Vertebra Shape Classification using MLP for Content-Based Image Retrieval

A desirable content-based image retrieval (CBIR) system would classify extracted image features to support some form of semantic retrieval. The Lister Hill National Center for Biomedical Communications, an intramural R&D division of the National Library for Medicine (NLM), maintains an archive of digitized X-rays of the cervical and lumbar spine taken as part of the second national health and nutrition examination survey (NHANES II). It is our goal to provide shape-based access to digitized X-rays including retrieval on automatically detected and classified pathology, e.g., anterior osteophytes. This is done using radius of curvature analysis along the anterior portion, and morphological analysis for quantifying protrusion regions along the vertebra boundary. Experimental results are presented for the classification of 704 cervical spine vertebrae by evaluating the features using a multi-layer perceptron (MLP) based approach. In this paper, we describe the design and current status of the content-based image retrieval (CBIR) system and the role of neural networks in the design of an effective multimedia information retrieval system

Machine Learning Approaches to Human Body Shape Analysis

Soft biometrics, biomedical sciences, and many other fields of study pay particular attention to the study of the geometric description of the human body, and its variations. Although multiple contributions, the interest is particularly high given the non-rigid nature of the human body, capable of assuming different poses, and numerous shapes due to variable body composition. Unfortunately, a well-known costly requirement in data-driven machine learning, and particularly in the human-based analysis, is the availability of data, in the form of geometric information (body measurements) with related vision information (natural images, 3D mesh, etc.). We introduce a computer graphics framework able to generate thousands of synthetic human body meshes, representing a population of individuals with stratified information: gender, Body Fat Percentage (BFP), anthropometric measurements, and pose. This contribution permits an extensive analysis of different bodies in different poses, avoiding the demanding, and expensive acquisition process. We design a virtual environment able to take advantage of the generated bodies, to infer the body surface area (BSA) from a single view. The framework permits to simulate the acquisition process of newly introduced RGB-D devices disentangling different noise components (sensor noise, optical distortion, body part occlusions). Common geometric descriptors in soft biometric, as well as in biomedical sciences, are based on body measurements. Unfortunately, as we prove, these descriptors are not pose invariant, constraining the usability in controlled scenarios. We introduce a differential geometry approach assuming body pose variations as isometric transformations of the body surface, and body composition changes covariant to the body surface area. This setting permits the use of the Laplace-Beltrami operator on the 2D body manifold, describing the body with a compact, efficient, and pose invariant representation. We design a neural network architecture able to infer important body semantics from spectral descriptors, closing the gap between abstract spectral features, and traditional measurement-based indices. Studying the manifold of body shapes, we propose an innovative generative adversarial model able to learn the body shapes. The method permits to generate new bodies with unseen geometries as a walk on the latent space, constituting a significant advantage over traditional generative methods

Can dietary intake influence perception of and measured appearance? A systematic review : dietary intake and appearance

Appearance-based interventions have had some success in reducing smoking and sun exposure. Appearance may also motivate dietary behavior change if it was established that dietary improvement had a positive impact on appearance. The aims of this review are to evaluate the current evidence examining the relationship between dietary intake and appearance and to determine the effectiveness of dietary interventions on perceived or actual appearance. An electronic search of English language studies up to August 2012 was conducted using Cochrane, MEDLINE, Embase, CINAHL, Web of Science, SCOPUS and PsycINFO databases. Studies that included participants aged ≥ 18 years, that observed or altered dietary intake from actual food or dietary supplement use and assessed appearance-related outcomes were considered eligible. Data from 27 studies were extracted and assessed for quality using standardized tools. Nineteen studies were assessed as being of “positive” and four of “neutral” quality. All observational studies (n = 4741 participants) indicated that there was a significant association between various aspects of dietary intake and skin coloration and skin aging. The majority (16 studies, 769 participants) evaluated the effect of dietary supplements on skin appearance amongst females. Only one study examined the effect of actual food intake on appearance. Significant improvements in at least one actual or perceived appearance-related outcome (facial wrinkling, skin elasticity, roughness and skin color) following dietary intervention were shown as a result of supplementation. Further studies are needed in representative populations that examine actual food intake on appearance, using validated tools in a well-designed high quality RCTs.PostprintPeer reviewe

X-ray Image Segmentation and An Internet-based Tool for Medical Validation

Segmentation of vertebrae in X-ray images is a difficult task that requires an effective segmentation procedure. Noise, poor image contrast, occlusions and shape variability are some of the challenges in many of the spine X-ray images archived at the U.S. National Library of Medicine (NLM). In this thesis, we propose a curvature-based corner matching approach, which exploits the posterior corners of the vertebra to estimate the location and orientation of the vertebrae. The key advantage of the proposed approach is execution time, roughly about one-fifth of the previous approach that uses the generalized Hough transform when tested on a sizeable set of cervical spine images. This thesis also presents the first ever effort to develop a prototype internet-based medical image segmentation and pathology validation tool, which enables radiologists to validate computer generated image segmentations, modify existing or create new segmentation in addition to identifying pertinent pathology data

Nutrition monitoring in the United States : the directory of federal and state nutrition monitoring activities

Nutrition monitoring in the United States is a complex system of coordinated activities that provides information about the dietary, nutritional, and related health status of Americans; the relationships between diet and health; and the factors affecting dietary and nutritional status. Surveys, surveillance systems, and other monitoring activities comprise the measurement component areas of the National Nutrition Monitoring and Related Research Program (NNMRRP), which was recently strengthened with the passage of the National Nutrition Monitoring and Related Research Act of 1990. The Act required the development of a 10-Year Comprehensive Plan for Nutrition Monitoring and Related Research that has as its primary goal the establishment of a comprehensive national nutrition monitoring and related research program by:\u2022 collecting quality data that are continuous, coordinated, timely, and reliable\u2022 using comparable methods for data collection and reporting of results\u2022 conducting relevant research, and\u2022 efficiently and effectively disseminating and exchanging information with data users.The Interagency Board for Nutrition Monitoring and Related Research (IBNMRR), co-chaired by the Assistant Secretary for Health, Department of Health and Human Services and the Assistant Secretary for Food and Consumer Services, Department of Agriculture, is responsible for overseeing implementation of this 10-Year Plan. A roster of the member agencies of the Board can be found on page iii. Correspondence to the Board or its members can be directed to the Executive Secretary/Department Liaison for the appropriate Department.The Directory of Federal and State Nutrition Monitoring Activities, which is to be updated every 3 years, is part of the effort to improve dissemination of nutrition monitoring data. Published under the guidance of the Working Group on Federal-State Relations and Information Dissemination of the IBNMRR, it is an updated version of the first Directory published in 1989.Suggested citation: Interagency Board for Nutrition Monitoring and Related Research. Wright J, ed. Nutrition Monitoring in the United States: The Directory of Federal and State Nutrition Monitoring Activities. Hyattsville, Maryland: Public Health Service, 1992.ISBN 0-16 -038045-61992706

Assessment of Blood Pressure Using Only a Smartphone and Machine Learning Techniques: A Systematic Review

Regular monitoring of blood pressure (BP) allows for early detection of hypertension and symptoms related to cardiovascular disease. Measuring BP with a cuff requires equipment that is not always readily available and it may be impractical for some patients. Smartphones are an integral part of the lives of most people; thus, detecting and monitoring hypertension with a smartphone is likely to increase the ability to monitor BP due to the convenience of use for many patients. Smartphones lend themselves to assessing cardiovascular health because their built-in sensors and cameras provide a means of detecting arterial pulsations. To this end, several image processing and machine learning (ML) techniques for predicting BP using a smartphone have been developed. Several ML models that utilize smartphones are discussed in this literature review. Of the 53 papers identified, seven publications were evaluated. The performance of the ML models was assessed based on their accuracy for classification, the mean error measure, and the standard deviation of error for regression. It was found that artificial neural networks and support vector machines were often used. Because a variety of influencing factors determines the performance of an ML model, no clear preference could be determined. The number of input features ranged from five to 233, with the most commonly used being demographic data and the features extracted from photoplethysmogram signals. Each study had a different number of participants, ranging from 17 to 5,992. Comparisons of the cuff-based measures were mostly used to validate the results. Some of these ML models are already used to detect hypertension and BP but, to satisfy possible regulatory demands, improved reliability is needed under a wider range of conditions, including controlled and uncontrolled environments. A discussion of the advantages of various ML techniques and the selected features is offered at the end of this systematic review

The Relationship Between Creativity And Self-Esteem In Elders Living In The Community

The purpose of this study is to further explore literature regarding the role of the nurse practitioner in health promotion and childhood obesity

Glaucoma And Quality Of Life And The Role Of The Nurse Practitioner

The purpose of this study is to further explore literature regarding the role of the nurse practitioner in health promotion and childhood obesity