Wavelet-Based Identification and Classification of Local Symmetries in Microscopy Images

We present a method for the identification and classification of local symmetries in biological images. We aim at obtaining a precise estimate of symmetric junctions in a scale and rotation invariant way. The proposed method is template-free, which allows the test of any combination of arbitrary symmetry orders in an effective way. Our measure of local symmetry is derived from a circular harmonic wavelet analysis. The basis functions exhibit different symmetry orders. We use this measure to formulate a classifier to label the different junctions into one of several symmetry classes. We present experimental results, and validate our method using both on synthetic images and biological micrographs.


Published in:
Proceedings of the Eleventh IEEE International Symposium on Biomedical Imaging: From Nano to Macro (ISBI'14), Beijing, People's Republic of China, 1035–1038
Year:
2014
Publisher:
IEEE
Laboratories:




 Record created 2015-09-18, last modified 2018-03-17

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