Bayesian Compensated Microscopy

  • Ameneh Boroomand
  • Jason Deglint
  • Alexander Wong

Abstract

We present a novel Bayesian compensated microscopy (BCM) technique
designed for enhancing microscopy image quality. The proposed
BCM technique provides a computational approach to jointly
compensate for microscopy image degradations due to (1) optical
aberrations, (2) illumination non-uniformities, and (3) imaging noise
within a probabilistic framework. Experimental results based on a
stained pathology sample of spleen tissue with leukemia demonstrate
the effectiveness of the proposed BCM technique for the
quality enhancement in microscopy imaging. The proposed BCM
technique can lead to improved visualization of fine tissue structures
as well as a more consistent visualization across the entire
sample, which can be beneficial for accurate analysis and better
interpretation of microscopy samples.

Published
2016-10-03
How to Cite
Boroomand, A., Deglint, J., & Wong, A. (2016). Bayesian Compensated Microscopy. Journal of Computational Vision and Imaging Systems, 2(1). https://doi.org/10.15353/vsnl.v2i1.108
Section
Articles