Automatic segmentation of granular objects in images: Combining local density clustering and gradient-barrier watershed

Huiguang Yang, Narendra Ahuja

Research output: Contribution to journalArticlepeer-review


Blob or granular object recognition is an image processing task with a rich application background, ranging from cell/nuclei segmentation in biology to nanoparticle recognition in physics. In this study, we establish a new and comprehensive framework for granular object recognition. Local density clustering and connected component analysis constitute the first stage. To separate overlapping objects, we further propose a modified watershed approach called the gradient-barrier watershed, which better incorporates intensity gradient information into the geometrical watershed framework. We also revise the marker-finding procedure to incorporate a clustering step on all the markers initially found, potentially grouping multiple markers within the same object. The gradient-barrier watershed is then conducted based on those markers, and the intensity gradient in the image directly guides the water flow during the flooding process. We also propose an important scheme for edge detection and fore/background separation called the intensity moment approach. Experimental results for a wide variety of objects in different disciplines - including cell/nuclei images, biological colony images, and nanoparticle images - demonstrate the effectiveness of the proposed framework.

Original languageEnglish (US)
Pages (from-to)2266-2279
Number of pages14
JournalPattern Recognition
Issue number6
StatePublished - Jun 2014


  • Cell/nuclei segmentation
  • Edge detection
  • Gradient-barrier watershed
  • Granular object recognition
  • Image segmentation
  • Intensity moment
  • Local density clustering
  • Nanoparticle segmentation

ASJC Scopus subject areas

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


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