Data structures using Pascal (2nd ed.)
Data structures using Pascal (2nd ed.)
Region Competition: Unifying Snakes, Region Growing, and Bayes/MDL for Multiband Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
Advanced algorithmic approaches to medical image segmentation
Machine Graphics & Vision International Journal
Pyramidal Seeded Region Growing Algorithm and Its Use in Image Segmentation
CAIP '99 Proceedings of the 8th International Conference on Computer Analysis of Images and Patterns
Seeded region growing: an extensive and comparative study
Pattern Recognition Letters
Image segmentation based on merging of sub-optimal segmentations
Pattern Recognition Letters
Iterative area filtering of multichannel images
Image and Vision Computing
Volumetric Curved Planar Reformation for Virtual Endoscopy
IEEE Transactions on Visualization and Computer Graphics
Blood cell identification and segmentation by means of statistical models
ISCGAV'07 Proceedings of the 7th WSEAS International Conference on Signal Processing, Computational Geometry & Artificial Vision
Enabling access to geo-referenced information: Atlas.txt
W4A '08 Proceedings of the 2008 international cross-disciplinary conference on Web accessibility (W4A)
Segmentation of small objects in color images
Programming and Computing Software
Journal of Visual Communication and Image Representation
An efficient image-mosaicing method based on multifeature matching
Machine Vision and Applications
Proceedings of the International Conference on Advances in Computing, Communication and Control
International Journal of Remote Sensing
Automatic seeded region growing for color image segmentation
Image and Vision Computing
Connectivity-based local adaptive thresholding for carotid artery segmentation using MRA images
Image and Vision Computing
Beyond self-duality in morphological image analysis
Image and Vision Computing
Advances in constrained connectivity
DGCI'08 Proceedings of the 14th IAPR international conference on Discrete geometry for computer imagery
Multi-seed segmentation of tomographic volumes based on fuzzy connectedness
ICIC'10 Proceedings of the Advanced intelligent computing theories and applications, and 6th international conference on Intelligent computing
Application of fuzzy connectedness in 3D blood vessel extraction
International Journal of Bioinformatics Research and Applications
ICICA'10 Proceedings of the First international conference on Information computing and applications
A clustering technique for defect inspection
EC'05 Proceedings of the 6th WSEAS international conference on Evolutionary computing
Interactive segmentation based on component-trees
Pattern Recognition
Image foresting transform: on-the-fly computation of segmentation boundaries
SCIA'11 Proceedings of the 17th Scandinavian conference on Image analysis
Region growing with automatic seeding for semantic video object segmentation
ICAPR'05 Proceedings of the Third international conference on Pattern Recognition and Image Analysis - Volume Part II
Technique for preprocessing of digital mammogram
Computer Methods and Programs in Biomedicine
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Recently Adams and Bischof (1994) proposed a novel region growing algorithm for segmenting intensity images. The inputs to the algorithm are the intensity image and a set of seeds - individual points or connected components - that identify the individual regions to be segmented. The algorithm grows these seed regions until all of the image pixels have been assimilated. Unfortunately the algorithm is inherently dependent on the order of pixel processing. This means, for example, that raster order processing and anti-raster order processing do not, in general, lead to the same tessellation. In this paper we propose an improved seeded region growing algorithm that retains the advantages of the Adams and Bischof algorithm - fast execution, robust segmentation, and no tuning parameters - but is pixel order independent.