Next Article in Journal
Fog over Virtualized IoT: New Opportunity for Context-Aware Networked Applications and a Case Study
Previous Article in Journal
Evaluating the High Frequency Behavior of the Modified Grounding Scheme in Wind Farms
Open AccessFeature PaperArticle

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
*
Author to whom correspondence should be addressed.
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.
Appl. Sci. 2017, 7(12), 1327; https://doi.org/10.3390/app7121327
Received: 21 November 2017 / Revised: 15 December 2017 / Accepted: 15 December 2017 / Published: 19 December 2017
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
Show Figures

Graphical abstract

MDPI and ACS Style

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.

Show more citation formats Show less citations formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Search more from Scilit
 
Search
Back to TopTop