Statistical analysis of motion patterns of body joints is potentially useful for detecting and quantifying pathologies. However, building a statistical motion model across different subjects remains a challenging task, especially for a complex joint like the wrist. We present a novel framework for simultaneous registration and segmentation of multiple 3-D (CT or MR) volumes of different subjects at various articulated positions. The framework starts with a pose model generated from 3-D volumes captured at different articulated positions of a single subject (template). This initial pose model is used to register the template volume to image volumes from new subjects. During this process, the Grow-Cut algorithm is used in an iterative refinement of the segmentation of the bone along with the pose parameters. As each new subject is registered and segmented, the pose model is updated, improving the accuracy of successive registrations. We applied the algorithm to CT images of the wrist from 25 subjects, each at five different wrist positions and demonstrated that it performed robustly and accurately. More importantly, the resulting segmentations allowed a statistical pose model of the carpal bones to be generated automatically without interaction. The evaluation results show that our proposed framework achieved accurate registration with an average mean target registration error of 0.34 ±0.27 mm. The automatic segmentation results also show high consistency with the ground truth obtained semi-automatically. Furthermore, we demonstrated the capability of the resulting statistical pose and shape models by using them to generate a measurement tool for scaphoid-lunate dissociation diagnosis, which achieved 90% sensitivity and specificity.

We present a novel framework for estimating the 3D poses and shapes of the carpal bones from single view fluoroscopic sequences. A hybrid statistical model representing both the pose and shape variation of the carpal bones is built, based on a number of 3D CT data sets obtained from different subjects at different poses. Given a fluoroscopic sequence, the wrist pose, carpal bone pose and bone shapes are estimated iteratively by matching the statistical model with the 2D images. A specially designed cost function enables smoothed parameter estimation across frames and constrains local bone pose with a penalty term. We have evaluated the proposed method on both simulated data and real fluoroscopic sequences and demonstrated that the relative poses of carpal bones can be accurately estimated. One condition that may be assessed using this measurement is dissociation, where the distance between the bones is larger than normal. Scaphoid-Lunate dissociation is one of the most common of these. The error of the measured 3D Scaphoid-Lunate distances were 0.75±0.50 mm for simulated data (25 subjects) and 0.93±0.47 mm for real data (15 subjects). We also propose a method for constructing a "standard" pathology measurement tool for automatically detecting Scaphoid-Lunate dissociation conditions, based on single-view fluoroscopic sequences. For the simulated data, it produced 100% sensitivity and specificity. For the real data, it achieved 83% sensitivity and 78% specificity.

A novel framework is presented in this paper for simultaneous multi-label segmentation and registration of carpal bones which leads to efficient statistical model building. It combines the Grow Cut segmentation algorithm with rigid image registration for propagating the segmentation of bones to new poses or different individuals. The proposed framework compares favourably to the conventional segmentation and non- rigid registration methods, in terms of flexibility and computational time, for our CT data of carpal bones. The segmentation code was implemented in a GPU, running about 15 times faster than CPU code.

Corneal Confocal Microscopy (CCM) imaging is a non-invasive surrogate of detecting, quantifying and monitoring diabetic peripheral neuropathy. This paper presents an automated method for detecting nerve-fibres from CCM images using a dual-model detection algorithm and compares the performance to well-established texture and feature detection methods. The algorithm comprises two separate models, one for the background and another for the foreground (nerve-fibres), which work interactively. Our evaluation shows significant improvement (p approximately 0) in both error rate and signal-to-noise ratio of this model over the competitor methods. The automatic method is also evaluated in comparison with manual ground truth analysis in assessing diabetic neuropathy on the basis of nerve-fibre length, and shows a strong correlation (r = 0.92). Both analyses significantly separate diabetic patients from control subjects (p approximately 0).

Citizen science websites are emerging as a common way for volunteers to collect and report geographic ecological data. Engaging the public in citizen science is challenging and, when involving online participation, data entry, and map use, becomes even more daunting. Given these new challenges, citizen science websites must be easy to use, result in positive overall satisfaction for many different users, support many different tasks, and ensure data quality. To begin reaching these goals, we built a geospatially enabled citizen science website, evaluated its usability, and gained experience by working with and listening to citizens using the website. We sought to determine general perceptions, discover potential problems, and iteratively improve website features. Although the website was rated positively overall, map-based tasks identified a wide range of problems. Given our results, we redesigned the website, improved the content, enhanced the ease of use, simplified the map interface, and added features. We discuss citizen science websites in relation to online Public Participation Geographic Information Systems, examine the role(s) websites may play in the citizen science research model, discuss how citizen science research advances GIScience, and offer guidelines to improve citizen-based web mapping applications.

Point-matching is a widely applied image registration method and many algorithms have been developed. Registration of 2-D electrophoresis gels is an important problem in biological research that presents many of the technical difficulties that beset point-matching: large numbers of points with variable densities, large nonrigid transformations between point sets, paucity of structural information and large numbers of unmatchable points (outliers) in either set. In seeking the most suitable algorithm for gel registration we have evaluated a number of approaches for accuracy and robustness in the face of these difficulties. Using synthetic images we test combinations of three algorithm components: correspondence assignment, distance metrics and image transformation. We show that a version of the iterated closest point (ICP) algorithm using a non-Euclidean distance metric and a robust estimation of transform parameters provides best performance, equalling SoftAssign in the presence of moderate image distortion, and providing superior robustness against large distortions and high outlier proportions. From this evaluation we develop a gel registration algorithm based on robust ICP and a novel distance metric combining Euclidean, shape context and image-related features. We demonstrate the accuracy of gel matching using synthetic distortions of real gels and show that robust estimation of transform parameters using M-estimators can enforce inverse consistency, ensuring that matching results are independent of the order of the images.

We describe a novel method of estimating reduced bone mineral density (BMD) from dental panoramic tomograms (DPTs), which show the entire mandible. Careful expert width measurement of the inferior mandibular cortex has been shown to be predictive of BMD in hip and spine osteopenia and osteoporosis. We have implemented a method of automatic measurement of the width by active shape model search, using as training data 132 DPTs of female subjects whose BMD has been established by dual-energy X-ray absorptiometry. We demonstrate that widths measured after fully automatic search are significantly correlated with BMD, and exhibit less variability than manual measurements made by different experts. The correlation is highest towards the lateral region of the mandible, in a position different from that previously employed for manual width measurement. An receiver-operator characterstic (ROC) analysis for identifying osteopenia (T < -1: BMD more than one standard deviation below that of young healthy females) gives an area under curve (AUC) value of 0.64. Using a minimal interaction to initiate active shape model (ASM) search, the measurement can be made at the optimum region of the mandible, resulting in an AUC value of 0.71. Using an independent test set, AUC for detection of osteoporosis (T < -2.5) is 0.81.

Proteomics research relies heavily on electrophoresis gels, which are complex images containing many protein ‘spots’. The identification and quantification of these spots is a bottleneck in the proteomics workflow. We describe a statistical model of protein spot appearance that is both general enough to represent unusual spots, and specific enough to introduce constraints on the interpretation of complex images. We propose a robust method of automatic model construction that is used to circumvent manual model construction which is subjective and time-consuming. We show that the statistical model of spot appearance is able to fit to image data more closely than the commonly used spot parameterisations which are based solely on Gaussian and diffusion formulations.

Pointdistributionmodelshavebeensuccessfulindescribingtheshape constraintsontwodimensionalobjectsforshapedescriptionandimage search. It is often the case that a class of objects to be modelled contains certain features which may be wholly present or absent in difierentinstances. Moustachesonfacesareacommonexample. Here we describe a method of coding the presence or absence of a feature within the PDM framework. We show that the method captures the intermittentnatureofthefeatureasoneofthemodesofvariation,and demonstrate that, where features are intermittently present, greater model speciflcity is achieved.

The usefulness of advanced machine vision techniques for automated inspection is restricted by long development cycles for the inspection software and a lack of general applicability of the resulting system. These difficulties arise because in the majority of cases the particulars of the inspection are embedded in the software structure resulting in severe restrictions on user reconfiguration. This paper describes the structure and major components of an inspection system which is capable of tackling a wide range of inspection problems without reprogramming. User configurability is based around a simple, interpreted geometric description language which provides support for both dimensional and surface quality measurements. The operation of the system is illustrated using the inspection of automotive brake assemblies as an example. 1 Introduction There is considerable potential for the use of automated visual inspection in manufacturing industry. Some inspection problems require specif...

The authors describe a technique for building compact models of the shape and appearance of flexible objects seen in 2-D images. The models are derived from the statistics of sets of labeled images of example objects. Each model consists of a flexible shape template, describing how important points of the object can vary, and a statistical model of the expected grey levels in regions around each model point. Such models have proved useful in a wide variety of applications. A description is given on how the models can be used in local image search, and examples of their application are included.>