Biomedical subjects

T J Hutton

Publications and source records attributed to T J Hutton.

3 recordsLinked to original sources

PubMed2002

Combining biometric and symbolic models for customized, automated prosthesis design.

In a previous paper [Artif. Intell. Med. 5 (1993) 431] we described RaPiD, a knowledge-based system for designing dental prostheses. The present paper discusses how RaPiD has been extended using techniques from computer vision and logic grammars. The first employs point distribution and active shape models (ASMs) to determine dentition from images of casts of patient's jaws. This enables a design to be customized to, and visualised against, an image of a patient's dentition. The second is based on the notion of a path grammar, a form of logic grammar, to generate a path linking an ordered sequence of subcomponents. The shape of an important and complex prosthesis component can be automatically seeded in this fashion. Combining these models now substantially automates the design process, beginning with a photograph of a dental cast and ending with an annotated and validated design diagram ready to guide manufacture.

Anthropometry↗

PubMed2001

Classifying vertical facial deformity using supervised and unsupervised learning.

OBJECTIVES: To evaluate the potential for machine learning techniques to identify objective criteria for classifying vertical facial deformity. METHODS: 19 parameters were determined from 131 lateral skull radiographs. Classifications were induced from raw data with simple visualisation, C5.0 and Kohonen feature maps; and using a Point Distribution Model (PDM) of shape templates comprising points taken from digitised radiographs. RESULTS: The induced decision trees enable a direct comparison of clinicians' idiosyncrasies in classification. Unsupervised algorithms induce models that are potentially more objective, but their blackbox nature makes them unsuitable for clinical application. The PDM methodology gives dramatic visualisations of two modes separating horizontal and vertical facial growth. Kohonen feature maps favour one clinician and PDM the other. Clinical response suggests that while Clinician 1 places greater weight on 5 of 6 parameters, Clinician 2 relies on more parameters that capture facial shape. CONCLUSIONS: While machine learning and statistical analyses classify subjects for vertical facial height, they have limited application in their present form. The supervised learning algorithm C5.0 is effective for generating rules for individual clinicians but its inherent bias invalidates its use for objective classification of facial form for research purposes. On the other hand, promising results from unsupervised strategies (especially the PDM) suggest a potential use for objective classification and further identification and analysis of ambiguous cases. At present, such methodologies may be unsuitable for clinical application because of the invisibility of their underlying processes. Further study is required with additional patient data and a wider group of clinicians.

Algorithms↗

PubMed2000

An evaluation of active shape models for the automatic identification of cephalometric landmarks.

This paper describes an evaluation of the application of active shape models to cephalometric landmarking. Permissible deformations of a template were established from a training set of hand-annotated images and the resulting model was used to fit to unseen images. An evaluation of this technique in comparison to the accuracy achieved by previous methods is presented. Sixty-three randomly selected cephalograms were tested using a drop-one-out method. On average, 13 per cent of 16 landmarks were within 1 mm, 35 per cent within 2 mm, and 74 per cent within 5 mm. It was concluded that the current implementation does not give sufficient accuracy for completely automated landmarking, but could be used as a time-saving tool to provide a first-estimate location of the landmarks. The method is also of interest because it provides a framework for a range of future improvements.

Adolescent↗