CT image segmentation using structural analysis

  • Authors:
  • Hiroyuki Hishida;Takashi Michikawa;Yutaka Ohtake;Hiromasa Suzuki;Satoshi Oota

  • Affiliations:
  • The University of Tokyo;The University of Tokyo;The University of Tokyo;The University of Tokyo;RIKEN BioResource Center

  • Venue:
  • ISVC'10 Proceedings of the 6th international conference on Advances in visual computing - Volume Part III
  • Year:
  • 2010

Quantified Score

Hi-index 0.00

Visualization

Abstract

We propose a segmentation method for blurred and low-resolution CT images focusing physical properties. The basic idea of our research is simple: two objects can be easily separated in areas of structural weakness. Given CT images of an object, we assign a physical property such as Young's modulus to each voxel and create functional images (e.g., von Mises strain at the voxel). We then remove the voxel with the largest value in the functional image, and these steps are reiterated until the input model is decomposed into multiple parts. This simple and unique approach provides various advantages over conventional segmentation methods, including preciousness and noise robustness. This paper also demonstrates the efficiency of our approach using the results of various types of CT images, including biological representations and those of engineering objects.