2026-09-20T00:11:35Zhttps://dspace.mit.edu/server/oai/requestoai:dspace.mit.edu:1721.1/1002992026-06-06T00:48:38Zcom_1721.1_7582com_1721.1_7581col_1721.1_131023
Randall Davis.
Chien, Jonathan
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science.
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
2015-12-16T15:54:17Z
2015-12-16T15:54:17Z
2015
2015
http://hdl.handle.net/1721.1/100299
930617504
Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2015.
This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.
Cataloged from student-submitted PDF version of thesis.
Includes bibliographical references (pages 85-86).
In this thesis, I designed and implemented improvements to an automatic classifier for the digitized clock drawing test, a diagnostic tool for assessing cognitive impairment, which asks the patient to draw an analog clock face using a digital pen. The classifier handles both the grouping of strokes into clock components and the subsequent labeling of those groups. Despite the domain-specificity, classification is a challenging problem because the subject often has cognitive or motor impairments. It is thus important for the classifier to be able to handle a wide range of input with distorted, overwritten, or missing components. I improve the robustness of the classifier, particularly for messy clinical data, by incorporating intrinsic stroke properties, developing additional symbol recognizers, and creating a global context evaluator. I describe in this thesis properties for isolated symbol recognition, features for symbol recognition and match scoring, as well as common sense rules based on a symbol's local and global context in a drawing. I combine these elements into a new system that locally maximizes a global label assignment based on match quality and context. I demonstrate that this system accurately recognizes a wide variety of clinical input, improving overall classification performance.
by Jonathan Chien.
M.Eng.
86 pages
eng
Massachusetts Institute of Technology
M.I.T. theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. See provided URL for inquiries about permission.
http://dspace.mit.edu/handle/1721.1/7582
Electrical Engineering and Computer Science.
Marking Time : increased scope and accuracy for sketch classification of the clock drawing test
Increased scope and accuracy for sketch classification of the clock drawing test
Thesis
application/pdf
false
Publication
unknown
unknown
<Publication xmlns="https://www.openaire.eu/cerif-profile/1.1/" id="0875c490-d2cb-4c19-9b30-edfc4fea3e49">
<Type xmlns="https://www.openaire.eu/cerif-profile/vocab/COAR_Publication_Types">http://purl.org/coar/resource_type/c_1843</Type>
<Language>eng</Language>
<Title>Marking Time : increased scope and accuracy for sketch classification of the clock drawing test</Title>
<Subtitle>Increased scope and accuracy for sketch classification of the clock drawing test</Subtitle>
<PublishedIn>
<Publication>
</Publication>
</PublishedIn>
<PublicationDate>2015</PublicationDate>
<Authors>
<Author>
<DisplayName>Chien, Jonathan</DisplayName>
<Affiliation>
<OrgUnit>
</OrgUnit>
</Affiliation>
</Author>
</Authors>
<Editors>
</Editors>
<Publishers>
<Publisher>
<DisplayName>Massachusetts Institute of Technology</DisplayName>
<OrgUnit />
</Publisher>
</Publishers>
<License>http://dspace.mit.edu/handle/1721.1/7582</License>
<Keyword>Electrical Engineering and Computer Science.</Keyword>
<Abstract>In this thesis, I designed and implemented improvements to an automatic classifier for the digitized clock drawing test, a diagnostic tool for assessing cognitive impairment, which asks the patient to draw an analog clock face using a digital pen. The classifier handles both the grouping of strokes into clock components and the subsequent labeling of those groups. Despite the domain-specificity, classification is a challenging problem because the subject often has cognitive or motor impairments. It is thus important for the classifier to be able to handle a wide range of input with distorted, overwritten, or missing components. I improve the robustness of the classifier, particularly for messy clinical data, by incorporating intrinsic stroke properties, developing additional symbol recognizers, and creating a global context evaluator. I describe in this thesis properties for isolated symbol recognition, features for symbol recognition and match scoring, as well as common sense rules based on a symbol's local and global context in a drawing. I combine these elements into a new system that locally maximizes a global label assignment based on match quality and context. I demonstrate that this system accurately recognizes a wide variety of clinical input, improving overall classification performance.</Abstract>
<Access xmlns="http://purl.org/coar/access_right"
>
</Access>
</Publication>