Rule-based image segmentation: A dynamic control strategy approach

https://doi.org/10.1016/0734-189X(85)90004-0Get rights and content

The structure and functional aspects of a rule-based image segmentation system are briefly described. This paper focuses mainly on the control aspects of the system. A set of measurements is defined that evaluates the quality of an image segmentation and thereby determines the control. To accomplish this, focus of attention areas are created within the image. These are processed in an order that depends on their properties, as reflected by the set of performance measures, computed for each area. A dynamic control strategy within each area is also determined based on the individual characteristics of that area. One aspect involves determining the spatial order in which the system will process the data entries inside the area. In addition, an ordering must be established among all the rules included in the model. Dynamic strategy setting is formulated as a fuzzy decision-making problem, whose solution depends on the performance parameters. Because the individual characteristics of areas are reflected in different performance measurements, the resulting control strategies will vary from one area to the next. In addition, the strategy within each area will vary with time to reflect the changing properties. This spatial and temporal updating process is designed to ensure both efficiency and improved output.

References (17)

There are more references available in the full text version of this article.

Cited by (26)

  • Radiomics in breast cancer classification and prediction

    2021, Seminars in Cancer Biology
    Citation Excerpt :

    Atlas-based segmentation approaches uses a reference image (i.e. atlas) to segment and label anatomical structures [47,50]. Rule-based methods are based on various measurements, named rules, used to iteratively evaluate segmentation quality [51]. Also, so-called shape models segment a ROI by fitting an estimating shape to the desired area of the image [52].

  • Object based image analysis for remote sensing

    2010, ISPRS Journal of Photogrammetry and Remote Sensing
    Citation Excerpt :

    The subject of OBIA is related to concepts of object-oriented software and to object handling in the GIS world; the reader is referred to a recent review paper on object-oriented presentation in GIS by Bian (2007). It is generally agreed (Blaschke et al., 2000; Blaschke and Strobl, 2001; Schiewe, 2002; Hay et al., 2003; Burnett and Blaschke, 2003; Koch et al., 2003; Flanders et al., 2003; Benz et al., 2004; Blaschke et al., 2004; Zhang et al., 2005a; Liu et al., 2006; Navulur, 2007; Lang, 2008; Hay and Castilla, 2008) that OBIA builds on older segmentation, edge-detection, feature extraction and classification concepts that have been used in remote sensing image analysis for decades (Kettig and Landgrebe, 1976; Haralick, 1983; Haralick and Shapiro, 1985; Levine and Nazif, 1985; Strahler et al., 1986; McKeown et al., 1989; Pal and Pal, 1993; Câmara et al., 1996; Hay et al., 1996; Lobo et al., 1996; Ryherd and Woodcock, 1996; Wulder, 1998; Aplin et al., 1999; Baltsavias, 2004). Its emergence has nevertheless provided a new, critical bridge between the spatial concepts applied in multiscale landscape analysis (Wu, 1999; Hay et al., 2001; Wu and David, 2002; Burnett and Blaschke, 2003), Geographic Information Systems (GIS, (Câmara et al., 1996; Yu et al., 2006)), Geographic Information Science (abbreviated to GIScience, see (Goodchild, 1992, 2004)), and the synergy between image-objects and their radiometric characteristics and analyses in Earth Observation data (Benz et al., 2004; Blaschke et al., 2004; Langanke et al., 2007; Laliberte et al., 2007; Navulur, 2007; Möller et al., 2007; Jobin et al., 2008; Stow et al., 2008; Tiede et al., 2008; Trias-Sanz et al., 2008; Aubrecht et al., 2008; van der Werff and van der Meer, 2008; Weinke et al., 2008).

  • Image Segmentation with Topological Maps and Inter-pixel Representation

    1998, Journal of Visual Communication and Image Representation
  • Knowledge-Based Image Understanding Systems: A Survey

    1997, Computer Vision and Image Understanding
View all citing articles on Scopus

This research was supported in part by the Natural Sciences and Engineering Research Council under Grant A4156, and in part by an FCAC grant awarded by the Department of Education, Province of Quebec, under Grant EQ-633.

View full text