Connected-component labeling

Connected-component labeling (CCL), connected-component analysis (CCA), blob extraction, region labeling, blob discovery, or region extraction is an algorithmic application of graph theory, where subsets of connected components are uniquely labeled based on a given heuristic. Connected-component labeling is not to be confused with segmentation.

Connected-component labeling is used in computer vision to detect connected regions in binary digital images, although color images and data with higher dimensionality can also be processed.[1][2] When integrated into an image recognition system or human-computer interaction interface, connected component labeling can operate on a variety of information.[3][4] Blob extraction is generally performed on the resulting binary image from a thresholding step, but it can be applicable to gray-scale and color images as well. Blobs may be counted, filtered, and tracked.

Blob extraction is related to but distinct from blob detection.

  1. ^ Samet, H.; Tamminen, M. (1988). "Efficient Component Labeling of Images of Arbitrary Dimension Represented by Linear Bintrees". IEEE Transactions on Pattern Analysis and Machine Intelligence. 10 (4): 579. doi:10.1109/34.3918. S2CID 15911227.
  2. ^ Michael B. Dillencourt; Hannan Samet; Markku Tamminen (1992). "A general approach to connected-component labeling for arbitrary image representations". Journal of the ACM. 39 (2): 253. CiteSeerX 10.1.1.73.8846. doi:10.1145/128749.128750. S2CID 1869184.
  3. ^ Weijie Chen; Maryellen L. Giger; Ulrich Bick (2006). "A Fuzzy C-Means (FCM)-Based Approach for Computerized Segmentation of Breast Lesions in Dynamic Contrast-Enhanced MR Images". Academic Radiology. 13 (1): 63–72. doi:10.1016/j.acra.2005.08.035. PMID 16399033.
  4. ^ Kesheng Wu; Wendy Koegler; Jacqueline Chen; Arie Shoshani (2003). "Using Bitmap Index for Interactive Exploration of Large part Datasets". SSDBM.