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Segmentation is one of the key enabling technologies in medical image analysis with a great variety of methods proposed (Histace et al. 2009; Zhang et al. 2010, 2013; Matuszewski et al. 2011). Methods based on deep learning, with the features learned directly from data rather than handcrafted, showed significant improvement in the quality of the segmentation including the analysis of colonoscopy images.

National heat demand time series are important inputs into national energy system models. Although time series for primary fuel such as gas might be available, heat demand is not and measuring heat demand is only possible for individual buildings. Four different methods are used in this work to generate daily heat demand time series for Great Britain for 2016-2018 from temperature and wind speed and are validated against heat demand derived from national grid gas demand. All seem to model heat demand well.

In this paper, we analyse patterns in face shape variation due to weight gain. We propose the use of persistent homology descriptors to get geometric and topological information about the configuration of anthropometric 3D face landmarks. In this way, evaluating face changes boils down to comparing the descriptors computed on 3D face scans taken at different times. By applying dimensionality reduction techniques to the dissimilarity matrix of descriptors, we get a space in which each face is a point and face shape variations are encoded as trajectories in that space. Our results show that persistent homology is able to identify features which are well related to overweight and may help assessing individual weight trends. The research was carried out in the context of the European project SEMEOTICONS, which developed a multisensory platform which detects and monitors over time facial signs of cardio-metabolic risk.

Existing approaches to recover structure of 3D deformable objects and camera motion parameters from an uncalibrated images assume the object’s shape could be modelled well by a linear subspace. These methods have been proven effective and well suited when the deformations are relatively small, but fail to reconstruct the objects with relatively large deformations. This paper describes a novel approach for 3D non-rigid shape reconstruction, based on manifold decision forest technique. The use of this technique can be justified by noting that a specific type of shape variations might be governed by only a small number of parameters, and therefore can be well represented in a low-dimensional manifold. The key contributions of this work are the use of random decision forests for the shape manifold learning and robust metric for calculation of the re-projection error. The learned manifold defines constraints imposed on the reconstructed shapes. Due to a nonlinear structure of the learned manifold, this approach is more suitable to deal with large and complex object deformations when compared to the linear constraints. The robust metric is applied to reduce the effect of measurement outliers on the quality of the reconstruction. In many practical applications outliers cannot be completely removed and therefore the use of robust techniques is of particular practical interest. The proposed method is validated on 2D points sequences projected from the 3D motion capture data for ground truth comparison and also on real 2D video sequences. Experiments show that the newly proposed method provides better performance compared to previously proposed ones, including the robustness with respect to measurement noise, missing measurements and outliers present in the data.

3-D face recognition research has received significant attention in the past two decades because of the rapid development in imaging technology and ever increasing security demand of modern society. One of its challenges is to cope with non-rigid deformation among faces, which is often caused by the changes of appearance and facial expression. Popular solutions to deal with this problem are to detect the deformable parts of the face and exclude them, or to represent a face in terms of sparse signature points, curves or patterns that are invariant to deformation. Such approaches, however, may lead to loss of information which is important for classification. In this paper, we propose a new geodesic-map representation with statistical shape modelling for handling the non-rigid deformation challenge in face recognition. The proposed representation captures all geometrical information from the entire 3-D face and provides a compact and expression-free map that preserves intrinsic geometrical information. As a result, the search for dense points correspondence in the face recognition task can be speeded up by using a simple image-based method instead of time-consuming, recursive closest distance search in 3-D space. An experimental investigation was conducted on 3-D face scans using publicly available databases and compared with the benchmark approaches. The experimental results demonstrate that the proposed scheme provides a highly competitive new solution for 3-D face recognition.

Paper introduces a 3-D shape representation scheme for automatic face analysis and identification, and demonstrates its invariance to facial expression. The core of this scheme lies on the combination of statistical shape modelling and non-rigid deformation matching. While the former matches 3-D faces with facial expression, the latter provides a low-dimensional feature vector that controls the deformation of model for matching the shape of new input, thereby enabling robust identification of 3-D faces. The proposed scheme is also able to handle the pose variation without large part of missing data. To assist the establishment of dense point correspondences, a modified freeform-deformation based on B-spline warping is applied with the help of extracted landmarks. The hybrid iterative closest point method is introduced for matching the models and new data. The feasibility and effectiveness of the proposed method was investigated using standard publicly available Gavab and BU-3DFE datasets, which contain faces with expression and pose changes. The performance of the system was compared with that of nine benchmark approaches. The experimental results demonstrate that the proposed scheme provides a competitive solution for face recognition.

A common approach to recover structure of 3D deformable scene and camera motion from uncalibrated 2D video sequences is to assume that shapes can be accurately represented in linear subspaces. These methods are simple and have been proven effective for reconstructions of objects with relatively small deformations, but have considerable limitations when the deformations are large or complex. This paper describes a novel approach to reconstruction of deformable objects utilising a manifold decision forest technique. The key contribution of this work is the use of random decision forests for the shape manifold learning. The learned manifold defines constraints imposed on the reconstructed shapes. Due to nonlinear structure of the learned manifold, this approach is more suitable to deal with large and complex object deformations when compared to the linear constraints. © 2013 Springer-Verlag.

This paper describes a novel method for shape representation and robust image segmentation. The proposed method combines two well known methodologies, namely, statistical shape models and active contours implemented in level set framework. The shape detection is achieved by maximizing a posterior function that consists of a prior shape probability model and image likelihood function conditioned on shapes. The statistical shape model is built as a result of a learning process based on nonparametric probability estimation in a PCA reduced feature space formed by the Legendre moments of training silhouette images. A greedy strategy is applied to optimize the proposed cost function by iteratively evolving an implicit active contour in the image space and subsequent constrained optimization of the evolved shape in the reduced shape feature space. Experimental results presented in the paper demonstrate that the proposed method, contrary to many other active contour segmentation methods, is highly resilient to severe random and structural noise that could be present in the data.

This paper presents a new method for facial expression modelling and recognition based on diffeomorphic image registration parameterised via stationary velocity fields in the log-Euclidean framework. The validation and comparison are done using different statistical shape models (SSM) built using the Point Distribution Model (PDM), velocity fields and deformation fields. The obtained results show that the facial expression representation based on stationary velocity fields can be successfully utilised in facial expression recognition, and this parameterisation produces a higher recognition rate than the facial expression representation based on deformation fields.

This paper describes a novel method for shape detection and image segmentation. The proposed method combines statistical shape models and active contours implemented in a level set framework. The shape detection is achieved by minimizing the Gibbs energy of the posterior probability function. The statistical shape model is built as a result of a learning process based on nonparametric probability estimation in a PCA reduced feature space formed by the Legendre moments of training silhouette images. The proposed energy is minimized by iteratively evolving an implicit active contour in the image space and subsequent constrained optimization of the evolved shape in the reduced shape feature space. Experimental results are also presented to show that the proposed method has very robust performances for images with a large amount of noise.

Purpose Non-invasive imaging assessment of cardiac function is important in cardiovascular disease diagnosis, especially for evaluation of local cardiac motion. Tagged Cardiac MRI has been developed for this purpose, but evaluation of the results requires quantification and automation. Method: Two methods utilizing active contour modeling for wall motion extraction based on tagged cardiac MRI scans were evaluated based on properties of tracking methods in the image domain and frequency domain. Three criteria were used: accuracy, inter-subject and intra-subject sensitivity. The tracking results were evaluated by a medical expert. The evaluation methodology and its possible generalization to other diagnostic methods were considered. Results: Image domain and frequency domain analysis of tagged cardiac MRI data sets were evaluated demonstrating that the image domain method provides better results. The image-domain method is much more resistant to changes in the data, this time, due to a different subject being scanned. The frequency domain approach is not suitable for clinical applications, as the global error is significantly increased (more than 20%). Conclusion: The image domain method was found most effective, and it can generate a set of clearly identified parameters. The evaluation approach can be an interesting alternative to classical psychvisual studies which are time consuming and often fastidious for clinicians.

This paper describes details of a method for robust and accurate marker position estimation in projection CB images. The method is based on previously proposed tracking algorithms which can cope with multiple proximate markers and image clutter. The algorithm described in this paper can be seen as a post processing algorithm which uses all the calculated hypothetical marker positions, from the tracking algorithm, for all the markers and all projection images in a single combinatorial optimisation process. The algorithm has been design to estimate intra fraction motion during image guided radiation therapy. The results from the algorithm can be used in treatment planning, subsequent treatment monitoring and correction of motion artefacts in cone beam CT. The proposed post processing algorithm reduced the maximum marker position error from 5.6 pixels, using tracker alone, to 2.6 pixels after post processing. This should be compared to estimated 2.5 pixels maximum error present in the ground truth data. For the total number of 3,840 tracked markers after post processing 1.61% and 0.02% of their positional errors were respectively above three and six standard deviation of the ground truth, estimated separately for each marker and each projection image, whereas corresponding results after using tracker alone were 2.86% and 0.23%.

Active contour methods are often methods of choice for demanding segmentation problems, yet segmentation of medical images with complex intensity patterns still remains a challenge for these methods. This paper proposes a method to incorporate interactively specified foreground/background regions into the active model framework while keeping the user interaction to the minimum. To achieve that, the proposed functional to be minimized includes a term to encourage active contour to separate the points close to the specified foreground region from the points close to the specified background region in terms of geodesic distance. The experiments on multi-modal prostate images demonstrate that the proposed method not only can achieve robust and accurate results, but also provides an efficient way to interactively improve the results.

This paper describes a novel approach for model based estimation of a dense deformation field utilizing an implicit representation of shape changes. Unlike existing methods based on the Point Distribution Model (PDM), the proposed method is not affected by an incorrect point correspondence which is a major limiting factor in practical applications of the PDM with clinical data. The proposed method uses regression between parametric representations of pelvic organs’ shape and corresponding dense displacement field parameterized by the stationary vector field. The regression function is learned based on the training data sets including subjects with representative organ deformations, where the inter- and intra- subject correspondences are established via the log-Euclidean diffeomorphic formulation. The evaluation of the proposed method is conducted both on synthetic examples to provide systematic experimental evidence of correctness of the implicit shape representation for shape-driven prediction of the deformation field and, real MRI data to show accuracy in terms of deformation and prostate position prediction. The results show an increased robustness of the proposed framework in comparison to PDM approaches and suggest potential of its application for adaptive radiation therapy of prostate.

This paper describes a novel approach for model based estimation of a dense deformation field utilizing an implicit representation of shape changes. Unlike existing methods based on the Point Distribution Model (PDM), the proposed method is not affected by an incorrect point correspondence which is a major limiting factor in practical applications of the PDM with clinical data. The proposed method uses regression between parametric representations of pelvic organs’ shape and corresponding dense displacement field parameterized by the stationary vector field. The regression function is learned based on the training data sets including subjects with representative organ deformations, where the inter- and intra- subject correspondences are established via the log-Euclidean diffeomorphic formulation. The evaluation of the proposed method is conducted both on synthetic examples to provide systematic experimental evidence of correctness of the implicit shape representation for shape-driven prediction of the deformation field and, real MRI data to show accuracy in terms of deformation and prostate position prediction. The results show an increased robustness of the proposed framework in comparison to PDM approaches and suggest potential of its application for adaptive radiation therapy of prostate.