Download or read book Development of Computer aided Diagnostic System for Breast MRI Lesion Classification written by Lina Arbash Meinel and published by . This book was released on 2005 with total page 22 pages. Available in PDF, EPUB and Kindle. Book excerpt: The MRMC analysis showed that the human reader performance with and without CAD system assistance can be generalized over the population of cases and still maintain a statistically significant improvement (F(1, 74) = 6.805, p = 0.0110

Download or read book Development of Breast MRI Computer aided Diagnosis System written by Ke Nie and published by . This book was released on 2009 with total page 225 pages. Available in PDF, EPUB and Kindle. Book excerpt: The goal of this dissertation is to develop analysis techniques to improve the management of breast diseases using 3D MRI. With nearly 20 years of research, breast MRI has finally evolved from a research tool to an important clinical screening modality. Considering the overwhelming 3D information, there is a critical need to develop computer-aided diagnosis (CAD) technology to fully utilize the wealth of imaging information. However, there is very limited work in developing "a true CAD" in either commercial products or in the literature. To respond to this need, the first part of the dissertation described the development of a CAD system that is capable of providing intelligent diagnostic impression. Automated computer algorithms were implemented to simulate the entire procedure of radiologists' interpretation from lesion detection to final classification. The second part of the dissertation described mathematical simulation models to understand the impact of environmental constraints on the tumor growth. Previous published models included only functional information, few of them considered the effect of environmental structure. As such, those models were only good for simulating in-vitro growth, and could not be correlated with in vivo phenotypes. A specific model was developed to simulate the growth of cancer in the milk duct by considering the constraint from the ductal membrane. The result was further correlated with the lesion phenotype shown on MRI. Understanding these biological growth patterns may aid in a better diagnosis, and prediction of the presence of invasive component in DCIS. The third part of the dissertation described the analysis of breast tissue environment (commonly referred as "breast density). A computer-based algorithm was developed to segment the fibroglandular tissue and to analyze the morphological distribution of adipose and dense tissue in addition to the mount of dense tissue. A consensus has been reached by the Breast Cancer Prevention Collaborative Group (BCPCG) to incorporate quantitative breast density into cancer risk prediction models. The methods presented here can be used to evaluate the role of the MRI density for risk prediction or as a surrogate marker predicting the effect of hormone related therapies.

Download or read book Computer Aided Diagnosis of Breast Lesions written by and published by . This book was released on 2002 with total page 48 pages. Available in PDF, EPUB and Kindle. Book excerpt: The long-term goal of our research is to develop computer-aided diagnosis (CAD) techniques to improve the detection and diagnosis of breast cancer. The hypothesis to be tested in the present project is that radiologists' ability to differentiate malignant from benign breast lesions can be improved by integrating radiologists' perceptual expertise in the interpretation of mammograms with the advantages of automated computer classification. This project has 3 objectives: 1. To combine radiologist-extracted Breast Imaging Reporting and Data System (BI-RADS) features with image features extracted by a computer to classify malignant and benign clustered microcalcifications in mammograms. 2. To optimally combine radiologists' diagnosis with the result of computer classification. 3. To optimize computer classification for full-field digital mammograms.

Download or read book Computer Aided Diagnosis of Digital Mammograms written by and published by . This book was released on 2003 with total page 54 pages. Available in PDF, EPUB and Kindle. Book excerpt: The long-term goal of our research is to develop computer-aided diagnosis (CAD) techniques to improve the detection and diagnosis of breast cancer. We have developed a computer technique that can classify breast calcifications in mammograms accurately, and this technique as a diagnostic aid has been shown to be able to improve radiologists' diagnostic accuracy. We have determined that Breast Imaging Report and Data System (BI- RADS) lesion descriptions provided by radiologists can be used as supplemental data to computer-extracted image features to improve the performance of computer classification of malignant and benign breast lesions. We have also found that our computer classification technique developed on screen-film mammograms, can achieve equally high performance on full-filed digital mammograms. This high performance is little affected by variability in the way in which radiologists indicate the general location of calcifications to the computer, which is designed as a means for the radiologist to query the computer aid. These results suggest that the computer technique has the potential to become a clinically useful and viable tool for diagnostic mammography.

Download or read book Contrast Enhanced Mammography written by Marc Lobbes and published by Springer. This book was released on 2019-04-29 with total page 160 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book is a comprehensive guide to contrast-enhanced mammography (CEM), a novel advanced mammography technique using dual-energy mammography in combination with intravenous contrast administration in order to increase the diagnostic performance of digital mammography. Readers will find helpful information on the principles of CEM and indications for the technique. Detailed attention is devoted to image interpretation, with presentation of case examples and highlighting of pitfalls and artifacts. Other topics to be addressed include the establishment of a CEM program, the comparative merits of CEM and MRI, and the roles of CEM in screening populations and monitoring of response to neoadjuvant chemotherapy. CEM became commercially available in 2011 and is increasingly being used in clinical practice owing to its superiority over full-field digital mammography. This book will be an ideal source of knowledge and guidance for all who wish to start using the technique or to learn more about it.

Download or read book Digital Mammography written by Nico Karssemeijer and published by Springer Science & Business Media. This book was released on 2012-12-06 with total page 520 pages. Available in PDF, EPUB and Kindle. Book excerpt: In June 1998 the Fourth International Workshop on Digital Mammography was held in Nijmegen, The Netherlands, where it was hosted by the department of Radiology of the University Hospital Nijmegen. This series of meetings was initiated at the 1993 SPIE Biomedical Image Processing Conference in San Jose, USA, where a number of sessions were entirely devoted to mammographic image analysis. At very successful subsequent workshops held in York, UK (1994) and Chicago, USA (1996), the scope of the conference was broadened, establishing a platform for presentation and discussion of new developments in digital mammog raphy. Topics that are addressed at these meetings are computer-aided diagnosis, image processing, detector development, system design, observer performance and clinical evaluation. The goal is to bring researchers from universities, breast cancer experts, and engineers together, to exchange information and present new scientific developments in this rapidly evolving field. This book contains all the scientific papers and posters presented at the work shop in Nijmegen. Contributions came from as many as 20 different countries and 190 participants attended the meeting. At a technical exhibit companies demon strated new products and work in progress. Abstracts of all papers were reviewed by members of the scientific committee. Many of the accepted papers had excellent quality, but due to limited space not all of them could be included as full papers in these proceedings. Papers that were rated high by the reviewers are included as long or short papers, others appear as extended abstracts in the last chapter.

Download or read book Computer Aided Detection for Breast Lesion in Ultrasound and Mammography written by Richa Agarwal and published by . This book was released on 2020 with total page 108 pages. Available in PDF, EPUB and Kindle. Book excerpt: In the field of breast cancer imaging, traditional Computer Aided Detection (CAD) systems were designed using limited computing resources and used scanned films (poor image quality), resulting in less robust application process. Currently, with the advancements in technologies, it is possible to perform 3D imaging and also acquire high quality Full-Field Digital Mammogram (FFDM). Automated Breast Ultrasound (ABUS) has been proposed to produce a full 3D scan of the breast automatically with reduced operator dependency. When using ABUS, lesion segmentation and tracking changes over time are challenging tasks, as the 3D nature of the images make the analysis difficult and tedious for radiologists. One of the goals of this thesis is to develop a framework for breast lesion segmentation in ABUS volumes. The 3D lesion volume in combination with texture and contour analysis, could provide valuable information to assist radiologists in the diagnosis.Although ABUS volumes are of great interest, x-ray mammography is still the gold standard imaging modality used for breast cancer screening due to its fast acquisition and cost-effectiveness. Moreover, with the advent of deep learning methods based on Convolutional Neural Network (CNN), the modern CAD Systems are able to learn automatically which imaging features are more relevant to perform a diagnosis, boosting the usefulness of these systems. One of the limitations of CNNs is that they require large training datasets, which are very limited in the field of medical imaging.In this thesis, the issue of limited amount of dataset is addressed using two strategies: (i) by using image patches as inputs rather than full sized image, and (ii) use the concept of transfer learning, in which the knowledge obtained by training for one task is used for another related task (also known as domain adaptation). In this regard, firstly the CNN trained on a very large dataset of natural images is adapted to classify between mass and non-mass image patches in the Screen-Film Mammogram (SFM), and secondly the newly trained CNN model is adapted to detect masses in FFDM. The prospects of using transfer learning between natural images and FFDM is also investigated. Two public datasets CBIS-DDSM and INbreast have been used for the purpose. In the final phase of research, a fully automatic mass detection framework is proposed which uses the whole mammogram as the input (instead of image patches) and provides the localisation of the lesion within this mammogram as the output. For this purpose, OPTIMAM Mammography Image Database (OMI-DB) is used. The results obtained as part of this thesis showed higher performances compared to state-of-the-art methods, indicating that the proposed methods and frameworks have the potential to be implemented within advanced CAD systems, which can be used by radiologists in the breast cancer screening.

Download or read book Development of a Computer aided Diagnosis System for Early Detection of Masses Using Retrospectively Detected Cancers on Prior Mammograms written by and published by . This book was released on 2009 with total page 128 pages. Available in PDF, EPUB and Kindle. Book excerpt: The performance of a CAD system for subtle lesions is generally much lower than their performance for less subtle lesions. The goal of this project is to develop a CAD system using advanced computer vision techniques aiming at improved detection of retrospectively seen cancers on prior mammograms and incorporate the developed CAD system into our current CAD system. During the project years, we have performed the following tasks: (1) collect the data sets of digitized film mammograms for training and testing our CAD system, (2) develop a series of single-view computer vision techniques for mass detection and classification in prior mammograms, (3) reduce FPs by correlation of image information from multiple view mammograms of the same patient, (4) develop a information fusion scheme to combine the new CAD system with the existing CAD system for mass detection, and (5) evaluate the effects of the newly developed CAD scheme with a large data set. We have found that our new computer-vision techniques can significantly improve the performance of the CAD system for mass detection by JAFROC analysis. The significance of this project is that the newly developed CAD system may be able to aid radiologists in detecting breast cancers at an early stage. Since early detection and treatment can reduce breast cancer mortality rate and health care costs, the proposed CAD system will improve the efficacy of mammography for breast cancer screening.

Download or read book Pattern Recognition Applied to the Computer aided Detection and Diagnosis of Breast Cancer from Dynamic Contrast enhanced Magnetic Resonance Breast Images written by Jacob Levman and published by . This book was released on 2010 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: The goal of this research is to improve the breast cancer screening process based on magnetic resonance imaging (MRI). In a typical MRI breast examination, a radiologist is responsible for visually examining the MR images acquired during the examination and identifying suspect tissues for biopsy. It is known that if multiple radiologists independently analyze the same examinations and we biopsy any lesion that any of our radiologists flagged as suspicious then the overall screening process becomes more sensitive but less specific. Unfortunately cost factors prohibit the use of multiple radiologists for the screening of every breast MR examination. It is thought that instead of having a second expert human radiologist to examine each set of images, that the act of second reading of the examination can be performed by a computer-aided detection and diagnosis system. The research presented in this thesis is focused on the development of a computer-aided detection and diagnosis system for breast cancer screening from dynamic contrast-enhanced magnetic resonance imaging examinations. This thesis presents new computational techniques in supervised learning, unsupervised learning and classifier visualization. The techniques have been applied to breast MR lesion data and have been shown to outperform existing methods yielding a computer aided detection and diagnosis system with a sensitivity of 89% and a specificity of 70%.

Download or read book Mammography and Beyond written by National Research Council and published by National Academies Press. This book was released on 2001-07-23 with total page 311 pages. Available in PDF, EPUB and Kindle. Book excerpt: Each year more than 180,000 new cases of breast cancer are diagnosed in women in the U.S. If cancer is detected when small and local, treatment options are less dangerous, intrusive, and costly-and more likely to lead to a cure. Yet those simple facts belie the complexity of developing and disseminating acceptable techniques for breast cancer diagnosis. Even the most exciting new technologies remain clouded with uncertainty. Mammography and Beyond provides a comprehensive and up-to-date perspective on the state of breast cancer screening and diagnosis and recommends steps for developing the most reliable breast cancer detection methods possible. This book reviews the dramatic expansion of breast cancer awareness and screening, examining the capabilities and limitations of current and emerging technologies for breast cancer detection and their effectiveness at actually reducing deaths. The committee discusses issues including national policy toward breast cancer detection, roles of public and private agencies, problems in determining the success of a technique, availability of detection methods to specific populations of women, women's experience during the detection process, cost-benefit analyses, and more. Examining current practices and specifying research and other needs, Mammography and Beyond will be an indispensable resource to policy makers, public health officials, medical practitioners, researchers, women's health advocates, and concerned women and their families.

Download or read book Breast MRI for High risk Screening written by Francesco Sardanelli and published by Springer Nature. This book was released on 2020-10-13 with total page 376 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book offers a comprehensive overview of the use of breast MRI for screening high-risk women, including those with familial-genetic hereditary predisposition and previous chest radiation therapy, typically lymphoma survivors. It discusses the historical background of studies and research that provided the body of evidence in favor of MRI screening of these women. Technical and clinical topics are treated in dedicated chapters, including models for individualized risk estimation, radiogenomics of breast cancer in high-risk women, computer-aided detection/diagnosis and machine learning systems applied to breast MRI, and psycho-oncology issues. Alternatives to breast MRI screening such as pharmaco-prevention and prophylactic mastectomy are also discussed, taking into account the public debate on the “Angelina Jolie” effect. The high breast cancer risk model is proposed as a paradigm for personalized medicine. This book will be of interest to radiologists, surgeons, oncologists and to all professionals devoted to female healthcare.

Download or read book Proceedings of 2023 International Conference on Medical Imaging and Computer Aided Diagnosis MICAD 2023 written by Ruidan Su and published by Springer Nature. This book was released on with total page 414 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Classification of Mammogram Images written by Supriya Salve and published by Anchor Academic Publishing. This book was released on 2017-05 with total page 53 pages. Available in PDF, EPUB and Kindle. Book excerpt: Breast cancer is the most common type of cancer in women, which also causes the most cancer deaths among them today. Mammography is the only reliable method to detect breast cancer in the early stage among all diagnostic methods available currently. Breast cancer can occur in both men and women and is defined as an abnormal growth of cells in the breast that multiply uncontrollably. The main factors which cause breast cancer are either hormonal or genetic. Masses are quite subtle, and have many shapes such as circumscribed, speculated or ill-defined. These tumors can be either benign or malignant. Computer-aided methods are powerful tools to assist the medical staff in hospitals and lead to better and more accurate diagnosis. The main objective of this research is to develop a Computer Aided Diagnosis (CAD) system for finding the tumors in the mammographic images and classifying the tumors as benign or malignant. There are five main phases involved in the proposed CAD system: image pre-processing, extraction of features from mammographic images using Gabor Wavelet and Discrete Wavelet Transform (DWT), dimensionality reduction using Principal Component Analysis (PCA) and classification using Support Vector Machine (SVM) classifier.

Download or read book Computational Image Analysis of Mass Lesions on Dynamic Contrast enhanced Breast MRI written by Qiu Wu and published by . This book was released on 2009 with total page 246 pages. Available in PDF, EPUB and Kindle. Book excerpt: This dissertation presents results of a medical image analysis project leading towards development of a comprehensive set of methods and tools for computational image analysis of dynamic contrast-enhanced (DCE) breast magnetic resonance image (MRI), with the aim to aid the physician in interpreting DCE breast MRI examinations. Toward this goal, we developed image analysis methods that would be needed in a breast MRI computer aided diagnosis (CADx) system. A novel contribution of this dissertation is the performance evaluation for each of the major algorithm components developed in this dissertation project. This dissertation begins with reviewing breast imaging techniques, including routinely used modalities in current clinical practice and emerging techniques still in development. We discuss at length the principles of DCE breast MRI, a very sensitive breast imaging modality that has been increasingly used in clinical practice. Then we review the diagnostic guidelines for interpreting DCE breast MRI, and explain the needs and challenges that arise in developing computational image analysis system for breast MRI applications. In this dissertation project, both the morphological and kinetic features of the lesion are automatically extracted for diagnostic purpose. In order to extract morphological features from the segmented lesions, the lesion needs to be accurately segmented out from its surrounding tissues. We utilized a probabilistic method to obtain an optimal segmentation map based on several algorithmic segmentation outputs. In evaluating the performance of segmentation algorithms, we compared the algorithmic segmentation results against manually segmented lesions, and further assessed the segmentation impact on subsequent classification stage. In order to extract accurate kinetic information, the motion needs to be compensated across image volumes acquired sequentially. In this dissertation, we comparatively assessed the similarity metric in registering DCE breast MR images. The performance of cross correlation(CC) coefficient, and mutual information (MI) were studied in both rigid and non-rigid registration schemes. Numerical results and statistical properties were reported. The resultant image quality after registration is discussed both qualitatively and quantitatively. In this dissertation we implemented a classification system based upon quantitative morphological and kinetic features in improving the specificity of breast MRI. Morphological and kinetic features of the lesion were extracted automatically, and then the feature selection step was utilized to select the most relevant features to maximize the classifier performance. In our study, the area under the receiver operating curve (AUC) is used as the performance metric of the classifier, and our results are competitive with those of previous studies. The dissertation concludes by summarizing the contribution of this project and suggesting the future directions of quantitative and highly automated approaches to breast MR image analysis.

Download or read book Computer Aided Breast Cancer Diagnosis written by and published by . This book was released on 1998 with total page 16 pages. Available in PDF, EPUB and Kindle. Book excerpt: The long range goal of this project is to improve the accuracy and consistency of breast cancer diagnosis by developing a Computer Aided Diagnosis (CAD) system for early prediction of breast cancer from patients' mammographic findings and medical history. Specifically, this system will predict the malignancy of non-palpable lesions that are examined with diagnostic mammography and are considered for biopsy. The goal is to improve the specificity of diagnosis with little loss of sensitivity thus significantly improving the positive predictive value of breast biopsy. Toward this goal, we have developed an artificial neural network (ANN) to predict biopsy outcome from mammographic and history findings. In the first four years of the grant we have 1) developed a user interface for acquiring mammographic findings, 2) acquired 700 cases using the standardized BI-RADS. reporting system, 3) trained and evaluated several ANN predictive models, 4) conducted a small prospective study, 5) examined the inter-and intra-observer variability of the reporting lexicon, 6) investigated reducing the number of active input features, and 7) examined the sensitivity of the system to the techniques used for sampling the data.

Download or read book Computer Aided Diagnosis Medical Image Analysis Techniques written by Bhagirathi Halalli and published by . This book was released on 2018 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Breast cancer is the second leading cause of death among women worldwide. Mammography is the basic tool available for screening to find the abnormality at the earliest. It is shown to be effective in reducing mortality rates caused by breast cancer. Mammograms produced by low radiation X-ray are difficult to interpret, especially in screening context. The sensitivity of screening depends on image quality and unclear evidence available in the image. The radiologists find it difficult to interpret the digital mammography; hence, computer-aided diagnosis (CAD) technology helps to improve the performance of radiologists by increasing sensitivity rate in a cost-effective way. Current research is focused toward the designing and development of medical imaging and analysis system by using digital image processing tools and the techniques of artificial intelligence, which can detect the abnormality features, classify them, and provide visual proofs to the radiologists. The computer-based techniques are more suitable for detection of mass in mammography, feature extraction, and classification. The proposed CAD system addresses the several steps such as preprocessing, segmentation, feature extraction, and classification. Though commercial CAD systems are available, identification of subtle signs for breast cancer detection and classification remains difficult. The proposed system presents some advanced techniques in medical imaging to overcome these difficulties.

Download or read book Diagnostic Breast Imaging written by Sylvia H. Heywang-Köbrunner and published by Thieme. This book was released on 2014-01-15 with total page 712 pages. Available in PDF, EPUB and Kindle. Book excerpt: Encompassing the entire spectrum of breast imaging and diagnostics, this acclaimed text provides a systematic and pragmatic guide for all clinicians involved in diagnosing breast disease. The new third edition has been fully updated to include advances in mammography, ultrasound, breast MRI, percutaneous interventions, and emerging technologies, with pros and cons and evidence-based approaches throughout. Special features of the third edition: Coverage of the field, with comprehensive sections on examination procedures and technical requirements; histologic, clinical, and radiologic appearance of a wide range of breast pathologies; results of international screening studies; and much more Nearly 1,200 clear radiographic images showing normal findings, benign and malignant disorders, and post-traumatic, post-surgical, and post-therapeutic changes to the breast Innovations in digital mammography, tomosynthesis, and computer assisted detection (CAD); new chapters on imaging of implants, lesions of uncertain malignant potential, developing technologies; and more A systematic, highly reproducible methodology for detection, diagnosis, and assessment of findings Easy-to-follow flowcharts for the diagnostic work-up of both typical and atypical cases Written by world-renowned authorities with decades of clinical experience, this book provides a brilliant orientation to the multimodality diagnostic approach and therapeutic significance of breast imaging findings. It is an essential reference and board review for radiologists, residents and fellows, gynecologists, oncologists, surgeons, technologists, and any other interdisciplinary specialist working to improve outcomes in breast disease.