Relevant bibliographies by topics / Mammogram

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Academic literature on the topic 'Mammogram'

Author: Grafiati
Published: 4 June 2021
Last updated: 21 February 2023

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Journal articles on the topic "Mammogram"

1

Hegar, Veronica, Kristin Oliveira, Bharat Kakarala, Alicia Mangram, and Ernest Dunn. "Annual Mammography Screening: Is it Necessary?" American Surgeon 78, no. 1 (January 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 mammograms were defined as either Breast Imaging Reporting And Data System 1 or 2. Analysis of the time elapse between benign mammogram and subsequent mammogram indicative of the diagnosis of breast cancer was performed. A total of 205 patients were included. The average age was 64 years. From our results, 48 patients, 23 per cent of the total, had a documented benign mammogram at 12 months or less before a breast cancer diagnosis. One hundred forty-three (70%) patients had a benign mammogram at 18 months or less prior. This study raises concern that 2 years between screening mammograms may delay diagnosis and possible treatment options for many women.
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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 (October 13, 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-processed mammograms are then fed into Deep Convolutional Neural Network for the classification process. Two approaches are used and compared to classify mammograms; Training model from scratch and Transfer Learning. Transfer Learning is an excellent approach to dealing with the small-sized training set, allowing us to consume the extendibility of deep learning entirely. By employing VGG16 as a pre-trained network on the pre-processed MIAS dataset, we improved training accuracy (96.14\%) compared to the model developed from scratch and other strategies described in the literature.
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Manik, Ritika, Connor B. Grady, Sara Ginzberg, Christine E. Edmonds, Emily F. Conant, Rebecca A. Hubbard, and Oluwadamilola Motunrayo Fayanju. "Racial disparities in diagnostic follow-up following BIRADS 0 mammogram." Journal of Clinical Oncology 40, no. 16_suppl (June 1, 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 Pennsylvania with an assessment of BIRADS 0 between September 2010 and February 2018. The distributions of time from screening to diagnostic mammogram and from BIRADS 4 or 5 diagnostic mammogram to biopsy were estimated using the Kaplan Meier method. Follow-up was censored at 365 days. Case-mix adjusted Cox proportional hazards models were used to estimate the association between race/ethnicity and time to diagnostic mammogram and biopsy. Results: We identified 6299 women (Asian/PI=257, Black=3223, Hispanic=124, White=2420, Other/Unknown=275) with 6880 BIRADS 0 screening mammograms during the study period. Following these BIRADS 0 mammograms, the overall rate of diagnostic mammograms within 365 days was 87.3% (n=6006 mammograms), with a rate of 90.6% (2432) for White women and 85.3% (2971) for Black women. For the 1151 BIRADS 4-5 diagnostic mammograms in the cohort, the overall rate of follow-up biopsies within 365 days was 91.8% (n=1057 biopsies), with a rate of 93.8% (396) for White women and 91.1% (575) for Black women. Compared to mammograms obtained by White women, those obtained by Black women were less likely to be followed up with a diagnostic mammogram (HR 0.71, 95% CI 0.63-0.80, p<0.001) and biopsy (HR 0.74, 95% CI 0.55-0.98, p=0.037) when indicated (Table). Almost 1/4 (24.2%, 95% CI 23.1-25.9%) of BIRADS 0 screening mammograms among Black women were not followed by diagnostic imaging within 30 days as compared to 14.6% among White women (95% CI 13.3-16.0%, p<0.001). 23.6% (95% CI 20.5-27.2%) of BIRADS 4-5 diagnostic mammograms among Black women were not followed up with biopsy within 30 days vs 18.7% for White women (95% CI 15.4-22.8%, p=0.61) (Table). These disparities persisted at 90 days. Conclusions: Racial disparities exist in rates of follow-up after BIRADS 0 mammograms. The additive effects of delays at each diagnostic step put Black women at disproportionately greater risk for worse outcomes. [Table: see text]
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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) interpolation based on corresponding points on the two breasts, and acquire the mammogram registration image. Performance of the proposed algorithm is evaluated by three criterions. The experimental results show that our method is accurate and closely to the source images.
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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 (May 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 and any change to the original management plan as a result of mammography was recorded. RESULTS In two-thirds (67%) of 218 patients, the clinician felt a mammogram was indicated. Half (46%) of these mammograms showed an abnormality; of these abnormal mammograms, 41% were breast cancer. Among the third (n = 71) of mammograms felt not to be indicated, 3 showed abnormalities of which 2 were breast cancer. One cancer was not suspected clinically or mammographically but was diagnosed on cyto/histopathological assessment. CONCLUSIONS A significant proportion of patients attending a symptomatic breast clinic have a non-suspicious history and normal clinical findings on examination. However, even in this group avoiding mammograms risks missing clinically occult breast cancers. It would appear sensible to offer mammograms to all symptomatic women over 35 years of age.
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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 (May 17, 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 screening mammograms was 46.0%. About 45.5% of women with high breast cancer risk had never undergone a mammogram. Older participants, aged 50 to 74 (OR = 2.57, 95% CI: 1.05, 6.29, p-value = 0.039) and those who received a physician’s recommendation (OR = 7.61, 95% CI: 3.81, 15.20, p-value < 0.001) were more likely to undergo screening mammography. Significant health beliefs associated with mammogram uptake were perceived barriers (OR = 0.81, 95% CI: 0.67, 0.97, p-value = 0.019) and cues to action (OR = 1.30, 95% CI: 1.06, 1.59, p-value = 0.012). Approximately half of the participants and those in the high-risk group had never undergone a mammogram. Older age, physician recommendation, perceived barriers and cues to action were significantly associated with mammogram uptake. Physicians need to play an active role in promoting breast cancer screening and addressing the barriers.
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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 how SONNET was used to produce a taxonomic organization of the mammography archive in an unsupervised manner. This process is subject to certain choices of SONNET parameters, in these numerical experiments using the craniocaudal view, and typically produced O(10), for example, 39 mammogram classes, by analysis of features from O() mammogram images. The mammogram taxonomy captured typical subtleties to discriminate mammograms, and it is submitted that this may be exploited to aid the detection of mammographic anomalies, for example, by acting as a preprocessing stage to simplify the task for a computational detection scheme, or by ordering mammography examinations by mammogram taxonomic class prior to screening in order to encourage more successful visual examination during screening. The resulting taxonomy may help train screening radiologists and conceivably help to settle legal cases concerning a mammography screening examination because the taxonomy can reveal the frequency of mammographic patterns in a population.
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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 (March 8, 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) seen at a SC or OC visit scheduled from January 1 to December 31, 2015. The primary outcome was women who received a diagnostic or screening mammogram during the 2015 calendar year. Demographic, clinical, and mammogram characteristics were also analyzed. Simple descriptive statistics were used to aggregate and compare data. Chi-square analysis tested for statistical significance. Results: A convenience sample of 354 cases was identified. Of those, 247 met the eligibility criteria for this analysis, SC=147 and OC=100. In this cohort, the mean age of diagnosis was 50.42 (±11.12), the majority were white (74.1%), and most received a diagnostic mammogram (64.4%). A greater proportion of diagnostic mammograms were ordered for women seen in OC (40.1%) vs SC (24. 3%). This finding was statistically significant (P=.00). Overall, 91.9 % of the mammogram results were negative and with a low proportion of “positive” (2.0%) or “need for additional imaging testing” (6.1%) reported. Recurrence rates in this cohort were also found to be low (1.6%). Conclusions: In this cohort, we found a difference in the type of mammogram ordered by providers in dedicated survivorship or medical oncology clinics. Our findings suggest that despite the greater use of diagnostic mammograms among this cohort of long-term breast cancer survivors, the overall proportion of positive findings, further imaging tests, or recurrence rates was low. It is concerning that many asymptomatic breast cancer survivors continue to receive a diagnostic mammogram as part of their surveillance visit. Further studies are needed to identify the emotional, financial, and physical toxicities associated with diagnostic mammograms.
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Eberl, M., G. Broffman, J. Pomerantz, N. Watroba, M. Reinhardt, M. C. Mahoney, C. Fox, and S. B. Edge. "Linked claims and medical records for case management of breast cancer diagnosis." Journal of Clinical Oncology 24, no. 18_suppl (June 20, 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 for all mammograms performed by practice radiologists. For each woman covered by the payer with a BI-RADS 0,3,4 or 5 mammogram, claims were searched for f/u breast procedures (imaging, biopsy, surgery). For women with more than 1 abnormal mammogram in the study period, only f/u of the first abnormal mammogram was studied. Cases were censured if their insurance coverage with the payer terminated before the required period of follow-up. Medical records of cases defined by claims as not having recommended f/u were reviewed to determine the accuracy of claims analysis. Results: 17,329 women covered by the payer had at least one mammogram in the practice from 1/1/2001 to 12/31/2003. The BI-RADS was 0, 3, 4 or 5 in 1,319 (7.5%). Among 1,206 eligible for f/u, 189 (16%) did not receive the BI-RADS recommended f/u (see table ). Medical record review showed that the claims search accurately identified the follow-up care in 95% of these cases. Conclusions: Administrative claims accurately identify care for diagnostic management of abnormal mammograms. Real-time linkage of claims to mammogram BI-RADS data is being tested as a regional case management system to assist physicians in assuring approrpriate follow-up for abnormal mammograms. [Table: see text] No significant financial relationships to disclose.
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Hermawan, Edy. "Active Contour Lankton untuk Segmentasi Kanker Payudara pada Citra Mammogram." Eksplora Informatika 9, no. 1 (September 30, 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 dari ahli radiologi. Penelitian ini merupakan upaya untuk melakukan deteksi dan segmentasi dengan menggunakan teknik pemrosesan citra terhadap objek yang dicurigai sebagai lesi kanker payudara pada citra mammogram. Untuk meningkatkan akurasi deteksi dan segmentasi maka dilakukan preprocessing untuk mengurangi noise dan meningkatkan homogenitas aras keabuan mammogram. Deteksi dan segmentasi terhadap keberadaan lesi kanker dilakukan dengan menerapkan metode active contour Lankton. Hasil penelitian menunjukkan metode yang diajukan mampu melakukan deteksi dan segmentasi terhadap lesi kanker payudara dengan baik. Wilayah kanker payudara dapat terdeteksi sesuai dengan wilayah kanker payudara yang dideteksi radiolog dan tersegmentasi dengan jelas. Fitur FO dan GLCM hasil ekstraksi dari lesi kanker payudara dapat diperoleh signifikan tanpa terlalu banyak terkontaminasi dari fitur non lesi kanker payudara. Fitur FO dan GLCM dari lesi kanker payudara hasil ekstraksi dapat dipergunakan sebagai input untuk analisis lanjutan berupa klasifikasi lesi kanker.
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Dissertations / Theses on the topic "Mammogram"

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Carlsson, Hampus. "Fully Convolutional Networks for Mammogram Segmentation." Thesis, Linköpings universitet, Statistik och maskininlärning, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-158127.

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Segmentation of mammograms pertains to assigning a meaningful label to each pixel found in the image. The segmented mammogram facilitates both the function of Computer Aided Diagnosis Systems and the development of tools used by radiologists during examination. Over the years many approaches to this problem have been presented. A surge in the popularity of new methods to image processing involving deep neural networks present new possibilities in this domain, and this thesis evaluates mammogram segmentation as an application of a specialized neural network architecture, U-net. Results are produced on publicly available datasets mini-MIAS and CBIS-DDSM. Using these two datasets together with mammograms from Hologic and FUJI, instances of U-net are trained and evaluated within and across the different datasets. A total of 10 experiments are conducted using 4 different models. Averaged over classes Pectoral, Breast and Background the best Dice scores are: 0.987 for Hologic, 0.978 for FUJI, 0.967 for mini-MIAS and 0.971 for CBIS-DDSM.
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Riley, Graeme Alexander. "Optimised mammogram displays for improved breast cancer detection." Thesis, University of Plymouth, 2016. http://hdl.handle.net/10026.1/5150.

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In current mammography practice, radiologists typically view mammograms in a symmetric, side-by-side, configuration in the belief that abnormalities will be made salient because they break the perceived symmetry. The literature on the use of symmetry as an aid to signal detection is limited and this thesis has taken a psychophysical approach to investigate the radiologist’s task of detecting a small mass (a blob) in paired mammogram backgrounds. Initial experiments used Gaussian white noise and synthetic mammogram backgrounds to test observer performance for the radiologist’s task using symmetric (side-by-side) displays and animated (the two images of a pair alternated sequentially in the same location) displays. The use of animated displays was then tested using real mammogram backgrounds in the subsequent experiments. The results showed that side-by-side presentation of paired images does not provide any benefit for the detection of a blob, whereas, alternated presentation enabled the observer to use the correlation present between the paired images to improve detection performance. The effect of alternation was not evident when applied to the task of detecting a small mass in real mammogram pairs and subsequent investigation suggested that the loss of effect resulted from the lack of scale invariance of real images. This meant that, regardless of the level of global correlation between two images, the localised correlation, at a region size reflecting the visual angle subtended by the fovea, was much lower. Thus, decorrelation by the visual system was ineffective and performance for the detection of a blob in the paired images was also ineffective. This thesis suggests that, whilst animated displays can be a powerful tool for the identification of differences between paired images, the underpinning mechanism of decorrelation makes them unsuited for mammograms where scale invariance means that correlation at local levels is a fraction of the global correlation level.
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Stewart, Brian D. "Automatic mammogram interpretation using knowledge-based computer vision." Thesis, University of Dundee, 1992. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.333875.

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Copeland, Valerie Anne. "Factors that influence follow-up after an abnormal mammogram." [College Station, Tex. : Texas A&M University, 2006. http://hdl.handle.net/1969.1/ETD-TAMU-1130.

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Kok-Wiles, Siewli. "Comparing mammogram pairs in the detection of mammographic lesions." Thesis, University of Oxford, 1998. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.298421.

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Majeed, Taban Fouad. "Segmentation, super-resolution and fusion for digital mammogram classification." Thesis, University of Buckingham, 2016. http://bear.buckingham.ac.uk/162/.

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Mammography is one of the most common and effective techniques used by radiologists for the early detection of breast cancer. Recently, computer-aided detection/diagnosis (CAD) has become a major research topic in medical imaging and has been widely applied in clinical situations. According to statics, early detection of cancer can reduce the mortality rates by 30% to 70%, therefore detection and diagnosis in the early stage are very important. CAD systems are designed primarily to assist radiologists in detecting and classifying abnormalities in medical scan images, but the main challenges hindering their wider deployment is the difficulty in achieving accuracy rates that help improve radiologists’ performance. The detection and diagnosis of breast cancer face two main issues: the accuracy of the CAD system, and the radiologists’ performance in reading and diagnosing mammograms. This thesis focused on the accuracy of CAD systems. In particular, we investigated two main steps of CAD systems; pre-processing (enhancement and segmentation), feature extraction and classification. Through this investigation, we make five main contributions to the field of automatic mammogram analysis. In automated mammogram analysis, image segmentation techniques are employed in breast boundary or region-of-interest (ROI) extraction. In most Medio-Lateral Oblique (MLO) views of mammograms, the pectoral muscle represents a predominant density region and it is important to detect and segment out this muscle region during pre-processing because it could be bias to the detection of breast cancer. An important reason for the breast border extraction is that it will limit the search-zone for abnormalities in the region of the breast without undue influence from the background of the mammogram. Therefore, we propose a new scheme for breast border extraction, artifact removal and removal of annotations, which are found in the background of mammograms. This was achieved using an local adaptive threshold that creates a binary mask for the images, followed by the use of morphological operations. Furthermore, an adaptive algorithm is proposed to detect and remove the pectoral muscle automatically. Feature extraction is another important step of any image-based pattern classification system. The performance of the corresponding classification depends very much on how well the extracted features represent the object of interest. We investigated a range of different texture feature sets such as Local Binary Pattern Histogram (LBPH), Histogram of Oriented Gradients (HOG) descriptor, and Gray Level Co-occurrence Matrix (GLCM). We propose the use of multi-scale features based on wavelet and local binary patterns for mammogram classification. We extract histograms of LBP codes from the original image as well as the wavelet sub-bands. Extracted features are combined into a single feature set. Experimental results show that our proposed method of combining LBPH features obtained from the original image and with LBPH features obtained from the wavelet domain increase the classification accuracy (sensitivity and specificity) when compared with LBPH extracted from the original image. The feature vector size could be large for some types of feature extraction schemes and they may contain redundant features that could have a negative effect on the performance of classification accuracy. Therefore, feature vector size reduction is needed to achieve higher accuracy as well as efficiency (processing and storage). We reduced the size of the features by applying principle component analysis (PCA) on the feature set and only chose a small number of eigen components to represent the features. Experimental results showed enhancement in the mammogram classification accuracy with a small set of features when compared with using original feature vector. Then we investigated and propose the use of the feature and decision fusion in mammogram classification. In feature-level fusion, two or more extracted feature sets of the same mammogram are concatenated into a single larger fused feature vector to represent the mammogram. Whereas in decision-level fusion, the results of individual classifiers based on distinct features extracted from the same mammogram are combined into a single decision. In this case the final decision is made by majority voting among the results of individual classifiers. Finally, we investigated the use of super resolution as a pre-processing step to enhance the mammograms prior to extracting features. From the preliminary experimental results we conclude that using enhanced mammograms have a positive effect on the performance of the system. Overall, our combination of proposals outperforms several existing schemes published in the literature.
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Alshabibi, Abdulaziz Saad Abdullah. "Factors Causing Variability When Reading Mammograms." Thesis, The University of Sydney, 2022. https://hdl.handle.net/2123/29415.

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Aims: This thesis investigated the impacts on radiologist performance of 1) the time of day at which an image is read, 2) how long a radiologist has been awake and the hours slept the night before and 3) the number of cases read without a break. Studies 2 and 3 also examined whether the influence of the studied factors varied according to reader experience. Methods: Data were collected during conference workshops, and radiologist accuracy was evaluated with BreastScreen Reader Assessment Strategy test sets, each containing 60 digital mammography cases. In study 1, 197 reader assessments were used to assess specificity, lesion sensitivity and JAFROC; in study 2, 133 reader assessments were used to assess sensitivity, specificity, lesion sensitivity, JAFROC and ROC AUC; and in study 3, 10 radiologists completed a test set without breaks to assess sensitivity, specificity, lesion sensitivity and ROC AUC; interactions between the fixed-series sequence and radiologist experience were also examined. Results: Study 1: The specificity was lower in the late morning and late afternoon than the early morning or mid-afternoon. Study 2: The lesion sensitivity of less experienced radiologists was lower for those awake less than 2 hours than those awake 8 to 10 hours and lower for those awake 4 to 6 hours than those awake 8 to 12 hours. The ROC AUC of less experienced radiologists was also lower for those with less than 6 hours of sleep than for those who slept longer. Study 3: Linear interactions were found between experience and the fixed-series sequences for sensitivity and lesion sensitivity, with experienced readers improving during the last series and less experienced readers deteriorating in performance. Conclusions: Time of day, hours awake, hours slept and the number of cases read without a break can all impact radiologist performance, which has significant implications for workday planning and accurate reporting.
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Azevedo, Roger. "Expert problem solving in mammogram interpretation, a visual cognitive task." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1998. http://www.collectionscanada.ca/obj/s4/f2/dsk1/tape11/PQDD_0002/NQ44353.pdf.

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McLoughlin, Kirstin J. "Computer aided detection of microcalcification clusters in digital mammogram images." Thesis, University of Canterbury. Electrical and Computer Engineering, 2004. http://hdl.handle.net/10092/6536.

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Recent advancements in computer technology have ensured that early detection of breast cancer, via computer aided detection (CAD) schemes, has become a rapidly expanding field of research. There is a desire to improve the detection accuracy of breast cancer without increasing the number of falsely identified cancers. The CAD scheme considered here is intended to assist radiologists in the detection of micro calcification clusters, providing a real contribution to the mammography screening process. Factors that affect the detection accuracy of micro calcifications in digital mammograms include the presence of high spatial frequency noise, and locally linear high intensity structures known as curvilinear structures (CLS). The two issues considered are how to compensate for the high frequency image noise and how to detect CLS thus removing their influence on micro calcification detection. First, an adaptive approach to modelling the image noise is adopted. This is derived directly from each mammogram and is adaptable to varying imaging conditions. It is found that compensating for the high frequency image noise significantly improves micro calcification detection accuracy. Second, due to the varying size and orientation of CLS in mammogram images, a shape parameter is designed for their detection using a multiresolution wavelet filter bank. The shape parameter leads to an efficient way of distinguishing curvilinear structures from faint micro calcifications. This improves micro calcification detection performance by reducing the number of false positive detections related to CLS. The detection and segmentation of micro calcification clusters is achieved by the development of a stochastic model, which classifies individual pixels within a mammogram into separate classes based on Bayesian decision theory. Both the high frequency noise model and CLS shape parameters are used as input to this segmentation process. The CAD scheme is specifically designed to be independent of the modality used, simultaneously exploiting the image data and prior knowledge available for micro calcification detection. A new hybrid clustering scheme enables the distinction between individual and clustered micro calcifications, where clustered micro calcifications are considered more clinically suspicious. The scheme utilises the observed properties of genuine clusters (such as a uniform distribution) providing a practical approach to the clustering process. The results obtained are encouraging with a high percentage of genuine clusters detected at the expense of very few false positive detections. An extensive performance evaluation of the CAD scheme helps determine the accuracy of the system and hence the potential contribution to the mammography screening process. Comparing the CAD scheme developed with previously developed micro calcification detection schemes shows that the performance of this method is highly competitive. The best results presented here give a sensitivity of 91% at an average false positive detection rate of 0.8 false positives per image.
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Al-Hinnawi, Abdel-Razzak. "Computer aided detection of clustered micro-calcifications in the digitised mammogram." Thesis, University of Aberdeen, 1999. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.301076.

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The presence of distributed micro-calcifications can be an indicator of early breast cancer. On the mammogram, they appear as bright smooth particles superimposed on the normal breast image background. Radiologists determine the occurrence of this lesion by detecting the individual micro-calcifications and then examining their distribution within the breast tissue. Due to the visual complexity of the mammogram, the detection sensitivity is usually less than 100%. The digital environment has the potential to increase the radiologist's accuracy. We have developed a computer aided detection (CAD) scheme that can identify clinically indicative clusters of micro-calcifications. The CAD algorithm emulates some aspects of the radiologists' approach by using contrast texture energy segmentation and morphological distribution analysis. On a local database of 61 mammograms digitised at 100μm with 8 bits intensity resolution, the CAD returns: a) 85% sensitivity (91% for malignant lesions and 78% for those that are benign), b) 0.33 false positive clusters (FPC) per image and c) 92% specificity. Therefore, the output from the CAD is shown to compare favourably with the performance of an expert radiologist. It also compares favourably with other CAD techniques, exceeding many algorithms which employ a higher level of mathematical complexity. The scheme is tested on an international database provided by the Mammographic Image Analysis Society. In this case it returns a) 96.4% sensitivity (100% for malignant lesions and 92% for those that are benign) b) 2.35 FPC rate per image and c) 33% specificity. The higher FPC rate is attributed to the different acquisition and production of the digital mammograms. It is concluded that this can be reduced by employing a shape analysis procedure to the CAD's final output. It is shown that the image processing principles we have implemented are generally successful on databases which are produced at other centres under different technical conditions.
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Books on the topic "Mammogram"

1

Illinois. Vehicle Services Dept. Mammogram license plates. Springfield, Ill.]: Jesse White, Secretary of State [Vehicle Services Dept., 2007.

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Illinois. Vehicle Services Department. Mammogram license plates. Springfield, Ill.]: Jesse White, Secretary of State [Vehicle Services Dept.], 2003.

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New York (N.Y.). City Council. Make mammogram appointments more accessible. New York]: The Council of the City of New York, 2002.

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Bhateja, Vikrant, Mukul Misra, and Shabana Urooj. Non-Linear Filters for Mammogram Enhancement. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-0442-6.

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Parker, James N., and Philip M. Parker. Mammogram: A medical dictionary, bibliography, and annotated research guide to Internet references. San Diego, CA: ICON Health Publications, 2004.

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The big squeeze: A social and political history of the controversial mammogram. Ithaca: ILR Press, 2012.

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Lanyi, Marton. Brustkrankheiten im Mammogramm. Berlin, Heidelberg: Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/978-3-662-06177-0.

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Aydell, Carole. Baring your breast: Mammograms : a positive experience. Bloomington, Ind: AuthorHouse, 2008.

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Cabral, Thanh M., and Rangaraj M. Rangayyan. Fractal Analysis of Breast Masses in Mammograms. Cham: Springer International Publishing, 2012. http://dx.doi.org/10.1007/978-3-031-01654-7.

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National Cancer Institute (U.S.). Mammograms: Not just once, but for a lifetime. [Washington, D.C.?]: U.S. Dept. of Health and Human Services, Public Health Service, National Institutes of Health, National Cancer Institute, 1997.

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Book chapters on the topic "Mammogram"

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Godellas, Constantine V. "Abnormal Mammogram." In Common Surgical Diseases, 390–92. New York, NY: Springer New York, 1998. http://dx.doi.org/10.1007/978-1-4757-2945-0_89.

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Kopkash, Katherine. "Abnormal Mammogram." In Common Surgical Diseases, 253–54. New York, NY: Springer New York, 2014. http://dx.doi.org/10.1007/978-1-4939-1565-1_62.

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Yaghmai, Nazanin, Tiffany Yu, Regan Ferraro, and Guita Rahbar. "Screening Mammogram." In Absolute Breast Imaging Review, 75–120. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-08274-0_3.

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Bhateja, Vikrant, Mukul Misra, and Shabana Urooj. "Mammogram Benchmarking Databases." In Non-Linear Filters for Mammogram Enhancement, 95–99. Singapore: Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-15-0442-6_11.

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Grigorian, Areg, Christian de Virgilio, and Danielle M. Hari. "Abnormal Screening Mammogram." In Surgery, 43–49. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-05387-1_5.

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Grigorian, Areg, Christian de Virgilio, and Danielle M. Hari. "Abnormal Screening Mammogram." In Surgery, 37–44. New York, NY: Springer New York, 2015. http://dx.doi.org/10.1007/978-1-4939-1726-6_4.

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Chan, Tiffany L., Tiffany Yu, and Irene Tsai. "Diagnostic Mammogram and Ultrasound." In Absolute Breast Imaging Review, 121–91. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-08274-0_4.

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Bhateja, Vikrant, Mukul Misra, and Shabana Urooj. "Mammogram Enhancement and Associated Challenges." In Non-Linear Filters for Mammogram Enhancement, 31–34. Singapore: Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-15-0442-6_4.

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Bhateja, Vikrant, Mukul Misra, and Shabana Urooj. "Performance Evaluation of Mammogram Enhancement Approaches." In Non-Linear Filters for Mammogram Enhancement, 79–86. Singapore: Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-15-0442-6_9.

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Bhale, Aparna, and Manish Joshi. "Enhancement of Screen Film Mammogram Up to a Level of Digital Mammogram." In Communications in Computer and Information Science, 133–42. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-37463-0_12.

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Conference papers on the topic "Mammogram"

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Suresh, R., A. Nagaraja Rao, and B. Eswara Reddy. "Improving the mammogram images by intelligibility mammogram enhancement method." In 2018 2nd International Conference on Inventive Systems and Control (ICISC). IEEE, 2018. http://dx.doi.org/10.1109/icisc.2018.8398954.

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Duan Zhu, Tian Hong, and Sun Lei. "Research on mammogram enhancement." In 2010 3rd IEEE International Conference on Computer Science and Information Technology (ICCSIT 2010). IEEE, 2010. http://dx.doi.org/10.1109/iccsit.2010.5564127.

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Muttarak, M. "The false negative mammogram." In Asian Breast Diseases Association (ABDA) 3rd Teaching Course: Advances in the Management of Breast Diseases. Kuantan, Malaysia: Asian Breast Diseases Association, 2005. http://dx.doi.org/10.2349/biij.1.1.e6-29.

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Samant, Neha, and Poonam Sonar. "Mammogram Classification in Transform Domain." In 2018 5th International Conference on Signal Processing and Integrated Networks (SPIN). IEEE, 2018. http://dx.doi.org/10.1109/spin.2018.8474186.

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Lochanambal, K. P., and M. Karnan. "Hybrid heuristics for mammogram segmentation." In 2010 IEEE International Conference on Computational Intelligence and Computing Research (ICCIC). IEEE, 2010. http://dx.doi.org/10.1109/iccic.2010.5705894.

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Gandhi, K. Rajiv, and M. Karnan. "Mammogram image enhancement and segmentation." In 2010 IEEE International Conference on Computational Intelligence and Computing Research (ICCIC). IEEE, 2010. http://dx.doi.org/10.1109/iccic.2010.5705895.

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Liszka, Gyorgy, Tamas Roska, Akos Zarandy, J. Hegyesi, L. Kek, and Csaba Rekeczky. "Mammogram analysis using CNN algorithms." In Medical Imaging 1995, edited by Murray H. Loew. SPIE, 1995. http://dx.doi.org/10.1117/12.208717.

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Bakic, Predrag R., and Dragana P. Brzakovic. "Simulation of digital mammogram acquisition." In Medical Imaging '99, edited by John M. Boone and James T. Dobbins III. SPIE, 1999. http://dx.doi.org/10.1117/12.349569.

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Chiang, Hung-Chih, Jhao-Ming Yu, Liang-Yu Chen, Min-Cheng Pan, Sheng-Yih Sun, Chia-Cheng Chou, and Min-Chun Pan. "Mammogram-based diffuse optical tomography." In SPIE BiOS, edited by Fred S. Azar and Xavier Intes. SPIE, 2012. http://dx.doi.org/10.1117/12.910402.

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Maeder, Anthony J. "Mammogram compression using adaptive prediction." In Medical Imaging 1995, edited by Yongmin Kim. SPIE, 1995. http://dx.doi.org/10.1117/12.207616.

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Reports on the topic "Mammogram"

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Brzakovic, Dragana. Mammogram Screening by Automated Followup: A Feasibility Study. Fort Belvoir, VA: Defense Technical Information Center, July 1999. http://dx.doi.org/10.21236/ada381307.

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Sanchez-Ayendez, Melba M. Mammogram Compliance Among Low-Income Middle-Women in Puerto Rico. Fort Belvoir, VA: Defense Technical Information Center, September 2001. http://dx.doi.org/10.21236/ada400641.

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Goldgof, Dmitry. Accurate 3D Modeling of Breast Deformation for Temporal Mammogram Registration. Fort Belvoir, VA: Defense Technical Information Center, September 2008. http://dx.doi.org/10.21236/ada495322.

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Li, Lihua. Computer Analysis and Detection of Missed Cancer in Screening Mammogram. Fort Belvoir, VA: Defense Technical Information Center, April 2006. http://dx.doi.org/10.21236/ada455785.

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Li, Lihua. Computerized Analysis and Detection of Missed Cancer in Screening Mammogram. Fort Belvoir, VA: Defense Technical Information Center, April 2004. http://dx.doi.org/10.21236/ada428194.

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Goldgof, Dmitry. Accurate 3D Modeling of Breast Deformation for Temporal Mammogram Registration. Fort Belvoir, VA: Defense Technical Information Center, September 2009. http://dx.doi.org/10.21236/ada516476.

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Li, Lihua. Computerized Analysis and Detection of Missed Cancer in Screening Mammogram. Fort Belvoir, VA: Defense Technical Information Center, April 2007. http://dx.doi.org/10.21236/ada474863.

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Sanchez-Ayendez, Melba M. Mammogram Compliance Among Low-Income Middle-Aged Women in Puerto Rico. Fort Belvoir, VA: Defense Technical Information Center, September 2002. http://dx.doi.org/10.21236/ada411452.

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Nishikawa, Robert. Computerized Identification of Normal Mammograms. Fort Belvoir, VA: Defense Technical Information Center, October 2005. http://dx.doi.org/10.21236/ada448474.

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Nishikawa, Robert M. Computerized Identification of Normal Mammograms. Fort Belvoir, VA: Defense Technical Information Center, October 2004. http://dx.doi.org/10.21236/ada433850.

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