Statistical Tomography of Microscopic Life

Aviad Levis, Yoav Y. Schechner, Ronen Talmon; The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2018, pp. 6411-6420

Abstract


We achieve tomography of 3D volumetric natural objects, where each projected 2D image corresponds to a different specimen. Each specimen has unknown random 3D orientation, location, and scale. This imaging scenario is relevant to microscopic and mesoscopic organisms, aerosols and hydrosols viewed naturally by a microscope. In-class scale variation inhibits prior single-particle reconstruction methods. We thus generalize tomographic recovery to account for all degrees of freedom of a similarity transformation. This enables geometric self-calibration in imaging of transparent objects. We make the computational load manageable and reach good quality reconstruction in a short time. This enables extraction of statistics that are important for a scientific study of specimen populations, specifically size distribution parameters. We apply the method to study of plankton.

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[bibtex]
@InProceedings{Levis_2018_CVPR,
author = {Levis, Aviad and Schechner, Yoav Y. and Talmon, Ronen},
title = {Statistical Tomography of Microscopic Life},
booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2018}
}