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A. Madabhushi, M. Feldman, D. Metaxas, D. Chute, and J. Tomaszewski. (2003) “A novel stochastic combination of 3D tex-ture features for automated segmentation of prostatic adenocarci-noma from high resolution MRI.” Medical Image Computing and Computer-Assisted Intervention, volume 2878 of Lecture Notes in Computer Science, pp. 581-591. Springer-Verlag.

has been cited by the following article:

• TITLE: Texture feature based automated seeded region growing in abdominal MRI segmentation

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

  KEYWORDS: Image Segmentation, Seeded Region Growing, Texture Analysis

  JOURNAL NAME: Journal of Biomedical Science and Engineering, Vol.2 No.1, February 13, 2009

  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.