Hands: a pattern theoretic study of biological shapes
Hands: a pattern theoretic study of biological shapes
Watersheds in Digital Spaces: An Efficient Algorithm Based on Immersion Simulations
IEEE Transactions on Pattern Analysis and Machine Intelligence
Fuzzy Sets and Systems - Special issue: fuzzy sets: where do we stand? Where do we go?
Unsupervised cell nucleus segmentation with active contours
Signal Processing - Special issue on deformable models and techniques for image and signal processing
Unsupervised segmentation based on robust estimation and color active contour models
IEEE Transactions on Information Technology in Biomedicine
IEEE Transactions on Information Technology in Biomedicine
Effect of rule weights in fuzzy rule-based classification systems
IEEE Transactions on Fuzzy Systems
Multiscale gradient watersheds of color images
IEEE Transactions on Image Processing
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Objective: This paper presents an algorithm based on multi-level watershed segmentation combined with three fuzzy systems to segment a large number of myelinated nerve fibers in microscope images. The method can estimate various geometrical parameters of myelinated nerve fibers in peripheral nerves. It is expected to be a promising tool for the quantitative assessment of myelinated nerve fibers in related research. Materials and methods: A novel multi-level watershed scheme iteratively detects pre-candidate nerve fibers. At each immersion level, watershed segmentation extracts the initial axon locations and obtains meaningful myelinated nerve fiber features. Thereafter, according to a priori characteristics of the myelinated nerve fibers, fuzzy rules reject unlikely pre-candidates and collect a set of candidates. Initial candidate boundaries are then refined by a fuzzy active contour model, which flexibly deforms contours according to the observed features of each nerve fiber. A final scan with a different set of fuzzy rules based on the a priori properties of the myelinated nerve fibers removes false detections. A particle swarm optimization method is employed to efficiently train the large number of parameters in the proposed fuzzy systems. Results: The proposed method can automatically segment the transverse cross-sections of nerve fibers obtained from optical microscope images. Although the microscope image is usually noisy with weak or variable levels of contrast, the proposed system can handle images with a large number of myelinated nerve fibers and achieve a high fiber detection ratio. As compared to manual segmentation by experts, the proposed system achieved an average accuracy of 91% across different data sets. Conclusion: We developed an image segmentation system that automatically handles myelinated nerve fibers in microscope images. Experimental results showed the efficacy of this system and its superiority to other nerve fiber segmentation approaches. Moreover, the proposed method can be extended to other applications of automatic segmentation of microscopic images.