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Prediction analysis and quality assessment of microwell array images
Author(s) -
Mazumdar Hirak,
Kim Tae Hyeon,
Lee Jong Min,
Ha Jang Ho,
Ahrberg Christian D.,
Chung Bong Geun
Publication year - 2018
Publication title -
electrophoresis
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.666
H-Index - 158
eISSN - 1522-2683
pISSN - 0173-0835
DOI - 10.1002/elps.201700460
Subject(s) - support vector machine , pattern recognition (psychology) , artificial intelligence , computer science , feature selection , filter (signal processing) , computer vision
Microwell arrays are widely used for the analysis of fluorescent‐labelled biomaterials. For rapid detection and automated analysis of microwell arrays, the computational image analysis is required. Support Vector Machines (SVM) can be used for this task. Here, we present a SVM‐based approach for the analysis of microwell arrays consisting of three distinct steps: labeling, training for feature selection, and classification into three classes. The three classes are filled, partially filled, and unfilled microwells. Next, the partially filled wells are analyzed by SVM and their tendency towards filled or unfilled tested through applying a Gaussian filter. Through this, all microwells can be categorized as either filled or unfilled by our algorithm. Therefore, this SVM‐based computational image analysis allows for an accurate and simple classification of microwell arrays.