[Algorithm for automatic endocardium identification in digital echocardiography image sequences]. / Algorithmus zur automatischen Endokarderkennung in digitalen echokardiographischen Bildsequenzen.

The present paper describes an automated procedure for the detection of left ventricular internal wall edges in digital echocardiographic image sequences. The proposed procedure is divided into three steps and programmed in C/UNIX. It includes the use of a specially designed, application-specific adaptive spatio-temporal filter for noise reduction in echocardiographic image sequences, local 3-D histogram equalization for augmented contrast, and segmentation of the left ventricle using a regional growth method. When designing the adaptive spatio-temporal filter, we took into account the fact that the background noise is correlated in tangential orientation due to beam deflection at interfaces characterized by a large impedance "jump". Using the specially designed filter, the background noise is successfully reduced without degrading the ventricular contours. The simulation results presented highlight the performance of the proposed method in an exemplary manner.

An unsupervised texture segmentation algorithm with feature space reduction and knowledge feedback.

This paper presents an unsupervised texture segmentation algorithm based on feature extraction using multichannel Gabor filtering. It is shown that feature contrast, a criterion derived for Gabor filter parameter selection, is well suited for feature coordinate weighting in order to reduce the feature space dimension. The central idea of the proposed segmentation algorithm is to decompose the actual segmented image into disjunct areas called scrap images and use them after lowpass filtering as additional features for repeated k-means clustering and minimum distance classification. This yields a classification of texture regions with an improved degree of homogeneity while preserving precise texture boundaries.

[Computer-assisted analysis of ultrasound sequences of the left ventricle for determining ejection fraction]. / Rechnergestützte Analyse von Ultraschallsequenzen des linken Ventrikels zur Bestimmung der Ejektionsfraktion.

The present study describes an automatic procedure for calculating the ejection fraction on the basis of ultrasound images. This procedure is divided into three steps, and programmed in C/UNIX. In an initial step, an algorithm, known as sequence analysis, is used to identify the enddiastolic and endsystolic images from a series of ultrasound images. With the use of a template matching procedure for calculating displacement vectors, a method has been developed for the first time for analysing moving images for identification purposes. The subsequent image analysis employs various filter processes for noise suppression, contrast enhancement, image sharpening, edge detection and segmentation. On the basis of the geometrical data of the segmented left ventricle obtained in this way the respective volumes and ejection fraction are calculated. To validate this automatic procedure, the series of ultrasound images were examined by a cardiologist. It was found that the identification of the endsystolic and enddiastolic image was correct, and that there was good agreement between the automatically determined and manually determined ejection fraction.

Unsupervised texture segmentation of images using tuned matched Gabor filters.

Recent studies have confirmed that the multichannel Gabor decomposition represents an excellent tool for image segmentation and boundary detection. Unfortunately, this approach when used for unsupervised image analysis tasks imposes excessive storage requirements due to the nonorthogonality of the basis functions and is computationally highly demanding. In this correspondence, we propose a novel method for efficient image analysis that uses tuned matched Gabor filters. The algorithmic determination of the parameters of the Gabor filters is based on the analysis of spectral feature contrasts obtained from iterative computation of pyramidal Gabor transforms with progressive dyadic decrease of elementary cell sizes. The method requires no a priori knowledge of the analyzed image so that the analysis is unsupervised. Computer simulations applied to different classes of textures illustrate the matching property of the tuned Gabor filters derived using our determination algorithm. Also, their capability to extract significant image information and thus enable an easy and efficient low-level image analysis will be demonstrated.

[Metabolism studies with coffee and caffeine in healthy subjects, diabetics and patients with liver disease]. / Stoffwechseluntersuchungen mit Kaffee und Coffein bei Gesunden, Diabetikern und Leberkranken

Peroral application of caffeine or theophylline causes a significant increase of the free fatty acids and the free glycerol of the serum; blood sugar and cholesterol are not influenced. FFA and free glycerol decrease during the day by food intake, blood sugar and triglycerides rise. The ingestion of coffee (1-3 cups) does not influence fat and carbohydrate metabolism. To induce a significant increase of lipolysis and of serum triglyceride levels high caffeine doses (1000 mg) are required. Summarizing the data presented, the authors conclude that coffee intake (at dosages compatible with normal nutrition) does not deteriorate glucose tolerance and does not interfere with the fat metabolism. Our findings are confirmed by epidemiologic investigations which have shown that coffee intake is no risk factor for myocardial infarction.