AVS 50th International Symposium
    QSA-10 Topical Conference Tuesday Sessions
       Session QS-TuP

Paper QS-TuP2
A Multi-modal Imaging and Visualization System for 3-D Materials Characterization

Tuesday, November 4, 2003, 5:30 pm, Room Hall A-C

Session: Aspects of Quantitative Surface Analysis
Presenter: J.E. Fulghum, University of New Mexico
Authors: J.E. Fulghum, University of New Mexico
K. Artyushkova, University of New Mexico
J. Farrar, University of New Mexico
L. Broadwater, Kent State University
J. Fenton, University of New Mexico
S. Pylypenko, University of New Mexico
D. Barbash, University of New Mexico
Correspondent: Click to Email
Rapid image acquisition has transformed laboratory XPS imaging from a novelty to an increasingly routine analysis method for surface chemical characterization on the scale of microns. A variety of techniques, including FTIR, AFM and confocal microscopy (CM) have fields-of-view which are comparable to imaging XPS, making correlative analyses possible. With appropriate sample marking, information can be acquired from the same area on samples using multiple techniques. Correlating and combining this information allows us to model chemical changes within the sample through visualization techniques. The end result will be a three-dimensional model of the complex chemical structures and morphologies formed in multicomponent, heterogeneous samples. An additional goal is to utilize multivariate analysis methods to extract quantitative data from images and link them to chemical information. Proper integration of useful data from the separate techniques is essential. A comprehensive image analysis system - the Active Knowledge Mesh Model (AKM) - is currently under development in our laboratories. Image analysis involved in AKM has several steps, depending on the properties of the images and prior knowledge of the system and the experiment. Correlating the data from multiple modalities requires experimental matching and marking, image registration, multivariate image analysis, image quantification and image fusion. A prototype interface of AKM architecture involving all these steps will be shown. This work has been partially supported by NSF ALCOM (DMR89-20147), NSF CHE-0113724 and UNM.