Fully Automated Image Preprocessing for Feature Extraction from Knife-edge Scanning Microscopy Image Stacks - Towards a Fully Automated Image Processing Pipeline for Light Microscopic Images

Knife-Edge Scanning Microscopy (KESM) stands out as a fast physical sectioning approach for imaging tissues at sub-micrometer resolution. To implement high-throughput and high-resolution, KESM images a tissue ribbon on the knife edge as the sample is being sectioned. This simultaneous sectioning and imaging approach has following benefits: (1) No image registration is required. (2) No manual job is required for tissue sectioning, placement or microscope imaging. However spurious pixels are present at the left and right side of the image, since the field of view of the objective is larger than the tissue width. The tissue region needs to be extracted from these images. Moreover, unwanted artifacts are introduced by KESM’s imaging mechanism, namely: (1) Vertical stripes caused by unevenly worn knife edge. (2) Horizontal artifacts due to vibration of the knife while cutting plastic embedded tissue. (3) Uneven intensity within an image due to knife misalignment. (4) Uneven intensity levels across images due to the variation of cutting speed. This paper outlines an image processing pipeline for extracting features from KESM images and proposes an algorithm to extract tissue region from physical sectioning-based light microscope images like KESM data for automating feature extraction from these data sets.