2026-09-19T04:28:43Zhttps://dspace.mit.edu/server/oai/requestoai:dspace.mit.edu:1721.1/918412026-06-06T00:49:13Zcom_1721.1_7582com_1721.1_7581col_1721.1_131023
Randall Davis.
Ma, Kăichén
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science.
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
2014-11-24T18:39:04Z
2014-11-24T18:39:04Z
2013
2014
http://hdl.handle.net/1721.1/91841
894244475
Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, June 2014.
Cataloged from PDF version of thesis. "May 2013."
Includes bibliographical references (page 61).
I present an automatic classifier for the digitized clock drawing test, a neurological diagnostic exam used to assess patients' mental acuity by having them draw an analog clock face using a digitizing pen. This classifier assists human examiners in clock drawing interpretation by labeling several basic components of a drawing, including its outline, numerals, hands, and noise, thereby freeing examiners to concentrate on more complex labeling problems. This is a challenging problem despite its specificity, because the average user of the clock drawing test has a high likelihood of cognitive or motor impairment. As a result, mistakes such as crossed-out numerals, messiness, missing components, and noise will be common in drawings, and a well-designed classifier must be capable of handling and correcting for various types of error. I describe in this thesis the construction of a system that is both accurate and robust enough to handle variable input, laying out its components and the principles behind its design. I demonstrate that this system accurately recognizes and classifies the basic components of a drawing, even when applied to a wide range of clinical input, and that it is able to do so because it relies both on statistical analysis and on common-sense observations about the structure of the problem at hand.
by Kaichen Ma.
M.Eng.
75 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.
Robust dynamic symbol recognition : the ClockSketch classifier
Thesis
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<Abstract>I present an automatic classifier for the digitized clock drawing test, a neurological diagnostic exam used to assess patients' mental acuity by having them draw an analog clock face using a digitizing pen. This classifier assists human examiners in clock drawing interpretation by labeling several basic components of a drawing, including its outline, numerals, hands, and noise, thereby freeing examiners to concentrate on more complex labeling problems. This is a challenging problem despite its specificity, because the average user of the clock drawing test has a high likelihood of cognitive or motor impairment. As a result, mistakes such as crossed-out numerals, messiness, missing components, and noise will be common in drawings, and a well-designed classifier must be capable of handling and correcting for various types of error. I describe in this thesis the construction of a system that is both accurate and robust enough to handle variable input, laying out its components and the principles behind its design. I demonstrate that this system accurately recognizes and classifies the basic components of a drawing, even when applied to a wide range of clinical input, and that it is able to do so because it relies both on statistical analysis and on common-sense observations about the structure of the problem at hand.</Abstract>
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