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Framework for morphometric classification of cells in imaging flow cytometry

Publication Type : Journal Article

Source : Journal of Microscopy 261 (3), 307-319, 2016, DOI: 10.1111/jmi.12335

Keywords : Classification; imaging flow cytometry; leukaemia; morphology; segmentation; texture features

Campus : Amritapuri

School : Department of Computer Science and Engineering, School of Engineering

Center : Computer Vision and Robotics, Research & Projects

Department : Computer Science

Verified : No

Year : 2016

Abstract : Imaging flow cytometry is an emerging technology that combines the statistical power of flow cytometry with spatial and quantitative morphology of digital microscopy. It allows high-throughput imaging of cells with good spatial resolution, while they are in flow. This paper proposes a general framework for the processing/classification of cells imaged using imaging flow cytometer. Each cell is localized by finding an accurate cell contour. Then, features reflecting cell size, circularity and complexity are extracted for the classification using SVM. Unlike the conventional iterative, semi-automatic segmentation algorithms such as active contour, we propose a noniterative, fully automatic graph-based cell localization. In order to evaluate the performance of the proposed framework, we have successfully classified unstained label-free leukaemia cell-lines MOLT, K562 and HL60 from video streams captured using custom fabricated cost-effective microfluidics-based imaging flow cytometer. The proposed system is a significant development in the direction of building a cost-effective cell analysis platform that would facilitate affordable mass screening camps looking cellular morphology for disease diagnosis.

Cite this Research Publication : G Gopakumar, VK Jagannadh, SS Gorthi, GRKS Subrahmanyam, "Framework for morphometric classification of cells in imaging flow cytometry", Journal of Microscopy 261 (3), 307-319, 2016, DOI: 10.1111/jmi.12335

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