Dissertations / Theses on the topic 'Mammogram'

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.

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.

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.

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.

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.

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.

X-ray screening for breast cancer is an important public health initiative in the management of a leading cause of death for women. However, screening is expensive if mammograms are required to be manually assessed by radiologists. Moreover, manual screening is subject to perception and interpretation errors. Computer aided detection/diagnosis (CAD) systems can help radiologists as computer algorithms are good at performing image analysis consistently and repetitively. However, image features that enhance CAD classification accuracies are necessary for CAD systems to be deployed. Many CAD systems have been developed but the specificity and sensitivity is not high; in part because of challenges inherent in identifying effective features to be initially extracted from raw images. Existing feature extraction techniques can be grouped under three main approaches; statistical, spectral and structural. Statistical and spectral techniques provide global image features but often fail to distinguish between local pattern variations within an image. On the other hand, structural approach have given rise to the Bag-of-Visual-Words (BoVW) model, which captures local variations in an image, but typically do not consider spatial relationships between the visual “words”. Moreover, statistical features and features based on BoVW models are computationally very expensive. Similarly, structural feature computation methods other than BoVW are also computationally expensive and strongly dependent upon algorithms that can segment an image to localize a region of interest likely to contain the tumour. Thus, classification algorithms using structural features require high resource computers. In order for a radiologist to classify the lesions on low resource computers such as Ipads, Tablets, and Mobile phones, in a remote location, it is necessary to develop computationally inexpensive classification algorithms. Therefore, the overarching aim of this research is to discover a feature extraction/image representation model which can be used to classify mammographic lesions with high accuracy, sensitivity and specificity along with low computational cost. For this purpose a novel feature extraction technique called ‘Pixel N-grams’ is proposed. The Pixel N-grams approach is inspired from the character N-gram concept in text categorization. Here, N number of consecutive pixel intensities are considered in a particular direction. The image is then represented with the help of histogram of occurrences of the Pixel N-grams in an image. Shape and texture of mammographic lesions play an important role in determining the malignancy of the lesion. It was hypothesized that the Pixel N-grams would be able to distinguish between various textures and shapes. Experiments carried out on benchmark texture databases and binary basic shapes database have demonstrated that the hypothesis was correct. Moreover, the Pixel N-grams were able to distinguish between various shapes irrespective of size and location of shape in an image. The efficacy of the Pixel N-gram technique was tested on mammographic database of primary digital mammograms sourced from a radiological facility in Australia (LakeImaging Pty Ltd) and secondary digital mammograms (benchmark miniMIAS database). A senior radiologist from LakeImaging provided real time de-identified high resolution mammogram images with annotated regions of interests (which were used as groundtruth), and valuable radiological diagnostic knowledge. Two types of classifications were observed on these two datasets. Normal/abnormal classification useful for automated screening and circumscribed/speculation/normal classification useful for automated diagnosis of breast cancer. The classification results on both the mammography datasets using Pixel N-grams were promising. Classification performance (Fscore, sensitivity and specificity) using Pixel N-gram technique was observed to be significantly better than the existing techniques such as intensity histogram, co-occurrence matrix based features and comparable with the BoVW features. Further, Pixel N-gram features are found to be computationally less complex than the co-occurrence matrix based features as well as BoVW features paving the way for mammogram classification on low resource computers. Although, the Pixel N-gram technique was designed for mammographic classification, it could be applied to other image classification applications such as diabetic retinopathy, histopathological image classification, lung tumour detection using CT images, brain tumour detection using MRI images, wound image classification and tooth decay classification using dentistry x-ray images. Further, texture and shape classification is also useful for classification of real world images outside the medical domain. Therefore, the pixel N-gram technique could be extended for applications such as classification of satellite imagery and other object detection tasks.
Doctor of Philosophy

Disparities exist in the health-seeking behavior of African American (AA) women in the United States. Specifically, AA women 40 years and older often do not adhere to guidelines for breast cancer screening because of demographic and socioeconomic factors that have impacted health disparities. The purpose of this study was to research negative health-seeking behavior toward early-stage breast cancer detection in AA women 40 years and older. The main research questions addressed whether there is a relationship between negative health-seeking behavior, operationally defined as lack of a personal doctor, lack of health insurance, and lack of doctor visits within the past 12 months, and early-stage breast cancer detection, operationally defined as lack of mammogram screening within the past 2 years, in AA women 40 years and older. This quantitative study was guided by the health belief model. A cross-sectional design was used along with secondary data from the 2016 Behavioral Risk Factor Surveillance System survey. Wald chi-square was used to examine the relationship between the dependent variables and the independent variable. The relationship between lack of a personal doctor, lack of health insurance, lack of doctor visits within the past 12 months, and lack of mammogram screening within the past 2 years was statistically significant at p < .05. The findings based on the significance between the variables confirmed that negative health- seeking behavior affects early-stage breast cancer detection in AA women 40 years and older. The results of this study may inform the development of educational programs that are instrumental in promoting and improving mammogram screening and early-stage breast cancer detection among AA women age 40 years and older.

Thesis (M.A.)--East Tennessee State University, 2004.
Title from electronic submission form. ETSU ETD database URN: etd-0430104-084147 Includes bibliographical references. Also available via Internet at the UMI web site.

Even with access to well-known breast cancer treatment centers, older African American women continue to have higher breast cancer mortality compared to their European American counterparts. Researchers have theorized relationships among diagnostic delay, socioeconomic status (SES) factors, beliefs, culture, and breast cancer mortality in African American women ages 40 to 64, but these same relationships among African American women ages 65 to 80 have not been investigated. The purpose of this qualitative study was to describe older African American women's experiences with abnormal mammograms. The quality-caring model and critical race theory were used through narration to show the association of structure and process within the context of race. Purposeful, criterion-based sampling was used to select and interview 12 African American women ages 65 to 80 who had an abnormal mammogram result after breast cancer screening (BCS) within the previous 2 years. Through narrative analysis with triangulation it was demonstrated that clinical and social systems within the process of BCS affected the women's perceptions of providers and outcome. Their independence, motivation, health outlook, and spiritualistic beliefs kept them adherent to BCS and longterm follow-up. Health promotion activities were supported by family, friends, and spirituality. Variations in mammography practices and poor provider communication were obstacles to health maintenance. Positive social change is supported through health care providers' understanding of the barriers that impede older African American women's follow-up of abnormal mammogram results. Removing these barriers may assist in the reduction of breast cancer mortality.

The aim of my project was to associate breast density with cancer risk by comparing SMF to the conventional density measurement methods. This case control study comprised cancers with age-matched controls from the Cambridge and Norwich Breast Screening Programmes. Data collection involved assessing the films based on Wolfe’s patterns, SCC and 21-categorisation classification, and then digitising the films for computer analyses (Cumulus and SMF). Conditional logistic regression was used to produce odds ratios associated with mammographic density. Results from a pilot study of 220 cancers and 220 controls demonstrated that compared to the conventional methods the SMF measurement was the most effective means of predicting breast cancer risk. This was shown by a methods comparison graph illustrating that the SMF percent volume measurement scheme had the highest density-risk association. Compared to the other density assessment methods, SMF measure of percent volume most clearly discriminated between women at high risk for breast cancer from those at low risk (odds ratio up to 27.1). The study was then expanded to increase the population size to 505 cancers with 1830 controls. Results from the full study contradicted those from the pilot study by showing that SMF measurements were not as effective at predicting breast cancer risk as the gold standard Cumulus measurements. The odds ratios showed a risk of 1.79 (95% CI 1.26-2.55) using SMF percent volume method compared to 3.38 (95% CI 2.32-4.92) as obtained by Cumulus absolute area measurements. I looked into the inconsistencies in the results from the pilot and full studies. After I examined several factors including digitiser effect, reader subjectivity and SMF version effect, I concluded that the promising results in the pilot study were most likely due to chance as I could not explain them by epidemiological or clinical means.

Breast cancer is the most common form of cancer among women, with around 9000 new diagnoses in Sweden yearly. Detecting and localizing risk of breast cancer could give the opportunity for individualized examination programs and preventative measures if necessary, and potentially be lifesaving. In this study, two deep learning methods have been designed, trained and evaluated on mammograms from healthy patients whom were later diagnosed with breast cancer, to examine how well deep learning models can localize suspicious areas in mammograms. The first proposed model is a ResNet-18 regression model which predicts the pixel coordinates of the annotated target pixel in the prior mammograms. The regression model produces predictions with an average of 44.25mm between the predictions and targets on the test set, which for average sized breasts correspond to a general area of the breast, and not a specific location. The regression network is hence not able to accurately localize suspicious areas in mammograms. The second model is a U-net segmentation model that segments out a risk area in the mammograms. The segmentation model had a 25% IoU, meaning that there is on average a 25% overlap between the target area and the prediction area. 57% of the predictions of the segmentation network had some overlap with the target mask, and predictions that did not overlap with the target often marked high density areas that are traditionally associated with high risk. Overall, the segmentation model did better than the regression model, but needs further improvement before it can be considered adequate to merge with a risk value model and used in practice. However, it is evident that there is sufficient information present in many of the mammogram images to localize the risk, and the research area holds potential for future improvements.
Bröstcancer är den vanligaste cancerformen bland kvinnor, med cirka 9000 nya diagnoser i Sverige årligen. Att upptäcka och lokalisera risken för bröstcancer kan möjliggöra individualiserade undersökningsprogram och förebyggande åtgärder vid behov och kan vara livräddande. I denna studie har två djupinlärningsmodeller designats, tränats och utvärderats på mammogram från friska patienter som senare diagnostiserades med bröstcancer, för att undersöka hur väl djupinlärningsmodeller kan lokalisera misstänkta områden i mammogram. Den första föreslagna modellen är en ResNet-baserad regressionsmodell som förutsäger pixelkoordinaterna för den utmarkerade målpixeln i de friska mammogrammen. Regressionsmodellen producerar förutsägelser med ett genomsnitt på 44,25 mm mellan förutsägelserna och målpunkterna för testbilderna, vilket för medelstora bröst motsvarar ett allmänt bröstområde och inte en specifik plats i bröstet. Regressionsnätverket kan därför inte med precision lokalisera misstänkta områden i mammogram. Den andra modellen är en U-net segmenteringsmodell som segmenterar ut ett riskområde ur mammogrammen. Segmenteringsmodellen hade ett IoU på 25%, vilket innebär att det i genomsnitt fanns en 25-procentig överlappning mellan målområdet och förutsägelsen. 57% av förutsägelserna från segmenteringsnätverket hade viss överlappning med målområdet, och förutsägelser som inte överlappade med målet markerade ofta områden med hög täthet som traditionellt är förknippade med hög risk. Sammantaget presterade segmenteringsmodellen bättre än regressionsmodellen, men behöver ytterligare förbättring innan den kan anses vara adekvat nog att sammanfogas med en riskvärdesmodell och användas i praktiken. Det är dock uppenbart att det finns tillräcklig information i många av mammogrambilderna för att lokalisera risken, och att forskningsområdet har potential för framtida förbättringar.

Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2018.
This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.
Cataloged from student-submitted PDF version of thesis.
Includes bibliographical references (pages 43-44).
This thesis presents a registration method for mammogram images with extended flexibility for manual inputs from medical specialists. The algorithm was developed as part of the Mammography project led by Professor. Regina Barzilay at MIT CSAIL. Given a sequence of mammogram images, the algorithm finds an optimal registration by considering both the global and local constraints as well as user-defined constraints such as manually selected matching points. This allows the registration process to be guided by both the algorithm itself and human experts. The second half of the thesis focuses on evaluating well-known optical flow and medical registration algorithms on mammogram images. It provides insights into how they perform when encountered by challenges and constraints that are unique in mammogram images.
by Hae Jin Song.
M. Eng.

Breast cancer is one of the leading causes of death and disability globally. Although mammogram has been identified as a significant breast screening tool in the United States, researchers have indicated that African-born women in the United States are diagnosed with advanced stages of breast cancer because of underutilization of mammogram from diverse reasons. The purpose of this quantitative study was to determine the association of demographic factors, breast cancer knowledge, health beliefs, and the utilization of mammogram among Nigerian women, 40 years and older in the United States (N=200). The study was guided by the health belief model and questionnaire was the data collection instrument used. Logistic regression analysis revealed that demographic variables, specifically age and length of residency in the United States have statistically significant effect on the odds of utilization of mammogram among the Nigerian women in the U.S. at p < 0.05. Also, according to the study results, breast cancer knowledge has a statistically significant effect on the utilization of mammogram at p <0.05. Additionally, health beliefs regarding breast cancer have significant effect on utilization of mammogram among Nigerian women 40 years and older in the U.S at p <0.05. The study findings will help in developing breast health programs for immigrant women, especially Nigerians in the U.S. to make informed decisions about timely utilization of mammographic services. Furthermore, the outcome of this study could enhance research, enlighten the health providers, and policymakers to develop culture sensitive preventive breast health programs that are appropriate to diverse women populations in the United States.

Mammography is currently used to screen women in targeted risk classes for breast cancer. Computer assisted diagnosis of mammograms attempts to lower the workload on radiologists by either automating some of their tasks or acting as a second reader. The task of mammogram segmentation based on pixel intensity is addressed in this thesis. The mammographic process leads to images where intensity in the image is related to the composition of tissue in the breast; it is therefore possible to segment a mammogram into several regions using a combination of global thresholds, local thresholds and higher-level information based on the intensity histogram. A hierarchical view is taken of the segmentation process, with a series of steps that feed into each other. Methods are presented for segmentation of: 1. image background regions; 2. skin-air interface; 3. pectoral muscle; and 4. segmentation of the database by classification of mammograms into tissue types and determining a similarity measure between mammograms. All methods are automatic. After a detailed analysis of minimum cross-entropy thresholding, multi-level thresholding is used to segment the main breast tissue from the background. Scanning artefacts and high intensity noise are separated from the breast tissue using binary image operations, rectangular labels are identified from the binary image by their shape, the Radon transform is used to locate the edges of tape artefacts, and a filter is used to locate vertical running roller scratching. Orientation of the image is determined using the shape of the breast and properties of the breast tissue near the breast edge. Unlike most existing orientation algorithms, which only distinguish between left facing or right facing breasts, the algorithm developed determines orientation for images flipped upside down or rotated onto their side and works successfully on all images of the testing database. Orientation is an integral part of the segmentation process, as skin-air interface and pectoral muscle extraction rely on it. A novel way to view the skin-line on the mammogram is as two sets of functions, one set with the x-axis along the rows, and the other with the x-axis along the columns. Using this view, a local thresholding algorithm, and a more sophisticated optimisation based algorithm are presented. Using fitted polynomials along the skin-air interface, the error between polynomial and breast boundary extracted by a threshold is minimised by optimising the threshold and the degree of the polynomial. The final fitted line exhibits the inherent smoothness of the polynomial and provides a more accurate estimate of the skin-line when compared to another established technique. The edge of the pectoral muscle is a boundary between two relatively homogenous regions. A new algorithm is developed to obtain a threshold to separate adjacent regions distinguishable by intensity. Taking several local windows containing different proportions of the two regions, the threshold is found by examining the behaviour of either the median intensity or a modified cross-entropy intensity as the proportion changes. Image orientation is used to anchor the window corner in the pectoral muscle corner of the image and straight-line fitting is used to generate a more accurate result from the final threshold. An algorithm is also presented to evaluate the accuracy of different pectoral edge estimates. Identification of the image background and the pectoral muscle allows the breast tissue to be isolated in the mammogram. The density and pattern of the breast tissue is correlated with 1. Breast cancer risk, and 2. Difficulty of reading for the radiologist. Computerised density assessment methods have in the past been feature-based, a number of features extracted from the tissue or its histogram and used as input into a classifier. Here, histogram distance measures have been used to classify mammograms into density types, and ii also to order the image database according to image similarity. The advantage of histogram distance measures is that they are less reliant on the accuracy of segmentation and the quality of extracted features, as the whole histogram is used to determine distance, rather than quantifying it into a set of features. Existing histogram distance measures have been applied, and a new histogram distance presented, showing higher accuracy than other such measures, and also better performance than an established feature-based technique.

Title from PDF of title page.
Document formatted into pages; contains 64 pages.
Thesis (M.S.C.S.)--University of South Florida, 2003.
Includes bibliographical references.
Text (Electronic thesis) in PDF format.
ABSTRACT: Predicting breast tissue deformation is of great significance in several medical applications such as surgery, biopsy and imaging. In breast surgery, surgeons are often concerned with a specific portion of the breast, e.g., tumor, which must be located accurately beforehand. Also clinically it is important for combining the information provided by images from several modalities or at different times, for the planning and guidance of interventions. Multi-modality imaging of the breast obtained by mammography, MRI and PET is thought to be best achieved through some form of data fusion technique. However, images taken by these various techniques are often obtained under entirely different tissue configurations, compression, orientation or body position. In these cases some form of spatial transformation of image data from one geometry to another is required such that the tissues are represented in an equivalent configuration.
ABSTRACT: We constructed the 3D biomechanical models for this purpose using Finite Element Methods (FEM). The models were based on phantom and patient MRIs and could be used to model the interrelation between different types of tissue by applying displacements of forces and to register multimodality medical images.
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Mode of access: World Wide Web.

Den senaste utvecklingen av artificiell intelligens (AI) och djupinlärning (DL) har gjort bild- och funktionsmedicin till en högst trolig kandidat att tidigt anta tekniken. AI inom mammografiscreening syftar till hälsofrämjande effekter genom en förhoppning om säkrare bilddiagnostik. Röntgensjuksköterskans (RSS) arbete präglas av korrekt utförd bildtagning och ett aktivt aktualiserande av den egna yrkesrollen gällande såväl tekniska framsteg som förnyade arbetssätt. Litteraturstudien har upprättats i syfte att belysa potentiella effekter av AI på bilddiagnostik inom mammografiscreening. Genom manifest innehållsanalys av resultat erhållna ur ämnesrelevanta vetenskapliga studier publicerade i databaserna Cinahl och Medline under år 2019–2020 identifierades och beskrevs kategorier sammanställda av subkategorier med liknande innehåll. Effekter inom granskningsprocessen och diagnostisk säkerhet skildrar flera perspektiv gällande AI:s effekter på bilddiagnostik. Utöver en stundtals ökad förmåga till cancerdetektion vid AI-assistans har artificiell bildgranskning även visat sig kunna reducera arbetsbördan för radiologer i form av friskrivning av mammogram med låg sannolikhet för bröstcancer. Vid tillämpning av AI ses lovande effekter inom framförallt klassificering av bröstvävnad samt vid reducering av falska positiva svar. Forskningen förbehålls dock med kvarstående etiska dilemman och avsaknad av ett juridiskt ramverk, vilket lämnar utrymme för vidare studier.
Recent developments in artificial intelligence (AI) and deep learning (DL) have made diagnostic imaging a prime candidate to adopt the technology. AI in mammography screening aims at promoting health with hopes of higher diagnostic accuracy. The radiographers work is characterized by properly performed imaging and actively updating the profession regarding technical developments and renewed working methods. The aim of this systematic review was to illustrate feasible effects of AI on diagnostic imaging within mammography screening. Through manifest content analysis of results obtained from subject related scientific studies published 2019–2020 in the databases Cinahl and Medline the authors identified and described categories compiled by subcategories with similar contents. Effects within the image interpretation process and diagnostic accuracy describes several perspectives regarding the outputs of AI on diagnostic imaging. AI-systems have proven to be useful in both assisting with image interpretation and reducing the workload for radiologists by disclaiming mammograms with low probability of breast cancer. Most promising effects are seen in the classification of breast tissue and reduction of false positives, but research is challenged by ethical dilemmas and the need for a legal framework, which are areas suggested for future research.

Mammographic density, the amount of radiodense tissue on a mammogram, is a strong risk factor for breast cancer, with properties that could be an asset in screening and prevention programmes. Its use in risk prediction contexts is currently limited, however, mainly due to di culties in measuring and interpreting density. This research investigates rstly, the properties of density as an independent marker of breast cancer risk and secondly, how density should be measured. The rst question was addressed by analysing data from a chemoprevention trial, a trial of hormonal treatment, and a cohort study of women with a family history of breast cancer . Tamoxifen-induced density reduction was observed to be a good predictor of breast cancer risk reduction in high-risk una ected subjects. Density and its changes did not predict risk or treatment outcome in subjects with a primary invasive breast tumour. Finally absolute density predicted risk better than percent density and showed a potential to improve existing risk-prediction models, even in a population at enhanced familial risk of breast cancer. The second part of thesis focuses on density measurement and in particular evaluates two fully-automated volumetric methods, Quantra and Volpara. These two methods are highly correlated and in both cases absolute density (cm3) discriminated cases from controls better than percent density. Finally, we evaluated and compared di erent measurement methods. Our ndings suggested good reliability of the Cumulus and visual assessments. Quantra volumetric estimates appeared negligibly a ected by measurement error, but were less variable than visual bi-dimensional ones, a ecting their ability to discriminate cases from controls. Overall, visual assessments showed the strongest association with breast cancer risk in comparison to computerised methods. Our research supports the hypothesis that density should have a role in personalising screening programs and risk management. Volumetric density measuring methods, though promising, could be improved.

Benign breast biopsy (BBB) can be a stressful experience for many women. Few studies have examined the specific aspects of the BBB more and less distressing. However, no research studies have examined demographic and clinical variables as they relate to distress associated with specific aspects of the BBB or the informational interests of women following a BBB. This study evaluated the magnitude of distress associated with each aspect of the BBB (additional mammography, waiting for the results of the mammography, being informed of needing a biopsy, etc.) as well as the clinical (family history of BC in first degree relative, history of BBB, and type of biopsy) and demographic (age and education) variables as correlates of distress associated with each aspect of a BBB. Additionally, we examined health information interests in women following a BBB and the manner in which women preferred to have this health information communicated.

Bakgrund: I Sverige inbjuds alla kvinnor i åldern 40 till 74 år regelbundet till mammografi som hälsokontroll. Undersökningen är frivillig och syftet är att minska dödligheten i bröstcancer. För att kvinnor ska kunna fatta ett välgrundat beslut om att medverka vid mammografi som hälsokontroll eller inte, bör informationen vara relevant och sanningsenlig. Evidensbaserad information som beskriver både för- och nackdelar med mammografi som hälsokontroll är därför av stor vikt. Syftet: Syftet med denna studie var att undersöka innehållet i den information som skickas till kvinnor i Sverige i samband med kallelsen till mammografi som hälsokontroll. Metod: Studien var av deskriptiv design med en kvantitativ och kvalitativ ansats. Kallelser som skickas till kvinnor inför sin mammografi som hälsokontroll, samlades in från alla mammografimottagningarna i Sverige som bedriver mammografi som hälsokontroll. Resultat: Studien visade tydligt att de allra flesta kallelser som skickas till kvinnor i Sverige inför mammografi som hälsokontroll saknade information som beskriver både för- och nackdelar med mammografiscreening. De flesta kvinnor i Sverige får allmän information och till viss del information som betonar fördelarna med mammografiscreening. Beträffande nackdelar med mammografi som hälsokontroll saknades den informationen i de allra flesta kallelser. Slutsats: De flesta kvinnorna i Sverige får inte relevant evidensbaserad information inför sin mammografi som hälsokontroll.

In Australia, early detection plays a leading role in reducing mortality from breast cancer. Little is known about how Chinese-Australian women engage in breast cancer screenings. This study aimed to investigate breast cancer screening practices and the associated factors amongst Chinese-Australian women. A cross-sectional quantitative method including convenient and snowball sampling was used to recruit 115 Chinese-Australian women living in Sydney (aged 35 years and older). The data was collected by self-administered questionnaires between July and September 2016. The results showed that the majority of participants performed breast self-examinations (78.1%), clinical breast examinations (69.8%) and mammograms (73.3%). Educational level was positively associated with having a breast self-examination. Age, religion, employment status and length of residence were significantly associated with performing a clinical breast examination. Income was related to having a mammogram. Significant associations between knowledge of breast cancer, cancer-related beliefs, and screening participation were also found. The length of residence was the strongest predictor of having a clinical breast examination and mammogram. The most common barrier to mammography screening was that doctors did not recommend it to participants. These findings can be used to develop tailored programs to promote the early detection of breast cancer among Chinese–Australian women.

Image processing is one area of signal analysis. This thesis is involved in feature extraction from image data and its implementation using Java programming language. The main contribution of this thesis lies in develop features extractors and their implementation in the program RapidMiner. The result is a robust tool for image analysis. The functionality of each operator is tested on mammogram images. A function model was developed for the removal of artifacts from the mammography images. The success rate of removal is comparable with other similar works. Furthermore, learning algorithms were compared on example detection of ventricle in ultrasound image.

La tomographie ultrasonore assistée par ordinateur possède un fort potentiel en tant que moyen d'inspection des tissus mammaires pour le dépistage du cancer du sein; cette technique permet de réduire la dépendance à l'opérateur constatée avec l'échographie conventionnelle. Une antenne de transducteurs (3 MHz) à géométrie semi-circulaire conformée à l'anatomie du sein a été développée pour réaliser une imagerie de réflectivité des structures d'intérêt en employant une procédure de reconstruction tomographique. L'antenne comporte 1024 éléments répartis sur un arc de 190 degrés ayant un rayon de courbure de 100 mm. Les acquisitions sont gérées par une électronique à 32 voies parallèles indépendantes en émission/réception et par un multiplexer pour l'adressage des voies vers les éléments de l'antenne. Les circuits d'émission et de réception ont une fréquence d'échantillonnage allant jusqu'à 80 MHz avec une précision de 12 bits. Des formes d'ondes arbitraires (pseudo-chirp, codes de Golay) sont transmises afin d'améliorer le rapport signal sur bruit. L'électroacoustique a été caractérisée avec des objets académiques et un hydrophone afin de déterminer les propriétés d'émission du système d'imagerie (réponses impulsionnelles et distribution spatiale du champ) et de développer des outils de correction des données; ces résultats sont mis en regard avec le formalisme de résolution du problème inverse (algorithme de sommation des rétroprojections elliptiques filtrées en champ proche). L'évaluation du système d'imagerie est réalisées sur des objets ponctuels, des objets bidimensionnels à faible contraste d'impédance et un fantôme anthropomorphique de sein contenant des inclusions
Ultrasound computed tomography has considerable potential as a means of breast cancer detection since it reduces the operator-dependency observed in echography. A half-ring transducer array was designed based on the breast anatomy, to obtain reflectivity images of the ductolobular structures using tomographic reconstruction procedures. The 3-MHz transducer array comprises 1024 elements set, in a 190-degree circular arc with a radius of 100 mm. The front-end electronics incorporate 32 independent parallel transmit/receive channels and a 32-to-1024 multiplexer unit. The transmit and receive circuitries have a variable sampling frequency of up to 80 MHz and a 12-bit precision. Arbitrary waveforms are synthesized to improve the signal-to-noise ratio. The set-up was calibrated with academic objects and a needle hydrophone to develop the data correction tools and specify the properties of the system; results are compared with the formalism of inverse problem (elliptical back-projection summation algorithm).The backscattering field was recorded using a restricted aperture, and tomographic acquisitions were performed with a pair of 0.08 mm diameter steel threads, a low contrast 2-D breast phantom, and a breast-shaped phantom containing inclusions. The pulse compression is used and the contribution of this technique to ultrasound computed tomography is evaluated with respect to the use of a standard broadband pulse. Prospects for development of inspection methods and also adaptations of the electroacoustic set-up dedicated to the anatomical tomographic imaging are proposed relative to conducted studies during this thesis

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Breast Cancer remains the type of cancer with the largest incidence and mortality in women. The best method of prevention is early diagnosis, which is carried out with mammography. However, a mammogram is not effective when the breast has a composition of greater than 50% fibroglandular tissue, or dense tissue. Studies show that high breast density is identified as a risk factor for developing the disease, and because of this new diagnostic technique for cancer in patients with dense breasts are being studied. This thesis proposes a method for early diagnosis of cancer in dense breasts, considered in the literature as hard scanning and detection. The methodology applied in this work used MIAS database for tests, equalization adaptive of histogram and contrast stretching techniques for segmentation step, and independent component analysis maxima-relevance-minimal-redundance and support vector machine for classification step. The tests were carried out with 76 breast mammograms whose dense parenchyma s make detection difficult. From the tests, we obtained accuracy of 97.36% in the segmentation stage. Already in the classification stage was an accuracy of 97.2% with a sensitivity of 81.88% and specificity of 100%. Based on the results, considering that the method was performed only on mammograms difficult to detect, it can be considered that the method achieved excellent performance, justifying the test in larger databases, and eventually enabling their use in hospitals and radiology clinics.
O câncer de mama continua sendo o tipo de câncer de maior incidência e mortalidade entre as mulheres. O melhor método de prevenção é o diagnóstico precoce, que é realizado com o auxilio da mamografia. Contudo, a mamografia não é eficaz quando a mama apresenta uma composição superior a 50 % de tecido fibroglandular, ou seja, de tecido denso. Estudos comprovam que a densidade mamária elevada é apontada como um fator de risco para o desenvolvimento da doença, e devido a isso novas técnicas de diagnóstico de câncer em pacientes com mamas densas estão sendo estudados. Esta tese propõe um método de diagnóstico precoce de câncer, em mamas densas, consideradas pela literatura de difícil rastreio e detecção, com o objetivo de aumentar as chances de cura da paciente, e diminuir os casos de mortalidade da doença. A metodologia empregada no trabalho utilizou a base de dados MIAS para teste, técnicas de equalização adaptativa e alargamento de contraste, na fase de segmentação, e análise de componentes independentes, máxima relevância - mínima redundância e máquinas de vetor de suporte, na etapa de classificação. Os testes foram realizados com 76 mamogramas de mamas em que o parênquima denso dificulta a detecção. A partir dos testes realizados, obteve-se média de acerto de 97.36 % na etapa de segmentação. Já na etapa de classificação foi encontrada uma média de acerto de 97,2% com sensibilidade de 81.88% e especificidade de 100%. Baseado nos resultados encontrados, considerando que o método foi realizado apenas em mamogramas de difícil detecção, pode-se considerar que o método obteve excelente desempenho, justificando o teste em bases de dados maiores, e futuramente viabilizando seu uso em hospitais e clinicas de radiologia.

Orientador: Marilisa Berti de Azevedo Barros
Dissertação (mestrado) - Universidade Estadual de Campinas, Faculdade de Ciencias Medicas
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Resumo:Justificativa: O câncer de mama e do colo de útero são neoplasias com altas taxas de incidência e mortalidade no Brasil e que dispõem de métodos eficazes de rastreamento para detecção precoce, oferecidos pelo Sistema Único de Saúde. Existe a necessidade de se identificar os subgrupos de mulheres que não realizam as práticas preventivas para esses agravos, como forma de se desenvolver estratégias, nas três esferas de governo, que venham minimizar as desigualdades ainda existentes em relação ao acesso aos serviços de saúde, a oferta de cuidados básicos, ao diagnóstico, tratamento e reabilitação nas questões relativas à saúde da mulher. Objetivos: Analisar as práticas de detecção precoce para o câncer de mama e do colo uterino, segundo características sociodemográficas, morbidade e comportamentos relacionados à saúde. Material e Métodos: Estudo do tipo transversal, de base populacional, tendo como população de estudo todos os indivíduos do sexo feminino com idade igual ou superior a 40 anos, não institucionalizados, residentes na área urbana de Campinas. Para a obtenção da amostra, os setores censitários do município de Campinas, foram agrupados em três estratos, segundo o percentual de chefes de família com nível universitário. Foram sorteados 10 setores censitários de cada estrato, e de cada setor censitário foram sorteados os domicílios e selecionados os indivíduos que seriam entrevistados, segundo os domínios de sexo e idade. As informações foram obtidas por meio de questionário estruturado em 19 blocos temáticos, com a maioria das questões fechadas, aplicado diretamente à pessoa sorteada.. O presente estudo incluiu 290 indivíduos pertencentes a dois domínios: mulheres de 20 a 59 anos e mulheres de 60 anos ou mais. Foram incluídos na análise dois grupos de variáveis: as independentes, compostas por variáveis sociodemográficas, comportamentos relacionados à saúde e estado da saúde e as dependentes, referentes à realização das práticas preventivas para a detecção do câncer de mama e do colo de útero. Para as análises estatísticas foi utilizado o programa STATA 7.0, que possibilitou levar em consideração as variáveis do plano de amostragem e o efeito de delineamento. As análises incluíram estimativas de prevalência, de razões de odds brutas e modelos de regressão logística múltipla. Resultados: O presente estudo possibilitou verificar que 83,3% das mulheres com 40 anos ou mais de idade, residentes em Campinas, encontram-se com a prática adequada em relação ao exame de Papanicolaou; 8,5% das mulheres entre 40 e 59 anos de idade e 11,1% das mulheres com 60 anos ou mais nunca haviam realizado o exame de Papanicolaou. Entre os principais motivos alegados pelas mulheres que nunca realizaram o exame citológico, destacam-se: achar que a realização deste exame não é necessária (43,5%), sentir vergonha (28,1%) e dificuldades relacionadas ao serviço prestador do exame (13,7%). Foram detectados, na análise univariada, os seguintes fatores associados à não-realização do exame citológico: idade, raça/cor, escolaridade, número de pessoas no domicílio, posse de bens, a não realização de exames preventivos para o câncer de mama. Os resultados da análise de regressão logística múltipla hierarquizada apontaram que não estar com a prática adequada quanto ao exame de Papanicolaou é mais freqüente nas mulheres entre 40 a 59 anos de idade, com escolaridade de até 4 anos, não brancas, e que não tiveram consulta odontológica no último ano. Verificou-se que 43,2% das mulheres que haviam feito o Papanicolaou tinham-no realizado em serviços do SUS. Em relação às práticas relativas à detecção precoce do câncer de mama, 50,8% das mulheres não fizeram mamografia nos últimos dois anos e dessas, 42,5% nunca haviam feito a mamografia e 8,3% realizaram-na há mais de 2 anos; 38,2 % não foram submetidas ao exame físico das mamas no ano que antecedeu a entrevista. Entre as mulheres com 70 anos ou mais de idade foram encontradas as maiores proporções de não realização da mamografia (67,7%) e do exame clínico das mamas (56,5%). Para a não realização do exame físico anual das mamas, nas análises univariadas, foram encontradas associações com: idade, raça/cor, situação conjugal, escolaridade, posse de bens, consumo de bebidas alcoólicas, prática de atividade física, do auto exame da mama, da mamografia e da citologia oncótica, e o uso de serviços odontológicos. Para a não realização da mamografia nos dois anos que antecederam a entrevista foram encontrados, nas análises univariadas, os seguintes fatores associados: idade, raça/cor, renda familiar per capita, posse de bens, consumo de bebidas alcoólicas, a prática do exame físico anual das mamas e da citologia oncótica. Os resultados do modelo de regressão logística múltipla mostraram que a não-realização do exame clínico das mamas foi mais freqüente entre as mulheres que vivem sem companheiro, que residem em domicílios com mais de quatro moradores, que não ingerem bebidas alcoólicas, que não realizaram o auto-exame das mamas e que não fizeram consulta odontológica no último ano. A não-realização da mamografia foi mais prevalente nas mulheres idade igual ou superior a 70 anos, não brancas, e que não ingeriam bebida alcoólica. Dos exames relatados, 28,8% das mamografias e 38,1% dos exames clínicos de mamas foram realizados pelo Sistema Único de Saúde (SUS). Conclusão: Esse estudo mostrou importantes características das mulheres que não realizam de forma adequada as práticas preventivas para o câncer de colo de útero e o de mama e que estratégias necessitam serem desenvolvidas pelos gestores da saúde, nos três níveis de governo, com o objetivo de minimizar as desigualdades de acesso, garantindo-se os princípios da equidade e da integralidade das ações pertinentes ao programa da saúde da mulher
Abstract: Background: Brazil has high incidence and mortality rates of breast and cervical cancer even though effective screening methods for early detection are provided by the Unified Health System-SUS. There is a need to identify subgroups of women who do not undergo preventive practices for these conditions, so as to develop strategies at the three levels of government in order to minimize the inequalities that still exist in terms of access to health services, offer of basic care, diagnosis, treatment and rehabilitation in issues related to women¿s health. Objectives: To analyze early detection practices for breast and cervical cancer, according to socio-demographic characteristics, morbidity and health-related behaviors. Methods: Cross-sectional, population-based study of all non-institutionalized women, 40 years old and over, and living in the urban area of Campinas. The sample was constructed by dividing the census sectors of Campinas into three strata groups according to the percentage of heads of households with college education. Ten census sectors were drawn from each stratum, and households were drawn and individuals selected for interviews from each census sector, according to gender and age. Information was derived from a questionnaire structured in 19 theme blocks, mostly with closed questions asked directly to the individual drawn. The present study included 290 individuals as follows: women, 20 to 59 years old and women 60 and over. Two groups of variables were analyzed: independent variables, encompassing socio-demographic variables, health-related behaviors and individual and family members¿ health status in terms of undergoing preventive practices to detect breast and cervical cancer. Statistical analysis was performed by using the STATA 7.0 program, which enabled taking into account the variables of the sample plan and design effect. Analyses included prevalence estimates, overall odds ratio and multiple regression logistic models. Results: The present study made it possible to verify that 83.3% of women 40 years and over, living in Campinas, have an inappropriate practice in relation to Pap smears; 8.5% of women between 40 and 59 years of age and that 11.1% of women 60 and over had never undergone a Pap smear. Among the major reasons pointed out by the women that had never had oncotic cytology, the following stand out: believing the test is not necessary (43.5%), being embarrassed (28.1%), and obstacles related to the service performing the test (13.7%). The univariate analysis detected the following factors associated with not having a cytology smear: age, race/color, schooling, number of individuals in the household, having assets, not doing preventive exams for breast cancer. The result of the hierarchy multiple regression logistic analysis pointed out that not having an appropriate practice in relation to Pap smears is more frequent in non-white women between 40 and 59 years of age, with up to 4 years of schooling and that had not had a dental appointment in the past year. The study verified that 43.2% of women that had been submitted to a Pap smear had done so in a SUS service. Regarding practices related to early detection of breast cancer, 50.8% of women had not had a mammogram in the past two years, and of these, 42.5% had never had a mammogram, and 8.3% had had one more than 2 years before; 38.2% had not been submitted to a breast examination in the year preceding the interview. The largest ratios of not having a mammogram (67.7%) and of not having a clinical breast exam (56.5%) were found among women 70 years old and over. The following associations were found in the univariate analyses for not having an annual breast exam: age, race/color, marital status, schooling, having assets, liquor consumption, exercising, breast self-examination, mammogram and cytology, and utilization of dental services. The following associated factors were found in the univariate analyses for not having a mammogram in the two years preceding the interview: age, race/color, per capita family income, having assets, liquor consumption, having had an annual breast exam and cytology. The results of the multiple regression logistic model showed that not having a clinical breast exam was more frequent among women that: live without a companion or in households with more than four residents, do not drink liquor, do not perform self breast examination and did not have a dental appointment in the past year. Not having a mammogram was more prevalent in non-white women 70 years or over, and that did not drink liquor. Of the tests mentioned, 28.8% of mammograms and 38.1% of clinical breast exams were performed by the SUS. Conclusion: The study showed the major features of women that did not have appropriate preventive practices for cervical and breast cancer, and that health managers should develop strategies at the three levels of government in order to minimize access inequalities and to guarantee the principles of equity and integrality of the actions of women¿s health programs
Mestrado
Mestre em Saude Coletiva

The number of women diagnosed with the breast cancer continues to rise year on year. Breast cancer is now the most common type of cancer in the UK, with over 55000 cases reported last year. In most cases, mammography is the first step towards diagnosing breast cancer. However, it continues to have many practical limitations as compared to more sophisticated modalities such as MRI. The relatively low cost of mammography, together with the ever increasing risk of women contracting the disease, has led to many developed countries having a breast screening program. These routine breast screens are taken at different points in time and are called temporal mammograms. Currently, a radiologist tends to qualitatively assess temporal mammograms and look for any abnormalities or suspicious regions that might be of a concern. In this thesis, we develop an automatic shape analysis model that can detect and quantify such changes inside the breast. This will not only help in early diagnosis of the disease, which is key to survival, but will potentially aid prognosis and post treatment care. The core to this thesis is the use of Circular Integral Invariants. We explore its multi-scale properties and use it for image smoothing to reduce image noise and enhance features for segmentation. We implement, modify and enhance a segmentation method which previously has been successfully used to acquire breast regions of interest. We applied such Integral Invariants for shape description, to be used for shape matching as well as for subdividing shapes into sub-regions and quantifying the differences between two such shapes. We combine boundary information with the information from inside a shape, thus eccentrically transforming shapes before describing their structure. We develop a novel false positives reduction method based on Integral Invariants scale space. A second aspect of the thesis is the evaluation of and emphasis on the use of breast density maps against the commonly used intensity maps or x-rays. We find density maps sufficient to use in clinical practice. The methods developed in this thesis aim to help clinicians in making diagnostic decision at the point of case. Our shape analysis model is easy to compute, fast and very general in nature that could be deployed in a wide range of applications, beyond mammography.

Breast cancer is a serious illness that often has fatal consequences. Adherence to the recommendations for breast cancer surveillance is poorly practiced among African American women. The purpose of this phenomenological study was to seek individual professed barriers to breast cancer screening among African American women to better understand why breast cancer continues to be one of the principal basis of mortality among African American women. The theoretical framework for this study was the behavioral model of health services use. Purposeful selection was used to invite 14 African American women to participate in the in-depth interview process. Interview data were transcribed and then coded for recurring themes and meaning. The findings of this study demonstrate that these women's perceived barriers to breast cancer screening were lack of information, a belief that genetics dictates who gets breast cancer, embarrassment, a norm of people not going for health checkups, the procedure of breast cancer screening, and fear. Participants noted that the improved method of mammography may promote utilization within the population. Breast cancer disparities among African American women may decline if healthcare providers promote awareness of the availability and accessibility of breast cancer prevention resources and if African American women understand the barriers to breast cancer prevention and change their own screening practices.

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Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)
Mammography is considered the “gold standard"in the early detection of breast cancer, being this disease one of the greatest health problems of women worldwide. Clustered microcalcifications detected on mammograms are very important findings in asymptomatic patients with early breast cancer and may be considered one of the first signs of malignancy. However, due to the small size of these structures, associated with the visual fatigue of radiologists resulting from the analysis of a large volume of images, clinical studies indicate that from 10 to 30% of microcalcifications presented in mammograms are lost during diagnosis. Within this scenario, this master thesis aims to develop an automatic system for the detection of clustered microcalcifications in digitized mammography images. To solve this problem, we use the transformed dua-three complex wavelet to detect the microcalsifications since this technique has some important characteristics for the signal analysis, for instance, good directional selectivity, approximate shift invariance and it provides both information - magnitude and phase. After the detection of isolated microcalcifications, a post-processing step is used to automatically demarcate regions containing clusters of microcalcifications. Furthermore, three techniques were investigated for the analysis of each clustered detection in order to identify false-positive clusters, such as: the Hessian matrix, the groups exclusion and the gray level co-occurrence matrix technique and SVM classifiers. For the development and testing of the algorithms one digitized mammogram database were used. The analysis of the results were performed by using ROC and FROC curves. The method achieved good results when compared to the mark made by experts.
A mamografia é considerada o "padrão ouro"na detecção precoce do câncer de mama, sendo essa doença um dos maiores problemas de saúde da mulher no mundo. Agrupamentos de microcalcificações detectados nos mamogramas são achados muito importantes em pacientes assintomáticas com câncer de mama e podem representar o primeiro sinal de malignidade. No entanto, devido ao reduzido tamanho dessas estruturas, associado à fadiga visual dos radiologistas resultante da análise de grandes volumes de imagens, estudos clínicos indicam que de 10 a 30% das microcalcificações presentes nos mamogramas são perdidas durante o diagnóstico. Diante deste quadro, este trabalho de mestrado tem por objetivo o desenvolvimento de um sistema automático para a detecção de agrupamentos de microcalcificações em imagens de mamogramas digitalizados. Para isso, utilizou-se a transformada wavelet complexa de árvore dupla (DT-CWT) para a detecção das microcalcificações, visto que essa técnica possui características importantes para a análise de sinais como, por exemplo, boa seletividade direcional, invariância aproximada ao deslocamento e fornece ambas informações – magnitude e fase. Após a detecção das microcalcificações isoladas, uma etapa de pós-processamento foi utilizada para demarcar automaticamente regiões contendo agrupamentos de microcalcificações. Além disso, três técnicas foram investigadas para a análise de cada agrupamento detectado, com o intuito de identificar agrupamentos falsopositivos, sendo elas: a matriz Hessiana, a exclusão de agrupamentos e a técnica de matriz de coocorrência de níveis de cinza e classificadores SVMs. Uma base de dados de mamogramas digitalizados foi utilizada para o desenvolvimento e testes dos algoritmos. A análise dos resultados foi realizada usando curvas ROC e FROC. O método obteve bons resultados quando comparado às marcações realizadas por especialistas e presentes na base de dados.

Metastasised breast cancer kills. There is no known cure, there are no known preventative measures, there are no drugs available with proven capacity to abate its effects. Early identification and excision of a malignancy prior to metastasis is the only method currently available for reducing the mortality due to breast disease. Automated analysis of mammograms has been proposed as a tool to aid radiologists detect breast disease earlier and with greater efficiency and success. This thesis addresses some of the major difficulties associated with the automated analysis of mammograms, in particular the difficulties caused by the high-frequency, relatively insignificant curvi-linear structures (CLS) comprising the blood vessels, milk-ducts and fibrous tissues. Previous attempts at automation have been overlooked these structures and the resultant complexity of that oversight has been handled inappropriately. We develop a model-based analysis of the CLS features, from the very anatomy of the breast, through mammography and digitisation to the image intensities. The model immediately dictates an algorithm for extracting a high-level feature description of the CLS features. This high-level feature description allows a systematic treatment of these image features prior to searching for instances of breast disease. We demonstrate a procedure for implementing such prior treatment by 'removing' the CLS features from the images. Furthermore, we develop a model of the expected appearance of mammographic densities in the CLS-removed image, which leads directly to an algorithm for their identification. Unfortunately the model also extracts many regions of the image that are not significant mammographic densities, and this therefore requires a subsequent segmentation stage. Unlike previous attempts which apply neural networks to this task, and therefore incorporate inherent insignificance as a consequence of insufficient data availability describing the significant mammographic densities, we illustrate the application of a new statistical method (novelty analysis) for achieving a statistically significant segmentation of the mammographic densities from the plethora of candidates identified at the previous stage. We demonstrate the ability of the CLS feature description to identify instances of radial-scar in mammograms, and note the suitability of the CLS and density descriptions for assessment of bilateral and temporal asymmetry. Some additional potential applications of these feature descriptions in arenas other than mammogram analysis are also noted.

Background: Age estimation has attracted attention because of its various clinical and medical applications. There are many studies on human age estimation from biomedical images such as X-ray images, MRI, facial images, dental images etc. However, there is no research done on mammograms for age estimation. Therefore, in our research, we focus on age estimation from mammogram images. Objectives: The purpose of this study is to make an AI-based model for estimating age from mammogram images based on the pectoral muscle segment and check its accuracy. At first, we segment the pectoral muscle from mammograms. Then we extract deep learning features and handcrafted features from the pectoral muscle segment as well as other regions for comparison. From these features, we built models to estimate the age. Methods: We have selected an experiment method to answer our research question. We have used the U-net model for pectoral muscle segmentation. After that, we have extracted handcrafted features and deep learning features from pectoral muscle using ResNet-50 and Xception. Then we trained Support Vector Regression and Random Forest models to estimate the age based on the pectoral muscle of mammograms. Finally, we observed how accurately these models are in estimating the age by comparing the MSE and MAE values. We have considered breast region (BR) and the whole MLO to answer our research question. Results: The MAE values for both SVR and RF models from handcrafted features is around 10 in years in all cases. On the other hand, with deep learning features MAE is less as compared to handcrafted features. In our experiment, the least observed error value for MAE was around 8.4656 years for the model that extracted the features from the whole MLO using ResNet50 and SVR as the regression model. Conclusions: We have concluded that the breast region (BR) is more accurate in estimating the age compared to PM by having least MAE and MSE values in its models. Moreover, we were able to observe that handcrafted feature models are not as accurate as deep feature models in estimating the age from mammograms.

This dissertation material provided in this work details the techniques that are developed to aid in the Classification of tumors, non-tumors, and dense masses in a Mammogram, certain characteristics such as texture in a mammographic image are used to identify the regions of interest as a part of classification. Pattern recognizing techniques such as nearest mean classifier and Support vector machine classifier are also used to classify the features. The initial stages include the processing of mammographic image to extract the relevant features that would be necessary for classification and during the final stage the features are classified using the pattern recognizing techniques mentioned above. The goal of this research work is to provide the Medical Experts and Researchers an effective method which would aid them in identifying the tumors, non-tumors, and dense masses in a mammogram. At first the breast region extraction is carried using the entire mammogram. The extraction is carried out by creating the masks and using those masks to extract the region of interest pertaining to the tumor. A chain code is employed to extract the various regions, the extracted regions could potentially be classified as tumors, non-tumors, and dense regions. Adaptive histogram equalization technique is employed to enhance the contrast of an image. After applying the adaptive histogram equalization for several times which will provide a saturated image which would contain only bright spots of the mammographic image which appear like dense regions of the mammogram. These dense masses could be potential tumors which would need treatment. Relevant Characteristics such as texture in the mammographic image are used for feature extraction by using the nearest mean and support vector machine classifier. A total of thirteen Haralick features are used to classify the three classes. Support vector machine classifier is used to classify two class problems and radial basis function (RBF) kernel is used to find the best possible (c and gamma) values. Results obtained in this research suggest the best classification accuracy was achieved by using the support vector machines for both Tumor vs Non-Tumor and Tumor vs Dense masses. The maximum accuracies achieved for the tumor and non-tumor is above 90 % and for the dense masses is 70.8% using 11 features for support vector machines. Support vector machines performed better than the nearest mean majority classifier in the classification of the classes. Various case studies were performed using two distinct datasets in which each dataset consisting of 24 patients’ data in two individual views. Each patient data will consist of both the cranio caudal view and medio lateral oblique views. From these views the region of interest which could possibly be a tumor, non-tumor, or a dense regions(mass).

[Truncated abstract] In this thesis, we introduce a mammogram analysis system developed for the automatic segmentation and analysis of mammograms. This original system has been designed to aid radiologists to detect breast cancer on mammograms. The system embodies attribute-driven segmentation in which the attributes of an image are extracted progressively in a step-by-step, hierarchical fashion. Global, low-level attributes obtained in the early stages are used to derive local, high-level attributes in later stages, leading to increasing refinement and accuracy in image segmentation and analysis. The proposed system can be characterized as: • a bootstrap engine driven by the attributes of the images; • a solid framework supporting the process of hierarchical segmentation; • a universal platform for the development and integration of segmentation and analysis techniques; and • an extensible database in which knowledge about the image is accumulated. Central to this system are three major components: 1. a series of applications for attribute acquisition; 2. a standard format for attribute normalization; and 3. a database for attribute storage and data exchange between applications. The first step of the automatic process is to segment the mammogram hierarchically into several distinctive regions that represent the anatomy of the breast. The adequacy and quality of the mammogram are then assessed using the anatomical features obtained from segmentation. Further image analysis, such as breast density classification and lesion detection, may then be carried out inside the breast region. Several domain-specific algorithms have therefore been developed for the attribute acquisition component in the system. These include: 1. automatic pectoral muscle segmentation; 2. adequacy assessment of positioning and exposure; and 3. contrast enhancement of mass lesions. An adaptive algorithm is described for automatic segmentation of the pectoral muscle on mammograms of mediolateral oblique (MLO) views

The study of the correlation between the radiological characteristics of the breast and the likelihood of the breast containing, or subsequently developing, a malignant lesion is termed breast density. In this thesis, a technique is developed for measuring the x-rav attenuation properties of breast tissue from an x-ray mammogram. The measure is independent of the characteristics and configuration of the x-ray equipment used to acquire the image. The intention is to provide a tool which facilitates the study of breast density by providing quantitative measurements of tissue characteristics, which it is hoped will ultimately form the basis of future risk estimation and malignant lesion diagnosis techniques. Part I of the thesis discusses the various area and volumetric measurements of breast density presented in the literature, together with the current hypotheses explaining the statistical results observed in epidemiological studies from a histopathological standpoint. The aim is to develop an inter-disciplinary understanding of the area under investigation to facilitate optimal development. A novel physics based model describing the complete process of mammographic image formation is presented in part II of the thesis. The design, build and test methodology executed for each component of the mammographic equipment is discussed through the chapters: the x-rav tube; the image receptor; and the anti-scatter grid. A ray tracing algorithm for computing the details of traversal paths is presented, as is an algorithm for calculating the scattered energy incident upon the image receptor. In each chapter, the underlying physical model is described, followed by the software design and implementation details, and finally the results of the experimental phantom validations studies to verify the implementation and underlying model. Where appropriate, sensitivity analysis is included so that uncertainty bounds may be estimated. Part III of the thesis proposes a novel measure of breast density, and describes how it overcomes the limitations of many existing techniques, in particular the Highnam and Brady 1996 hint model. The results of a clinical trial are included which demonstrate a favourable correlation between the proposed measure and the occurrence of malignancy compared to that observed using other methods.

This thesis evaluated the accuracy of radiographers screen-reading mammograms. This was undertaken as a potential solution to current radiologist workforce shortages that may contribute to delays in women receiving their screening mammogram results. This large, well-designed Australian study undertook extensive analysis and imparts evidence that even prior to any formal reading training, radiographers have good accuracy levels when screen-reading mammograms. It is expected that with formal screen-reading training these accuracy levels will further improve, such that radiographers have the potential to be one of the two screen-readers within the BreastScreen Australia program, contributing to timeliness and improved accuracy outcomes.

Bakgrund: Bröstcancer är den allra vanligaste cancerformen hos kvinnor. I Sverige drabbas omkring 8000 kvinnor av denna sjukdom varje år och cirka 1500 avlider. Från och med 1986 erbjuds alla kvinnor mellan 40-74 år att delta i regelbundna mammografikontroller. Det har gjort att allt fler kvinnor diagnostiseras tidigt i sjukdomsförloppet och att antalet dödsfall reducerats. Trots fördelarna med mammografi upplever många kvinnor undersökningen som smärtsam och påfrestande. Det är därför viktigt att få en ökad förståelse för kvinnors upplevelser för att på så sätt skapa en förutsättning för ökat välbefinnande. Syfte: Syftet med litteraturstudien var att belysa kvinnors upplevelse i samband med mammografiscreening. Metod: Litteraturstudien baserades på 8 vetenskapliga artiklar. Sökningen av artiklar gjordes i databaserna CINAHL och PsychINFO. Artiklarna granskades och analyserades av författarna innan ett resultat sammanställdes. Resultat: Resultatet visade att tidigare erfarenheter påverkade attityderna kring mammografiscreening. Majoriteten fann undersökningen smärtsam och att bemötandet av personalen var en viktig aspekt som påverkade upplevelsen. Det visade sig att det fanns ett genomgående behov av information kring undersökningen samt att väntan på resultatet var en tid fylld med oro och ovisshet. Slutsats: Trots att många kvinnor upplever fysiska såväl som psykiska obehag i samband med mammografi är de motiverade att fortsätta medverka i screeningundersökningar. Med anledning av detta är det viktigt för röntgensjuksköterskan att ha en förståelse och kunskap om kvinnors upplevelser så det finns möjlighet att motverka dessa obehag så långt det är möjligt.

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We propose a method for discrimination and classification of mammograms with benign, malignant and normal tissues using independent component analysis and neural networks. The method was tested for a mammogram set from MIAS database, and multilayer perceptron. The method obtained a success rate of 97.83% , with 97.5% of specificity and 98% of sensitivity.
Neste trabalho, propomos um método para discriminação e classificação de mamogramas, com diagnóstico maligno, benigno e normal, usando análise de componentes independentes e redes neurais. O método foi testado com mamogramas da MIAS database, e com redes perceptron multicamadas. O método obteve uma taxa de sucesso média de 97.83%, com 97.5% de especificidade, e 98% de sensibilidade.

Using the MCMIS (Monte Carlo for Mammography Image Simulation) code, several possible scenarios of microcalcification images were simulated for the Fischer SenoscanTM digital mammography system, which has been approved for clinical use by the F.D.A. The cases simulated included detectors that have 100 ?Ým x 100 ?Ým, 50 ?Ým x 50 ?Ým, and 25 ?Ým x 25 ?Ým pixels in order to determine how much improvement can be obtained through decreased pixel size in the detection of microcalcification clusters in mammograms. Breast thickness was also varied for each modality from 4 to 7 cm in order to determine the effect that reduced breast compression will have on image quality under ideal conditions. The breast phantom used for each simulation included a region of microcalcification pairs of varying size and pair spacing. This microcalcification cluster phantom was designed such that simulated images would indicate the minimum required size and spacing for microcalcification clusters to become distinctly discernable in each of the modalities under scrutiny. Both qualitative and quantitative analyses were performed for each simulated image produced. A decrease in detector pixel size did not show the expected result of significant improvement in cluster detection ability, even under ideal conditions. However, for the range of breast thickness studied, results indicate that decreasing the amount of compression during a mammogram did not significantly affect the image quality in terms of image resolution or contrast for all detector modalities tested. These results suggest that new detector modalities incorporating smaller detector pixel sizes may not show significant improvement over current modalities. However, they also suggest that doctors may be able to make the mammogram process less painful for the patient while maintaining image quality.