Texture feature based automated seeded region growing in abdominal MRI segmentation

Jie Wu, Skip Poehlman, Michael D. Noseworthy, Markad V. Kamath

Brain-Body Institute, St. Joseph’s Healthcare, Hamilton, Ontario, Canada.
D epartment of Medicine, McMaster University, Hamilton, Ontario, Canada.
Department of Computing and Software.

DOI: 10.4236/jbise.2009.21001                

Abstract

A new texture feature-based seeded region growing algorithm is proposed for automated segmentation of organs in abdominal MR images. 2D Co-occurrence texture feature, Gabor texture feature, and both 2D and 3D Semi- variogram texture features are extracted from the image and a seeded region growing algorithm is run on these feature spaces. With a given Region of Interest (ROI), a seed point is automatically se-lected based on three homogeneity criteria. A threshold is then obtained by taking a lower value just before the one causing ‘explosion’. This algorithm is tested on 12 series of 3D ab-dominal MR images.

Keywords

Image Segmentation, Seeded Region Growing, Texture Analysis

Conflicts of Interest

The authors declare no conflicts of interest.

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