Dynamic Programming Using Polar Variance for Image Segmentation

Jose A. Rosado-Toro, Maria I Altbach, Jeffrey J Rodriguez

Research output: Contribution to journalArticle

3 Scopus citations

Abstract

When using polar dynamic programming (PDP) for image segmentation, the object size is one of the main features used. This is because if size is left unconstrained the final segmentation may include high-gradient regions that are not associated with the object. In this paper, we propose a new feature, polar variance, which allows the algorithm to segment the objects of different sizes without the need for training data. The polar variance is the variance in a polar region between a user-selected origin and a pixel we want to analyze. We also incorporate a new technique that allows PDP to segment complex shapes by finding low-gradient regions and growing them. The experimental analysis consisted on comparing our technique with different active contour segmentation techniques on a series of tests. The tests consisted on robustness to additive Gaussian noise, segmentation accuracy with different grayscale images and finally robustness to algorithm-specific parameters. Experimental results show that our technique performs favorably when compared with other segmentation techniques.

Original languageEnglish (US)
Article number7585054
Pages (from-to)5857-5866
Number of pages10
JournalIEEE Transactions on Image Processing
Volume25
Issue number12
DOIs
StatePublished - Dec 1 2016

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Keywords

  • Dynamic programming
  • image segmentation
  • polar variance
  • region growing

ASJC Scopus subject areas

  • Software
  • Medicine(all)
  • Computer Graphics and Computer-Aided Design

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