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Segmentation of clock drawings based on spatial and temporal features

Harbi, Zainab, Hicks, Yulia Alexandrovna, Setchi, Rossitza and Bayer, Antony James 2015. Segmentation of clock drawings based on spatial and temporal features. Procedia Computer Science 60 , pp. 1640-1648. 10.1016/j.procs.2015.08.274

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Abstract

The Clock Drawing Test (CDT) is an inexpensive and effective measure for early detection of cognitive impairment in the elderly, which is important for timely diagnosis and initiation of appropriate treatment. Currently, medical experts assess the drawings based on their judgement and a number of available scoring systems. An automatic system for assessment of CDT drawings would simultaneously decrease the waiting time for a specialist appointment and improve accessibility of the test to the patients. Published research has only started to address the problem of automatic assessment of CDT drawings and existing systems require user intervention during the segmentation of the CDT drawing into its composing parts, such as numbers and clock hands. In this paper, a new set of temporal and spatial features automatically extracted from the CDT data acquired using a graphics tablet is proposed. Consequently, a Support Vector Machine (SVM) classifier is employed to segment the CDT drawings into their elements, such as numbers and clock hands, on the basis of the extracted features. The proposed algorithm is tested on two data sets, the first set consisting of 65 drawings made by healthy people, and the second consisting of 100 drawings reproduced from actual drawings of dementia patients. The test on both data sets shows that the proposed method outperforms the current state-of-the-art method for CDT drawing segmentation.

Item Type: Article
Date Type: Published Online
Status: Published
Schools: Engineering
Medicine
Subjects: R Medicine > RC Internal medicine > RC0321 Neuroscience. Biological psychiatry. Neuropsychiatry
T Technology > T Technology (General)
Uncontrolled Keywords: Clock Drawing Test (CDT); cognitive impairment diagnosis; dementia; segmentation; dynamic features.
Publisher: Elsevier
ISSN: 1877-0509
Date of First Compliant Deposit: 21 December 2016
Last Modified: 28 Nov 2017 21:31
URI: http://orca.cf.ac.uk/id/eprint/96887

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