A color clustering technique for image segmentation
Computer Vision, Graphics, and Image Processing
Watersheds in Digital Spaces: An Efficient Algorithm Based on Immersion Simulations
IEEE Transactions on Pattern Analysis and Machine Intelligence
The watershed transform: definitions, algorithms and parallelization strategies
Fundamenta Informaticae - Special issue on mathematical morphology
A region growing and merging algorithm to color segmentation
Pattern Recognition
An adaptive nonlinear diffusion algorithm for filtering medical images
IEEE Transactions on Information Technology in Biomedicine
Adaptive color segmentation-a comparison of neural and statistical methods
IEEE Transactions on Neural Networks
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The computer-assisted microscopy systems can increase the accuracy of the analysis. To guarantee correct results in computer-assisted microscopy, accurate nuclei segmentation is crucially important since images segmentation is the first step towards image understanding and image analysis. In this paper, we present clustering techniques to segment homogeneous clusters in RGB color space and then label each cluster as a different region. According to the evaluation process, 97% of nuclei pixels were correctly delineated with our algorithm and on average 90% of nuclei were correctly detected. Our methods could be of value to computer-based systems designed to objectively interpret microscopic images by accurate nuclei segmentation.