X-ray Image Segmentation and An Internet-based Tool for Medical Validation

Abstract

Segmentation of vertebrae in X-ray images is a difficult task that requires an effective segmentation procedure. Noise, poor image contrast, occlusions and shape variability are some of the challenges in many of the spine X-ray images archived at the U.S. National Library of Medicine (NLM). In this thesis, we propose a curvature-based corner matching approach, which exploits the posterior corners of the vertebra to estimate the location and orientation of the vertebrae. The key advantage of the proposed approach is execution time, roughly about one-fifth of the previous approach that uses the generalized Hough transform when tested on a sizeable set of cervical spine images.

This thesis also presents the first ever effort to develop a prototype internet-based medical image segmentation and pathology validation tool, which enables radiologists to validate computer generated image segmentations, modify existing or create new segmentation in addition to identifying pertinent pathology data.