http://dx.doi.org/10.2298/ntrp1402108s.

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A fast and simple method for the visual enhancement of small bright details in digital mam- mograms based on mathematical morphology is proposed. By a proper choice of the shape and size of the structuring element, an algorithm for a particular processing task - in this case, for the visual enhancement of microcalcifications in digital mammograms - was designed. The efficiency of the proposed algorithm was tested on publicly available mammograms from the mammographic image analysis society database. In all tested cases (23 mammograms), the proposed method successfully segmented and enhanced th

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Jiang, Shu, and Graham A. Colditz. "Abstract PD14-04: PD14-04 Whole mammogram image-based Cox regression improves 5-year breast cancer prediction." Cancer Research 83, no. 5_Supplement (2023): PD14–04—PD14–04. http://dx.doi.org/10.1158/1538-7445.sabcs22-pd14-04.

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Abstract To efficiently capture data from mammographic breast images and classify long-term risk of breast cancer, we developed FLIP, a novel Cox regression-based framework that fully utilizes data in the mammograms beyond current density measures. FLIP use the extensive existing data that are currently ignored in the context of breast cancer risk stratification. More than 20 studies support texture features add value to risk prediction beyond breast density. However, the entire mammogram imaging data has a high dimension of pixels (~13 million per image), greatly exceeding the number of women

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Popli, Manju Bala, Rahul Teotia, Meenakshi Narang, and Hare Krishna. "Breast Positioning during Mammography: Mistakes to be Avoided." Breast Cancer: Basic and Clinical Research 8 (January 2014): BCBCR.S17617. http://dx.doi.org/10.4137/bcbcr.s17617.

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Aims and Objectives Breast positioning is the key factor affecting a mammogram. If care is taken during positioning, it maximizes the amount of breast tissue being imaged, eliminates most of the artifacts, and increases sensitivity of the mammogram. This retrospective study was carried out in our department to assess correctness, and also the incorrectness of breast positioning, which need to be avoided to obtain an ideal mammogram. Material and Methods A total of 1369 female patients were included in this study. Mammography was performed on full field detector digital mammography equipment. C

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Liu, Chen Chung, Shyr Shen Yu, Chung Yen Tsai, and Ta Shan Tsui. "Pectoral Muscle Segmentation for Digital Mammograms Based on Otsu Thresholding." Applied Mechanics and Materials 121-126 (October 2011): 4537–41. http://dx.doi.org/10.4028/www.scientific.net/amm.121-126.4537.

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The appearance of pectoral muscle in medio-lateral oblique (MLO) views of mammograms can increase the false positive in computer aided detection (CAD) of breast cancer detection. Pectoral muscle has to be identified and segmented from the breast region in a mammogram before further analysis. The main goal of this paper is to propose an accurate and efficient algorithm of pectoral muscle extraction on MLO mammograms. The proposed algorithm bases on the positional characteristic of pectoral muscle in a breast region to combine the iterative Otsu thresholding scheme and the mathematic morphologic

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Jiang, Shu, and Graham A. Colditz. "Abstract LB161: Whole mammogram image improves breast cancer prediction." Cancer Research 82, no. 12_Supplement (2022): LB161. http://dx.doi.org/10.1158/1538-7445.am2022-lb161.

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Abstract To efficiently capture data from mammographic breast images and classify long term risk of breast cancer, we developed methods that use the extensive existing data that are currently ignored in the context of breast cancer risk stratification. More than 20 studies support texture features add value to risk prediction beyond breast density. However, the entire mammogram imaging data has a high dimension of pixels (~13 million per image), greatly exceeding the number of women in a cohort. We apply functional principal component analysis methods to predict 5-years breast cancer incidence

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Pairawan, Seyed S., Luis Olmedo Temich, Sebastian de Armas, et al. "Recovery of Screening Mammogram Cancellations During COVID-19." American Surgeon 87, no. 10 (2021): 1651–55. http://dx.doi.org/10.1177/00031348211051695.

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Background In response to the COVID-19 pandemic, the American Society of Breast Surgeons and American College of Radiology released a joint statement recommending that all breast screening studies be postponed effective March 26, 2020. Study Design A retrospective review of all canceled mammograms at a single tertiary care institution from January 1-August 31, 2020 was performed to evaluate the effect of this recommendation by quantifying both the number and reason for mammogram cancellations before and after March 26, 2020. Utilization of the electronic patient portal for appointment cancella

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Stokes, Jeffrey E., Danielle A. Waldron, Jacquelin Sauer, and Emily Hartford. "HEALTH CARE ACCESS AND USE AMONG ADULTS AGING WITH INTELLECTUAL AND DEVELOPMENTAL DISABILITIES." Innovation in Aging 7, Supplement_1 (2023): 563–64. http://dx.doi.org/10.1093/geroni/igad104.1848.

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Abstract Adults with intellectual and developmental disabilities (IDD) are living longer than ever before, a testament to advancements in healthcare. However, adults with IDD face a number of challenges accessing healthcare, and particularly preventative healthcare targeted towards midlife and older adults, which have previously been far less utilized by adults aging with IDD. This study uses longitudinal data from the National Core Indicators In-Person Survey (NCI-IPS) to examine factors associated with adults with IDD’s utilization of two preventative healthcare measures specific to mid- and

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Loizidou, Kosmia, Galateia Skouroumouni, Christos Nikolaou, and Costas Pitris. "A Review of Computer-Aided Breast Cancer Diagnosis Using Sequential Mammograms." Tomography 8, no. 6 (2022): 2874–92. http://dx.doi.org/10.3390/tomography8060241.

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Radiologists assess the results of mammography, the key screening tool for the detection of breast cancer, to determine the presence of malignancy. They, routinely, compare recent and prior mammographic views to identify changes between the screenings. In case a new lesion appears in a mammogram, or a region is changing rapidly, it is more likely to be suspicious, compared to a lesion that remains unchanged and it is usually benign. However, visual evaluation of mammograms is challenging even for expert radiologists. For this reason, various Computer-Aided Diagnosis (CAD) algorithms are being

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Kosmia, Loizidou, Skouroumouni Galateia, Nikolaou Christos, and Pitris Costas. "A Review of Computer-Aided Breast Cancer Diagnosis Using Sequential Mammograms." Tomography 8, no. 6 (2022): 2874–92. https://doi.org/10.3390/tomography8060241.

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Radiologists assess the results of mammography, the key screening tool for the detection of breast cancer, to determine the presence of malignancy. They, routinely, compare recent and prior mammographic views to identify changes between the screenings. In case a new lesion appears in a mammogram, or a region is changing rapidly, it is more likely to be suspicious, compared to a lesion that remains unchanged and it is usually benign. However, visual evaluation of mammograms is challenging even for expert radiologists. For this reason, various Computer-Aided Diagnosis (CAD) algorithms are being

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Joshi, Birendra Raj, Bishnu Paudel, and Anamika Jha. "Comparison of Sonographic Findings with Diagnostic Mammography." Journal of Nepal Health Research Council 22, no. 01 (2024): 87–90. http://dx.doi.org/10.33314/jnhrc.v22i01.4193.

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Background: Breast cancer is the leading female cancer worldwide with a high mortality rate. Early detection of the suspicious lesion is crucial for better prognosis. Higher breast density decreases the sensitivity of mammogram. Ultrasound can differentiate between cystic and solid masses and further characterize these as benign or possibly malignant. Our objective was to compare the findings of sonography with diagnostic mammography.Methods: This was a cross sectional study including 125 females who underwent diagnostic mammogram in a tertiary care center. The mammograms were evaluated and th

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Eberl, M. M., C. H. Fox, G. Broffman, et al. "An automated practice-based intervention for case management of abnormal mammograms." Journal of Clinical Oncology 25, no. 18_suppl (2007): 6563. http://dx.doi.org/10.1200/jco.2007.25.18_suppl.6563.

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6563 Background: Failure to obtain follow-up (f/u) of abnormal mammograms may delay cancer diagnosis and impact outcome; studies show up to 20% of women do not get recommended f/u. Our group previously established that administrative claims linked to medical records accurately identify care for management of abnormal mammograms. We are testing a case management system linking claims data to mammogram information to assist physicians in assuring appropriate f/u for abnormal mammograms. Methods: Electronic medical records in a staff model practice affiliated with a single payer are scanned to id

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Whittaker, Meredith, Kiah Farr, Preethika Potluri, et al. "Abstract P1-05-04: Physician Practice Patterns of Breast Imaging After Treatment: Survey of Real-World Practice." Cancer Research 83, no. 5_Supplement (2023): P1–05–04—P1–05–04. http://dx.doi.org/10.1158/1538-7445.sabcs22-p1-05-04.

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Abstract Introduction: Mammography is the cornerstone of breast cancer screening, diagnosis, and surveillance. After definitive treatment for breast cancer, mammograms are continued for surveillance. The current recommendations regarding surveillance after definitive treatment (surgery and radiation) lack consensus amongst various societies. There are no clear guidelines in regards to the type of mammogram recommended: diagnostic or screening mammogram or if a diagnostic mammogram is used, when to return to routine screening protocols. Current practice patterns are driven by physician’s prefer

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Khehra, Baljit Singh, and Amar Partap Singh Pharwaha. "DIGITAL MAMMOGRAM ENHANCEMENT USING KAPUR MEASURE OF ENTROPY AND MATHEMATICAL MORPHOLOGY." Biomedical Engineering: Applications, Basis and Communications 25, no. 03 (2013): 1350029. http://dx.doi.org/10.4015/s1016237213500294.

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Mammography is the most reliable, effective, low cost and highly sensitive method for early detection of breast cancer. Mammogram analysis usually refers to the processing of mammograms with the goal of finding abnormality presented in the mammogram. Mammogram enhancement is one of the most critical tasks in automatic mammogram image analysis. Main purpose of mammogram enhancement is to enhance the contrast of details and subtle features while suppressing the background heavily. In this paper, a hybrid approach is proposed to enhance the contrast of microcalcifications while suppressing the ba

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Bhuvaneswari, E., and T. Ravi. "Privacy Preserving with M-SVM Classifier of Tumor Classification in Mammography Images Using Multiple Otsu'S Thresholding Technique." Journal of Computational and Theoretical Nanoscience 15, no. 2 (2018): 697–705. http://dx.doi.org/10.1166/jctn.2018.7146.

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Breast Cancer is formed by an abnormal development of cells in breast. The cells of body separate in an incessant method and occupy to surrounding tissues. It is the important reason of death amongst women and after lung cancer breast cancer is second cause of women deaths. Early breast cancer detection can lead to death rate decrease. The mammography is executed to discover the breast cancer tumor at earlier stages. Early breast cancer tumors detection based on the both the radiologists capability to read mammogram images and image quality. The tumors classification is a medical application t

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Manik, Ritika, Connor B. Grady, Sara Ginzberg, et al. "Mammographic follow-up before and during the COVID-19 pandemic." Journal of Clinical Oncology 40, no. 28_suppl (2022): 122. http://dx.doi.org/10.1200/jco.2022.40.28_suppl.122.

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122 Background: Mammography adapted during the COVID-19 pandemic to accommodate social-distancing guidelines and minimize risk of exposure, but it is unclear how these accommodations potentially provoked existing inefficiencies or illuminated opportunities to redress them. The goal of this study was to compare rates of (1) diagnostic follow-up after a BIRADS 0 (i.e., incomplete) screening mammogram and (2) biopsy following a BIRADS 4 or 5 (i.e., biopsy recommended) diagnostic mammogram before and after onset of the pandemic. Methods: We included women ≥18y who underwent a BIRADS 0 screening ma

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Bobo, Janet Kay, Denita Dean, Christina Stovall, Margaret Mendez, and Lee Caplan. "Factors That May Discourage Annual Mammography among Low-Income Women with Access to Free Mammograms: A Study Using Multi-Ethnic, Multiracial Focus Groups." Psychological Reports 85, no. 2 (1999): 405–16. http://dx.doi.org/10.2466/pr0.1999.85.2.405.

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Age-eligible women enrolled in the National Breast and Cervical Cancer Early Detection Program can obtain free or low-cost mammograms annually, but many do not routinely complete rescreening. This study investigated the rescreening behavior of low-income women by conducting 8 focus groups in Texas with enrollees who had access to free mammograms. Concerns mentioned in the focus groups included fear of radiation, anxiety that the test might not find a cancer that was there, and worries that cancer might be detected. In all groups, some women mentioned the embarrassment, discomfort, or pain they

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Suresh, Attili V. S., Rakesh Sharma, Praveen K. Dadireddy, et al. "Vascular calcifications in mammogram and correlate them with coronary artery diseases: a retrospective study." International Journal Of Community Medicine And Public Health 9, no. 7 (2022): 2961. http://dx.doi.org/10.18203/2394-6040.ijcmph20221766.

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Background: The aim was to evaluate the incidental findings of vascular calcifications in mammogram and correlate them with coronary artery diseases clinically and radiologically in retrospective manner from multiple institutes.Methods: It was a retrospective analysis of all mammograms done in past one year for routine screening from three institutes during 2020-2021. Those with positive calcifications were interviewed and for the willing population we evaluated the coronary artery disease status clinically and radiologically.Results: There were 335 subjects whose complete case records were av

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Mohamed, Basma A., and Heba M. Afify. "MAMMOGRAM COMPRESSION TECHNIQUES USING HAAR WAVELET AND QUADTREE DECOMPOSITION-BASED IMAGE ENHANCEMENT." Biomedical Engineering: Applications, Basis and Communications 29, no. 05 (2017): 1750038. http://dx.doi.org/10.4015/s1016237217500387.

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Biomedical image compression plays an important role in the medical field. Mammograms are medical images used in the early detection of breast cancer. Mammogram image compression is a challenging task because these images contain information that occupies huge size for storage. The aim of image compression is to reduce the image size and the time taken for recovering the original image without any loss. In this paper, two different techniques of mammogram compression are introduced. The proposed algorithm includes two main steps. First, a preprocessing step is applied to enhance the image, and

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