Weighted variational model for selective image segmentation with application to medical images

Chunxiao Liu, Kwok Po NG, Tieyong ZENG*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

35 Citations (Scopus)

Abstract

Selective image segmentation is an important topic in medical imaging and real applications. In this paper, we propose a weighted variational selective image segmentation model which contains two steps. The first stage is to obtain a smooth approximation related to Mumford-Shah model to the target region in the input image. Using weighted function, the approximation provides a larger value for the target region and smaller values for other regions. In the second stage, we make use of this approximation and perform a thresholding procedure to obtain the object of interest. The approximation can be obtained by the alternating direction method of multipliers and the convergence analysis of the method can be established. Experimental results for medical image selective segmentation are given to demonstrate the usefulness of the proposed method. We also do some comparisons and show that the performance of the proposed method is more competitive than other testing methods.

Original languageEnglish
Pages (from-to)367-379
Number of pages13
JournalPattern Recognition
Volume76
DOIs
Publication statusPublished - Apr 2018

Scopus Subject Areas

  • Software
  • Signal Processing
  • Computer Vision and Pattern Recognition
  • Artificial Intelligence

User-Defined Keywords

  • Iterative algorithm
  • Medical images
  • Mumford-Shah model
  • Selective segmentation
  • Thresholding

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