A generic concept for the implementation of medical image retrieval systems.

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, which improves software quality, shortens the development cycle for applications, and allows to introduce standardized end-user interfaces.

A generic concept for the implementation of medical image retrieval systems.

This work presents mechanisms to support the development and installation of content-based image retrieval in medical applications (IRMA). A strict separation of feature extraction, feature storage, feature comparison, and the user interfaces is suggested. The concept and implementation of a system following these guidelines is described.

Strategies to configure image analysis algorithms for clinical usage.

Medical imaging informatics must exceed the mere development of algorithms. The discipline is also responsible for the establishment of methods in clinical practice to assist physicians and improve health care. From our point of view, it is commonly accepted that model-based analysis of medical images is superior to other concepts, but only a few applications are found in daily clinical use.

Automatic categorization of medical images for content-based retrieval and data mining.

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.

IRMA--content-based image retrieval in medical applications.

The impact of content-based access to medical images is frequently reported but existing systems are designed for only a particular modality or context of diagnosis. Contrarily, our concept of image retrieval in medical applications (IRMA) aims at a general structure for semantic content analysis that is suitable for numerous applications in case-based reasoning or evidence-based medicine. Within IRMA, stepwise processing results in six layers of information modeling (raw data layer, registered data layer, feature layer, scheme layer, object layer, knowledge layer) incorporating medical expert knowledge.

Determining the view of chest radiographs.

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.

Colour texture analysis for quantitative laryngoscopy.

Objective: Whilst considerable progress has been made in enhancing the quality of indirect laryngoscopy and image processing, the evaluation of clinical findings is still based on the clinician's judgement. The aim of this paper was to examine the feasibility of an objective computer-based method for evaluating laryngeal disease.

Material And Methods: Digitally recorded images obtained by 90 degree- and 70 degree-angled indirect rod laryngoscopy using standardized white balance values were made of 16 patients and 19 healthy subjects.