Abstract
This paper proposes image segmentation methods for cell nucleuses and vacuoles in the liver fibrosis tissue images. The novel idea is to segment the objects by extracting the image features to determine the required cell in liver fibrosis images. In the proposed segmentation phase, some image processing methods are applied to segment the objects of nucleuses and vacuoles. Run Length method makes the object regions become obviously and the noises can be suppressed. The morphological opening operation is performed to split connecting objects. For vacuole regions segmentation, the opening operation applies the mode filter to stuff up the dark holes in the objects and keep the completeness of regions. Furthermore, the proposed method uses the Genetic Algorithm to find the most appropriate parameters and weights for the region segmentation. From the experimental results, the proposed method can achieve a good performance on the segmentation of cell nucleuses and vacuoles.
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Wang, CT., Wang, CL., Chan, YK., Tsai, MH., Wang, YS., Cheng, WY. (2013). Liver Cell Nucleuses and Vacuoles Segmentation by Using Genetic Algorithms for the Tissue Images. In: Ali, M., Bosse, T., Hindriks, K.V., Hoogendoorn, M., Jonker, C.M., Treur, J. (eds) Recent Trends in Applied Artificial Intelligence. IEA/AIE 2013. Lecture Notes in Computer Science(), vol 7906. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-38577-3_60
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DOI: https://doi.org/10.1007/978-3-642-38577-3_60
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