Multiresolution Gray-Scale and Rotation Invariant Texture Classification with Local Binary Patterns
IEEE Transactions on Pattern Analysis and Machine Intelligence
BIBE '01 Proceedings of the 2nd IEEE International Symposium on Bioinformatics and Bioengineering
Journal of VLSI Signal Processing Systems - Special issue on signal processing and neural networks for bioinformatics
Description of interest regions with local binary patterns
Pattern Recognition
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As one of the most significant characteristics of human cell, subcellular localization plays a critical role for understanding specific functions of mammalian proteins. In this study, we developed a novel computational protocol for predicting protein subcellular locations from microscope cell images in human reproductive tissues. Experiments are performed on a benchmark dataset consisting of 7 major subcellular classes in human reproductive tissues collected from Human Protein Atlas database. We first separated protein and DNA staining in the images with both linear and nonnegative matrix factorization separation methods; then we extracted protein multi-view texture features including wavelet haralick and local binary patterns; finally based on the selected important feature subset achieved by feature selection technique, we constructed ensemble classifier based on support vector machines for predictions. Our experimental results show that 84% accuracy can be achieved through current system, and when only considering the most confident classifications, the accuracy can rise to 98%.