Bio-medical Ontologies Maintenance and Change Management

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Multimedia Medical Databases 75 Being given a collection C of N images, it is required to find all images αT ∈ C that have at least R symbols existing in the same spatial arrangement as the R symbols of the query image. In the case of the absolute spatial images query it have to be solved the following problem [83, 86]: Being given a collection C of N images and a spatial distance function vf , it is required to find the most appropriate k images αT ∈ C that have at least R regions R R R such that uf (αQ ,αT )≤ σf ,where αQ is the query image with R regions, and σf is a limit for the spatial distance. The most powerful images retrieval system must allows queries where 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 regions and attributes which are the most important in the similarity computation. 1.3 The Need for Content-Based Visual Query in Multimedia Medical Databases The directions where content-based retrieval is needed in medical multimedia databases were fully specified. They are [67, 69]: • Diagnostic aid – from the conversation with the doctors, the following situation appears frequently: the doctor visualizes a medical image, he/she cannot establish the diagnosis exactly, he is aware of the fact that he has seen something similar before but doesn’t have the means to search for it in the database; the problem can be solved establishing that image as query image and the contentbased image query will provide the similar images from the database; it is very likely that among the retrieved images should be the searched image together with its diagnosis, observations, treatment; so the content-based image query can be directly used in the diagnosis process; • Medical teaching – there is a series of applications for content-based visual query including other ways for access (text-based, hierarchical methods). Students can see the images in the database and the attached information in a simple and direct manner: they choose the query image and they see the similar images; this method stimulates learning by comparing similar cases and their particularities or comparing similar cases with different diagnosis; • Medical research – using content-based visual query in this area brings up similar advantages for medical teaching. It can be used, for example, for finding certain types of images to be included in a study, for finding misclassified images, etc.; • PACS and electronic records for patients – the integration of the content-based visual retrieval techniques and the simple text-based retrieval in PACS and the electronic patients’ record might bring important benefits in clinical applications. The information that is stored in the image can be used together with the alphanumerical information.
76 L. Stanescu, D. Dan Burdescu, and M. Brezovan 1.4 Content-Based Image Retrieval Systems There is in present a series of database management systems capable to manage different types of media. Many of these systems permits indexing and searching for multimedia information taking into account only structured information, using traditional techniques. They work well only with short numeric and alphanumeric arrays. This includes traditional queries of the databases based on alphanumerical arrays. They cannot be used for multimedia information indexing and retrieval. That is why the researchers studied the possibility to create new systems that satisfy the high demands of the multimedia information. An exception is Oracle Multimedia (Formerly Oracle inter-Media), a feature of Oracle Database that enables the efficient management and retrieval of image, audio, and video data. Oracle Multimedia has knowledge of the most popular multimedia formats and makes automate metadata extraction and basic image processing [13, 50, 73]. Most of the medical informatics systems use for multimedia medical databases management traditional database management servers (MySQL, MS SQL Server, Interbase, and Oracle). There have been implemented alternative methods for content-based visual retrieval, taking into consideration different characteristics like color, texture and shape. For medical multimedia databases content-based query it is generally used the method called QBE (Query by example). It implies the selection of an image or a region as a query image (region). For improving the results, the simple text-based query and content-based visual query can be combined [67]. In general, the images are represented in the databases by automatically extracted visual features that are supposed to correspond to the visual image content or the way we perceive it. The features mainly used for image retrieval, are [30, 67]: • Grey levels and color descriptors, in a local or global fashion • Texture descriptors • Shapes of segmented objects Content-based retrieval has been investigated in a number of important projects. It can be mentioned the QBIC project from IBM [1, 26, 103] and Virage, a commercial project for content-based retrieval [103]. Most of the projects are academically projects: Photobook system from MIT [103], MARS (Multimedia Analysis and Retrieval System) developed to the University of Illinois [79, 103], Chabot system for image retrieval [101], WebSeek system, VisualSEEk and SaFe implemented to the University of Columbia [83, 84, 87, 103]. Using higher-level information, such as segmented parts of the image for queries, was introduced by the Blobworld system [9]. A system that is available free of charge is the GNU Image Finding Tool (GIFT). Some systems are available as demonstration versions on the web such as Viper, WIPE or Compass [67].
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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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Bio-medical Ontologies Maintenance
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TextToOnto (Maedche and Volz 2001)
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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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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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