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Appl. Sci. 2017, 7(12), 1327; https://doi.org/10.3390/app7121327

An Accurate Perception Method for Low Contrast Bright Field Microscopy in Heterogeneous Microenvironments

1
Department of Mechanical Engineering, University of Maryland, College Park, MD 20742, USA
2
Department of Electrical Engineering, Indian Institute of Technology, Gandhinagar, Palaj 382355, Guajarat, India
3
Department of Mechanical Engineering, University of Washington, Seattle, WA 98195, USA
4
Department of Industrial & Systems Engineering, University of Washington, Seattle, WA 98195, USA
This paper is an expanded version of our paper published in the Proceedings of the International Conference on Manipulation, Automation and Robotics at Small Scales, Paris, France, 18–22 July 2016.
*
Author to whom correspondence should be addressed.
Received: 21 November 2017 / Revised: 15 December 2017 / Accepted: 15 December 2017 / Published: 19 December 2017

Abstract

Automated optical tweezers-based robotic manipulation of microscale objects requires real-time visual perception for estimating the states, i.e., positions and orientations, of the objects. Such visual perception is particularly challenging in heterogeneous environments comprising mixtures of biological and colloidal objects, such as cells and microspheres, when the popular imaging modality of low contrast bright field microscopy is used. In this paper, we present an accurate method to address this challenge. Our method combines many well-established image processing techniques such as blob detection, histogram equalization, erosion, and dilation with a convolutional neural network in a novel manner. We demonstrate the effectiveness of our processing pipeline in perceiving objects of both regular and irregular shapes in heterogeneous microenvironments of varying compositions. The neural network, in particular, helps in distinguishing the individual microspheres present in dense clusters. View Full-Text
Keywords: bright field imaging; cell and microsphere perception; blob and feature detection; convolutional neural network bright field imaging; cell and microsphere perception; blob and feature detection; convolutional neural network
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).
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Rajasekaran, K.; Samani, E.; Bollavaram, M.; Stewart, J.; Banerjee, A.G. An Accurate Perception Method for Low Contrast Bright Field Microscopy in Heterogeneous Microenvironments. Appl. Sci. 2017, 7, 1327.

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