Classification of subcellular phenotype images by decision templates for classifier ensemble

Subcellular localization is a key functional characteristic of proteins. An automatic, reliable and efficient prediction system for protein subcellular localization is needed for large-scale genome analysis. The automated cell phenotype image classification problem is an interesting "bio-image informatics" application. It can be used for establishing knowledge of the spatial distribution of proteins within living cells and permits to screen systems for drug discovery or for early diagnosis of a disease. In this paper, three well-known texture feature extraction methods including local binary patterns (LBP), Gabor filtering and Gray Level Coocurrence Matrix (GLCM) have been applied to cell phenotype images and the multiple layer perceptron (MLP) method has been used to classify cell phenotype image. After classification of the extracted features, decision-templates ensemble algorithm (DT) is used to combine base classifiers built on the different feature sets. Different texture feature sets can provide sufficient diversity among base classifiers, which is known as a necessary condition for improvement in ensemble performance. For the HeLa cells, the human classification error rate on this task is of 17% as reported in previous publications. We obtain with our method an error rate of 4.8%.