Towards segmentation of pedicles on posteroanterior x-ray views of scoliotic patients

Authors:
Vincent Doré;Luc Duong;Farida Cheriet;Mohamed Cheriet
Affiliations:
Department of GPA, École de Technologie Supérieure, Montreal, Canada;Research Center, Sainte-Justine Hospital and Department of Computer Engineering, École Polytechnique de Montréal, Montreal, Canada;Research Center, Sainte-Justine Hospital and Department of Computer Engineering, École Polytechnique de Montréal, Montreal, Canada;Department of GPA, École de Technologie Supérieure, Montreal, Canada
Venue:
ICIAR'07 Proceedings of the 4th international conference on Image Analysis and Recognition
Year:
2007

Citing 7
Cited 1

Diffusion Snakes: Introducing Statistical Shape Knowledge into the Mumford-Shah Functional

International Journal of Computer Vision
Shape Priors for Level Set Representations

ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part II
Handbook of Mathematical Models in Computer Vision

Handbook of Mathematical Models in Computer Vision
Pre-Indexing for Fast Partial Shape Matching of Vertebrae Images

CBMS '06 Proceedings of the 19th IEEE Symposium on Computer-Based Medical Systems
Automatic Classification System for Lumbar Spine X-ray Images

CBMS '06 Proceedings of the 19th IEEE Symposium on Computer-Based Medical Systems
Shape Representation based on Integral Kernels: Application to Image Matching and Segmentation

CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 1
Active contours without edges

IEEE Transactions on Image Processing

Pedicle detection in planar radiographs based on image descriptors

ICIAR'12 Proceedings of the 9th international conference on Image Analysis and Recognition - Volume Part II

Quantified Score

Hi-index	0.00

Visualization

Abstract

The objective of this work is to provide a feasible study to develop an automatic segmentation of pedicles of vertebrae on X-ray images of scoliotic patients, with the ultimate goal of the extraction of high level primitives leading to an accurate 3D spine reconstruction based on stereo-radiographic views. Our approach relies on coarse and fine parameter free segmentation. First, active contour is performed on a probability score table built from the input pedicle sub-space yielding to a coarse shape. The prior knowledge induced from the latter shape is introduced within a level set model to refine the segmentation, resulting in a fine shape. For validation purposes, the result obtained by the estimation of the rotation of scoliotic deformations using the resulting fine shape is compared with a gold standard obtained by manual identification by an expert. The results are promising in finding the orientation of scoliotic deformations, and hence can be used for subsequent tools for clinicians.