Use this URL to cite or link to this record in EThOS: https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.777675
Title: Spatial description-based approach towards integration of biomedical atlases
Author: Zaizi, Nurzi Juana Binti Mohd
ISNI:       0000 0004 7963 4494
Awarding Body: Heriot-Watt University
Current Institution: Heriot-Watt University
Date of Award: 2015
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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.
Supervisor: Burger, Albert ; Baldock, Richard Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID: uk.bl.ethos.777675  DOI: Not available
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