Tissue characterization using dimensionality reduction and fluorescence imaging.
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.
Lekadir
Karim
K
Visual Information Processing Group, Department of Computing, Imperial College London, United Kingdom.
Elson
Daniel S
DS
Requejo-Isidro
Jose
J
Dunsby
Christopher
C
McGinty
James
J
Galletly
Neil
N
Stamp
Gordon
G
French
Paul M W
PM
Yang
Guang-Zhong
GZ
D000465:Algorithms; D001185:Artificial Intelligence; D003198:Computer Simulation; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D036641:Microscopy, Fluorescence, Multiphoton / Q000295:instrumentation / Q000379:methods*; D008954:Models, Biological; D010179:Pancreas / Q000166:cytology* / Q000502:physiology; D010363:Pattern Recognition, Automated / Q000379:methods*; D019047:Phantoms, Imaging; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity