Segmentation algorithm for objects with very low edge contrast

Stephen McCarthy, Timothy C. Miller, Isaac N. Bankman

Research output: Contribution to journalConference articlepeer-review


We present an algorithm for segmentation of objects with very low edge contrast, such as microcalcifications in mammogram images. Most methods used to segment microcalcifications have algorithmic aspects that could raise operational difficulties, such as thresholds or windows that must be selected manually, or parametric models of the data. The presented algorithm does not use any of these techniques and does not require that any parameters be set by a user. It builds upon an earlier algorithm presented in, but is much faster and also applicable to a wider range of objects to be segmented. The algorithm's approach is based on the extension of radial intensity profiles from a given seed point to the edge of the image. A first derivative analysis is used to find an edge point pixel along each directional intensity profile. These points are connected and the resulting object border is filled using a constrained dilatation operation to form a complete region. Results from the tested mammography images indicate that the segmented regions compare closely to those expected from visual inspection.

Original languageEnglish (US)
Article number19
Pages (from-to)162-168
Number of pages7
JournalProceedings of SPIE - The International Society for Optical Engineering
StatePublished - 2004
Externally publishedYes
EventIntellignt Robots and Computer Vision XXII: Algorithms, Techniques, and Active Vision - Philadelphia, PA, United States
Duration: Oct 25 2004Oct 27 2004


  • Automated image analysis system
  • Fast algorithm
  • Image segmentation
  • Low contrast image

ASJC Scopus subject areas

  • Electronic, Optical and Magnetic Materials
  • Condensed Matter Physics
  • Computer Science Applications
  • Applied Mathematics
  • Electrical and Electronic Engineering


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