Adaptive pixon represented segmentation (APRS) for 3D MR brain images based on mean shift and Markov random fields

  • Authors:
  • Lei Lin;Daniel Garcia-Lorenzo;Chong Li;Tianzi Jiang;Christian Barillot

  • Affiliations:
  • Department of Mathematics, Zhejiang University, Hangzhou 310027, PR China and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, PR C ...;INRIA, VisAGeS Unit/Project, IRISA, F-35042 Rennes, France and University of Rennes I - CNRS UMR 6074, IRISA, F-35042 Rennes, France and INSERM, VisAGeS U746 Unit/Project, IRISA, F-35042 Rennes, F ...;Department of Mathematics, Zhejiang University, Hangzhou 310027, PR China and Department of Mathematics, College of Sciences, King Saud University, P.O. Box 2455, Riyadh 11451, Saudi Arabia;National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, PR China;INRIA, VisAGeS Unit/Project, IRISA, F-35042 Rennes, France and University of Rennes I - CNRS UMR 6074, IRISA, F-35042 Rennes, France and INSERM, VisAGeS U746 Unit/Project, IRISA, F-35042 Rennes, F ...

  • Venue:
  • Pattern Recognition Letters
  • Year:
  • 2011

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Abstract

In this paper, we proposed an adaptive pixon represented segmentation (APRS) algorithm for 3D magnetic resonance (MR) brain images. Different from traditional method, an adaptive mean shift algorithm was adopted to adaptively smooth the query image and create a pixon-based image representation. Then K-means algorithm was employed to provide an initial segmentation by classifying the pixons in image into a predefined number of tissue classes. By using this segmentation as initialization, expectation-maximization (EM) iterations composed of bias correction, a priori digital brain atlas information, and Markov random field (MRF) segmentation were processed. Pixons were assigned with final labels when the algorithm converges. The adoption of bias correction and brain atlas made the current method more suitable for brain image segmentation than the previous pixon based segmentation algorithm. The proposed method was validated on both simulated normal brain images from BrainWeb and real brain images from the IBSR public dataset. Compared with some other popular MRI segmentation methods, the proposed method exhibited a higher degree of accuracy in segmenting both simulated and real 3D MRI brain data. The experimental results were numerically assessed using Dice and Tanimoto coefficients.