Two linear time Union-Find strategies for image processing
Theoretical Computer Science
Sequential Operations in Digital Picture Processing
Journal of the ACM (JACM)
Scale-Space Theory in Computer Vision
Scale-Space Theory in Computer Vision
Digital Image Processing
Linear-time connected-component labeling based on sequential local operations
Computer Vision and Image Understanding
A linear-time component-labeling algorithm using contour tracing technique
Computer Vision and Image Understanding
Breast segmentation with pectoral muscle suppression on digital mammograms
IbPRIA'05 Proceedings of the Second Iberian conference on Pattern Recognition and Image Analysis - Volume Part II
Real-Time Object-Based Video Segmentation Using Colour Segmentation and Connected Component Labeling
IVIC '09 Proceedings of the 1st International Visual Informatics Conference on Visual Informatics: Bridging Research and Practice
Pectoral muscle segmentation: A review
Computer Methods and Programs in Biomedicine
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A new algorithm for connected component labeling is presented in this paper. This algorithm requires only one scan through an image for labeling connected components. Once this algorithm encounters a starting pixel of a component, it completely traces all the contour pixels and all internal pixels of that particular component. This algorithm recognizes components one at a time in the image while scanning in raster order. This property will be very useful in areas such as image matching, image registration and content-based information retrieval etc. This algorithm is also capable of extracting contour pixels of an image and storing them in the order of clock-wise direction which will provide very useful information in many applications. Also this algorithm assigns consecutive label numbers for different components and hence needs a minimum number of labels. As our main research is on mammography image analysis for diagnosing breast cancers, we applied this algorithm to mammograms and measured performance of the algorithm in terms of processing time. This will be a useful algorithm in medical image analysis as a preprocessing tool.