A top-down region dividing approach for image segmentation

  • Authors:

  • Yi-Ta Wu;Frank Y. Shih;Jiazheng Shi;Yih-Tyng Wu

  • Affiliations:

  • Radiology Department, University of Michigan, Ann Arbor, MI 48109, USA;Computer Vision Laboratory, College of Computing Sciences, New Jersey Institute of Technology, Newark, NJ 07102, USA;Radiology Department, University of Michigan, Ann Arbor, MI 48109, USA;Graduate School of Computer and Information Sciences, NOVA Southeastern University, USA

  • Venue:
  • Pattern Recognition
  • Year:
  • 2008

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Abstract

Histogram-based and region-based segmentation approaches have been widely used in image segmentation. Difficulties arise when we use these techniques, such as the selection of a proper threshold value for the histogram-based technique and the over-segmentation followed by the time-consuming merge processing for the region-based technique. To provide efficient algorithms that not only produce better segmentation results but also maintain low computational complexity, a novel top-down region dividing based approach is developed for image segmentation, which combines the advantages of both histogram-based and region-based approaches. Experimental results show that our algorithm can efficiently perform image segmentation without distorting the spatial structure of an image. Furthermore, two potential applications in medical image analysis are presented to show the advantages of using the proposed algorithm.