Scanning Probe Recognition Microscopy Investigation of the Elastic Properties of Tissue Scaffolding
- PDF / 111,904 Bytes
- 6 Pages / 612 x 792 pts (letter) Page_size
- 55 Downloads / 163 Views
O15.2.1
Scanning Probe Recognition Microscopy Investigation of the Elastic Properties of Tissue Scaffolding Q. Chen1, Y. Fan1, V.M. Ayres1, L. Udpa1, M.S. Schindler1 and A. F. Rice2 Michigan State University, East Lansing, MI 48824 2 Veeco Metrology Group, Santa Barbara, CA 93117 1
ABSTRACT Scanning Probe Recognition Microscopy is a new scanning probe capability under development within our group to reliably return to and directly interact with a specific nanoscale feature of interest, without the use of a zoom box with its thermal drift and local origin difficulties. It is a recognition-driven and learning approach, made possible through combining SPM piezoelectric implementation with on-line image processing and dynamically adaptive learning algorithms. Segmentation plus a recognized pattern is implemented within a scan plan and used to guide the tip in a recognition-driven return to a specific site. The specific application focus of our group is on the development of Scanning Probe Recognition Microscopy for nanobiological investigations. In the present work, Scanning Probe Recognition Microscopy is used in a direct investigation of the surface and elastic properties along individual tubules within a tissue scaffolding matrix. Elastic properties are indicated as important influences on actin polymerization and consequent cell pseudopodia extension and contraction. INTRODUCTION Scanning probe recognition microscopy is a new scanning probe capability under development within our group to reliably return to and directly interact with a specific nanoscale feature of interest, without the use of a zoom box with its thermal drift and local origin difficulties. It is a recognition-driven and learning approach, made possible through combining SPM piezoelectric implementation with on-line image processing and dynamically adaptive learning algorithms. Segmentation plus a recognized pattern is implemented within a scan plan and used to guide the tip in a recognition-driven return to a specific site. The specific application focus of our group is on the development of Scanning probe recognition microscopy for nanobiological investigations. In previous work, we have successfully recognized and classified tubular versus globular biological objects from experimental atomic force microscope (AFM) images using a method based on normalized central moments1,2. Normalized central moments are translation, rotation and scale invariant. We have also extended this work to include recognition schemes appropriate for more subtle differences between biological objects of similar globular external boundaries with dissimilar internal features by adding the Continuous Wavelet Transform (CWT) with a differential Gaussian mother wavelet3. The 2-D continuous wavelet transform allows multi-scale analysis of images. Thus, these two methods together can be applied to analyze biological objects of any scale.
O15.2.2
In the present work, scanning probe recognition microscopy is used in a direct investigation of the surface and elastic properties
Data Loading...
