Authors:
Shruthi Raghavan
and
Jaerock Kwon
Affiliation:
Kettering University, United States
Keyword(s):
KESM, Artifact Removal, Preprocessing, Tissue Extraction, Image Normalization.
Related
Ontology
Subjects/Areas/Topics:
Bioimaging
;
Biomedical Engineering
;
Histology and Tissue Imaging
;
Image Processing Methods
Abstract:
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 leve
ls 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.
(More)