15 Fakultätsübergreifend / Sonstige Einrichtung
Permanent URI for this collectionhttps://elib.uni-stuttgart.de/handle/11682/16
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Item Open Access Die Ermittlung von Steuerprofilen zur Insulinabgabe mit programmierbaren Insulininfusionssystemen(1981) Schulz, Gerhard; Beyer, Jürgen; Nagel, Joachim H.; Strack, Thomas; Krause, Ulrich; Hassinger, Wieland; Cordes, UweDer Insulinverbrauch und die Form der Insulinabgabeprofile bei intravenöser Insulintherapie mit nicht rückgekoppelten Infusionsgeräten wurde bisher nur rein empirisch ermittelt. Dabei war die Form der Insulinabgabeprofile nicht zuletzt auch von den technischen Möglichkeiten des Dosiergerätes abhängig. Unser Ziel war es, Grundsätze für die Blutzuckerführung mit Open-Loop-Systemen zu entwickeln. Die Insulinprofile sollten dem Blutzuckeranstieg und Abfall nach den Mahlzeiten möglichst gut angepaßt sein und individuell programmiert werden können, so daß der Patient zu den Mahlzeiten das entsprechende Programm abrufen kann.Item Open Access Quantification of aortic stenosis based on the morphology of Doppler ultrasound signals using image processing techniques(1994) Zahn, Thomas P.; Nagel, Joachim H.; Agatston, Arthur S.A method was developed to evaluate the morphologic structure of continuous wave Doppler ultrasound signals in order to quantify aortic valve stenosis in the human heart. The systolic peaks of the Doppler signal were assigned digital images and the stability of shape information was tested for patients with low stroke volume and aortal fibrillation. The results indicate that the shape of the signal peaks remains stable despite variations in amplitude and duration. The shape information was then used to quantify the severity of aortic stenosis by optimally matching Doppler peak images to selected templates representing typical diagnostic patterns. The developed software is capable of automatic extraction of shape information from Doppler ultrasound signals in order to support the clinical decision about valve functioning and replacement.Item Open Access Digital signal processing of the fetal heart sound(1982) Kartmann, Peter; Schlotter, Claus M.; Zhou, Li Gao; Nagel, Joachim H.; Schaldach, Max-Item Open Access Diagnosis and modelling of Alzheimer's disease through neural network analyses of PET studies(1990) Kippenhan, Jonathan Shane; Nagel, Joachim H.The back-propagation neural network algorithm was applied to the analysis of regional patterns in cerebral function, as demonstrated in positron emission tomography (PET). A trained network was able to successfully distinguish PET scans of normal subjects from PET scans of Alzheimer's Disease patients. It is concluded that the combination of PET and neural networks is a useful diagnostic tool for Alzheimer's Disease. A new paradigm for back-propagation learning is discussed which emphasizes its similarity to template matching. It is demonstrated that, under certain circumstances, the back-propagation network can be used as an estimation tool, as well as a classification tool, i.e., a trained neural network can indicate the criteria by which its classifications are performed.Item Open Access Fast multi-modality image matching(1989) Apicella, Anthony; Kippenhan, Jonathan Shane; Nagel, Joachim H.Automated image matching has important applications, not only in the fields of machine vision and general pattern recognition, but also in modern diagnostic and therapeutic medical imaging. Image matching, including the recognition of objects within images as well as the combination of images that represent the same object or process using different descriptive parameters, is particularly important when complementary physiological and anatomical images, obtained with different imaging modalities, are to be combined. Correlation analysis offers a powerful technique for the computation of translational, rotational and scaling differences between the image data sets, and for the detection of objects or patterns within an image. Current correlation-based approaches do not efficiently deal with the coupling of the registration variables, and thus yield iterative and computationally-expensive algorithms. A new approach is presented which improves on previous solutions. In this new approach, the registration variables are de-coupled, resulting in a much less computationally expensive algorithm. The performance of the new technique is demonstrated in the matching of MRI and PET scans, and in an application of pattern recognition in linear accelerator images.Item Open Access Fast multimodality image matching(1988) Apicella, Anthony; Nagel, Joachim H.; Duara, RanjanThe diagnostic potential of medical images obtained at different times or from complimentary imaging modalities may be augmented by objective, accurate matching of the different data sets. Correlation analysis offers a powerful technique for the computation of translation, rotation, and scaling differences between image data sets, especially in the case of complimentary images containing similar but not exact information. So far, this technique suffers from the drawback of high computational expense. We have reformulated this approach, yielding a fast, computationally much less expensive algorithm. Reduction of computation time is about seventy five percent.Item Open Access Decomposition of heart rate variability by adaptive filtering for estimation of cardiac vagal tone(1991) Han, Kedu; Nagel, Joachim H.; Hurwitz, Barry E.; Schneiderman, NeilHeart rate fluctuations resulting from respiration and other influences upon the cardiovascular system are encoded into the patterns of heart rate variability (HRV). The fluctuations due to respiration are called respiratory sinus arrhythmia (RSA). Since RSA is primarily mediated through the autonomic nervous system (ANS), it is of interest to separate RSA from other influences to assess the underlying ANS function. On the other hand, the RSA may obscure heart rate responses to external manipulations in psychophysiological tests. A method of partitioning the HRV signal which can provide quantitative estimate of RSA as well as true heart rate responses without respiratory disturbances for psychophysiological studies is developed. The analysis of HRV signal is performed using an adaptive filtering system. With the simultaneously recorded respiration signal as a reference input, the HRV signal can be separated into two components, RSA and fluctuation due to other influences. After the separation, the variance of RSA, an estimate of cardiac vagal tone (ECVT), is readily obtained. The performance of the system was evaluated using artificial test signals as well as real HR V data. As a time domain approach, the method is simple, fast and robust.Item Open Access Optimization and evaluation of a neural network classifier for PET scans of memory disorder subjects(1991) Kippenhan, Jonathan Shane; Barker, Warren W.; Pascal, Shlomo; Duara, Ranjan; Nagel, Joachim H.Back-propagation neural networks were used to classify PET scans as either normal or abnormal, with abnormal subjects defined as subjects who had previously been clinically diagnosed with memory disorders. Numerous neural network experiments were performed in order to achieve optimization with respect to number of hidden units and training duration. Optimizations and performance evaluations were based on ROC analysis, in which the area under the ROC curve was the figure of merit. The neural network's performance was better than that of dlscrlminant analysis, and comparable to the expert's performance, despite the low resolution image data, which consisted of one value per brain lobe, provided to the network.Item Open Access Multi-scale segmentation of retinal images(1991) Cideciyan, Artur V.; Jacobson, Samuel G.; Nagel, Joachim H.Tiny bright golden patches can be seen on the dark retinal background of some carriers of X-linked retinitis pigmentosa, an incurable blinding disease. We are interested in analyzing quantitatively these unique "tapetal-like reflex" patches in order to increase our understanding of the cellular mechanisms of the disease. In this paper, we describe the multi-scale thresholding method and its application to the segmentation of the tapetal-like reflex in high resolution digital fundus images. Multi-scale thresholding is a local thresholding method that generates results very similar to that of human intuition. Unlike other local thresholding methods, our method successfuIly ignores small artifacts in dark regions and simultaneously generats high resolution definitions of objects. Other segmentation applications where there are many bright objects on a darker background should profit from the use of multi-scale thresholding.Item Open Access
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