Fronts propagating with curvature-dependent speed: algorithms based on Hamilton-Jacobi formulations
Journal of Computational Physics
Low-level grouping mechanisms for contour completion
Information Sciences—Applications: An International Journal
Morphological Image Analysis: Principles and Applications
Morphological Image Analysis: Principles and Applications
Level Set Evolution without Re-Initialization: A New Variational Formulation
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
An Automated Method for Segmentation of Epithelial Cervical Cells in Images of ThinPrep
Journal of Medical Systems
Cell Image Segmentation Based on an Improved Watershed Transformation
CASON '10 Proceedings of the 2010 International Conference on Computational Aspects of Social Networks
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The morphological analysis of muscle biopsy helps in diagnosis of neuromuscular disease. The presence, extent, size, shape, and other morphological appearance of the muscle fibres are important indicators for presence or severity of disease. However, estimation of these parameters by simple visual inspection is inaccurate and subjective and manual delineation of individual muscle fibres from muscle biopsy images is time-consuming and tedious. In this study, two automatic segmentation methods are proposed. Both methods operate on fluorescence microscopy images. The first uses a level set framework and the second one a marker-driven watershed transform. In a first stage, mathematical morphology is used to detect the presence of muscle fibres. The result of this step provides requirements for both segmentation methods (initial contour and markers). Experimental results demonstrate that segmentation of watershed detects fibres contours more accurately and with a lower computational cost.