An Accurate Perception Method for Low Contrast Bright Field Microscopy in Heterogeneous Microenvironments
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 biolog...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2017-12-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/7/12/1327 |
| _version_ | 1811281285118689280 |
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| author | Keshav Rajasekaran Ekta Samani Manasa Bollavaram John Stewart Ashis G. Banerjee |
| author_facet | Keshav Rajasekaran Ekta Samani Manasa Bollavaram John Stewart Ashis G. Banerjee |
| author_sort | Keshav Rajasekaran |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-13T01:29:49Z |
| format | Article |
| id | doaj.art-4f21858dd0904b0ebe6ec5f0f4d01c9b |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-04-13T01:29:49Z |
| publishDate | 2017-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-4f21858dd0904b0ebe6ec5f0f4d01c9b2022-12-22T03:08:32ZengMDPI AGApplied Sciences2076-34172017-12-01712132710.3390/app7121327app7121327An Accurate Perception Method for Low Contrast Bright Field Microscopy in Heterogeneous MicroenvironmentsKeshav Rajasekaran0Ekta Samani1Manasa Bollavaram2John Stewart3Ashis G. Banerjee4Department of Mechanical Engineering, University of Maryland, College Park, MD 20742, USADepartment of Electrical Engineering, Indian Institute of Technology, Gandhinagar, Palaj 382355, Guajarat, IndiaDepartment of Mechanical Engineering, University of Washington, Seattle, WA 98195, USADepartment of Industrial & Systems Engineering, University of Washington, Seattle, WA 98195, USADepartment of Mechanical Engineering, University of Washington, Seattle, WA 98195, USAAutomated 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.https://www.mdpi.com/2076-3417/7/12/1327bright field imagingcell and microsphere perceptionblob and feature detectionconvolutional neural network |
| spellingShingle | Keshav Rajasekaran Ekta Samani Manasa Bollavaram John Stewart Ashis G. Banerjee An Accurate Perception Method for Low Contrast Bright Field Microscopy in Heterogeneous Microenvironments Applied Sciences bright field imaging cell and microsphere perception blob and feature detection convolutional neural network |
| title | An Accurate Perception Method for Low Contrast Bright Field Microscopy in Heterogeneous Microenvironments |
| title_full | An Accurate Perception Method for Low Contrast Bright Field Microscopy in Heterogeneous Microenvironments |
| title_fullStr | An Accurate Perception Method for Low Contrast Bright Field Microscopy in Heterogeneous Microenvironments |
| title_full_unstemmed | An Accurate Perception Method for Low Contrast Bright Field Microscopy in Heterogeneous Microenvironments |
| title_short | An Accurate Perception Method for Low Contrast Bright Field Microscopy in Heterogeneous Microenvironments |
| title_sort | accurate perception method for low contrast bright field microscopy in heterogeneous microenvironments |
| topic | bright field imaging cell and microsphere perception blob and feature detection convolutional neural network |
| url | https://www.mdpi.com/2076-3417/7/12/1327 |
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