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

• Propose a general framework for granular object recognition and segmentation.
• Apply the scheme to a wide variety of applications.
• Propose a modified watershed approach called gradient-barrier watershed.
• Incorporate a clustering step in the marker finding procedure.
• Propose a scheme for edge detection and fore/background separation called intensity moment approach.

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.