Biomedical subjects

T F Cootes

Publications and source records attributed to T F Cootes.

5 recordsLinked to original sources

PubMed2004

Anatomical statistical models and their role in feature extraction.

A detailed model of the shape of anatomical structures can significantly improve the ability to segment such structures from medical images. Statistical models representing the variation of shape and appearance can be constructed from suitably annotated training sets. Such models can be used to synthesize images of anatomy, and to search new images to accurately locate the structures of interest, even in the presence of noise and clutter. In this paper we summarize recent work on constructing and using such models, and demonstrate their application to several domains.

Brain↗

PubMed2003

Improving appearance model matching using local image structure.

We show how non-linear representations of local image structure can be used to improve the performance of model matching algorithms in medical image analysis tasks. Rather than represent the image structure using intensity values or gradients, we use measures that indicate the reliability of a set of local image feature detector outputs. These features are image edges, corners, and gradients. Feature detector outputs in flat, noisy regions tend to be ignored whereas those near strong structure are favoured. We demonstrate that combinations of these features give more accurate and reliable matching between models and new images than modelling image intensity alone. We also show that the approach is robust to non-linear changes in contrast, such as those found in multi-modal imaging.

Algorithms↗

PubMed1997

Model-based interpretation of complex and variable images.

The ultimate goal of machine vision is image understanding-the ability not only to recover image structure but also to know what it represents. By definition, this involves the use of models which describe and label the expected structure of the world. Over the past decade, model-based vision has been applied successfully to images of man-made objects. It has proved much more difficult to develop model-based approaches to the interpretation of images of complex and variable structures such as faces or the internal organs of the human body (as visualized in medical images). In such cases it has been problematic even to recover image structure reliably, without a model to organize the often noisy and incomplete image evidence. The key problem is that of variability. To be useful, a model needs to be specific-that is, to be capable of representing only 'legal' examples of the modelled object(s). It has proved difficult to achieve this whilst allowing for natural variability. Recent developments have overcome this problem; it has been shown that specific patterns of variability in shape and grey-level appearance can be captured by statistical models that can be used directly in image interpretation. The details of the approach are outlined and practical examples from medical image interpretation and face recognition are used to illustrate how previously intractable problems can now be tackled successfully. It is also interesting to ask whether these results provide any possible insights into natural vision; for example, we show that the apparent changes in shape which result from viewing three-dimensional objects from different viewpoints can be modelled quite well in two dimensions; this may lend some support to the 'characteristic views' model of natural vision.

Form Perception↗

PubMed1994

Magnetic resonance image synthesis using a flexible model.

Image synthesis methods are based on the hypothesis that a magnetic resonance (MR) image with optimized contrast can be reproduced by synthesis from three calculated basic images of T1, T2 and spin density. This method, however, is limited by noise due to uncertainties in the initial measurements. The principal component analysis (PCA) method is based on an information theory approach that decomposes MR images into a small set of characteristic feature images. PCA images, or eigenimages, show morphology by condensing the structural information from the source images. Eigenimages have also been shown to improve contrast-to-noise ratio (CNR) compared with source images. In this study we have developed a method of synthesizing MR images using a flexible model, comprising a set of eigenimages derived from PCA. A matching process has been carried out to find the best fit between the model and a synthetic image calculated from the Bloch equations. The method has been applied to MR images obtained from a group of patients with intracranial lesions. The images derived from the flexible model show increased lesion conspicuity, reduced artefact and comparable CNR to the directly acquired images while maintaining the MR characteristic information for diagnosis.

Adult↗

PubMedDate not supplied

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.

Algorithms↗