Andrew Hill

Use of active shape models for locating structures in medical images

Abstract This paper describes a technique for building compact models of the shape and appearance of flexible objects (such as organs) seen in 2D images. The models are derived from the statistics of labelled images containing examples of the objects. Each model consists of a flexible shape template, describing how the relevant locations of important points on the objects can vary, and a statistical model of the expected grey levels in a region around each model point. We describe how the models can be used in local image search, and give examples of their application to medical images.

Model-based image interpretation using genetic algorithms

We describe the application of genetic algorithms in model-based image interpretation. The delineation of left ventricular boundaries in apical 4-ehamber echocardiograms is used as an illustrative exemplar. The suitability of genetic algorithms for the model/objective-function/search procedure is presented.

The Use of Active Shape Models for Locating Structures in Medical Images

This paper describes a technique for building compact models of the shape and appearance of flexible objects (such as organs) seen in 2-D images. The models are derived from the statistics of sets of labelled images of examples of the 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. The shape models are parameterised in such a way as to allow ‘legal’ configurations. Such models have proved useful in a wide variety of applications. We describe how the models can be used in local image search and give examples of their application to medical images. We also describe how the method can be simply extended to segment 3-D objects in volume images and to track structures in image sequences.

A framework for automatic landmark identification using a new method of nonrigid correspondence

A framework for automatic landmark identification is presented based on an algorithm for corresponding the boundaries of two shapes. The auto-landmarking framework employs a binary tree of corresponded pairs of shapes to generate landmarks automatically on each of a set of example shapes. The landmarks are used to train statistical shape models, known as point distribution models. The correspondence algorithm locates a matching pair of sparse polygonal approximations, one for each of a pair of boundaries by minimizing a cost function, using a greedy algorithm. The cost function expresses the dissimilarity in both the shape and representation error (with respect to the defining boundary) of the sparse polygons. Results are presented for three classes of shape which exhibit various types of nonrigid deformation.

Model based interpretation of 3D medical images.

The automatic segmentation and labelling of anatomical structures in 3D medical images is a challenging task of practical importance. We describe a model-based approach which allows robust and accurate interpretation using explicit anatomical knowledge. Our method is based on the extension to 3D of Point Distribution Models (PDMs) and associated image search algorithms. A combination of global, Genetic Algorithm (GA), and local, Active Shape Model (ASM), search is used. We have built a 3D PDM of the human brain describing a number of major structures. Using this model we have obtained automatic interpretations for 30 3D Magnetic Resonance head images from different individuals. The results have been evaluated quantitatively and support our claim of robust and accurate interpretation.

Active shape models and the shape approximation problem

Active Shape Models (ASM) use an iterative algorithm to match statistically defined models of known but variable objects to instances in images. Each iteration of ASM search involves two steps: image data interrogation and shape approximation. Here we consider the shape approximation step in detail. We present a new method of shape approximation which uses directional constraints. We show how the error term for the shape approximation problem can be extended to cope with directional constraints, and present iterative solutions to the 2D and 3D problems. We also present an efficient algorithm for the 2D problem in which a modification of the error term permits a closed-form approximate solution which can be used to produce starting estimates for the iterative solution.

Automatic landmark generation for Point Distribution Models

Point Distribution Models (PDMs) are statistically derived flexible templates which are trained on sets of examples of the object(s) to be modelled. They require that each example is represented by a set of points (landmaiks) and that each landmark represents the same location on each of the examples. Generating the landmarks from 2D boundaries or 3D surfaces has previously been a manual process. Here, we describe a method for automatically generating PDMs from a training set of pixel- lated boundaries in 2D. The algorithm is a two-stage process in which a pair-wise corresponder is first used to establish an approximate set of landmarks on each of the example boundaries; in the second phase the landmarks are refined using an iterative non-linear optimisation scheme to generate a more compact PDM. We present results for two objects - the right hand and a chamber of the heart. The mo- dels generated using the automatically placed landmarks are shown to be better than those derived from landmarks located manually.

Medical image interpretation: a generic approach using deformable templates

We describe a generic approach to image interpretation, based on combining a general method of building flexible template models with genetic algorithm (GA) search. The method can be applied to a given image interpretation problem simply by training a statistical shape model, using a set of examples of the image structure to be located. A local optimization technique has been incorporated into the GA search and shown to improve the speed of convergence and optimality of solution. We present results from three medical applications, demonstrating that the new method offers significant improvements when compared with previously reported approaches to flexible template matching, particularly the ability to deal with different domains of application using a standard method and the possibility of employing complex multipart models. We also describe how the method can be simply extended to track structures in image sequences and segment three dimensional objects in volume images.

A generic system for image interpretation using flexible templates.

We describe a generic approach to image interpretation, based on combining a general method of building flexible template models with Genetic Algorithm (GA) search. The method can be applied to a given image interpretation problem simply by training a Point Distribution Model (PDM), using a set of examples of the image structure to be located. A local optimisation technique, developed for use with PDMs, has been incorporated into the GA search with the aim of improving the speed of convergence and optimality of solution. We present results, from three practical applications, demonstrating that the new method offers significant improvements when compared to previously reported approaches to flexible template matching. The benefits include the ability to deal with different domains of application using a standard method, the ability to deal with complex multi-part models and improved search performance.

Application of point distribution models to the automated analysis of echocardiograms

We have recently described how a Point Distribution Model (PDM) can be used to automatically detect structures in medical images. The method has two stages. Initially a statistical model of the shape variability is generated from a set of training images. Structures are located in experimental data by placing an instance of the model in the image and iteratively refining its shape and location to better fit the data. Whilst fitting to the image the model can only adopt shapes which are consistent with the training data. We call the combination of a PDM and the iterative refinement procedure an Active Shape Model (ASM). We have trained models of the left ventricle and associated structures as seen in both long axis and short axis echocardiograms. We show that ASM search using these models is able to detect the structures of interest rapidly accurately and reliably.<<ETX>>