Extraction of diagnostic information from hand drawn images for the assessment of visuo-spatial neglect
Extraction of diagnostic information from hand drawn images for the assessment of visuo-spatial neglect
This paper describes the computer-based analysis of a series of hand-drawn images used to detect the severity of visuo-spatial neglect in stroke patients. Neglect is the inability of a brain damaged patient to respond to stimuli on one side of the visual field. Accurate assessment of the presence and extent of the condition is therefore important at the initial stages of treatment. Existing methods of assessment include simple `pencil and paper' tests where a patient is required to perform such tasks as the location and cancellation of targets and the drawing of simple geometric shapes. Scoring of the latter task is subjective as it requires the assessment of drawings without detailed reference models, resulting in the possibility of inter-rater disagreement and hence variation in clinical assessment. A computer based data capture and analysis system has been developed for the automated and computer-assisted assessment of these tasks. By capturing the data as a stream of co-ordinates using a standard graphics tablet, the `outcome' of the drawing can be measured with accuracy and consistency as well as facilitating the extraction of a range of novel data pertaining to the constructional aspects of the drawing. While the majority of the severe neglect cases are easy to detect by observing omission of components (elements of geometric shapes etc.) many of the diagnostic groupings are not obvious by assessing outcome alone. It is shown that the constructional features provide extra test sensitivity in predicting the outcome. A simple figure completion task is considered here to demonstrate its diagnostic features.
387-391
IET Conference Publications
Fairhurst, Michael
6a82d154-93fe-4657-bcee-934d5c888192
Guest, Richard
93533dbd-b101-491b-83cc-39ccfdc18165
Potter, Jonathan
9f0adcdb-fe43-4c3b-b087-cd0d7ca687fc
Donnelly, Nick
3f974c49-b11c-411c-bb42-e6e12448a74c
6 August 2002
Fairhurst, Michael
6a82d154-93fe-4657-bcee-934d5c888192
Guest, Richard
93533dbd-b101-491b-83cc-39ccfdc18165
Potter, Jonathan
9f0adcdb-fe43-4c3b-b087-cd0d7ca687fc
Donnelly, Nick
3f974c49-b11c-411c-bb42-e6e12448a74c
Fairhurst, Michael, Guest, Richard, Potter, Jonathan and Donnelly, Nick
(2002)
Extraction of diagnostic information from hand drawn images for the assessment of visuo-spatial neglect.
In,
Seventh International Conference on Image Processing And Its Applications, 1999.
(Conference Publications, 465)
IET Conference Publications, .
(doi:10.1049/cp:19990349).
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Abstract
This paper describes the computer-based analysis of a series of hand-drawn images used to detect the severity of visuo-spatial neglect in stroke patients. Neglect is the inability of a brain damaged patient to respond to stimuli on one side of the visual field. Accurate assessment of the presence and extent of the condition is therefore important at the initial stages of treatment. Existing methods of assessment include simple `pencil and paper' tests where a patient is required to perform such tasks as the location and cancellation of targets and the drawing of simple geometric shapes. Scoring of the latter task is subjective as it requires the assessment of drawings without detailed reference models, resulting in the possibility of inter-rater disagreement and hence variation in clinical assessment. A computer based data capture and analysis system has been developed for the automated and computer-assisted assessment of these tasks. By capturing the data as a stream of co-ordinates using a standard graphics tablet, the `outcome' of the drawing can be measured with accuracy and consistency as well as facilitating the extraction of a range of novel data pertaining to the constructional aspects of the drawing. While the majority of the severe neglect cases are easy to detect by observing omission of components (elements of geometric shapes etc.) many of the diagnostic groupings are not obvious by assessing outcome alone. It is shown that the constructional features provide extra test sensitivity in predicting the outcome. A simple figure completion task is considered here to demonstrate its diagnostic features.
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Published date: 6 August 2002
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Local EPrints ID: 489584
URI: http://eprints.soton.ac.uk/id/eprint/489584
ISSN: 0537-9989
PURE UUID: 66e0e477-cff2-4885-8f78-223873db663c
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Date deposited: 29 Apr 2024 16:35
Last modified: 30 Apr 2024 02:05
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Author:
Michael Fairhurst
Author:
Richard Guest
Author:
Jonathan Potter
Author:
Nick Donnelly
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