Prediction analysis and quality assessment of microwell array images.
Journal:
Electrophoresis
Published Date:
Apr 1, 2018
Abstract
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.