Semiautomatic segmentation with compact shape prior

  • Authors:

  • Piali Das;Olga Veksler;Vyacheslav Zavadsky;Yuri Boykov

  • Affiliations:

  • Atamai Inc., London, Ont., Canada;University of Western Ontario, Computer Science, Middlesex College, 361, London, Ont., Canada N6A 5B7;Semiconductor Insight Inc., Ottawa, Ont., Canada;University of Western Ontario, Computer Science, Middlesex College, 361, London, Ont., Canada N6A 5B7

  • Venue:
  • Image and Vision Computing
  • Year:
  • 2009

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Abstract

In recent years, interactive methods for segmentation are increasing in popularity due to their success in different domains such as medical image processing, photo editing, etc. We present an interactive segmentation algorithm that can segment an object of interest from its background with minimum guidance from the user, who just has to select a single seed pixel inside the object of interest. Due to minimal requirements from the user, we call our algorithm semiautomatic. To obtain a reliable and robust segmentation with such low user guidance, we have to make several assumptions. Our main assumption is that the object to be segmented is of compact shape, or can be approximated by several connected roughly collinear compact pieces. We base our work on the powerful graph cut segmentation algorithm of Boykov and Jolly, which allows straightforward incorporation of the compact shape constraint. In order to make the graph cut approach suitable for our semiautomatic framework, we address several well-known issues of graph cut segmentation technique. In particular, we counteract the bias towards shorter segmentation boundaries and develop a method for automatic selection of parameters. We demonstrate the effectiveness of our approach on the challenging industrial application of transistor gate segmentation in images of integrated chips. Our approach produces highly accurate results in real-time.