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Y. Tuduki, K. Murase, M. Izumida, H. Miki, K. Kikuchi, K. Murakami & J. Ikezoe (2000). “Automated Seeded Region Growing Algorithm for Extraction of Cerebral Blood Vessels from Magnetic Resonance Angiographic Data”. Proceedings of The 22nd Annual International. Conference of the IEEE Engineering in Medicine and Biology Society. 3. 1756-1759.

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.