Detection of Pulmonary Region in Medical Images through Improved Active Control Model

  • Kwon Yong-Jun (School of Electrical Engineering & Computer Science, Kyungpook National University) ;
  • Won Chul-Ho (Dept. of Computer Control Engineering, Kyungil University) ;
  • Kim Dong-Hun (School of Electrical Engineering & Computer Science, Kyungpook National University) ;
  • Kim Pil-Un (Dept. of Biological & Medical Engineering, Kyungpook National University) ;
  • Park Il-Yong (Advanced Research Center for Recovery of Human Sensibility, Kyungpook National University) ;
  • Park Hee-Jun (School of Electrical Engineering & Computer Science, Kyungpook National University) ;
  • Lee Jyung-Hyun (School of Electrical Engineering & Computer Science, Kyungpook National University) ;
  • Kim Myoung-Nam (Dept. of Biomedical Engineering, School of Medicine, Kyungpook National University) ;
  • Cho Jin-HO (School of Electrical Engineering & Computer Science, Kyungpook National University, Advanced Research Center for Recovery of Human Sensibility, Kyungpook National University)
  • Published : 2005.12.01

Abstract

Active contour models have been extensively used to segment, match, and track objects of interest in computer vision and image processing applications, particularly to locate object boundaries. With conventional methods an object boundary can be extracted by controlling the internal energy and external energy based on energy minimization. However, this still leaves a number of problems, such as initialization and poor convergence in concave regions. In particular, a contour is unable to enter a concave region based on the stretching and bending characteristic of the internal energy. Therefore, this study proposes a method that controls the internal energy by moving the local perpendicular bisector point of each control point on the contour, and determines the object boundary by minimizing the energy relative to the external energy. Convergence at a concave region can then be effectively implemented as regards the feature of interest using the internal energy, plus several objects can be detected using a multi-detection method based on the initial contour. The proposed method is compared with other conventional methods through objective validation and subjective consideration. As a result, it is anticipated that the proposed method can be efficiently applied to the detection of the pulmonary parenchyma region in medical images.

Keywords

References

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