Computer-aided classification of mammographic masses and normal tissue: linear discriminant analysis in texture feature space

The authors studied the effectiveness of using texture features derived from spatial grey level dependence (SGLD) matrices for classification of masses and normal breast tissue on mammograms. One hundred and sixty-eight regions of interest (ROIS) containing biopsy-proven masses and 504 ROIS containing normal breast tissue were extracted from digitized mammograms for this study. Eight features were calculated for each ROI. The importance of each feature in distinguishing masses from normal tissue was determined by stepwise linear discriminant analysis. Receiver operating characteristic (ROC) methodology was used to evaluate the classification accuracy. The authors investigated the dependence of classification accuracy on the input features, and on the pixel distance and bit depth in the construction of the SGLD matrices. It was found that five of the texture features were important for the classification. The dependence of classification accuracy on distance and bit depth was weak for distances greater than 12 pixels and bit depths greater than seven bits. By randomly and equally dividing the data set into two groups, the classifier was trained and tested on independent data sets. The classifier achieved an average area under the ROC curve, Az, of 0.84 during training and 0.82 during testing. The results demonstrate the feasibility of using linear discriminant analysis in the texture feature space for classification of true and false detections of masses on mammograms in a computer-aided diagnosis scheme.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/48960/2/pb950510.pd

Digital Image Processing Applications

Digital image processing can refer to a wide variety of techniques, concepts, and applications of different types of processing for different purposes. This book provides examples of digital image processing applications and presents recent research on processing concepts and techniques. Chapters cover such topics as image processing in medical physics, binarization, video processing, and more

Personalized Decision Modeling for Intervention and Prevention of Cancers

Personalized medicine has been utilized in all stages of cancer care in recent years, including the prevention, diagnosis, treatment and follow-up. Since prevention and early intervention are particularly crucial in reducing cancer mortalities, personalizing the corresponding strategies and decisions so as to provide the most appropriate or optimal medical services for different patients can greatly improve the current cancer control practices. This dissertation research performs an in-depth exploration of personalized decision modeling of cancer intervention and prevention problems. We investigate the patient-specific screening and vaccination strategies for breast cancer and the cancers related to human papillomavirus (HPV), representatively. Three popular healthcare analytics techniques, Markov models, regression-based predictive models, and discrete-event simulation, are developed in the context of personalized cancer medicine. We discuss multiple possibilities of incorporating patient-specific risk into personalized cancer prevention strategies and showcase three practical examples. The first study builds a Markov decision process model to optimize biopsy referral decisions for women who receives abnormal breast cancer screening results. The second study directly optimizes the annual breast cancer screening using a regression-based adaptive decision model. The study also proposes a novel model selection method for logistic regression with a large number of candidate variables. The third study addresses the personalized HPV vaccination strategies and develops a hybrid model combining discrete-event simulation with regression-based risk estimation. Our findings suggest that personalized screening and vaccination benefit patients by maximizing life expectancies and minimizing the possibilities of dying from cancer. Preventive screening and vaccination programs for other cancers or diseases, which have clearly identified risk factors and measurable risk, may all benefit from patient-specific policies

J Surv Stat Methodol

Record linkage is a valuable and efficient tool for connecting information from different data sources. The National Center for Health Statistics (NCHS) has linked its population-based health surveys with administrative data, including Medicare enrollment and claims records. However, the linked NCHS-Medicare files are subject to missing data; first, not all survey participants agree to record linkage, and second, Medicare claims data are only consistently available for beneficiaries enrolled in the Fee-for-Service (FFS) program, not in Medicare Advantage (MA) plans. In this research, we examine the usefulness of multiple imputation for handling missing data in linked National Health Interview Survey (NHIS)-Medicare files. The motivating example is a study of mammography status from 1999 to 2004 among women aged 65 years and older enrolled in the FFS program. In our example, mammography screening status and FFS/MA plan type are missing for NHIS survey participants who were not linkage eligible. Mammography status is also missing for linked participants in an MA plan. We explore three imputation approaches: (i) imputing screening status first, (ii) imputing FFS/MA plan type first, (iii) and imputing the two longitudinal processes simultaneously. We conduct simulation studies to evaluate these methods and compare them using the linked NHIS-Medicare files. The imputation procedures described in our paper would also be applicable to other public health-related research using linked data files with missing data issues arising from program characteristics (e.g., intermittent enrollment or data collection) reflected in administrative data and linkage eligibility by survey participants.CC999999/Intramural CDC HHS/United States2019-04-02T00:00:00Z30949519PMC64443666127vault:3186

Learning Representations for Novelty and Anomaly Detection

The problem of novelty or anomaly detection refers to the ability to automatically identify data samples that differ from a notion of normality. Techniques that address this problem are necessary in many applications, like in medical diagnosis, autonomous driving, fraud detection, or cyber-attack detection, just to mention a few. The problem is inherently challenging because of the openness of the space of distributions that characterize novelty or outlier data points. This is often matched with the inability to adequately represent such distributions due to the lack of representative data. In this dissertation we address the challenge above by making several contributions. (a)We introduce an unsupervised framework for novelty detection, which is based on deep learning techniques, and which does not require labeled data representing the distribution of outliers. (b) The framework is general and based on first principles by detecting anomalies via computing their probabilities according to the distribution representing normality. (c) The framework can handle high-dimensional data such as images, by performing a non-linear dimensionality reduction of the input space into an isometric lower-dimensional space, leading to a computationally efficient method. (d) The framework is guarded from the potential inclusion of distributions of outliers into the distribution of normality by favoring that only inlier data can be well represented by the model. (e) The methods are evaluated extensively on multiple computer vision benchmark datasets, where it is shown that they compare favorably with the state of the art

Complexity Reduction in Image-Based Breast Cancer Care

The diversity of malignancies of the breast requires personalized diagnostic and therapeutic decision making in a complex situation. This thesis contributes in three clinical areas: (1) For clinical diagnostic image evaluation, computer-aided detection and diagnosis of mass and non-mass lesions in breast MRI is developed. 4D texture features characterize mass lesions. For non-mass lesions, a combined detection/characterisation method utilizes the bilateral symmetry of the breast s contrast agent uptake. (2) To improve clinical workflows, a breast MRI reading paradigm is proposed, exemplified by a breast MRI reading workstation prototype. Instead of mouse and keyboard, it is operated using multi-touch gestures. The concept is extended to mammography screening, introducing efficient navigation aids. (3) Contributions to finite element modeling of breast tissue deformations tackle two clinical problems: surgery planning and the prediction of the breast deformation in a MRI biopsy device

The use of a figure-of-merit (FOM) for optimization in digital mammography: an exploratory study in Malta

This PhD thesis comprises an exploratory study in digital mammography physics that portrays two essential components. The first component (1) presents the first national survey of the technical performance of mammography equipment in Malta using the European Protocol [1-3]. This demonstrated considerable differences in the technical performance of the mammography units across the country with a wide range in performance, patient dose and image quality. A common problem was that many clinics had implemented computed radiography (CR) systems to replace existing film-screen (FS) systems without due consideration to optimization. All direct digital (DR) mammography units met current international technical performance standards and the effectiveness of DR mammography in reducing patient dose and maintaining high image quality compared to CR has been confirmed. The second component (2) was to explore the use of a figure-of-merit (FOM) for optimization and characterisation in digital mammography. The use of image quality parameters in digital mammography such as contrast-to-noise ratio (CNR) or signal-difference-to-noise ratio (SDNR), signal-to-noise ratio (SNR) and detective quantum efficiency (DQE) have been traditionally used for the quantitative evaluation of the system performance against international standards or guidelines. The use of FOMs is relatively new and may be considered as a new quality assurance tool in digital mammography permitting the quantitative and simultaneous assessment of image quality and patient dose. The main objective in having a FOM is to have a numerical expression representing the efficiency and efficacy of a given system gauging how good or poor a system is performing. This may be useful in optimization and in predicting a predetermined or expected image quality with a given amount of radiation dose for a given system. The most interesting aspect of the FOMs in this work will be to investigate and explore the possibility for inter-system comparison