Paper
8 August 2007 Quantitative determination of specimen properties using computational differential-interference contrast (DIC) microscopy
Author Affiliations +
Abstract
The theoretical development of a new iterative method based on a diffraction imaging model for the computation of a specimen's complex transmittance function (magnitude and phase) from DIC images is presented. This new method extends our initial work (RD method presented by Preza1) which was based on the assumption that the specimen does not absorb light and thus only the specimen's phase function or optical path length (OPL) distribution was computed from rotationally-diverse (RD) DIC images. In this paper, we quantify this approximation by modeling the magnitude of the synthetic object as a deviation from unity by a small perturbation. Synthetic, noiseless DIC data are generated from these test objects and processed with the RD method. Our results show that although for weakly absorbing objects the RD method may be adequate for some applications, in general the results can be quantitatively unacceptable. This supports the development of the new alternating minimization method presented in this paper. Preliminary results from the current implementation of the AM method show that the discrepancy measure utilized in the method goes to zero as iterations increase but a constrain on the estimated magnitude is necessary in order to obtain quantitative specimen properties.
© (2007) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Chrysanthe Preza and Joseph A. O’Sullivan "Quantitative determination of specimen properties using computational differential-interference contrast (DIC) microscopy", Proc. SPIE 6630, Confocal, Multiphoton, and Nonlinear Microscopic Imaging III, 66300E (8 August 2007); https://doi.org/10.1117/12.729527
Lens.org Logo
CITATIONS
Cited by 2 scholarly publications.
Advertisement
Advertisement
RIGHTS & PERMISSIONS
Get copyright permission  Get copyright permission on Copyright Marketplace
KEYWORDS
Digital image correlation

Expectation maximization algorithms

Microscopy

Algorithm development

Amplitude modulation

Error analysis

Point spread functions

Back to Top