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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)
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)
Content-based image retrieval requires a formal description of visual information. In medical applications, all relevant biological objects have to be represented by this description. Although color as the primary feature has proven successful in publicly available retrieval systems of general purpose, this description is not applicable to most medical(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)
Recent research has suggested that there is no general similarity measure, which can be applied on arbitrary databases without any parameterization. Hence, the optimal combination of similarity measures and parameters must be identified for each new image repository. This optimization loop is time consuming and depends on the experience of the designer as(More)
The classification and measuring of objects in medical images is important in radiological diagnostics and education , especially when using large databases as knowledge resources, for instance a picture archiving and communication system (PACS). The main challenge is the modeling of medical knowledge and the diagnostic context to label the sought objects.(More)
This paper presents a technical framework to support the development and installation of system for content-based image retrieval in medical applications (IRMA). A strict separation of feature extraction, feature storage, feature comparison, and the user interfaces is suggested. This allows to reuse implemented components in different retrieval algorithms,(More)
PURPOSE Maturity estimation by radiological bone age assessment (BAA) is a frequent task for pediatric radiologists. Following Greulich and Pyle, all hand bones are compared with a standard atlas, or a subset of bones is examined according to Tanner and Whitehouse. We support BAA comparing the epiphyses of a current case to similar cases with validated bone(More)
The content of medical images can often be described as a composition of relevant objects with distinct relationships. Each single object can then be represented as a graph node, and local features of the objects are associated as node attributes, e.g. the centroid coordinates. The relations between these objects are represented as graph edges with(More)