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Automatic Lumbar Vertebrae Segmentation in Fluoroscopic Images via Optimised Concurrent Hough Transform

Automatic Lumbar Vertebrae Segmentation in Fluoroscopic Images via Optimised Concurrent Hough Transform
Automatic Lumbar Vertebrae Segmentation in Fluoroscopic Images via Optimised Concurrent Hough Transform
Low back pain is a very common problem in the industrialised countries and its associated cost is enormous. Diagnosis of the underlying causes can be extremely difficult. Many studies have focused on mechanical disorders of the spine. Digital videofluoroscopy (DVF) was widely used to obtain images for motion studies. This can provide motion sequences of the lumbar spine, but the images obtained often suffer due to noise, exacerbated by the very low radiation dosage. Thus determining vertebrae position within the image sequence presents a considerable challenge. In this paper, we show how our new approach can automatically detect the positions and borders of vertebrae concurrently, relieving many of the problems experienced in other approaches. First, we use phase congruency to relieve difficulty associated with threshold selection in edge detection of the illumination variant DVF images. Then, our new Hough transform approach is applied to determine the moving vertebrae, concurrently. We include optimisation via a genetic algorithm as without it the extraction of moving multiple vertebrae is computationally daunting. Our results show that this new approach can indeed provide extractions of position and rotation which appear to be of sufficient quality to aid therapy and diagnosis of spinal disorders.
Zheng, Yalin
cf121a1d-9a54-4d7c-aeea-6354fe19d880
Nixon, Mark S
2b5b9804-5a81-462a-82e6-92ee5fa74e12
Allen, Robert
61929c7d-45ee-4a1d-ba45-164420621020
Zheng, Yalin
cf121a1d-9a54-4d7c-aeea-6354fe19d880
Nixon, Mark S
2b5b9804-5a81-462a-82e6-92ee5fa74e12
Allen, Robert
61929c7d-45ee-4a1d-ba45-164420621020

Zheng, Yalin, Nixon, Mark S and Allen, Robert (2001) Automatic Lumbar Vertebrae Segmentation in Fluoroscopic Images via Optimised Concurrent Hough Transform. 23rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

Record type: Conference or Workshop Item (Other)

Abstract

Low back pain is a very common problem in the industrialised countries and its associated cost is enormous. Diagnosis of the underlying causes can be extremely difficult. Many studies have focused on mechanical disorders of the spine. Digital videofluoroscopy (DVF) was widely used to obtain images for motion studies. This can provide motion sequences of the lumbar spine, but the images obtained often suffer due to noise, exacerbated by the very low radiation dosage. Thus determining vertebrae position within the image sequence presents a considerable challenge. In this paper, we show how our new approach can automatically detect the positions and borders of vertebrae concurrently, relieving many of the problems experienced in other approaches. First, we use phase congruency to relieve difficulty associated with threshold selection in edge detection of the illumination variant DVF images. Then, our new Hough transform approach is applied to determine the moving vertebrae, concurrently. We include optimisation via a genetic algorithm as without it the extraction of moving multiple vertebrae is computationally daunting. Our results show that this new approach can indeed provide extractions of position and rotation which appear to be of sufficient quality to aid therapy and diagnosis of spinal disorders.

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More information

Published date: October 2001
Additional Information: Organisation: IEEE Address: Istanbul, Turkey
Venue - Dates: 23rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2001-09-30
Organisations: Southampton Wireless Group

Identifiers

Local EPrints ID: 256043
URI: http://eprints.soton.ac.uk/id/eprint/256043
PURE UUID: e79fd1d2-895a-48fa-a796-feb87357657b
ORCID for Mark S Nixon: ORCID iD orcid.org/0000-0002-9174-5934

Catalogue record

Date deposited: 20 Nov 2003
Last modified: 07 Oct 2020 02:37

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Contributors

Author: Yalin Zheng
Author: Mark S Nixon ORCID iD
Author: Robert Allen

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