Marking Time : increased scope and accuracy for sketch classification of the clock drawing test

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.