Experimental investigation on breast tissue classification based on statistical feature extraction of mammograms
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Computer-Aided Diagnosis
Breast tissue
Feature (linguistics)
Breast cancer is one of the most important causes of death among women in world between the age of 40 and 55. Breast cancer can be treated effectively only if it is detected at a premature stage. Imaging techniques play a considerable role in assisting breast biopsies, especially of abnormal areas that cannot be felt but can be seen on a conventional mammogram or other techniques. To help radiologists provide an accurate diagnosis, a computer- aided detection (CADe) and computer-aided diagnosis (CADx) algorithms are being developed. CADe and CADx algorithms help reducing the number of false positives and they assist radiologists in deciding between follow up and biopsy. This chapter gives a survey of segmentation in mass detection algorithm for mammography and thermography. Patient will be first screened with thermal imaging and then by using mammography technique for breast cancer. The results of these techniques are studied and analysed.
Computer-Aided Diagnosis
Thermography
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Radiologists assess the results of mammography, the key screening tool for the detection of breast cancer, to determine the presence of malignancy. They, routinely, compare recent and prior mammographic views to identify changes between the screenings. In case a new lesion appears in a mammogram, or a region is changing rapidly, it is more likely to be suspicious, compared to a lesion that remains unchanged and it is usually benign. However, visual evaluation of mammograms is challenging even for expert radiologists. For this reason, various Computer-Aided Diagnosis (CAD) algorithms are being developed to assist in the diagnosis of abnormal breast findings using mammograms. Most of the current CAD systems do so using only the most recent mammogram. This paper provides a review of the development of methods to emulate the radiological approach and perform automatic segmentation and/or classification of breast abnormalities using sequential mammogram pairs. It begins with demonstrating the importance of utilizing prior views in mammography, through the review of studies where the performance of expert and less-trained radiologists was compared. Following, image registration techniques and their application to mammography are presented. Subsequently, studies that implemented temporal analysis or subtraction of temporally sequential mammograms are summarized. Finally, a description of the open access mammography datasets is provided. This comprehensive review can serve as a thorough introduction to the use of prior information in breast cancer CAD systems but also provides indicative directions to guide future applications.
Computer-Aided Diagnosis
Breast Cancer Screening
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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.
Breast tissue
Breast Cancer Screening
Gold standard (test)
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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.
Breast tissue
Breast Cancer Screening
Breast screening
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In this paper, we describe a novel approach for the automatic classification of candidate spiculated mass locations on mammography. Our approach is based on "Snakules" - an evidence-based active contour algorithm that we have recently developed for the annotation of spicules on mammography. We use snakules to extract features characteristic of spicules and spiculated masses, and use these features to classify whether a region of a mammogram contains a spiculated mass or not. The results from our initial classification experiment demonstrate the strong potential of snakules as an image analysis technique to extract features specific to spicules and spiculated masses, which can subsequently be used to distinguish true spiculated mass locations from non-lesion locations on a mammogram and improve the specificity of computer-aided detection (CADe) algorithms.
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The purpose of this study is the evaluation of the variation of performance in terms of sensitivity and specificity of two radiologists with different experience in mammography, with and without the assistance of two different CAD systems. The CAD considered are SecondLookTM (CADx Medical Systems, Canada), and CALMA (Computer Assisted Library in MAmmography). The first is a commercial system, the other is the result of a research project, supported by INFN (Istituto Nazionale di Fisica Nucleare, Italy); their characteristics have already been reported in literature. To compare the results with and without these tools, a dataset composed by 70 images of patients with cancer (biopsy proven) and 120 images of healthy breasts (with a three years follow up) has been collected. All the images have been digitized and analysed by two CAD, then two radiologists with respectively 6 and 2 years of experience in mammography indipendently made their diagnosis without and with, the support of the two CAD systems. In this work sensitivity and specificity variation, the Az area under the ROC curve, are reported. The results show that the use of a CAD allows for a substantial increment in sensitivity and a less pronounced decrement in specificity. The extent of these effects depends on the experience of the readers and is comparable for the two CAD considered.
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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.
Digital Mammography
Breast tissue
Breast imaging
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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.
Breast tissue
Microcalcification
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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.
Breast tissue
Breast Cancer Screening
Mammography screening
Screening mammography
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Single view mammography may be a less time consuming, more comfortable and radiation reduced alternative for young women, but there are no studies examining this approach after the implementation of digital mammography into clinical practice.Retrospective analysis of all mammographies performed in women younger than 40 years during a 24 month period. The sample consisted of 109 women with 212 examined breasts. All patients initially received standard two- view mammography. In the study setting the MLO- views were read by a single viewer and compared to a composite reference standard.In this sample 7 malignant findings were present and the review of the MLO-view detected 6 of them (85%). In patients with dense breasts 4 out of 5 malignant findings were found on the single-view (sensitivity 80%) and all 2 malignant findings were detected in patients with low breast density (sensitivity 100%). There were 7 false positive findings (3.3%). i.e. in total 8 out of 212 examined breasts were therefore misinterpreted (3.8%).Single view digital mammography detects the vast majority of malignant findings, especially in low density breast tissue and the rate of false-positive findings is within acceptable limits. Therefore this approach may be used in different scenarios (for example in increasing patient throughout in resource poor settings, reducing radiation burden in the young or in combination with ultrasound to use the strengths of both methods). More research on this topic is needed to establish its potential role in breast imaging.
Digital Mammography
Breast tissue
Breast density
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