Abstract
Fractional-order differentiation enhances the image nonlinearly, but only has been applied in the 2D image. The 2D fractional differentiation operator is extended to 3D and the 3D fractional differentiation discrete filtering masks are deduced. The 3D fractional differentiation is implemented to improve the traditional 3D image edge surface tracking algorithm in two aspects. Firstly, the 3D data fields of neuron slices are enhanced by the 3D fractional differentiation, ensures more detailed structures of edge surfaces with low contrast are extracted. Then integral-order gradient is modified by the fractional differentiation to get more 3D detailed structures. The proposed method has been applied to 3D confocal microscopy images, and more 3D detail structures of neuron are tracked compared to the traditional 3D edge surface tracking algorithm.
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Ma, Y., Zhang, Y., Wang, L. (2013). An Improved 3D Edge Surface Tracking Algorithm Based on 3D Fractional-Order Differentiation within Confocal Microscopy Images. In: Sun, C., Fang, F., Zhou, ZH., Yang, W., Liu, ZY. (eds) Intelligence Science and Big Data Engineering. IScIDE 2013. Lecture Notes in Computer Science, vol 8261. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-42057-3_63
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DOI: https://doi.org/10.1007/978-3-642-42057-3_63
Publisher Name: Springer, Berlin, Heidelberg
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