Abstract
Manual image segmentation performed by humans is time-intensive and inadequate for the quantification of segmentation parameters. Automatic feed-forward segmentation techniques suffer from restrictions in parameter selection and combination and are difficult in quantifying the direct parametric effect on segmentation outcome. Here, we introduce an automatic feedback-based image processing method that uses fuzzy a priori knowledge to adapt segmentation parameters. Therefore, a fuzzy evaluation of segment properties is performed for each parameter combination. The method was applied to biological cell imaging. An automatic tuning of the image segmentation process yields an optimal parameter set such that segments match known properties (a priori knowledge e.g. cell size, outline etc.).
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Khan, A.u.M., Mikut, R., Schweitzer, B., Weiss, C., Reischl, M. (2013). Automatic Tuning of Image Segmentation Parameters by Means of Fuzzy Feature Evaluation. In: Kruse, R., Berthold, M., Moewes, C., Gil, M., Grzegorzewski, P., Hryniewicz, O. (eds) Synergies of Soft Computing and Statistics for Intelligent Data Analysis. Advances in Intelligent Systems and Computing, vol 190. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-33042-1_49
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DOI: https://doi.org/10.1007/978-3-642-33042-1_49
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