Automated extraction of image segments from clinically diagnostic hand-drawn geometric shapes

Guest, Richard; Fairhurst, Michael; Potter, Jonathan

doi:10.1109/eurmic.2000.874527

Cited by 3 publications

(2 citation statements)

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“…Application of the assessment criteria by trained assessors can vary and, hence, is arguably unreliable, so it is desirable to produce an assessment mechanism which will be able to classify cube drawings in a completely objective way. Guest and Fairhurst [8] implement an algorithm to extract components from static hand-drawn responses for two figure copying tests and one figure completion test. First the image is 'skeletonised' then split it into its horizontal, vertical and diagonal components by using directional neighbourhood identification.…”

Section: Evaluation Of Visuo-spatial Abilitymentioning

confidence: 99%

“…By looking at these dynamic features they conclude that they can gain an additional understanding of the condition. However, the algorithms described in [7] and [8] use rigid sets of rules designed by the authors based on observed differences. This paper proposes a method in which this level of subjectivity is removed by using an evolutionary algorithm, Implicit Context Representation Cartesian Genetic Programming, to identify the features used for classifying subject responses.…”

Section: Evaluation Of Visuo-spatial Abilitymentioning

confidence: 99%

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Implicit Context Representation Cartesian Genetic Programming for the assessment of visuo-spatial ability

Smith

Lones

2009

2009 IEEE Congress on Evolutionary Computation

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Abstract-In this paper, a revised form of Implicit Context Representation Cartesian Genetic Programming is used in the development of a diagnostic tool for the assessment of patients with neurological dysfunction such as Alzheimer's disease. Specifically, visuo-spatial ability is assessed by analysing subjects' digitised responses to a simple figure copying task using a conventional test environment. The algorithm was trained to distinguish between classes of visuo-spatial ability based on responses to the figure copying test by 7-11 year old children in which visuo-spatial ability is at varying stages of maturity. Results from receiver operating characteristic (ROC) analysis are presented for the training and subsequent testing of the algorithm and demonstrate this technique has the potential to form the basis of an objective assessment of visuo-spatial ability.

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Section: Evaluation Of Visuo-spatial Abilitymentioning

confidence: 99%

Section: Evaluation Of Visuo-spatial Abilitymentioning

confidence: 99%