Applying and evaluating 3D bodyscanning technology and landmarking within the clothing product development process to improve garment fit for mature women aged 55+

Women aged 55+ are recognised to have non-standard body morphologies and may present with further functional considerations. Existing practice bases clothing development on younger bodies, exasperating misfit issues that exist already. This research therefore focuses on the assessment and provision of garment fit for mature women aged 55+. It applies and critically analyses the application of 3D bodyscanning technology and landmarking practice for the clothing product development process for mature women. Compared to traditional methods in anthropometric body measurement, 3D bodyscanning procedures have perceived benefits in speed, privacy and accuracy. It is therefore ideal in capturing the measurement of mature women aged 55+. However, bodyscanning may deal less well with non-standard bodies, which may complicate further pattern creation. Whilst bodyscanning has recognisable benefits (speed, convenience, consistency), the technology is not readily accessible to practitioners and necessitates its study and testing. A pragmatic, mixed method approach was developed to gather and analyse qualitative and quantitative data related to body scanning and pattern applications. A theoretical framework was established from the knowledge base informing six propositions, a null and alternative of hypothesis. This research applied a mixed methods approach, allowing the exploration of the technology, the application of the data in pattern practice and the testing of its success with a suitable 55+ population. The research developed novel approaches to understand the data and ensure its validity. Processes found that landmarking errors were not confined to 55+ demographic. Landmark errors concerning armscye, bust and crotch points were common; but the t-test revealed that older age was the variable most likely to impact on landmarking accuracy concerning bust and crotch points. Scan analysis added time to the scanning process which made the technology less time conserving as widely perceived. The study discovered that non-contact landmarking methods allowed errors that were not easily detectable without a reliable system in place; hence established a system for validation. Body measurements from the pattern guidance and body scan data measurements did not have comparable landmark definitions; therefore scanner landmark definitions needed to be modified for pattern construction, adding time to the process. Comparison of patterns constructed from unmodified and modified scan data revealed that landmark error had a substantial impact on key areas of pattern geometry. Changes in pattern shape translated into poor fit of the bodice, where armholes were either too tight/loose and the shoulder seam too short for the body. The bodice fit trials confirmed that participants favoured the fit of the bodice that had undergone landmark modification and had used their self-selected waist position. Methods are necessary to ensure scan data is suitable for the application of pattern construction, this study provides clear approaches that allow this

3D human body modelling from range data

This thesis describes the design, implementation and application of an integrated and fully automated system for interpreting whole-body range data. The system is shown to be capable of generating complete surface models of human bodies, and robustly extracting anatomical features for anthropometry, with minimal intrusion on the subject. The ability to automate this process has enormous potential for personalised digital models in medicine, ergonomics, design and manufacture and for populating virtual environments. The techniques developed within this thesis now form the basis of a commercial product. However, the technical difficulties are considerable. Human bodies are highly varied and many of the features of interest are extremely subtle. The underlying range data is typically noisy and is sparse at occluded areas. In addressing these problems this thesis makes five main research contributions. Firstly, the thesis describes the design, implementation and testing of the whole integrated and automated system from scratch, starting at the image capture hardware. At each stage the tradeoffs between performance criteria are discussed, and experiments are described to test the processes developed. Secondly, a combined data-driven and model-based approach is described and implemented, for surface reconstruction from the raw data. This method addresses the whole body surface, including areas where body segments touch, and other occluded areas. The third contribution is a library of operators, designed specifically for shape description and measurement of the human body. The library provides high-level relational attributes, an "electronic tape measure" to extract linear and curvilinear measurements,as well as low-level shape information, such as curvature. Application of the library is demonstrated by building a large set of detectors to find anthropometric features, based on the ISO 8559 specification. Output is compared against traditional manual measurements and a detailed analysis is presented. The discrepancy between these sets of data is only a few per cent on most dimensions, and the system's reproducibility is shown to be similar to that of skilled manual measurers. The final contribution is that the mesh models and anthropometric features, produced by the system, have been used as a starting point to facilitate other research, Such as registration of multiple body images,draping clothing and advanced surface modelling techniques

Detection of anatomical structures in medical datasets

Detection and localisation of anatomical structures is extremely helpful for many image analysis algorithms. This thesis is concerned with the automatic identification of landmark points, anatomical regions and vessel centre lines in three-dimensional medical datasets. We examine how machine learning and atlas-based ideas may be combined to produce efficient, context-aware algorithms. For the problem of anatomical landmark detection, we develop an analog to the idea of autocontext, termed atlas location autocontext, whereby spatial context is iteratively learnt by the machine learning algorithm as part of a feedback loop. We then extend our anatomical landmark detection algorithm from Computed Tomography to Magnetic Resonance images, using image features based on histograms of oriented gradients. A cross-modality landmark detector is demonstrated using unsigned gradient orientations. The problem of brain parcellation is approached by independently training a random forest and a multi-atlas segmentation algorithm, then combining them by a simple Bayesian product operation. It is shown that, given classifiers providing complementary information, the hybrid classifier provides a superior result. The Bayesian product method of combination outperforms simple averaging where the classifiers are sufficiently independent. Finally, we present a system for identifying and tracking major arteries in Magnetic Resonance Angiography datasets, using automatically detected vascular landmarks to seed the tracking. Knowledge of individual vessel characteristics is employed to guide the tracking algorithm by two means. Firstly, the data is pre-processed using a top-hat transform of size corresponding to the vessel diameter. Secondly, a vascular atlas is generated to inform the cost function employed in the minimum path algorithm. Fully automatic tracking of the major arteries of the body is satisfactorily demonstrated

Craneología funcional y evolución humana: relaciones estructurales y organización espacial en la evolución de las áreas fronto-parietales

Los humanos modernos se caracterizan por la forma globular del neurocráneo y retracción de la cara, atribuidos generalmente a la encefalización. La proximidad entre cara y cerebro podría implicar un conflicto espacial entre órbitas y lóbulos frontal y temporal. El abultamiento parietal puede deberse a cambios en la corteza parietal, por ejemplo, el precúneo. Esta tesis investiga la relación estructural cerebro-orbital y la anatomía del lóbulo parietal utilizando morfometría geométrica aplicada a imágenes biomedicas de humanos modernos y fósiles y de primates no humanos. Los resultados apuntan hacia un mayor conflicto estructural de las órbitas con los lóbulos temporales. Los lóbulos parietales son longitudinal y verticalmente más grandes en humanos modernos que en neandertales. La variación de la proporción longitudinal del precúneo se debe sobre todo a la región superior y es específica de humanos modernos. La dimensión vertical del precúneo está relacionada con la morfología del contorno parietal exterior.Modern humans are characterized by a globular braincase and a reduced face, two features that are generally attributed to encephalization. The anatomical proximity between the face and the brain involves nonetheless spatial conflicts between the orbits and the frontal and temporal lobes. Parietal bulging is likely due to changes in parietal cortical elements, like the precuneus. This thesis investigates the orbit-brain structural relationships and parietal lobe anatomy through geometric morphometrics and biomedical imaging, in modern and fossil humans, as well as in non-human primates. Results point to a greater structural conflict between orbits and temporal lobes. The parietal lobes are longitudinally and vertically larger in modern humans, when compared to those of Neanderthals. The variation in the longitudinal proportions of the precuneus is mostly due to the superior regions, and it is specific to modern humans. Precuneus vertical dimension is related to the morphology of the outer parietal contour

The Probabilistic Active Shape Model: From Model Construction to Flexible Medical Image Segmentation

Automatic processing of three-dimensional image data acquired with computed tomography or magnetic resonance imaging plays an increasingly important role in medicine. For example, the automatic segmentation of anatomical structures in tomographic images allows to generate three-dimensional visualizations of a patient’s anatomy and thereby supports surgeons during planning of various kinds of surgeries. Because organs in medical images often exhibit a low contrast to adjacent structures, and because the image quality may be hampered by noise or other image acquisition artifacts, the development of segmentation algorithms that are both robust and accurate is very challenging. In order to increase the robustness, the use of model-based algorithms is mandatory, as for example algorithms that incorporate prior knowledge about an organ’s shape into the segmentation process. Recent research has proven that Statistical Shape Models are especially appropriate for robust medical image segmentation. In these models, the typical shape of an organ is learned from a set of training examples. However, Statistical Shape Models have two major disadvantages: The construction of the models is relatively difficult, and the models are often used too restrictively, such that the resulting segmentation does not delineate the organ exactly. This thesis addresses both problems: The first part of the thesis introduces new methods for establishing correspondence between training shapes, which is a necessary prerequisite for shape model learning. The developed methods include consistent parameterization algorithms for organs with spherical and genus 1 topology, as well as a nonrigid mesh registration algorithm for shapes with arbitrary topology. The second part of the thesis presents a new shape model-based segmentation algorithm that allows for an accurate delineation of organs. In contrast to existing approaches, it is possible to integrate not only linear shape models into the algorithm, but also nonlinear shape models, which allow for a more specific description of an organ’s shape variation. The proposed segmentation algorithm is evaluated in three applications to medical image data: Liver and vertebra segmentation in contrast-enhanced computed tomography scans, and prostate segmentation in magnetic resonance images

Cultivate Quantitative Magnetic Resonance Imaging Methods to Measure Markers of Health and Translate to Large Scale Cohort Studies

Magnetic Resonance Imaging (MRI) is an indispensable tool in healthcare and research, with a growing demand for its services. The appeal of MRI stems from its non-ionizing radiation nature, ability to generate high-resolution images of internal organs and structures without invasive procedures, and capacity to provide quantitative assessments of tissue properties such as ectopic fat, body composition, and organ volume. All without long term side effects. Nine published papers are submitted which show the cultivation of quantitative measures of ectopic fat within the liver and pancreas using MRI, and the process of validating whole-body composition and organ volume measurements. All these techniques have been translated into large-scale studies to improve health measurements in large population cohorts. Translating this work into large-scale studies, including the use of artificial intelligence, is included. Additionally, an evaluation accompanies these published studies, appraising the evolution of these quantitative MRI techniques from the conception to their application in large cohort studies. Finally, this appraisal provides a summary of future work on crowdsourcing of ground truth training data to facilitate its use in wider applications of artificial intelligence.In conclusion, this body of work presents a portfolio of evidence to fulfil the requirements of a PhD by published works at the University of Salford