A Real-time Image Analysis System for Computer-Assisted Diagnosis of Neurological Disorders

Abstract

This paper describes the application of digital image processing and pattern recognition techniques to assist in diagnosing neurological disorders. In medical practices the posture and movement of a human subject through his/her gait cycle contains information that is used by an experienced clinician to determine the mental health of a patient. An image processing based system can be used to automate this process and produce quantified results. This is achieved by processing, extracting and classifying joint angle information from still images of a human subject's gait. Several new algorithms are devised to process and extract the required information from the images: a new edge extraction technique is used to assist in image segmentation; a technique based on correlating images from different parts of a gait is used to obtain complete information about a subject's posture; and an algorithm based on the Hough Transform is used to obtain the limb joint angles and swing distances of a subject. Joint angles and swing distances obtained from normal and patient subjects are then used in training and verifying classifications using a feed-forward neural network and a fuzzy clustering algorithm.