Phenotype Recognition with Combined Features and Random Subspace Classifier Ensemble

Background: Automated, image based high-content screening is a fundamental tool for discovery in biological science. Modern robotic fluorescence microscopes are able to capture thousands of images from massively parallel experiments such as RNA interference (RNAi) or small-molecule screens. As such, efficient computational methods are required for automatic cellular phenotype identification capable of dealing with large image data sets. In this paper we investigated an efficient method for the extraction of quantitative features from images by combining second order statistics, or Haralick features, with curvelet transform. A random subspace based classifier ensemble with multiple layer perceptron (MLP) as the base classifier was then exploited for classification. Haralick features estimate image properties related to second-order statistics based on the grey level co-occurrence matrix (GLCM), which has been extensively used for various image processing applications. The curvelet tran...

• Type: Journal
• Year: 2011
• Where: BMCBI
• Authors: Bailing Zhang, Tuan D. Pham
• Keywords: Artificial Intelligence, Base Classifier, BMCBI 2011, Curvelet Transform, Image