DocumentCode
2498766
Title
Automated scoring of a neuropsychological test: the Rey Osterrieth complex figure
Author
Canham, R.O. ; Smith, S.L. ; Tyrrell, A.M.
Author_Institution
Dept. of Electron., York Univ., UK
Volume
2
fYear
2000
fDate
2000
Firstpage
406
Abstract
The Rey Osterrieth Complex Figure (ROCF) is a widely used neuropsychological test for visual perception and long term visual memory. Many scoring systems are used to quantify the accuracy of the drawings; these are currently implemented by hand in a subjective manner. The paper gives details of the current progress of a novel technique to locate the scoring sections of the most common of these systems (the Osterrieth Scoring System), with the ultimate goal of automating the scoring system. High levels of distortion are possible, making this an extremely difficult task; however location and perceptual grading of the basic geometric features (triangles, rectangles and diamonds) have been most successful. All but one section in the test data was located (99.3% success) and 78% of the perceptual grades calculated were within 5% of grades generated by independent raters. Unary spatial metrics have been implemented to reduce the possible section candidates by an average of 75% without the loss of a single section
Keywords
medical information systems; patient care; psychology; testing; visual perception; ROCF; Rey Osterrieth complex figure; automated scoring; distortion levels; geometric features; independent raters; long term visual memory; neuropsychological test; perceptual grades; perceptual grading; scoring sections; scoring systems; unary spatial metrics; visual perception; Automatic testing; Data engineering; Dementia; Electronic equipment testing; Laser sintering; Lesions; Magnetic resonance imaging; Medical treatment; Visual perception;
fLanguage
English
Publisher
ieee
Conference_Titel
Euromicro Conference, 2000. Proceedings of the 26th
Conference_Location
Maastricht
ISSN
1089-6503
Print_ISBN
0-7695-0780-8
Type
conf
DOI
10.1109/EURMIC.2000.874519
Filename
874519
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