Abstract
We present a new method for detection and separation of individual nematode worms in a still image. After pre-processing stage, which includes image binarization, filling the small holes, obtaining the skeleton of the new image and pruning the extra branches of skeleton, we split a skeleton into several branches by eliminating the connection pixels (pixels with more than 2 neighbors). Then we compute angles of all branches and compare the angles of the neighboring branches. The neighbor branches with angle differences less than a threshold are connected. Our method has been applied to a database of 54 overlap worms and results in 82% accuracy as automatic and 89% as semi-automatic with some limited user interaction.
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Rizvandi, N.B., Pižurica, A., Philips, W. (2008). Automatic Individual Detection and Separation of Multiple Overlapped Nematode Worms Using Skeleton Analysis. In: Campilho, A., Kamel, M. (eds) Image Analysis and Recognition. ICIAR 2008. Lecture Notes in Computer Science, vol 5112. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-69812-8_81
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DOI: https://doi.org/10.1007/978-3-540-69812-8_81
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