Probabilistic segmentation of partial volume voxels
VIP '94 The international conference on volume image processing on Volume image processing
Multiscale Segmentation of Three-Dimensional MR Brain Images
International Journal of Computer Vision
High-Precision Boundary Length Estimation by Utilizing Gray-Level Information
IEEE Transactions on Pattern Analysis and Machine Intelligence
Quantification of bone remodeling in the proximity of implants
CAIP'07 Proceedings of the 12th international conference on Computer analysis of images and patterns
Estimation of moments of digitized objects with fuzzy borders
ICIAP'05 Proceedings of the 13th international conference on Image Analysis and Processing
Sub-pixel Segmentation with the Image Foresting Transform
IWCIA '09 Proceedings of the 13th International Workshop on Combinatorial Image Analysis
A graph-based framework for sub-pixel image segmentation
Theoretical Computer Science
Image foresting transform: on-the-fly computation of segmentation boundaries
SCIA'11 Proceedings of the 17th Scandinavian conference on Image analysis
Coverage segmentation based on linear unmixing and minimization of perimeter and boundary thickness
Pattern Recognition Letters
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By utilizing intensity information available in images, partial coverage of pixels at object borders can be estimated. Such information can, in turn, provide more precise feature estimates. We present a pixel coverage segmentation method which assigns pixel values corresponding to the area of a pixel that is covered by the imaged object(s). Starting from any suitable crisp segmentation, we extract a one-pixel thin 4-connected boundary between the observed image components where a local linear mixture model is used for estimating fractional pixel coverage values. We evaluate the presented segmentation method, as well as its usefulness for subsequent precise feature estimation, on synthetic test objects with increasing levels of noise added. We conclude that for reasonable noise levels the presented method outperforms the achievable results of a perfect crisp segmentation. Finally, we illustrate the application of the suggested method on a real histological colour image.