Automated segmentation and tracking of lumbar spine motion in low-dosage digital videofluoroscopic images

Abstract

Low back pain is one of the most frequent medical problems in the western world and its consequent cost is enormous. However, despite the high occurrence of low back pain, diagnosis of the causes is still a major problem. Research has indicated that low back pain is often related to mechanical disorders of the spinal or holding elements. Therefore, it could be very helpful for clinical diagnosis to study the motion of lumbar spine in order to determine where abnormal motion exists and hence any sources of mechanical instability. Digital videofluoroscopy (DVF) is currently the only practical medical imaging technique to obtain real-time, continuous motion sequences of the lumbar spine. However, DVF images suffer from the presence of noise, poor contrast and adjacent structures near the vertebrae due to the low radiation dosage. Recently, wavelet-based approaches have been applied in edge detection to acquire multi- scale gradient images. In multi-scale detection, the edges are more accurately located with low scales but some false edges are produced; with large scales, fewer false edges are identified but traded against a reduced accuracy in the edge location. This project presents a scale multiplication in the identification of spinal vertebrae as a basis for

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