Automatic segmentation of brain infarction in diffusion-weighted MR images

It is important to detect the site and size of infarction volume in stroke patients. An automatic method for segmenting brain infarction lesion from diffusion weighted magnetic resonance (MR) images of patients has been developed. The method uses an integrated approach which employs image processing techniques based on anisotropic filters and atlas-based registration techniques. It is a multi-stage process, involving first images preprocessing, then global and local registration between the anatomical brain atlas and the patient, and finally segmentation of infarction volume based on region splitting and merging and multi-scale adaptive statistical classification. The proposed multi-scale adaptive statistical classification model takes into account spatial, intensity gradient, and contextual information of the anatomical brain atlas and the patient. Application of the method to diffusion weighted imaging (DWI) scans of twenty patients with clinically determined infarction was carried out. It shows that the method got a satisfied segmentation even in the presence of radio frequency (RF) inhomogeneities. The results were compared with lesion delineations by human experts, showing the identification of infarction lesion with accuracy and reproducibility.

[1]  John F. Canny,et al.  A Computational Approach to Edge Detection , 1986, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[2]  S M Davis,et al.  Prediction of stroke outcome with echoplanar perfusion- and diffusion-weighted MRI , 1998, Neurology.

[3]  Jürgen Weese,et al.  Point-Based Elastic Registration of Medical Image Data Using Approximating Thin-Plate Splines , 1996, VBC.

[4]  Koenraad Van Leemput,et al.  Automated segmentation of multiple sclerosis lesions by model outlier detection , 2001, IEEE Transactions on Medical Imaging.

[5]  L H Schwamm,et al.  Diffusion-weighted MR imaging: diagnostic accuracy in patients imaged within 6 hours of stroke symptom onset. , 1999, Radiology.

[6]  J.L. Marroquin,et al.  An accurate and efficient Bayesian method for automatic segmentation of brain MRI , 2002, IEEE Transactions on Medical Imaging.

[7]  Jean-Philippe Thirion,et al.  Image matching as a diffusion process: an analogy with Maxwell's demons , 1998, Medical Image Anal..

[8]  R. Rabbitt,et al.  3D brain mapping using a deformable neuroanatomy. , 1994, Physics in medicine and biology.

[9]  G. Schlaug,et al.  Enlargement of human cerebral ischemic lesion volumes measured by diffusion‐weighted magnetic resonance imaging , 1997, Annals of neurology.

[10]  Baba C. Vemuri,et al.  An Accurate and Efficient Bayesian Method for Automatic Segmentation of Brain MRI , 2002, ECCV.

[11]  Jagath C. Rajapakse,et al.  Statistical approach to segmentation of single-channel cerebral MR images , 1997, IEEE Transactions on Medical Imaging.

[12]  Donald Geman,et al.  Stochastic relaxation, Gibbs distributions, and the Bayesian restoration of images , 1984 .

[13]  Jitendra Malik,et al.  Scale-Space and Edge Detection Using Anisotropic Diffusion , 1990, IEEE Trans. Pattern Anal. Mach. Intell..

[14]  Anne L. Martel,et al.  Measurement of Infarct Volume in Stroke Patients Using Adaptive Segmentation of Diffusion Weighted MR Images , 1999, MICCAI.

[15]  Julia Anne Schnabel,et al.  Multiscale shape description of MR brain images using active contour models , 1996, Medical Imaging.

[16]  Thrasyvoulos N. Pappas.  An adaptive clustering algorithm for image segmentation , 1992, IEEE Trans. Signal Process..

[17]  Koenraad Van Leemput,et al.  Automated model-based bias field correction of MR images of the brain , 1999, IEEE Transactions on Medical Imaging.

[18]  R R Edelman,et al.  Clinical experience with diffusion-weighted MR in patients with acute stroke. , 1998, AJNR. American journal of neuroradiology.

[19]  R Kikinis,et al.  Automatic identification of gray matter structures from MRI to improve the segmentation of white matter lesions. , 1995, Journal of image guided surgery.

[20]  Benoit M. Dawant,et al.  Morphometric analysis of white matter lesions in MR images: method and validation , 1994, IEEE Trans. Medical Imaging.

[21]  Guido Gerig,et al.  Automatic Brain and Tumor Segmentation , 2002, MICCAI.

[22]  Nuggehally Sampath Jayant,et al.  An adaptive clustering algorithm for image segmentation , 1989, International Conference on Acoustics, Speech, and Signal Processing,.

[23]  Koenraad Van Leemput,et al.  Automated model-based tissue classification of MR images of the brain , 1999, IEEE Transactions on Medical Imaging.

[24]  Donald Geman,et al.  Stochastic Relaxation, Gibbs Distributions, and the Bayesian Restoration of Images , 1984, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[25]  Pierre Vandergheynst,et al.  Segmentation of natural images using Scale-Space representations: A linear and a non-linear approach , 2002, 2002 11th European Signal Processing Conference.

[26]  Ron Kikinis,et al.  Adaptive, template moderated, spatially varying statistical classification , 2000, Medical Image Anal..

[27]  Gottfried Schlaug,et al.  Ischemic lesion volumes in acute stroke by diffusion‐weighted magnetic resonance imaging correlate with clinical outcome , 1997, Annals of neurology.

[28]  R. Kikinis,et al.  Nonlinear Registration and Template-Driven Segmentation , 1999 .