Berthold B. Wein

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OBJECTIVES To develop a general structure for semantic image analysis that is suitable for content-based image retrieval in medical applications and an architecture for its efficient implementation. METHODS Stepwise content analysis of medical images results in six layers of information modeling incorporating medical expert knowledge (raw data layer,(More)
Categorization of medical images means selecting the appropriate class for a given image out of a set of pre-defined categories. This is an important step for data mining and content-based image retrieval (CBIR). So far, published approaches are capable to distinguish up to 10 categories. In this paper, we evaluate automatic categorization into more than 80(More)
Modern communication standards such as Digital Imaging and Communication in Medicine (DICOM) include nonimage data for a standardized description of study, patient, or technical parameters. However, these tags are rather roughly structured, ambiguous, and often optional. In this paper, we present a mono-hierarchical multi-axial classification code for(More)
Picture archiving and communication systems (PACS) aim to efficiently provide the radiologists with all images in a suitable quality for diagnosis. Modern standards for digital imaging and communication in medicine (DICOM) comprise alphanumerical descriptions of study, patient, and technical parameters. Currently, this is the only information used to select(More)
Recently, research in the field of content-based image retrieval has attracted a lot of attention. Nevertheless, most existing methods cannot be easily applied to medical image databases, as global image descriptions based on color, texture, or shape do not supply sufficient semantics for medical applications. The concept for content-based image retrieval(More)
The objective of this work is to develop a general structure for semantic image analysis that is suitable for content-based image retrieval in medical applications and an architecture for its efficient implementation. Stepwise content analysis of medical images results in six layers of information modeling (raw data layer, registered data layer, feature(More)
Volumetric computed tomography (CT) scans ("spiral CT") were performed after intravenous (i.v.) cholangiography followed by additional 3D surface reconstructions of gallbladder and biliary ducts. 34 patients were investigated prior to cholecystectomy. No allergic adverse reactions were observed. The scan time was 24 s. Contrast enhancement in the(More)
Automatic identification of frontal (posteroanterior/anteroposterior) vs. lateral chest radiographs is an important preprocessing step in computer-assisted diagnosis, content-based image retrieval, as well as picture archiving and communication systems. Here, a new approach is presented. After the radiographs are reduced substantially in size, several(More)
Global features describe the image content by a small number of numerical values, which are usually combined into a vector of less than 1,024 components. Since color is not present in most medical images, grey-scale and texture features are analyzed in order to distinguish medical imagery from various modalities. The reference data is collected arbitrarily(More)
Zusammenfassung. Beim Aufbau eines Image{Retrieval{Systems, das inhaltsbasierte Anfragen an eine medizinische Bilddatenbank erlauben soll, mu das Zusammenf uhren der interdisziplinaren Kompetenzen aller Beteiligten durch eine entsprechend gestaltete Entwicklungsumgebung unterst utzt werden. Die Ressourcen eines Image{Retrieval{Systems sind das(More)