Image registration and visualization of in situ gene expression images.
Author(s) -
Ernur Saka
Publication year - 2011
Language(s) - English
Resource type - Dissertations/theses
DOI - 10.18297/etd/1249
Subject(s) - landmark , image registration , computer science , visualization , artificial intelligence , mutual information , computer vision , consistency (knowledge bases) , matlab , expression (computer science) , modality (human–computer interaction) , correlative , pattern recognition (psychology) , data mining , image (mathematics) , linguistics , philosophy , programming language , operating system
IMAGE REGISTRATION AND VISUALIZATION OF IN SITU GENE EXPRESSION IMAGES August 1,2011 In the age of high-throughput molecular biology techniques, scientists have incorporated the methodology of in-situ hybridization to map spatial patterns of gene expression. In order to compare expression patterns within a common tissue structure, these images need to be "registered" or organized into a common coordinate system for alignment to a reference or atlas images. We use three different image registration methodologies (manual; correlation based; mutual information based) to determine the common coordinate system for the reference and in-situ hybridization images. All three methodologies are incorporated into a Matlab tool to visualize the results in a user friendly way and save them for future work. Our results suggest that the user-defined landmark method is best when considering images from different modalities; automated landmark detection is best when the images are expected to have a high degree of consistency; and the mutual information methodology is useful when the images are from the same modality.
Accelerating Research
Robert Robinson Avenue,
Oxford Science Park, Oxford
OX4 4GP, United Kingdom
Address
John Eccles HouseRobert Robinson Avenue,
Oxford Science Park, Oxford
OX4 4GP, United Kingdom