Image based approaches have proven to be of great utility in the automated cell phenotype classification, it is very important to develop a method that efficiently quantifies, distinguishes and classifies sub-cellular images. In this work the invariant locally binary patterns (LBP) are applied, for the first time, to the classification of protein sub-cellular localization images. They are tested on three image datasets (available for download), in conjunction with support vector machines (SVMs) and random subspace ensembles of neural networks. Our method based on invariant LBP provides higher accuracy than other well known methods for feature extraction; moreover, our method does not require to (direct) crop the cells for the classification. The experimental results show that the random subspace ensemble of neural networks outperforms the SVM in this problem. The proposed approach based on the solely LBP features gives accuracies of 85%, 93.9% and 88.4% on the 2D HeLa dataset, LOCATE endogenous and transfected datasets, respectively, and in combination with other state-of-the-art methods for the cell phenotype image classification we obtain a classification accuracy of 94.2%, 98.4% and 96.5%.

A reliable method for cell phenotype image classification

NANNI, LORIS;LUMINI, ALESSANDRA
2008

Abstract

Image based approaches have proven to be of great utility in the automated cell phenotype classification, it is very important to develop a method that efficiently quantifies, distinguishes and classifies sub-cellular images. In this work the invariant locally binary patterns (LBP) are applied, for the first time, to the classification of protein sub-cellular localization images. They are tested on three image datasets (available for download), in conjunction with support vector machines (SVMs) and random subspace ensembles of neural networks. Our method based on invariant LBP provides higher accuracy than other well known methods for feature extraction; moreover, our method does not require to (direct) crop the cells for the classification. The experimental results show that the random subspace ensemble of neural networks outperforms the SVM in this problem. The proposed approach based on the solely LBP features gives accuracies of 85%, 93.9% and 88.4% on the 2D HeLa dataset, LOCATE endogenous and transfected datasets, respectively, and in combination with other state-of-the-art methods for the cell phenotype image classification we obtain a classification accuracy of 94.2%, 98.4% and 96.5%.
ARTIFICIAL INTELLIGENCE IN MEDICINE
Nanni, Loris; Lumini, Alessandra
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11585/63184
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