Fluorescence microscopy image segmentation based on graph and fuzzy methods: A comparison with ensemble method
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Accurate segmentation of fluorescence images has become increasingly important for recognizing cell nucleus that have the phenotype of interest in biomedical applications. In this study an ensemble based method is proposed for the segmentation of cell cancer microscopy images. The ensemble is constructed and compared using Bayes graph-cut algorithm, binary graph-cut algorithm, spatial fuzzy C-means, and fuzzy level set algorithm, which were chosen for their accuracy and efficiency in the segmentation area. We investigate the performance of each method separately and finally compare the results with the ensemble method. Experiments are conducted over two datasets with different cell types. At 95% confidence level, the ensemble based method represents the best among all the implemented algorithms. Also ensemble method depicts better results in comparison with other state-of-the-art segmentation methods.
Journal of Intelligent and Fuzzy Systems
Artificial Intelligence and Image Processing not elsewhere classified