Browsing by Author "Kippenhan, Jonathan Shane"

Now showing 1 - 7 of 7

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.
Open Access
Estimation and analysis of platelet-analogue concentration profiles in blood flow
(1988) Kippenhan, Jonathan Shane; Nagel, Joachim H.; Eckstein, Eugene C.
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Open Access
Evaluation of a neural-network classifier for PET scans of normal and Alzheimer's disease subjects
(1992) Kippenhan, Jonathan Shane; Barker, Warren W.; Pascal, Shlomo; Nagel, Joachim H.; Duara, Ranjan
The value of PET as an objective diagnostic tool for dementia may depend on the degree to which abnormal metabolic patterns can be detected by quantitative classification methods. In these studies, a neural-network classifier based on coarse region of interest analyses was used to classify normal and abnormal FDG-PET scans. The performance of neural networks and of an expert reader were evaluated by cross validation testing. When the "abnormal" class was represented by subjects with clinical diagnoses of "Probable Alzheimer's," the areas under the relative-operating-characteristic (ROC) curves were 0.85 and 0.89 for the neural network and the expert reader, respectively. When testing with abnormal subjects represented by "Possible AD" cases, ROC areas for both the network and the expert were 0.81. The neural network out-performed discriminant analysis. It is concluded that PET has potential for the detection of abnormal brain function in dementing diseases, and that the combination of neural networks and PET is a useful diagnostic tool. Despite the low-resolution "view" afforded the neural network, its performance was nearly equivalent to that of an expert reader.
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.
Open Access
Methods for analysis of data representing concentration profiles of platelet analogues in blood flow
(1987) Kippenhan, Jonathan Shane; Nagel, Joachim H.; Eckstein, Eugene C.
Methods for the estimation of particle concentration profiles from numerical data are presented. The estimation techniques described, which involve the use of Fourier transforms, make more efficient use of data than do simple histogram techniques. Additionally, Fourier methods of analysis have been used to test theoretical models of experimental data.
Open Access
Neural network classification of normal and Alzheimer's disease subjects using high-resolution and low-resolution PET cameras
(1994) Kippenhan, Jonathan Shane; Barker, Warren W.; Nagel, Joachim H.; Grady, Cheryl; Duara, Ranjan
Neural-network classification methods were applied to studies of FDG-PET images of the brain acquired from a total of 77 "probable" Alzheimer's disease and 124 normal subjects at two different centers. Methods: Classification performances, as determined by relative-operating-characteristic (ROC) analyses of cross-validation experiments, were measured for FDG PET images obtained with either a 15-mm FWHM PETT V or a 6-mm FWHM Scanditronix PC-1024-7B camera for various methods of data representation. Neural networks were trained to distinguish between normal and abnormal subjects on the basis of regional metabolic patterns. For both databases, classification performance could be improved by increasing the "resolution" of the representation (decreasing the region size) and by normalizing the regional metabolic values to the value of a reference region (occipital region). Results: The optimal classification performance for Scanditronix data (ROC area = 0.95) was higher than that for PETT V data (ROC area = 0.87). Under Bayesian theory, the classification performance with Scanditronix data corresponded to an ability to change a pre-test probability of disease of 50% to a post-test probability of either 90% for a positive classification or 10% for a negative classification. Conclusion: This classification can be used to either strongly confirm or rule out the presence of abnormalities.
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.