A Nonlinear Mapping for Data Structure Analysis
IEEE Transactions on Computers
Optimal Feature Selection Applied to Multispectral Fluorescence Imaging
MICCAI '08 Proceedings of the 11th International Conference on Medical Image Computing and Computer-Assisted Intervention, Part II
IROS'09 Proceedings of the 2009 IEEE/RSJ international conference on Intelligent robots and systems
High throughput analysis of breast cancer specimens on the grid
MICCAI'07 Proceedings of the 10th international conference on Medical image computing and computer-assisted intervention - Volume Part I
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Multidimensional fluorescence imaging is a powerful molecular imaging modality that is emerging as an important tool in the study of biological tissues. Due to the large volume of multi-spectral data associated with the technique, it is often difficult to find the best combination of parameters to maximize the contrast between different tissue types. This paper presents a novel framework for the characterization of tissue compositions based on the use of time resolved fluorescence imaging without the explicit modeling of the decays. The composition is characterized through soft clustering based on manifold embedding for reducing the dimensionality of the datasets and obtaining a consistent differentiation scheme for determining intrinsic constituents of the tissue. The proposed technique has the benefit of being fully automatic, which could have significant advantages for automated histopathology and increasing the speed of intraoperative decisions. Validation of the technique is carried out with both phantom data and tissue samples of the human pancreas.