Maximum a posteriori spatial probability segmentation

Chi Kin Leung, F. K. Lam

Research output: Journal article publicationJournal articleAcademic researchpeer-review

13 Citations (Scopus)

Abstract

An image segmentation algorithm that performs pixel-by-pixel segmentation on an image with consideration of spatial information is described. The spatial information is the joint grey level values of the pixel to be segmented and its neighbouring pixels. The conditional probability that a pixel belongs to a particular class under the condition that the spatial information has been observed is defined to be the a posteriori spatial probability. A maximum a posteriori spatial probability (MASP) segmentation algorithm is proposed to segment an image such that each pixel is segmented into a particular class when the a posteriori spatial probability is maximum. The proposed segmentation algorithm is implemented in an <, iterative form. During the iteration, a series of intermediate segmented images are produced among which the one that possesses the maximum amount of information in its spatial structure is chosen as the optimum segmented image. Results from segmenting synthetic and practical images demonstrate that the MASP algorithm is capable of achieving better results when compared with other global thresholding methods.
Original languageEnglish
Pages (from-to)161-167
Number of pages7
JournalIEE Proceedings: Vision, Image and Signal Processing
Volume144
Issue number3
DOIs
Publication statusPublished - 1 Jan 1997

Keywords

  • Entropy
  • Image segmentation
  • Spatial information
  • Thresholding

ASJC Scopus subject areas

  • Signal Processing
  • Electrical and Electronic Engineering

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