3D body scanning and healthcare applications

Developed largely for the clothing industry, 3D body-surface scanners are transforming our ability to accurately measure and visualize a person's body size, shape, and skin-surface area. Advancements in 3D whole-body scanning seem to offer even greater potential for healthcare applications

Face morphology: Can it tell us something about body weight and fat?

This paper proposes a method for an automatic extraction of geometric features, related to weight parameters, from 3D facial data acquired with low-cost depth scanners. The novelty of the method relies both on the processing of the 3D facial data and on the definition of the geometric features which are conceptually simple, robust against noise and pose estimation errors, computationally efficient, invariant with respect to rotation, translation, and scale changes. Experimental results show that these measurements are highly correlated with weight, BMI, and neck circumference, and well correlated with waist and hip circumference, which are markers of central obesity. Therefore the proposed method strongly supports the development of interactive, non-obtrusive systems able to provide a support for the detection of weight-related problems

High-quality dense stereo vision for whole body imaging and obesity assessment

textThe prevalence of obesity has necessitated developing safe and convenient tools for timely assessing and monitoring this condition for a broad range of population. Three-dimensional (3D) body imaging has become a new mean for obesity assessment. Moreover, it generates body shape information that is meaningful for fitness, ergonomics, and personalized clothing. In the previous work of our lab, we developed a prototype active stereo vision system that demonstrated a potential to fulfill this goal. But the prototype required four computer projectors to cast artificial textures on the body which facilitate the stereo-matching on texture-deficient images (e.g., skin). This decreases the mobility of the system when used to collect a large population data. In addition, the resolution of the generated 3D~images is limited by both cameras and projectors available during the project. The study reported in this dissertation highlights our continued effort in improving the capability of 3Dbody imaging through simplified hardware for passive stereo and advanced computation techniques. The system utilizes high-resolution single-lens reflex (SLR) cameras, which became widely available lately, and is configured in a two-stance design to image the front and back surfaces of a person. A total of eight cameras are used to form four pairs of stereo units. Each unit covers a quarter of the body surface. The stereo units are individually calibrated with a specific pattern to determine cameras' intrinsic and extrinsic parameters for stereo matching. The global orientation and position of each stereo unit within a common world coordinate system is calculated through a 3Dregistration step. The stereo calibration and 3Dregistration procedures do not need to be repeated for a deployed system if the cameras' relative positions have not changed. This property contributes to the portability of the system, and tremendously alleviates the maintenance task. The image acquisition time is around two seconds for a whole-body capture. The system works in an indoor environment with a moderate ambient light. Advanced stereo computation algorithms are developed by taking advantage of high-resolution images and by tackling the ambiguity problem in stereo matching. A multi-scale, coarse-to-fine matching framework is proposed to match large-scale textures at a low resolution and refine the matched results over higher resolutions. This matching strategy reduces the complexity of the computation and avoids ambiguous matching at the native resolution. The pixel-to-pixel stereo matching algorithm follows a classic, four-step strategy which consists of matching cost computation, cost aggregation, disparity computation and disparity refinement. The system performance has been evaluated on mannequins and human subjects in comparison with other measurement methods. It was found that the geometrical measurements from reconstructed 3Dbody models, including body circumferences and whole volume, are highly repeatable and consistent with manual and other instrumental measurements (CV 0.99). The agreement of percent body fat (%BF) estimation on human subjects between stereo and dual-energy X-ray absorptiometry (DEXA) was found to be improved over the previous active stereo system, and the limits of agreement with 95% confidence were reduced by half. Our achieved %BF estimation agreement is among the lowest ones of other comparative studies with commercialized air displacement plethysmography (ADP) and DEXA. In practice, %BF estimation through a two-component model is sensitive to body volume measurement, and the estimation of lung volume could be a source of variation. Protocols for this type of measurement should still be created with an awareness of this factor.Biomedical Engineerin

Three-dimensional body scanning: methods and applications for anthropometry

In questa tesi descriviamo i metodi informatici e gli esperimenti eseguiti per l\u2019applicazione della tecnologia whole body 3D scanner in supporto dell\u2019antropometria. I body scanner restituiscono in uscita una nuvola di punti, solitamente trasformata in mesh triangolare mediante l\u2019uso di algoritmi specifici per supportare la visualizzazione 3D della superficie e l\u2019estrazione di misure e landmarks antropometrici significativi. L\u2019antropometria digitale \ue8 gi\ue0 stata utilizzata con successo in vari studi per valutare importanti parametri medici. L\u2019analisi antropometrica digitale \ue8 solitamente eseguita utilizzando soluzioni software fornite dai costruttori che sono chiuse e specifiche per il prodotto, che richiedono attenzione nell\u2019acquisizione e dei forti limiti sulla posa assunta dal soggetto. Questo pu\uf2 portare a dei problemi nella comparazione di dati acquisiti in diversi luoghi, nella realizzazione di studi multicentrici su larga scala e nell\u2019applicazione di metodi avanzati di shape analysis sui modelli acquisiti. L\u2019obiettivo del nostro lavoro \ue8 di superare questi problemi selezionando e personalizzando strumenti di processing geometrico capaci di creare un sistema aperto ed indipendente dallo strumento per l\u2019analisi di dati da body scanner. Abbiamo inoltre sviluppato e validato dei metodi per estrarre automaticamente dei punti caratteristici, segmenti corporei e misure significative che possono essere utilizzate nella ricerca antropometrica e metabolica. Nello specifico, presentiamo tre esperimenti. Nel primo, utilizzando uno specifico software per l\u2019antropometria digitale, abbiamo valutato la performance dello scanner Breuckmann BodySCAN nelle misure antropometriche. I soggetti degli esperimenti sono 12 giovani adulti che sono stati sottoposti procedure di antropometria manuale e digitale tridimensionale (25 misurazioni) indossando abbigliamento intimo attillato. Le misure duplicate effettuate da un\u2019antropometrista esperto mostrano una correlazione r=0.975-0.999; la loro media \ue8 significativamente (secondo il test t di Student) diversa su 4 delle 25 misure. Le misure digitali effettuate in duplicato da un antropometrista esperto e da due antropometristi non esperti, mostrano indici di correlazione individuali r che variano nel range 0.975-0.999 e medie che che erano significativamente diverse in una misurazione su 25. La maggior parte delle misure effettuate dall\u2019antropometrista esperto, manuali e digitali, mostrano una correlazione significativa (coefficiente di correlazione intra-classe che variano nell\u2019intervallo 0.855-0.995, p<0.0001). Concludiamo che lo scanner Breuckmann BodySCAN \ue8 uno strumento affidabile ed efficace per le misure antropometriche. In un secondo esperimento, compariamo alcune caratteristiche geometriche facilmente misurabili ottenute dalle scansioni di femmine obese (BMI>30) con i parametri di composizione corporea (misurata con una DXA) dei soggetti stessi, per investigare quali misure dei descrittori di forma correlavano meglio con il grasso del torso e corporeo. I risultati ottenuti mostrano che alcuni dei parametri geometrici testati presentano una elevata correlazione, mentre altri non correlano fortemente con il grasso corporeo. Questi risultati supportano il ruolo dell\u2019antropometria digitale nell\u2019indagine sulle caratteristiche fisiche rilevanti per la salute, ed incoraggiano la realizzazione di ulteriori studi che analizzino la relazione tra descrittori di forma e composizione corporea. Infine, presentiamo un nuovo metodo per caratterizzare le superfici tridimensionali mediante il calcolo di una funzione chiamata \u201cArea projection transform\u201d, la quale misura la possibilit\ue0 dei punti dello spazio 3D di essere il centro di simmetria radiale della forma a predeterminati raggi. La trasformata pu\uf2 essere usata per rilevare e caratterizzare in maniera robusta i regioni salienti (approssimativamente parti sferiche e cilindriche) ed \ue8, quindi, adatta ad applicazioni come la detection di caratteristiche anatomiche. In particolare, mostriamo che \ue8 possibile costruire grafi che uniscono questi punti seguendo i valori massimali della MAPT (Radial Simmetry Graphs) e che questi grafi possono essere usati per estrarre rilevanti propriet\ue0 della forma o definire corrispondenze puntuali robuste nei confronti di problematiche quali parti mancanti, rumore topologico e deformazioni articolate. Concludiamo che le potenziali applicazioni della tecnologia della scansione tridimensionale applicata all\u2019antropometria sono innumerevoli, limitate solo dall\u2019abilit\ue0 della conoscienza scientifica di connettere il fenomeno biologico con le appropriate descrizioni matematiche/geometriche.In this thesis we describe the developed computer method and experiments performed in order to apply whole body 3D scanner technology in support to anthropometry. The output of whole body scanners is a cloud of points, usually transformed in a triangulated mesh through the use of specific algorithms in order to support the 3D visualization of the surface and the extraction of meaningful anthropometric landmarks and measurements. Digital anthropometry has been already used in various studies to assess important health-related parameters. Digital anthropometric analysis is usually performed using device-specific and closed software solutions provided by scanner manufacturers, and requires often a careful acquisition, with strong constraints on subject pose. This may create problems in comparing data acquired in different places and performing large-scale multi-centric studies as well as in applying advanced shape analysis tools on the captured models. The aim of our work is to overcome these problems by selecting and customizing geometrical processing tools able to create an open and device-independent method for the analysis of body scanner data. We also developed and validated methods to extract automatically feature points, body segments and relevant measurements that can be used in anthropometric and metabolic research. In particular we present three experiments. In the first, using specific digital anthropometry software, we evaluated the Breuckmann BodySCAN for performance in anthropometric measurement. Subjects of the experiment were 12 young adults underwent both manual and 3D digital anthropometry (25 measurements) wearing close-fitting underwear. Duplicated manual measurement taken by one experienced anthropometrist showed correlation r 0.975-0.999; their means were significantly different in four out of 25 measurements by Student\u2019s t test. Duplicate digital measurements taken by one experienced anthropometrist and two na\uefve anthropometrists showed individual correlation coefficients r ranging 0.975-0.999 and means were significantly different in one out of 25 measurements. Most measurements taken by the experienced anthropometrist in the manual and digital mode showed significant correlation (intraclass correlation coefficient ranging 0.855-0.995, p<0.0001). We conclude that the Breuckmann BodyScan is reliable and effective tool for digital anthropometry. In a second experiment, we compare easily detectable geometrical features obtained from 3D scans of female obese (BMI > 30) subjects with body composition (measured with a DXA device) of the same subjects, in order to investigate which measurements on shape descriptors better correlate with torso and body fat. The results obtained show that some of the tested geometrical parameters have a relevant correlation, while other ones do not strongly correlate with body fat. These results support the role of digital anthropometry in investigating health-related physical characteristics and encourage the realization of further studies analyzing the relationships between shape descriptors and body composition. Finally, we present a novel method to characterize 3D surfaces through the computation of a function called Area Projection Transform, measuring the likelihood of points in the 3D space to be center of radial symmetry at selected scales (radii). The transform can be used to detect and characterize robustly salient regions (approximately spherical and cylindrical parts) and it is, therefore, suitable for applications like anatomical features detection. In particular, we show that it is possible to build graphs joining these points following maximal values of the MAPT (Radial Symmetry Graphs) and that these graphs can be used to extract relevant shape properties or to establish point correspondences on models robustly against holes, topological noise and articulated deformations. It is concluded that whole body scanning technology application to anthropometry are potentially countless, limited only by the ability of science to connect the biological phenomenon with the appropriate mathematical/geometrical descriptions

Motion robust acquisition and reconstruction of quantitative T2* maps in the developing brain

The goal of the research presented in this thesis was to develop methods for quantitative T2* mapping of the developing brain. Brain maturation in the early period of life involves complex structural and physiological changes caused by synaptogenesis, myelination and growth of cells. Molecular structures and biological processes give rise to varying levels of T2* relaxation time, which is an inherent contrast mechanism in magnetic resonance imaging. The knowledge of T2* relaxation times in the brain can thus help with evaluation of pathology by establishing its normative values in the key areas of the brain. T2* relaxation values are a valuable biomarker for myelin microstructure and iron concentration, as well as an important guide towards achievement of optimal fMRI contrast. However, fetal MR imaging is a significant step up from neonatal or adult MR imaging due to the complexity of the acquisition and reconstruction techniques that are required to provide high quality artifact-free images in the presence of maternal respiration and unpredictable fetal motion. The first contribution of this thesis, described in Chapter 4, presents a novel acquisition method for measurement of fetal brain T2* values. At the time of publication, this was the first study of fetal brain T2* values. Single shot multi-echo gradient echo EPI was proposed as a rapid method for measuring fetal T2* values by effectively freezing intra-slice motion. The study concluded that fetal T2* values are higher than those previously reported for pre-term neonates and decline with a consistent trend across gestational age. The data also suggested that longer than usual echo times or direct T2* measurement should be considered when performing fetal fMRI in order to reach optimal BOLD sensitivity. For the second contribution, described in Chapter 5, measurements were extended to a higher field strength of 3T and reported, for the first time, both for fetal and neonatal subjects at this field strength. The technical contribution of this work is a fully automatic segmentation framework that propagates brain tissue labels onto the acquired T2* maps without the need for manual intervention. The third contribution, described in Chapter 6, proposed a new method for performing 3D fetal brain reconstruction where the available data is sparse and is therefore limited in the use of current state of the art techniques for 3D brain reconstruction in the presence of motion. To enable a high resolution reconstruction, a generative adversarial network was trained to perform image to image translation between T2 weighted and T2* weighted data. Translated images could then be served as a prior for slice alignment and super resolution reconstruction of 3D brain image.Open Acces

Equine body weight estimation using three-dimensional images

Includes bibliographical references.2015 Summer.Accurately estimating the body weight (BW) of a horse is important in order to make appropriate management and treatment decisions. Most field equine veterinarians and experienced equine people, however, visually estimate BW because large animal scales are impractical for field use due to the weight (>80 kg), size (length >200 cm), and cost (>$1,000). There are some alternative BW estimation methods such as a weight tape or BW estimation using a combination of heart girth and body length measurements. These methods, however, have 5 - 15% or even higher margin of error. According to human studies, there is a high correlation between BW and body volume (BV). Correlation coefficient (R) between these two variables is 0.996-0.998. Our study was designed to develop methods to estimate the BW of horses by using 3D image based BV measurement. 3D imaging technology allows easy and accurate measurement of diverse indices of an object, including the volume. Recent development of Structure-light 3D scanning technology allows 3D scanning of an object as large as 3 by 3 square meter in a short time. In this study, 3D images of 22 and 11 horses were obtained by using 3D scanning (3DScan) and photogrammetry (2Dto3D), respectively. BV and trunk volume (TV) of the horses were measured from the obtained 3D images. Measurements of BW using five conventional methods (visual estimation, 2 weight tapes (Purina, Shell), estimated BW by using heart girth and body length (Carroll’s formula), and a large animal scale) were also conducted, and the data of body condition score (BCS), sex, coat color, and coat type of the horses were collected. Linear regression models to estimate the BW of the horse based on the volume and other independent variables were developed using regression model stepwise selection procedures (P<0.05). Variables selected in 3DScan method were BV, sex, and coat type, and, in 2Dto3D method, BV (TV) was selected. The coefficient of determination of the developed regression models were 0.95 and 0.78-0.82, respectively, and the average percent errors of the predicted BW compared to the true BW of horses were 2.07 % and 2.67 %, respectively. The accuracy of the 3DScan method was significantly more accurate than WT, Carroll’s formual, and VE (P<0.05). 3D image based BW measurement method had higher accuracy and convenience compared to conventional alternative BW measuring methods. Accurate and easy determination of BW using 3D images will allow for regular BW measurement in the field and allow optimal equine health management by equine stakeholders and practitioners. The 3D images obtained in this study were highly detailed. Further graphical analysis of the obtained 3D images will make it possible to use this technology on automatic evaluation of body condition score, equine conformation evaluation, breed registration, and the study of pharmacokinetics and dynamics of newly developed drugs. This research findings may also have utility for application to wild or zoo animals such as the elephant, rhinoceros, or even the tiger where hands on collection of body weight would be challenging

Wize Mirror - a smart, multisensory cardio-metabolic risk monitoring system

In the recent years personal health monitoring systems have been gaining popularity, both as a result of the pull from the general population, keen to improve well-being and early detection of possibly serious health conditions and the push from the industry eager to translate the current significant progress in computer vision and machine learning into commercial products. One of such systems is the Wize Mirror, built as a result of the FP7 funded SEMEOTICONS (SEMEiotic Oriented Technology for Individuals CardiOmetabolic risk self-assessmeNt and Self-monitoring) project. The project aims to translate the semeiotic code of the human face into computational descriptors and measures, automatically extracted from videos, multispectral images, and 3D scans of the face. The multisensory platform, being developed as the result of that project, in the form of a smart mirror, looks for signs related to cardio-metabolic risks. The goal is to enable users to self-monitor their well-being status over time and improve their life-style via tailored user guidance. This paper is focused on the description of the part of that system, utilising computer vision and machine learning techniques to perform 3D morphological analysis of the face and recognition of psycho-somatic status both linked with cardio-metabolic risks. The paper describes the concepts, methods and the developed implementations as well as reports on the results obtained on both real and synthetic datasets

An Automatic Technique for MRI Based Murine Abdominal Fat Measurement

Because of the well-known relationship between obesity and high incidence of diseases, fat related research using mice models is being widely investigated in preclinical experiments. In the present study, we developed a technique to automatically measure mice abdominal adipose volume and determine the depot locations using Magnetic Resonance Imaging (MRI). Our technique includes an innovative method to detect fat tissues from MR images which not only utilizes the T1 weighted intensity information, but also takes advantage of the transverse relaxation time(T2) calculated from the multiple echo data. The technique contains both a fat optimized MRI imaging acquisition protocol that works well at 7T and a newly designed post processing methodology that can automatically accomplish the fat extraction and depot recognition without user intervention in the segmentation procedure. The post processing methodology has been integrated into easy-to-use software that we have made available via free download. The method was validated by comparing automated results with two independent manual analyses in 26 mice exhibiting different fat ratios from the obesity research project. The comparison confirms a close agreement between the results in total adipose tissue size and voxel-by-voxel overlaps