Title :
Object type recognition for automated analysis of protein subcellular location
Author :
Zhao, Ting ; Velliste, Meel ; Boland, Michael V. ; Murphy, Robert F.
Author_Institution :
Dept. of Biomed. Eng., Carnegie Mellon Univ., Pittsburgh, PA, USA
Abstract :
The new field of location proteomics seeks to provide a comprehensive, objective characterization of the subcellular locations of all proteins expressed in a given cell type. Previous work has demonstrated that automated classifiers can recognize the patterns of all major subcellular organelles and structures in fluorescence microscope images with high accuracy. However, since some proteins may be present in more than one organelle, this paper addresses a more difficult task: recognizing a pattern that is a mixture of two or more fundamental patterns. The approach utilizes an object-based image model, in which each image of a location pattern is represented by a set of objects of distinct, learned types. Using a two-stage approach in which object types are learned and then cell-level features are calculated based on the object types, the basic location patterns were well recognized. Given the object types, a multinomial mixture model was built to recognize mixture patterns. Under appropriate conditions, synthetic mixture patterns can be decomposed with over 80% accuracy, which, for the first time, shows that the problem of computationally decomposing subcellular patterns into fundamental organelle patterns can be solved.
Keywords :
biological organs; biological techniques; cellular biophysics; image recognition; object recognition; proteins; automated analysis; image recognition; object type recognition; organelle patterns; pattern recognition; protein subcellular location; Biological system modeling; Cells (biology); Displays; Fluorescence; Humans; Image recognition; Microscopy; Pattern recognition; Proteins; Proteomics; Fluorescence microscopy; image modeling; location proteomics; mixed-pattern decomposition; object type recognition; protein subcellular location; Algorithms; Artificial Intelligence; Hela Cells; Humans; Image Enhancement; Image Interpretation, Computer-Assisted; Information Storage and Retrieval; Microscopy, Fluorescence; Neoplasm Proteins; Pattern Recognition, Automated; Reproducibility of Results; Sensitivity and Specificity; Subcellular Fractions;
Journal_Title :
Image Processing, IEEE Transactions on
DOI :
10.1109/TIP.2005.852456