Skip to Main Content
Knife-edge scanning microscopy (KESM) is a recently developed technique that allows fast and automated imaging of several hundred cubic millimeters of tissue at sub-micron resolution. Successive sections are captured in registration by imaging the specimen concurrently with cutting by a diamond-knife ultramicrotome. Because this imaging technique is relatively new, we are currently investigating ways to improve image quality and data rate. In addition, certain imaging artifacts are unique to this technology and the time required to perform corrective image processing is a concern due to the high rate of image capture. In this paper, we describe algorithms that can be used to process KESM images in order to obtain the quality necessary for subsequent segmentation and modeling. There is also emphasis on making these algorithms independent of global information within the image so that they can be more easily parallelized.