Biomedical subjects

A C Kotcheff

Publications and source records attributed to A C Kotcheff.

3 recordsLinked to original sources

PubMed1998

Automatic construction of eigenshape models by direct optimization.

A new approach to the problem of automatic construction of eigenshape models is presented. Eigenshape models have proved to be successful in a variety of medical image analysis problems. However, automatic construction of eigenshape models has proved to be a difficult problem, and in many applications the models are built by hand-a painstaking process. We show that the fundamental problem is a choice of the correct pose and parametrization of each shape in the training set. Eigenshape models are not invariant under reparametrizations and pose transformations of the training shapes. Since there is no a priori correct choice for the pose and parametrization of each shape, their value should be chosen so as to produce a model that is compact and specific. This problem can be solved by finding an objective function that measures these properties and varying the pose and parametrization of each shape to optimize this function. We show that the appropriate objective function is the determinant of the covariance matrix. We go on to show how this objective function can be optimized by a genetic algorithm (GA) and thus give a practical method for building eigenshape models. The models produced are often better than hand-built ones. The advantages of a GA over other choices of optimization method are that no assumptions about the nature of the shapes being modelled is required and that the global minimum of the objective function can, in principle, be found.

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↗

PubMed1997

An automated method for assessing routine radiographs of patients with total hip replacements.

This paper describes a new, fully automated method of locating objects on radiographs of patients with total joint replacements (TJRs). A statistical computer model, known as an active shape model, was trained to identify the position of the femur, pelvis, stem and cup marker wire on radiographs of patients with Charnley total hip prostheses. Once trained, the model was able to locate these objects through a process of automatic image searching, despite their appearance depending on the orientation and anatomy of the patient. Experiments were carried out to test the accuracy with which the model was able to fit to previously unseen data and with which reference points could be calculated from the model points. The model was able to locate the femur and stem with a mean error of approximately 0.8 mm and a 95 per cent confidence limit of 1.7 mm. Once the model had successfully located these objects, the midpoint of the stem head could be calculated with a mean error of approximately 0.2 mm. Although the model has been trained on Charnley total hip replacements, the method is generic and so can be applied to radiographs of patients with any TJR. This paper shows that computer models can form the basis of a quick, automatic method of taking measurements from standard clinical radiographs.

Computer Simulation↗