626048
Corresponding Articular Cartilage Thickness Measurements in the Knee Joint by Modelling the Underlying Bone
We present a method for corresponding and combining cartilage thickness readings from a population of patients using the underlying bone structure as a reference. Knee joint femoral bone and cartilage surfaces are constructed from a set of parallel slice segmentations of MR scans. Correspondence points across a population of bone surfaces are defined and refined by minimising an objective function based on the Minimum Description Length of the resulting statistical shape model. The optimised bone model defines a set of corresponding locations from which 3D measurements of the cartilage thickness can be taken and combined for a population of patients. Results are presented for a small group of patients demonstrating the feasibility and potential of the approach as a means of detecting sub-millimetre cartilage thickness changes due to disease progression.
13018667
Transforming Pixel Signatures into an Improved Metric Space
17273589
Developing a Measure of Similarity between Pixel Signatures
Our previous work in computer-aided mammography has used scaleorientation pixel signatures that provide a rich description of local structure. We describe work using the transportation (`earth mover') algorithm to de ne a measure of similarity between two signatures that recognises similar structures whilst remaining robust to background variability and the presence of other structures. Speci cally, we investigate an un-normalised, normalised and novel best-partialmatching (BPM) approach to measuring this transportation distance and compare it with Euclidean distance. Receiver operating characteristic (ROC) methodology is used to compare performance and an example of clinical application is given. The BPM approach outperforms the other methods.
6527681
The use of kernel principal component analysis to model data distributions
Abstract We describe the use of kernel principal component analysis (KPCA) to model data distributions in high-dimensional spaces. We show that a previous approach to representing non-linear data constraints using KPCA is not generally valid, and introduce a new ‘proximity to data’ measure that behaves correctly. We investigate the relation between this measure and the actual density for various low-dimensional data distributions. We demonstrate the effectiveness of the method by applying it to the higher-dimensional case of modelling an ensemble of images of handwritten digits, showing how it can be used to extract the digit information from noisy input images.
35191926
Automatic Extraction of the Face Identity-Subspace
Facial variation divides into a number of functional subspaces, and ensemblespecific variation. An improved method of measuring these is presented, within the space defined by an Appearance Model. Initial estimates of the subspaces (lighting, pose, identity and expression) are obtained by Principal Components Analysis on appropriate groups of faces. An expectationmaximization algorithm is applied to image codings to maximise the probability of coding across these non-orthogonal subspaces. Ensemble specific variation is then removed by measuring the spatial predictability of the eigenvectors excluding those which are less predictable than the ensemble. These procedures significantly enhance identity recognition for a disjoint test set.
33520001
Active Appearance Models
We describe a new method of matching statistical models of appearance to images. A set of model parameters control modes of shape and gray-level variation learned from a training set. We construct an efficient iterative matching algorithm by learning the relationship between perturbations in the model parameters and the induced image errors.
3172134
VideoPlus: A Method for Capturing the Structure and Appearance of Immersive Environments
This paper describes an approach to capturing the appearance and structure of immersive environments based on the video imagery obtained with an omnidirectional camera system. The scheme proceeds by recovering the 3D positions of a set of point and line features in the world from image correspondences in a small set of key frames in the image sequence. Once the locations of these features have been recovered the position of the camera during every frame in the sequence can be determined by using these recovered features as fiducials and estimating camera pose based on the location of corresponding image features in each frame. The end result of the procedure is an omnidirectional video sequence where every frame is augmented with its pose with respect to an absolute reference frame and a 3D model of the environment composed of point and line features in the scene.By augmenting the video clip with pose information we provide the viewer with the ability to navigate the image sequence in new and interesting ways. More specifically the user can use the pose information to travel through the video sequence with a trajectory different from the one taken by the original camera operator. This freedom presents the end user with an opportunity to immerse themselves within a remote environment.
6301159
The Image Fidelity Assessor
In this paper we describe the Image Fidelity Assessor (IFA), a model of the human visual system designed to evaluate perceived fidelity. In particular, we describe the structure of the model, present results from a contrast discrimination experiment used within the model, and demonstrate preliminary results of the model. The IFA is designed to be both physiologically and psychophysically plausible. It consists of a multichannel Gabor pyramid decomposition. The fidelity at each spatial location within each channel is evaluated based on psychometric functions. The psychometric functions were determined experimentally and describe the discrimination ability of the visual system as a function of spatial frequency, orientation, and adaptation level. Limited memory probability summation across the visual channels is used to obtain a measure of perceived image fidelity. Preliminary results of the IFA are consistent with human evaluation of image fidelity.
290131
Internet-based system for simulation-based medical planning for cardiovascular disease
Current practice in vascular surgery utilizes only diagnostic and empirical data to plan treatments, which does not enable quantitative a priori prediction of the outcomes of interventions. We have previously described simulation-based medical planning methods to model blood flow in arteries and plan medical treatments based on physiologic models. An important consideration for the design of these patient-specific modeling systems is the accessibility to physicians with modest computational resources. We describe a simulation-based medical planning environment developed for the World Wide Web (WWW) using the Virtual Reality Modeling Language (VRML) and the Java programming language.
33669607
Patient-Specific Operative Planning for Aorto-Femoral Reconstruction Procedures
Traditionally, a surgeon will select a procedure for a particular patient based on past experience for patients with a similar state of disease. The experience gained from this patient will be selectively used when treating the next patient with similar symptoms. In this work, a surgical planning system was developed enabling a vascular surgeon to create and test alternative operative plans prior to surgery for a given patient. One dimensional and three dimensional hemodynamic (i.e. blood flow) simulations were performed for rest and exercise for operative plans for two aorto-femoral bypass patients and compared with actual postoperative data. The information that can be obtained from one-dimensional (volume flow distribution and pressure losses) and three-dimensional (wall shear stress) hemodynamic simulation may be clinically relevant to vascular surgeons planning interventions.
1646981
Reconstruction of Linearly Parameterized Models from Single Images with a Camera of Unknown Focal Length
This paper deals with the problem of recovering the dimensions of an object and its pose from a single image acquired with a camera of unknown focal length. It is assumed that the object in question can be modeled as a polyhedron where the coordinates of the vertices can be expressed as a linear function of a dimension vector. The reconstruction program takes as input, a set of correspondences between features in the model and features in the image. From this information, the program determines an appropriate projection model for the camera, the dimensions of the object, its pose relative to the camera and, in the case of perspective projection, the focal length of the camera. This paper describes how the reconstruction problem can be framed as an optimization over a compact set with low dimension (no more than four). This optimization problem can be solved efficiently by coupling standard nonlinear optimization techniques with a multistart method. The result is an efficient, reliable solution system that does not require initial estimates for any of the parameters being estimated.