Biomedical subjects

Thomas M Lehmann

Publications and source records attributed to Thomas M Lehmann.

3 recordsLinked to original sources

PubMed2004

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. At the scheme layer, medical images are represented by a hierarchical structure of ellipses (blobs) describing image regions. Hence, image retrieval transforms to graph matching. The multilayer processing is implemented using a distributed system designed with only three core elements. The central database holds program sources, process-ing schemes, images, features, and blob trees; the scheduler balances distributed computing by addressing daemons running on all connected workstations; and the web server provides graphical user interfaces for data entry and retrieval..

Computer Graphics↗

PubMed2003

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. After the radiographs are reduced substantially in size, several distance measures are applied for nearest-neighbor classification. Leaving-one-out experiments were performed based on 1,867 radiographs from clinical routine. For comparison to existing approaches, subsets of 430 and 5 training images are also considered. The overall best correctness of 99.7% is obtained for feature images of 32 x 32 pixels, the tangent distance, and a 5-nearest-neighbor classification scheme. Applying the normalized cross correlation function, correctness yields still 99.6% and 99.3% for feature images of 32 x 32 and 8 x 8 pixel, respectively. Remaining errors are caused by image altering pathologies, metal artifacts, or other interferences with routine conditions. The proposed algorithm outperforms existing but sophisticated approaches and is easily implemented at the same time.

Humans↗

PubMed2003

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. The digital images were evaluated manually by the clinician based on a standardized questionnaire, and suspect lesions were marked and classified on the image. Following colour separation, normal vocal cord areas as well as suspect lesions were analyzed automatically using co-occurrence matrices, which compare colour differences between neighbouring pixels over a predefined distance. RESULTS: Whilst colour histograms did not provide sufficient information for distinguishing between healthy and diseased tissues, consideration of the blue content of neighbouring pixels enabled a correct classification in 81.4% of cases. If all colour channels (red, green and blue) were regarded simultaneously, the best classification correctness obtained was 77.1%. CONCLUSIONS: Although only a very basic classification differentiating between healthy and diseased tissue was attempted, the results showed progress compared to grey-scale histograms, which have been evaluated before. The results document a first step towards an objective, machine-based classification of laryngeal images, which could provide the basis for further development of an expert system for use in indirect laryngoscopy.

Color↗