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 epidemiological studies, educational uses, clinical monitoring, or translational science purposes, the ability to integrate and compare such image-based data has become increasingly critical in the life sciences and eHealth domain. Ontology-based solutions often lack spatial precision. Image processing-based solutions may have difficulties when the underlying morphologies are too different. This thesis proposes a compromise 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 fiducial points and a set of spatial relations; and (3) spatial descriptions based on fiducial 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 biological data, suggests that the spatial description-based solution can provide good spatial precision. This dissertation discusses the need for biomedical image data integration, the shortcomings of existing solutions and proposes new algorithms based on spatial descriptions of anatomical details in the image. Evaluation studies, particularly in the context of gene expression data analysis, were carried out to study the performance of the new algorithms.

Spatial description-based approach towards integration of biomedical atlases

Image-based data integration in eHealth and life sciences is typically concerned with the method used for anatomical space mapping, needed to retrieve, compare and analyse large volumes of biomedical data. In mapping one image onto another image, a mechanism is used to match and find the corresponding spatial regions which have the same meaning between the source and the matching image. Image-based data integration is useful for integrating data of various information structures. Here we discuss a broad range of issues related to data integration of various information structures, review exemplary work on image representation and mapping, and discuss the challenges that these techniques may bring.

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Using image mapping towards biomedical and biological data sharing

Proposes and implement a content-based image retrieval system,introduces the system ar-chitecture with emphasis on the abstract approaches of color features and the image re-trieval engine,in which FCM arithmetic is used to improve the efficiency and precision of comparability measurement.Some experiment results are given to validate the system.

An Image Retrieval System Based on FCM

Grain yield is important in national economy so there is necessary for grain yield prediction. A novel predicting model based on spiking neural networks (SNNs) is presented for this purpose. SNNs are computationally more effective than conventional artificial neural networks. The spiking neurons act as basic elements in which information deliver from one neuron to another in forms of multiple spikes via plenty of synapses. Besides, the corresponding learning mechanism called Spikeprop is also discussed. An example, prediction of China annual grain yields as our experiment, is used to explain the principle of SNNs based method. Experimental results are demonstrated showing the feasibility and accuracy of our approach.

Prediction of grain yield based on spiking neural networks model

This paper discusses the principles of connectivity,similarity and comparability in image identification based on the mixed-graph structure.It proposes a reduction algorithm that compares each composite node in a mixed-graph against a reference image to find the possible matches and the corresponding maximal similarity value(i.e.,compound similarity),and reduces the mixed-graph using the best matches.To ease the calculation and better illustrate the reduction process,this paper labels the nodes in the mixed-graph with single attributes,and simplifies the similarity computation to sort and query operations with a linear complexity.The results show that the method is efficient and feasible.

Algorithm of Image Region Matching and Reduction Based on Mix-graph Structure

A novel method based on mixed graph structure is proposed for image representation and matching. The mixed graph structure is constructed according to the spatial relation of the regions within an image. This structure does not require redundant information to describe images. An image matching process focuses on evaluating region attributes and relationships contained in the corresponding mixed graph structure. An association graph based on this mixed graph structure is then constructed. By searching the largest cliques in the association graph, we are able to obtain the optimal similarity among images under this structure. Experimental results are presented showing the effectiveness of our approach. Keywords-image representation; image matching; mixed graph structure; association graph;

A Novel Approach for Image Representation and Matching Based on Mixed Graph Structure

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A Novel Approach for Image Representation and Matching Based on Mixed Graph Structure