Biomedical subjects

D N Davis

Publications and source records attributed to D N Davis.

6 recordsLinked to original sources

PubMed1996

Assessment of an automated cephalometric analysis system.

A system is described which automatically identifies cephalometric landmarks on digital cephalometric radiographs. The accuracy of the automated system in identifying nineteen cephalometric landmarks is assessed. The accuracy obtained with the automated system is less than that of manual tracing. The automated system has particular difficulty in identifying landmarks which lie on poorly defined structures where there is a poor signal to noise ratio.

Artifacts↗

PubMed1994

Knowledge-based cephalometric analysis: a comparison with clinicians using interactive computer methods.

In modern orthodontic practice great reliance is placed on systematic and objective methods of characterizing craniofacial forms, using measurements based on both hard and soft tissue landmarks. Lateral skull X-ray images are routinely used in cephalometric analysis to provide quantitative measurements useful to clinical orthodontists. It is argued that a model- and knowledge-based methodology provides the best approach in successfully interpreting digitized lateral skull radiographs. A rule-based segmentation system, making use of an image appearance model, is used to extract image features from gray-level images. Complex image features and cephalometric landmarks are constructed from these segmented component features. A predictive model, defining picture structure, allows location hypotheses to be made for image features. The underlaying structure of the location model provides the basis for a geometric constraint model of use in discriminating between image feature candidates. A blackboard system is used to organize these tasks hierarchically, with individual knowledge sources grouped according to function and the individual stages of the adopted image interpretation cycle. Quantitative results demonstrate the superiority of this complex system over its component segmentation system run on its own. Comparisons with clinicians demonstrate both the strengths and the weaknesses of the present system. Comparisons with previous systems are favorable.

Cephalometry↗

PubMed1991

Models for the movement of mono-oriented chromosomes.

The two contending theories of chromosome movement differ, principally, in the location of the motor that powers this motion: Is the motor at the kinetochore or is it instead distributed, in the form of "traction fibers", along the kinetochore fiber that connects the kinetochore to the pole? Dynamic mathematical models are developed for these two opposing theories in the relatively simple case of mono-oriented chromosomes. These models are then compared relative to their quality of fit to some available data concerning the X-chromosome of the grasshopper Melanoplus differentialis. Also, the traction fiber theory is used to predict the positions obtained by bi-oriented bivalents and trivalents at metaphase equilibrium.

Animals↗

PubMed1991

Reliability of cephalometric analysis using manual and interactive computer methods.

This study compares the results of cephalometric analyses using manual and interactive computer graphics methods. Results are statistically in favour of the interactive computer system. This study provides a basis for ongoing research into alternative methods of cephalometric analyses, such as digitization and automatic landmark identification using sophisticated computer vision systems.

Cephalometry↗

PubMedDate not supplied

A blackboard architecture for automating cephalometric analysis.

This paper describes a principled attempt to use artificial intelligence methodologies for interpretation of lateral skull X-ray images. Lateral skull X-ray images are routinely used in cephalometric analysis to provide quantitative measurements useful to clinical orthodontists. Manual and interactive methods of analysis are known to be error-prone, and time-consuming. Previous attempts have been made to automate this analysis, using conventional algorithmic approaches. Unfortunately such systems typically fail to capture the expertise and adaptability required to cope with the variability in biological structure and X-ray image quality found in cephalograms. The present system makes use of a blackboard architecture and multiple knowledge sources within an integrated model-based system. A data-gathering system allows models of feature appearance and location to be built from examples. Blackboard and task control modules allow specific knowledge-based modules to act on information available to the blackboard. Knowledge-based modules include location hypothesis, intelligent segmentation, and constraint propagation systems. Results from a working experimental system are given, and compare favourably with previous algorithmic solutions.

Artificial Intelligence↗

PubMedDate not supplied

The symbolic atlas of the brain: handling non-visual information associated with neuroanatomy.

The Symbolic Atlas of the brain is a novel software tool which enables the user to store and access non-visual information associated with the brain anatomy. The atlas is potentially capable of storing any information about neuroanatomical objects. The user can construct a number of atlases, each containing a different kind of information related to functionality, pathology, symptoms and other facts. The Prolog database that underpins the storage of knowledge frames provides scope for nearly unconstrained usage and manipulation of the stored data, including reasoning with and about data and complex querying. The access to the stored information is provided in an intuitive way, through a simple 'click' on an anatomical structure either in a two- or three-dimensional atlas, or on an outline of a structure superimposed on a real image slice. A pilot educational and clinical use of the atlas indicates its great potential, especially in the are of education.

Anatomy, Artistic↗