Nick Efford

Preliminary experience with medical applications of rapid prototyping by selective laser sintering

Rapid prototyping techniques, originally developed for building components from computer aided designs in the motor industry, are now being applied in medicine to build models of human anatomy from high resolution multiplanar imaging data such a computed tomography (CT). The established technique of stereolithography and the more recent selective laser sintering (SLS), both build up an object layer by layer. Models have applications in surgical planning, for the design of customised implants and for training. Preliminary experience of using the SLS technique for medical applications is described, addressing questions regarding image processing, data transfer and manufacture. Pilot models, built from nylon, included two skills (a child with craniosynoslosis and an adult with hypertetorism) and a normal femur which was modelled for use in a bioengineering test of an artificial hip. The dimensions of the models were found to be in good agreement with the CT data from which they were built-for the childs skull the difference between the model and the CT data was less than 1.0 +/- 0.5 mm in each direction. Our experience showed that, with care, a combination of existing software packages may be used for data conversion. Ideally, image data of high spatial resolution should be used. The pilot models generated sufficient clinical interest for the technique to be pursued in the orthopaedic field.

An efficient 3D deformable model with a self-optimising mesh

Deformable models are a powerful and popular tool for image segmentation, but in 3D imaging applications the high computational cost of fitting such models can be a problem. A further drawback is the need to select the initial size and position of a model in such a way that it is close to the desired solution. This task may require particular expertise on the part of the operator, and, furthermore, may be difficult to accomplish in three dimensions without the use of sophisticated visualisation techniques. This article describes a 3D deformable model that uses an adaptive mesh to increase computational efficiency and accuracy. The model employs a distance transform in order to overcome some of the problems caused by inaccurate initialisation. The performance of the model is illustrated by its application to the task of segmentation of 3D MR images of the human head and hand. A quantitative analysis of the performance is also provided using a synthetic test image.

AI-based algorithms for protein surface comparisons

Many current methods for protein analysis depend on the detection of similarity in either the primary sequence, or the overall tertiary structure (the Calpha atoms of the protein backbone). These common sequences or structures may imply similar functional characteristics or active properties. Active sites and ligand binding sites usually occur on or near the surface of the protein; so similarly shaped surface regions could imply similar functions. We investigate various methods for describing the shape properties of protein surfaces and for comparing them. Our current work uses algorithms from computer vision to describe the protein surfaces, and methods from graph theory to compare the surface regions. Early results indicate that we can successfully match a family of related ligand binding sites, and find their similarly shaped surface regions. This method of surface analysis could be extended to help identify unknown surface regions for possible ligand binding or active sites.

Taxing our Best Students

Abstract A significant challenge that faces any teacher of introductory programming is the diversity of the class. At one extreme there will be students who have never programmed before, while at the other there will be students who have many years experience of programming. Handling this diversity is difficult. The temptation for the instructor is often to focus on the novice group and to assume that the others will get by with minimal supervision. This is understandable, but it can be risky. There is a very real risk that the neglected group of experienced programmers become bored and disengage from the course. At the worst, they can lose motivation and fail or drop out altogether. This paper describes and presents the outcomes of a project aimed at challenging the more experienced programmers in four introductory programming classes at four different UK institutions. The project took the form of a competition in which students were asked to devise and solve a series of programming challenges.

Surface reflectance model estimation from daylight illuminated image sequences

Accurate surface reflection models derived from existing natural scenes can be used for a variety of tasks. This paper presents a machine vision approach for determining such models. We investigate the use of simplistic models of reflection and daylight illumination to determine surface reflection properties. We attempt to determine the gloss factor of both an individual surface and a multi-faceted object when illuminated by natural light with varying sun position. Experiments are performed using synthetic image sequences of surfaces illuminated by CIE standard clear and intermediate skies, viewed from a variety of camera positions. Results show that some success can be achieved using such simple illumination models. Enhancements to the proposed method are also discussed with a view to improving system performance.

Evolving patterns of working: do they matter?

We consider how changing attitudes to computer use may alter the habits of students in the use of location and times of the day. We discover for one community the extent to which they absent themselves from the physical university and see some trends. We suggest that this is a deep change of habit, influenced by the ‘digital generation’, and seek qualitative results on what determines the modern students keyboard behaviour. We present early evidence that, while students are indeed absenting themselves from formal computer facilities, some understanding of the benefits of the physical university remains intact.

An efficient 3D deformable model with a self-optimising topology

Deformable models are a powerful and popular tool for image segmentation, but in 3D imaging applications the high computational cost of fitting such models can be a problem. A further drawback is the need to select the initial size and position of a model in such a way that it is close to the desired solution. This task may require particular expertise on the part of the operator, and, furthermore, may be difficult to accomplish in three dimensions without the use of sophisticated visualisation techniques. This article describes a 3D deformable model that uses an adaptive mesh to increase computational efficiency and accuracy. The model employs a distance transform in order to overcome some of the problems caused by inaccurate initialisation. The performance of the model is illustrated by its application to the task of segmentation of 3D MR images of the human head.

Object-based approach to integrate remotely sensed data with geodata within a GIS context for land use classification at urban-rural fringe area

An object-based approach for producing land use maps will be described in this paper. This approach has been used for integrating Landsat TM data within a GIS context for producing land use maps of urban-rural fringe areas. A contextual image classification method based on the SMAP estimate was used to produce land cover maps which provide knowledge for inferring land use types. Objectized land cover information, thematic knowledge and spatial composition rulers were used to infer the land use type of each object are. The prototype of this approach has been built using the GRASS 4.1 GIS software package and tested using a dataset compiled for this purpose. Results indicate a significant improvement compared with land use maps produced using a contextual image classification approach alone.

Image processing: the art of science

We Live in a world where an increasing amount of information is stored digitally. Pictures are an important data source, and thus the technology for acquiring, manipulating and analysing digital images is becoming ever more important. Image processing (IP) already plays a critical role in many areas of modern life, with applications ranging from medical imaging through automated industrial inspection to remote sensing.

PETMiner—A Visual Analysis Tool for Petrophysical Properties of Core Sample Data

The aim of the PETMiner software is to reduce the time and monetary cost of analysing petrophysical data that is obtained from reservoir sample cores. Analysis of these data requires tacit knowledge to fill ‘gaps’ so that predictions can be made for incomplete data. Through discussions with 30 industry and academic specialists, we identified three analysis use cases that exemplified the limitations of current petrophysics analysis tools. We used those use cases to develop nine core requirements for PETMiner, which is innovative because of its ability to display detailed images of the samples as data points, directly plot multiple sample properties and derived measures for comparison, and substantially reduce interaction cost. An 11-month evaluation demonstrated benefits across all three use cases by allowing a consultant to: (1) generate more accurate reservoir flow models, (2) discover a previously unknown relationship between one easy-to-measure property and another that is costly, and (3) make a 100-fold reduction in the time required to produce plots for a report.