A run-based two-scan labeling algorithm

  • Authors:

  • Lifeng He;Yuyan Chao;Kenji Suzuki

  • Affiliations:

  • Graduate School of Information Science and Technology, Aichi Prefectural University, Aichi, Japan and Department of Radiology, The University of Chicago, Chicago, IL and Shannxi University of Scie ...;Graduate School of Environment Management, Nagoya Sangyo University, Owariasahi, Aichi, Japan and Shannxi University of Science and Technology, Xian, Shannxi, China;Department of Radiology, The University of Chicago, Chicago, IL

  • Venue:
  • ICIAR'07 Proceedings of the 4th international conference on Image Analysis and Recognition
  • Year:
  • 2007

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Abstract

Unlike conventional raster-scan based connected-component labeling algorithms which detect the connectivity of object pixels by processing pixels in an image one by one, this paper presents an efficient run-based two-scan labeling algorithm: the run data obtained during the scan are recorded in a queue, and are used for detecting the connectivity later. Moreover, unlike conventional label-equivalence-based algorithms which resolve label equivalences between provisional labels that are assigned during the first scan, our algorithm resolve label equivalences between the representative labels of equivalent provisional label sets. In our algorithm, at any time, all provisional labels that are assigned to a connected component are combined in a set, and the smallest label is used as the representative label. The corresponding relation of a provisional label to its representative label is recorded in a table. Whenever different connected components are found to be connected, all provisional label sets concerned with these connected components are merged together, and the smallest provisional label is taken as the representative label. When the first scan is finished, all provisional labels that were assigned to each connected component in the given image will have a unique representative label. During the second scan, we need only to replace each provisional label with its representative label. Experimental results on various types of images demonstrate that our algorithm is the fastest of all conventional labeling algorithms.