Spatial description-based approach towards integration of biomedical atlases
Abstract
Biomedical imaging has become ubiquitous in both basic research and the clinical
sciences. As technology advances the resulting multitude of imaging modalities has
led to a sharp rise in the quantity and quality of such images. Whether for epi-
demiological studies, educational uses, clinical monitoring, or translational science
purposes, the ability to integrate and compare such image-based data has become in-
creasingly critical in the life sciences and eHealth domain. Ontology-based solutions
often lack spatial precision. Image processing-based solutions may have di culties
when the underlying morphologies are too di erent. This thesis proposes a compro-
mise solution which captures location in biomedical images via spatial descriptions.
Three approaches of spatial descriptions have been explored. These include: (1)
spatial descriptions based on spatial relationships between segmented regions; (2)
spatial descriptions based on ducial points and a set of spatial relations; and (3)
spatial descriptions based on ducial points and a set of spatial relations, integrated
with spatial relations between segmented regions. Evaluation, particularly in the
context of mouse gene expression data, a good representative of spatio-temporal bi-
ological data, suggests that the spatial description-based solution can provide good
spatial precision. This dissertation discusses the need for biomedical image data in-
tegration, the shortcomings of existing solutions and proposes new algorithms based
on spatial descriptions of anatomical details in the image. Evaluation studies, par-
ticularly in the context of gene expression data analysis, were carried out to study
the performance of the new algorithms.