| Summary: | 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.
|
|---|
