Automatic detection of breast border and nipple in digital mammograms
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Breast tissue
Breast cancer is the most frequently diagnosed neoplasm in women in Italy. There are several risk factors, but thanks to screening and increased awareness, most breast cancers are diagnosed at an early stage when surgical treatment can most often be conservative and the adopted therapy is more effective. Regular screening is essential but advanced technology is needed to achieve quality diagnoses. Mammography is the gold standard for early detection of breast cancer. It is a specialized technique for detecting breast cancer and, thus, distinguishing normal tissue from cancerous breast tissue. Mammography techniques are based on physical principles: through the proper use of X-rays, the structures of different tissues can be observed. This first part of the paper attempts to explain the physical principles used in mammography. In particular, we will see how a mammogram is composed and what physical principles are used to obtain diagnostic images.
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Breast Cancer Screening
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Breast density
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The nationwide breast cancer screening programme using mammography has been in full operation in the Netherlands since 1997. Quality control of the screening programme has been assigned to the National Expert and Training Centre for Breast Cancer Screening. Limits are set to the mean glandular dose and the centre monitors these for all facilities engaged in the screening programme. This procedure is restricted to the determination of the entrance dose on a 5 cm thick polymethylmethacrylate (PMMA) phantom. The mean glandular dose for a compressed breast is estimated from these data. Individual breasts may deviate largely from this 5 cm PMMA breast model. Not only may the compressed breast size vary from 2 to 10 cm, but breast composition varies also. The mean glandular dose is dependent on the fraction of glandular tissue (glandularity) of the breast. To estimate the risk related to individual mammograms requires the development of a method for determination of the glandularity of individual breasts. A method has been developed to derive the glandularity using the attenuation of mammography x-rays in the breast. The method was applied to a series of mammograms at a screening unit. The results, i.e., a glandularity of 93% within the range of 0 to 1, were comparable with data in the literature. The glandularity as a function of compressed breast thickness is similar to results from other investigators using differing methods.
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Mammographic Density
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Breast cancer, a potentially fatal condition, affects one in twenty-eight women in India. Breast density, which is defined as the ratio of the volume of breast tissue to that of fatty tissue, is an important criterion in measuring the likelihood of a person developing breast cancer. In breast cancer, it is almost always the breast tissue that turns cancerous, not the fatty tissue. Women who have dense breast tissue, are four to six times more likely to develop breast cancer than women who do not have dense breast tissue. In our study, we examine breast mammograms and aim to develop a technique to automatically quantify breast density objectively, which is important in the prediction of the risk of breast cancer. While increased breast density greatly predisposes the risk of breast cancer, it is important to assess the risk quantitatively, which is difficult. In our technique, we develop a technique based on U-Net to segment breast tissues. We aim to classify the breasts based on the amount of breast tissue they have compared with the total volume of the breast. Therefore, we utilize a training database of 20 human-segmented images to train our algorithm and test it on a separate set of 15 human-segmented images. Examining the accuracy of the segmentation, we find that we obtain an accuracy of $0.79\pm 0.12$ in the Dice similarity coefficient. The qualitative images have also been examined and as may be observed from the qualitative results. A fully replicable, open-source code that everyone can use to segment breast tissue from mammogram images is the project's final goal.
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3.1 Introduction Breast imaging is currently undergoing major changes following the widespread implementation of full-field digital mammography (FFDM) equipment. The advantages of digital mammography over film-screen mammography (FSM) stem from the ability to manipulate the image electronically; brightness or contrast can be adjusted, and the image can be magnified after the digital mammogram is completed. These features make it easier to see subtle differences between tissues. Mammographic breast density appears to be a major risk factor for interval breast cancer. The ability to increase contrast when imaging dense tissue is very important. Digital mammography is more accurate in women under the age of 50, women with radiographically dense breasts,and premenopausal or perimenopausal women. Despite recent major advances, mammography remains a very technically demanding imaging procedure. This is partly because the breast is entirely composed of soft tissue, and normal and abnormal tissues have very similar radiographic properties. In addition, the appearance of "normal" breast tissue varies enormously from woman to woman. Asymmetric breast tissue is encountered relatively frequently, having been reported to occur in 3% of screening and diagnostic mammograms. The American College of Radiology (ACR) BI-RADS® defines four different types of asymmetric breast findings: asymmetric breast tissue, densities seen in one projection, architectural distortion, and focal asymmetric densities. These findings are significant because they can indicate a neoplasm, especially if an associated palpable mass is present.
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To analyze the mammographic and ultrasonographic findings of ductal carcinoma in situ (DCIS) and determine the sensitivity in Thai women.Mammograms and bilateral whole-breast ultrasonograms of 37 proven cases of DCIS were reviewed. The former was assessed for microcalcifications and soft tissue densities while the latter was evaluated for masses and thickened ducts. Ultrasonography was used to spot the areas to visualize soft tissue densities in mammogram.Mammography detected 22 cases of DCIS having pure microcalcifications, eight cases with mixed microcalcifications and soft tissue densities, six cases with pure abnormal soft tissue densities and one case showing negative finding. The ultrasonography detected 13 cases showing masses, seven cases as showing thickened ducts and 17 cases as negative findings.Microcalcifications are characteristic findings in mammogram accounting for 81% of DCIS in the present study. Ultrasonography shows abnormalities including mass and thickened duct lesions in 54% of DCIS. The combined modalities can give the detection of abnormalities in 97% of DCIS.
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Breast cancer is the most frequently occurring cancer among women worldwide. One of the most important risk factors for breast cancer is high mammographic density. Mammographic density represents the amount of fibroglandular tissue relative to the fat tissue in the breast. Women with >75% of their breast composed of dense tissue have a three to six fold increase in breast cancer risk. Mammographic density usually decreases with increasing age. Especially after menopause, mammographic density declines rapidly in some women, although not all women do show such a decline. In this thesis it was investigated if women with a more rapid decline in mammographic density have a lower breast cancer risk than women who show a slower decline in mammographic density. Although we found that women with high mammographic density have an increased breast cancer risk, we did not find that the average decline in mammographic density was different for those who developed and those who did not develop breast cancer. However, we found some indication that very large declines in density do lower breast cancer risk. In this thesis we also had a closer look at the effect of the fatty breast tissue on breast cancer risk. Although the role of the fat breast tissue has not often been studied, there are indications that in postmenopausal women the fat tissue in general can play an important role in breast cancer risk. In our studies we found that women who had a large amount of fat breast tissue in combination with a large amount of dense tissue had the largest breast cancer risk. This does not necessarily imply that women with larger breasts have a higher breast cancer risk, as we also found in our studies that women with larger breasts often have less dense breast tissue, not only on a relative scale but also on an absolute scale. The risk increasing effect of the fatty breast tissue, can be explained by the fact that in the fat tissue androgens are being converted to estrogens, that have a carcinogenic effect. Endogenous sex hormone levels in the blood and reproductive factors are known to be associated with breast cancer risk. In this thesis we describe that these effects were independent of mammographic density. In addition we found that high mammographic density and being nulliparous work synergistically; the harmful effect on breast cancer risk of being nulliparous was stronger among women with high mammographic density than among women with low mammographic density. In this thesis we also evaluated new methods for measuring mammographic density. The replacement of the conventional film-screen mammography by full-field digital mammography in the last ten years, has provided opportunities to measure not only the two-dimensional area of the dense tissue but also its volume. Despite its alleged higher precision, the volumetric method was not more strongly related to breast cancer risk factors. However, the definitive relationship between volumetric density and breast cancer risk still needs to be investigated.
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Background Breast compression is important in mammography in order to improve image quality, better separate tissue components, and reduce absorbed dose to the breast. In this study we use a method to measure and visualize the distribution of pressure over a compressed breast in mammography. Purpose To measure and describe the pressure distribution over the breast as a result of applied breast compression in mammography. Material and Methods One hundred and three women aged 40.7-74.3 years (median, 48.9 years) invited for mammographic screening consented to take part in this study. They were subjected to two additional breast compressions of the left breast (standard force and approximately 50% reduction). Pressure images of the compressed breast were obtained using force sensing resistor (FSR) sensors placed underneath the compression plate. Subjects rated their experience of pain on a visual analogue scale (VAS). Results Four pressure patterns were identified, fitting 81 of the 103 breasts, which were grouped accordingly. The remaining 22 breasts were found to correspond to a combination of any two patterns. Two groups (43 breasts) showed pressure mainly over the juxtathoracic part of the breast, had significantly greater breast thickness ( P = 0.003) and had a lower mean pressure over dense tissue ( P < 0.0001) than those with more evenly distributed pressure. Reducing compression force increased average breast thickness by 1.8 mm ( P < 0.0001). Conclusion The distribution of pressure differed greatly between breasts. In a large proportion of breasts the compression plate did not provide optimal compression of the breast, the compression force being absorbed in juxtathoracic structures.
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Background: Screening mammography has limited sensitivity for detecting breast cancer in dense breast tissue. This study estimates the maximal number of breast cancers undetected by mammography in individuals with dense breast tissue participating in screening mammography in the United States. Methods: Published data on supplemental screening incremental cancer detection rates (ICDRs), dense breast tissue prevalence, and total annual screening mammography exams in the United States are utilized for study estimates. Results: Assuming an ICDR of 16 cancers beyond mammography per 1,000 individuals with dense breast tissue, 38.8 million mammograms in the U.S. in 2021, and a prevalence of dense breast tissue of 43%, the number of cancers undetected by mammography in individuals with dense breast tissue participating in screening in the U.S. is estimated at 267,000. Conclusion: A large number of undiagnosed breast cancers in the population of individuals with dense breast tissue participating in screening mammography is estimated.
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Breast Cancer Screening
Mammography screening
Screening mammography
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