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 in medical applications (IRMA) is therefore based on the separation of the following processing steps: categorization of the entire image; registration with respect to prototypes; extraction and query-dependent selection of local features; hierarchical blob representation including object identification; and finally, image retrieval. Within the first step of processing, images are classified according to image modality, body orientation, anatomic region, and biological system. The statistical classifier for the anatomic region is based on Gaussian kernel densities within a probabilistic framework for multiobject recognition. Special emphasis is placed on invariance, employing a probabilistic model of variability based on tangent distance and an image distortion model. The performance of the classifier is evaluated using a set of 1617 radiographs from daily routine, where the error rate of 8.0% in this six-class problem is an excellent result, taking into account the difficulty of the task. The computed posterior probabilities are furthermore used in the subsequent steps of the retrieval process. © 2003 SPIE and IS&T.

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An essential part of the IRMA-project (Image Retrieval in Medical Applications) is the categorization of digitized images into predefined classes using a combination of different independent features. To obtain an automated and content-based categorization, the following features are extracted from the image data: Fourier coefficients of normalized projections are computed to supply a scale-and translation-invariant description. Furthermore, histogram information and Co-occurrence matrices are calculated to supply information about the grey value distribution and textural information. But the key part of the feature extraction is the shape information of the objects represented by an Active Shape Model. The Active Shape Model supports various form variations given by a representative training set; we use one particular Active Shape Model for each image class. These different Active Shape Models are matched on preprocessed image data with a simulated annealing optimization. The different extracted features were chosen with regard to the different characteristics of the image content. They give a comprehensive description of image content using only few different features. Using this combination of different features for categorization results in a robust classification of image data, which is a basic step towards medical archives that allow retrieval results for queries of diagnostic relevance.

Image retrieval in medical applications (IRMA) requires the cooperation of experts in the field of medicine, image analysis, feature analysis and systems engineering. A distributed developing platform was implemented to support the progress of the IRMA-system. As the concept for this system strictly separates the steps for medical image retrieval, its components can be developed separately by work groups in different departments. The development platform provides location and access transparency for its resources. These resources are images and extracted features as well as methods which all are distributed automatically between the work groups. Replications are created to avoid repeated network transfers. All resources are administered in one central database. Computationally expensive feature extraction tasks are distributed also automatically to be processed on concurring workstations of different work groups. The developing platform intensifies and simplifies the cooperation of the interdisciplinary IRMAdevelopment-team by providing fast and automated deliveries of components from software developers to physicians for evaluation.