A Framework of Vertebra Segmentation Using the Active Shape Model-Based Approach

We propose a medical image segmentation approach based on the Active Shape Model theory. We apply this method for cervical vertebra detection. The main advantage of this approach is the application of a statistical model created after a training stage. Thus, the knowledge and interaction of the domain expert intervene in this approach. Our application allows the use of two different models, that is, a global one (with several vertebrae) and a local one (with a single vertebra). Two modes of segmentation are also proposed: manual and semiautomatic. For the manual mode, only two points are selected by the user on a given image. The first point needs to be close to the lower anterior corner of the last vertebra and the second near the upper anterior corner of the first vertebra. These two points are required to initialize the segmentation process. We propose to use the Harris corner detector combined with three successive filters to carry out the semiautomatic process. The results obtained on a large set of X-ray images are very promising

Spatial description-based approach towards integration of biomedical atlases

Biomedical imaging has become ubiquitous in both basic research and the clinical sciences. As technology advances the resulting multitude of imaging modalities has led to a sharp rise in the quantity and quality of such images. Whether for epi- demiological studies, educational uses, clinical monitoring, or translational science purposes, the ability to integrate and compare such image-based data has become in- creasingly critical in the life sciences and eHealth domain. Ontology-based solutions often lack spatial precision. Image processing-based solutions may have di culties when the underlying morphologies are too di erent. This thesis proposes a compro- mise solution which captures location in biomedical images via spatial descriptions. Three approaches of spatial descriptions have been explored. These include: (1) spatial descriptions based on spatial relationships between segmented regions; (2) spatial descriptions based on ducial points and a set of spatial relations; and (3) spatial descriptions based on ducial points and a set of spatial relations, integrated with spatial relations between segmented regions. Evaluation, particularly in the context of mouse gene expression data, a good representative of spatio-temporal bi- ological data, suggests that the spatial description-based solution can provide good spatial precision. This dissertation discusses the need for biomedical image data in- tegration, the shortcomings of existing solutions and proposes new algorithms based on spatial descriptions of anatomical details in the image. Evaluation studies, par- ticularly in the context of gene expression data analysis, were carried out to study the performance of the new algorithms

Analysis and Design of Footwear Antennas

Wearable technologies are found in an increasing number of applications including sport and medical monitoring, gaming and consumer electronics. Sensors are used to monitor vital signs and are located on various parts of the body. Footwear sensors permit the collection of data relating to gait, running style, physiotherapy and research. The data is sent from sensors to on-body hubs, often using wired technology, which can impact gait characteristics. This thesis describes the design of footwear antennas for wireless sensor telemetry. The work addresses the challenges of placing antennas close to the foot as well as the proximity to the ground. Guidelines for polarization are presented. The channel link between footwear and wrist is investigated for both narrowband and wideband channels across different frequencies. The effects of the body proximity and movement were gauged for walking subjects and are described in terms of the Rician Distribution K-factor. Different antenna solutions are presented including UWB antennas on various footwear locations as well as 433 MHz integrated antennas in the insole. Both directional and omnidirectional antennas were considered for UWB and the evaluation was for both time-domain and frequencydomain. The research established new ideas that challenge the old paradigm of the waist as the best hub position, demonstrating that a hub on the footwear using directional antennas outperforms a hub on the waist using an omnidirectional antenna. The cumulative distribution functions of measured path gains are evaluated and the results are described in terms of the achievable minimum data rate considering the Body Area Network standard

Fully automatic image analysis framework for cervical vertebra in X-ray images

Despite the advancement in imaging technologies, a fifth of the injuries in the cervical spine remain unnoticed in the X-ray radiological exam. About a two-third of the subjects with unnoticed injuries suffer tragic consequences. Based on the success of computer-aided systems in several medical image modalities to enhance clinical interpretation, we have proposed a fully automatic image analysis framework for cervical vertebrae in X-ray images. The framework takes an X-ray image as input and highlights different vertebral features at the output. To the best of our knowledge, this is the first fully automatic system in the literature for the analysis of the cervical vertebrae. The complete framework has been built by cascading specialized modules, each of which addresses a specific computer vision problem. This dissertation explores data-driven supervised machine learning solutions to these problems. Given an input X-ray image, the first module localizes the spinal region. The second module predicts vertebral centers from the spinal region which are then used to generate vertebral image patches. These patches are then passed through machine learning modules that detect vertebral corners, highlight vertebral boundaries, segment vertebral body and predict vertebral shapes. In the process of building the complete framework, we have proposed and compared different solutions to the problems addressed by each of the modules. A novel region-aware dense classification deep neural network has been proposed for the first module to address the spine localization problem. The proposed network outperformed the standard dense classification network and random forestbased methods. Location of the vertebral centers and corners vary based on human interpretation and thus are better represented by probability maps than single points. To learn the mapping between the vertebral image patches and the probability maps, a novel neural network capable of predicting a spatially distributed probabilistic distribution has been proposed. The network achieved expert-level performance in localizing vertebral centers and outperform the Harris corner detector and Hough forest-based methods for corner localization. The proposed network has also shown its capability for detecting vertebral boundaries and produced visually better results than the dense classification network-based boundary detectors. Segmentation of the vertebral body is a crucial part of the proposed framework. A new shapeaware loss function has been proposed for training a segmentation network to encourage prediction of vertebra-like structures. The segmentation performance improved significantly, however, the pixel-wise nature of proposed loss function was not able to constrain the predictions adequately. To solve the problem a novel neural network was proposed which predicts vertebral shapes and trains on a loss function defined in the shape space. The proposed shape predictor network was capable of learning better topological information about the vertebra than the shape-aware segmentation network. The methods proposed in this dissertation have been trained and tested on a challenging dataset of X-ray images collected from medical emergency rooms. The proposed, first-of-its-kind, fully automatic framework produces state-of-the-art results both quantitatively and qualitatively

Testing And Data Recovery Excavations At The Jayroe Site (41HM51), Hamilton County, Texas

In 2003–2004, Prewitt and Associates, Inc., performed National Register of Historic Places testing and subsequent data recovery excavations at the Jayroe site (41HM51) in Hamilton County for the Texas Department of Transportation, Environmental Affairs Division, under Texas Antiquities Permit Nos. 3211 and 3405. The investigations were prompted by the planned replacement of the County Road 294 bridge at the Leon River (CSJ No. 0909-29-030), in compliance with Section 106 of the National Historic Preservation Act and its implementing regulations (36 CFR Part 800) and the Antiquities Code of Texas. Testing consisted of the excavation of 6 backhoe trenches and 19 test units, and the data recovery work consisted mainly of hand excavation of 153 contiguous 1x1-m units within a single block, with 2 backhoe trenches and 2 manual units apart from the block excavation. Combined, the testing and data recovery identified 16 cultural features interpreted as 3 open hearths, 4 shallow earth ovens or surface hearths, 8 scatters of various kinds of debris, and 1 knapping station. The excavations recovered 322 chipped stone tools, 26 cores, 6,589 pieces of unmodified debitage, 21 ground or battered stone tools, 38 potential pigment sources, 43 ceramic sherds, 15 modified bone artifacts, 7,649 animal bones, 1,200 mussel shells, and macrobotanical remains. Four analytical units are defined for the site, only one of which—the Toyah phase component— has much interpretive potential. It is interpreted as a campsite used at least several times, mostly in the a.d. 1470s, at which butchering of mostly bison and deer, late-stage lithic tool manufacture and repair, and other maintenance tasks figured prominently in the site activities. The artifacts recovered and records generated by the project are curated at the Center for Archaeological Studies, Texas State University

commemorative volume in celebration of the 60th birthday of Joachim Reitner

This volume contains papers presented in part at a symposium held in May 2012 at Göttingen University, to honour Professor Joachim Reitner for his numerous contributions to the fields of geobiology, geology, and palaeontology. Our present volume reflects the breadth of Reitnerś interests and accomplishement with tributes and research or review papers by his students, former students, collaborators, and friends. The symposium was held in conjunction with Joachim Reitnerś 60th birthday.researc

Quantification of vascular perfusion in the spinal cord after injury.

Traumatic injury destroys blood vessels at the injury epicenter and is followed by local angiogenesis and regional inflammation. Healing from injury depends on vascular health because blood supply is directly responsible for the health and function of surrounding tissue. This work establishes a new method for qualitatively and quantitatively measuring the blood supply of spinal cord (SC) tissue. Systemically injecting fluorescent microspheres (FMs) and cryostat sectioning SC tissue reveals a novel and potentially powerful way of assessing blood supply. This method is easily incorporated with existing tissue processing protocols because it does not require chemical digestion of the tissue region of interest. FM blood supply measurements show that after mild contusion injury, the epicenter has less blood flow while the blood flow several millimeters rostral and caudal to the epicenter is elevated compared to uninjured controls. The time course for vascular repair after spinal cord injury (SCI) has been widely studied and this pilot experiment was carried out seven days post-injury, at which point angiogenesis has reached its zenith and vascular pruning is minimal. A custom MATLAB program is used to automatically analyze FM distribution

Dynamical models and machine learning for supervised segmentation

This thesis is concerned with the problem of how to outline regions of interest in medical images, when the boundaries are weak or ambiguous and the region shapes are irregular. The focus on machine learning and interactivity leads to a common theme of the need to balance conflicting requirements. First, any machine learning method must strike a balance between how much it can learn and how well it generalises. Second, interactive methods must balance minimal user demand with maximal user control. To address the problem of weak boundaries,methods of supervised texture classification are investigated that do not use explicit texture features. These methods enable prior knowledge about the image to benefit any segmentation framework. A chosen dynamic contour model, based on probabilistic boundary tracking, combines these image priors with efficient modes of interaction. We show the benefits of the texture classifiers over intensity and gradient-based image models, in both classification and boundary extraction. To address the problem of irregular region shape, we devise a new type of statistical shape model (SSM) that does not use explicit boundary features or assume high-level similarity between region shapes. First, the models are used for shape discrimination, to constrain any segmentation framework by way of regularisation. Second, the SSMs are used for shape generation, allowing probabilistic segmentation frameworks to draw shapes from a prior distribution. The generative models also include novel methods to constrain shape generation according to information from both the image and user interactions. The shape models are first evaluated in terms of discrimination capability, and shown to out-perform other shape descriptors. Experiments also show that the shape models can benefit a standard type of segmentation algorithm by providing shape regularisers. We finally show how to exploit the shape models in supervised segmentation frameworks, and evaluate their benefits in user trials

Automated shape analysis and visualization of the human back.

Spinal and back deformities can lead to pain and discomfort, disrupting productivity, and may require prolonged treatment. The conventional method of assessing and monitoring tile de-formity using radiographs has known radiation hazards. An alternative approach for monitoring the deformity is to base the assessment on the shape of back surface. Though three-dimensional data acquisition methods exist, techniques to extract relevant information for clinical use have not been widely developed. Thi's thesis presentsthe content and progression of research into automated analysis and visu-alization of three-dimensional laser scans of the human back. Using mathematical shape analysis, methods have been developed to compute stable curvature of the back surface and to detect the anatomic landmarks from the curvature maps. Compared with manual palpation, the landmarks have been detected to within accuracy of 1.15mm and precision of 0.8111m.Based on the detected spinous process landmarks, the back midline which is the closest surface approximation of the spine, has been derived using constrained polynomial fitting and statistical techniques. Three-dimensional geometric measurementsbasedon the midline were then corn-puted to quantify the deformity. Visualization plays a crucial role in back shape analysis since it enables the exploration of back deformities without the need for physical manipulation of the subject. In the third phase,various visualization techniques have been developed, namely, continuous and discrete colour maps, contour maps and three-dimensional views. In the last phase of the research,a software system has been developed for automating the tasks involved in analysing, visualizing and quantifying of the back shape. The novel aspectsof this research lie in the development of effective noise smoothing methods for stable curvature computation; improved shape analysis and landmark detection algorithm; effective techniques for visualizing the shape of the back; derivation of the back midline using constrained polynomials and computation of three dimensional surface measurements.