Bio-medical Ontologies Maintenance and Change Management

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Multimedia Medical Databases 125 Each image is automatically segmented into regions named "blobs" with associated color and texture descriptors. Querying is based on the attributes of one or two regions of interest, rather than a description of the entire image. The blob descriptions were indexing using a tree in order to make a faster retrieval. Experiments showed encouraging results for both querying and indexing [3, 8, 10]. In [64] the Schema Reference System is presented. This is a content-based image retrieval system that employs multiple segmentation algorithms and indexing and retrieval subsystems. These algorithms are the following: • Pseudo Flat Zone Loop algorithm (PFZL), contributed by Munich University of Technology -Institute for Integrated Systems. • Modified Recursive Shortest Spanning Tree algorithm (MRSST), contributed by Dublin City University • K-Means-with-Connectivity-Constraint algorithm (KMCC), contributed by the Informatics and Telematics Institute/Centre for Research and Technology - Hellas. • Expectation Maximization algorithm (EM) in a 6D colour/texture space, contributed by Queen Mary University of London. The automatic segmentation techniques were applied on various imaging modalities: brain imaging, chest radiography, computed tomography, digital mammography or ultrasound imaging. A lot of studies were made on MR brain images in order to extract the brain volume, to outline structures such as the cerebral cortex or the hippocampus or to segment the brain tissue into gray matter, white matter and cerebrospinal fluid with the help of classifier approaches, clustering approaches, neuronal network and Markov random fields. For the segmentation of the cerebral cortex or other structures (the ventricles, the corpus callosum, the hippocampus) the deformable models were especially used. The atlas-guided methods are capable of fully segmentation of the brain structures [76]. Automatic segmentation techniques in computed tomography were applied to bone scans (thresholding, region growing, Markov random fields or deformable models), to thoracic scans (deformable models, region growing combined with watershed algorithms, region growing combined with fuzzy logic) or liver images (deformable models) [53, 76, 68]. The automatic segmentation applied on digital mammography tries to distinguish the tumors, the microcalcification clusters or other pathologies. In reference materials two approaches can be found: • Image initial segmentation and labeling the candidate regions as normal or suspicious • Image processing in order to detect the presence of pathology and then image segmentation to determine its precise location The most used techniques are: thresholding, region growing and Markov random fields, because pathological regions have often different texture characteristics [76].
126 L. Stanescu, D. Dan Burdescu, and M. Brezovan In ultrasound imaging the deformable models were successfully applied for segmentation of the echocardiograms, to detect the boundary of the fetus and the fetus head, or to outline the cysts in breast images. Though, the segmentation algorithms are limited in ultrasound images because of the high level of speckling present in this type of medical images [76]. 5.2 The Color Set Back-Projection Algorithm For detecting color regions, at Software Engineering Department Craiova, it was chosen the color set back-projection algorithm, introduced initially by Swain and Ballard and then developed in the research projects at Columbia University, in the content-based visual retrieval domain [83, 86, 87]. This technique provides the automatic extraction of regions and the representation of their color content. The extraction system for color regions has four steps [83, 86, 87]: 1. The image transformation, quantization and filtering (the transformation from the RGB color space to HSV color space and the quantization of the HSV color space at 166 colors) 2. Back-projection of binary color sets 3. The labeling of regions 4. The extraction of the region features The algorithm reduces the insignificant color information and makes evident the significant color regions, followed by the generation, in automatic way, of the regions of a single color, of the two colors, of three colors. To conclude with, the second step of the color set back-projection algorithm is the following [83, 86, 87]: 1. Detection of single color regions – Having the image histogram, H[m], all the values m ' = m for which H[ m] ≥ p0 are detected. – For each m' the color set c having the property c[ k] = 1for k = m and c [ k] = 0 in other cases is found. On the image R[m,n] the back-projection algorithm for each color set c is applied and the color regions are found. For each region n the local histogram Ln [m] is stored. – The residue histogram [ m] = H [ m] − ∑ L [ m] is computed H r n n 2. Detection of two colors regions – The values l '= 1 and m ' = m , l ≠ m , H[ 1] ≥ p , 0 H[ m] ≥ p and 0 – H r[ 1] ≥ p , 1 H r[ m] ≥ p are found. 1 For each set l', m' the color set c having the property c [ k] = 1 for k = l' or k = m' and c[ k] = 0 in other cases is found. On the image R[m,n] the
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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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