Bio-medical Ontologies Maintenance and Change Management

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Multimedia Medical Databases 129 procedure FindRegions (Graph G, colorset C) : InitQueue(Q) Visited = ∅ for *each node P in the G do if *color of P is in C then PUSH(P) Visited ← Visited ∪ {P} while not Empty(q) do CrtPoint
130 L. Stanescu, D. Dan Burdescu, and M. Brezovan Region 1 Region 2 Region 3 Region 4 Region 5 Region 6 Fig. 5.2. Color regions detected by the algorithm For each region, the color, the minimum bounding rectangle and the number of pixels that roughly indicates the dimension of the sick region, were stored in the database. The region localization is given by the minimal bounding rectangle (MBR). The region area is represented by the number of color pixels, and can be smaller than the minimum-bounding rectangle. Other experiments can be found in [90]. 5.3 Content-Based Region Query – Experiments and Results The regions detected by the image automatic segmentation techniques can be utilized in content-based region query of the multimedia medical databases [86]. In a content-based region query, the images are compared based on their regions. In the first step of the query, content-based visual queries are effectuated on the regions, and not on the images. Then, in the final step of the query, there are determined the images corresponding to the regions and there is computed the total distance between the images by the weighting of the distances between regions. So, the user selects one or several query regions in order to find in the database images containing similar regions from the color, size or spatial extent point of view. The content–based visual query may be improved by adding spatial information to the query. So, the total measure of the dissimilarity takes into consideration both the values of the features (color, texture), and the spatial values of the regions. There are two types of spatial indexing, namely: relative and absolute [83, 86, 87, 91]. The most powerful images retrieval system is the one that allows queries in which are specified both the visual features and spatial properties for the desired images. Such query offers to the user the possibility to control the selection of
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Amandeep S. Sidhu and Tharam S. Dil
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Amandeep S. Sidhu and Tharam S. Dil
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Preface The molecular biology commu
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Preface VII biomedical systems, and
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X Contents Mining Clinical, Immunol
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2 A.S. Sidhu, M. Bellgard, and T.S.
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Towards Bioinformatics Resourceomes
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2 The New Waves Towards Bioinformat
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2.4 Semantic Web Towards Bioinforma
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A Summary of Genomic Databases: Ove
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Appendix A Summary of Genomic Datab
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Protein Data Integration Problem Am
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Protein Data Integration Problem 57
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Fig. 3. Class Hierarchy of Protein
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72 L. Stanescu, D. Dan Burdescu, an
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Extraction of Constraints from Biol
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Classifying Patterns in Bioinformat
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Mining Clinical, Immunological, and
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Substructure Analysis of Metabolic
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entry compound relation subtype com
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Running Time 4500 4000 3500 3000 25
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6 Conclusion Substructure Analysis
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266 J.Y. Chen, S. Taduri, and F. Ll
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Completing the Total Wellbeing Puzz
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296 M. Fernandez, M. Villasana, and
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Genetic Algorithm in Ab Initio Prot
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Author Index Apiletti, Daniele 169
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Bio-medical Ontologies Maintenance and Change Management

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