A three-dimensional imaging system for archiving dental study casts: a preliminary report.

Dental study casts form an essential part of patient records for both diagnostic and medicolegal purposes. Storage of study casts poses major problems because of inconvenience and cost of mass storage. Various methods for recording and storage of study casts are discussed. A new biostereometric technique for digitally recording and storing dental casts, and using the recent advances in stereophotogrammetry, is presented. Archiving dental casts in a digital format would reduce problems of mass storage and provide new benefits such as duplication of casts and use in clinical and research studies.

Mapping between MR brain images and a voxel model.

This paper describes an approach to establish the correspondence between a magnetic resonance (MR) image of the brain and a slice through a 3D anatomical model. The model is of voxel structure that symbolically labels primary tissue types such as grey matter, white matter, CSF, etc. In this approach a slice is first searched for in the model to achieve the best general match with the brain MR image in question. The operation involves a minimization of parameters such as position, rotation, slant, tilt and enlargement. Having thus found a globally good registration between the image and the model, local matches that link every pixel in the image through to the model slice are then searched for. This pixel-by-pixel match is expressed within a pair of maps, one for the vertical deformation and the other for the horizontal one. The matching algorithm consists of a series of octave separated blurring convolutions combined with exhaustive grey-valued correlation. Because every pixel in the model slice is labelled in terms of its tissue type, and because every pixel in the image has been matched directly to the model, every pixel in the image is now classified. This classification is used directly to perform segmentation which serves as a basis for the computation of medically relevant indices.

Automatic detection of gait events: a case study using inductive learning techniques.

One of the problems which occurs in the development of a control system for functional electrical stimulation of the lower limbs is to detect accurately specific events within the gait cycle. We present a method for the classification of phases of the gait cycle using the artificial intelligence technique of inductive learning. Both the terminology of inductive learning and the algorithm used for the analyses are fully explained. Given a set of examples of sensor data from the gait events that are to be detected, the inductive learning algorithm is able to produce a decision tree (or set of rules) which classify the data using a minimum number of sensors. The nature of the redundancy of the sensor set is examined by progressively removing combinations of sensors and noting the effect on both the size of the decision trees produced and their classification accuracy on 'unseen' testing data. Since the algorithm is able to calculate which sensors are more important (informative), comparisons with the intuitive appreciation of sensor importance of five researchers in the fields were made, revealing that those sensors which appear intuitively most informative may, in fact, provide the least information. Comparison results with the standard statistical classification technique of linear discriminant analysis are also presented, showing the relative simplicity of the inductively derived rules together with their good classification accuracy. In addition to the control of FES, such techniques are also applicable to automatic gait analysis and the construction of expert systems for diagnosis of gait pathologies.

Vergence eye movements made in response to spatial-frequency-filtered random-dot stereograms.

Vergence responses were recorded from practised observers viewing narrow-band spatial-frequency-filtered planar random-dot stereograms. It was found that low spatial frequencies of 1.75-3.5 cycles deg-1 could trigger appropriate vergence responses to larger disparities than could the relatively high spatial frequency of 7.0 cycles deg-1. Nevertheless, appropriate vergence shifts were observed reliably for spatial-frequency/disparity combinations well outside the range predicted by Marr and Poggio's (1979) model of stereo vision. It was also found that for large-disparity/high-spatial-frequency combinations which the subjects could not fuse, the vergence system went into oscillation with the eyes diverging and converging at a frequency of about 1.5 Hz and with an amplitude of about 10-20 min arc. Finally, it was demonstrated that when a prominent monocular cue was superimposed upon a large-disparity/high-spatial-frequency stereogram then a speedy vergence response occurred which resulted in successful fusion. This latter finding supports the hypothesis advanced earlier that monocular cues can facilitate stereopsis by triggering appropriate vergence shifts.