Framework for morphometric classification of cells in imaging flow cytometry

Summary 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. Lay description In this article, we propose a novel framework for processing the raw data generated using microfluidics based imaging flow cytometers. Microfluidics microscopy or microfluidics based imaging flow cytometry (mIFC) is a recent microscopy paradigm, that combines the statistical power of flow cytometry with spatial and quantitative morphology of digital microscopy, which allows us imaging cells while they are in flow. In comparison to the conventional slide‐based imaging systems, mIFC is a nascent technology enabling high throughput imaging of cells and is yet to take the form of a clinical diagnostic tool. The proposed framework process the raw data generated by the mIFC systems. The framework incorporates several steps: beginning from pre‐processing of the raw video frames to enhance the contents of the cell, localising the cell by a novel, fully automatic, non‐iterative graph based algorithm, extraction of different quantitative morphological parameters and subsequent classification of cells. 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 cost‐effective microfluidics based imaging flow cytometer