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