Abstract
The growing importance of nail-fold capillaroscopy imaging as a diagnostic tool in medicine increases the need to automate this process. One of the most important markers in capillaroscopy is capillary thickness. On this basis capillaries may be divided into three separate categories: healthy, capillaries with increased loops and megacapillaries. In the paper we describe the problem of capillary thickness analysis automation. First, data is extracted from a segmented capillary image. Then feature vectors are constructed. They are given as an input for capillary classification method. We applied different classifiers in the experiments. The best achieved accuracy reaches 97%, which can be considered as very high and satisfying.
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Paradowski, M., Markowska-Kaczmar, U., Kwasnicka, H., Borysewicz, K. (2009). Capillary Abnormalities Detection Using Vessel Thickness and Curvature Analysis. In: Velásquez, J.D., RÃos, S.A., Howlett, R.J., Jain, L.C. (eds) Knowledge-Based and Intelligent Information and Engineering Systems. KES 2009. Lecture Notes in Computer Science(), vol 5712. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-04592-9_19
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DOI: https://doi.org/10.1007/978-3-642-04592-9_19
Publisher Name: Springer, Berlin, Heidelberg
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