X-ray image segmentation for vertebral mobility analysis

Abstract

Objective The goal of this work is to extract the parameters determining vertebral motion and its variation during flexion-extension movements using a computer vision tool for estimating and analyzing vertebral mobility. Materials and MethodsTo compute vertebral body motion parameters we propose a comparative study between two segmentation methods proposed and applied to lateral X-ray images of the cervical spine. The two vertebra contour detection methods include (1) a discrete dynamic contour model (DDCM) and (2) a template matching process associated with a polar signature system. These two methods not only enable vertebra segmentation but also extract parameters that can be used to evaluate vertebral mobility. Lateral cervical spine views including 100 views in flexion, extension and neutral orientations were available for evaluation. Vertebral body motion was evaluated by human observers and using automatic methods. Results The results provided by the automated approaches were consistent with manual measures obtained by 15 human observers. Conclusion The automated techniques provide acceptable results for the assessment of vertebral body mobility in flexion and extension on lateral views of the cervical spine. Electronic supplementary materialThe online version of this article (

Special Session on Abdominal, Brain, Lumbar Spine CAD

“…The primary evaluation of vertebral deformity in individuals with osteoporosis is typically performed via radiographic studies, including morphometric assessment of vertebral bodies [2][3]. A few recent studies have proposed computed-aided methods for the detection and analysis of vertebral bodies in radiographic images [4][5][6]. The objective of this work is to develop digital image processing and pattern recognition techniques for the segmentation of vertebrae in lateral X-ray images of the lumbar spine using Gabor filters [7] and decision tree [8].…”

“…1f. The distances between the points were calculated automatically, and identified as D(1,2), D(2,3), D(3,4), and (4,5), as shown in Fig. 1f.…”

A Comparison of Mean Models and Clustering Techniques for Vertebra Detection and Region Separation from C-Spine X-Rays

“…They used a novel combination of GHT and Kmeans clustering for the localization of vertebra using template matching theory. Benjelloun et al [15] presented an analysis of vertebra based on segmentation from x-ray images. They described a relative study of two algorithms to segment out vertebra.…”

Semi-automatic detection of cervical vertebrae in X-ray images using generalized hough transform

“…Benjelloun et al [6] proposed a framework of two segmentation approaches applied to X-ray images. They developed a semiautomatic tool to estimate and analyze vertebral mobility.…”