Maximum Segmented Image Information Thresholding

Chi Kin Leung, F. K. Lam

Research output: Journal article publicationJournal articleAcademic researchpeer-review

27 Citations (Scopus)

Abstract

Utilizing information theory and considering image segmentation from a communication perspective, the image segmentation process is interpreted as a data processing step that operates on a gray-scale image and produces a segmented image. It is shown that the segmented image contains a certain amount of information about the scene, which is defined as segmented image information (SII). It is proposed that the SII should be maximized when an image is thresholded, and this is known as the maximum segmented image information (MSII) thresholding criterion. The MSII thresholding criterion possesses better properties as compared with the minimum error (MINE) and the uniform error (UNFE) thresholding criteria. Based on the MSII thresholding criterion, an MSII thresholding algorithm is proposed for the thresholding of real images. The MSII thresholding algorithm is evaluated against several well-known thresholding algorithms. The good thresholding results of both synthetic and real images confirm the capabilities of the proposed MSII thresholding algorithm.
Original languageEnglish
Pages (from-to)57-76
Number of pages20
JournalGraphical Models and Image Processing
Volume60
Issue number1
DOIs
Publication statusPublished - 1 Jan 1998

ASJC Scopus subject areas

  • Modelling and Simulation
  • Computer Vision and Pattern Recognition
  • Geometry and Topology
  • Computer Graphics and Computer-Aided Design

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