M. E. Jernigan

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This paper presents a study investigating the potential of artiicial neural networks (ANN's) for the classiication and segmentation of magnetic resonance (MR) images of the human brain. In this study, we present the application of a Learning Vector Quantization (LVQ) Artiicial Neural Network (ANN) for the multispectral supervised classiication of MR images.(More)
OBJECTIVE To test the authors' hypothesis that antibody deposition in autopsy specimens from patients with human T-lymphotropic virus type 1-associated myelopathy/tropical spastic paraparesis (HAM/TSP) would correlate with CNS damage. METHODS Endogenous immunoglobulin G (IgG) was detected using antihuman IgG in autopsy tissues from HAM/TSP and control(More)
An approach which uses regional entropy measures in the spatial frequency domain for texture discrimination is presented. The measures provide texture discriminating information independent of that contained in the usual summed energy within based frequency domain features. Performance of the entropy features as measured by a between-to-within-class scatter(More)
A syntactic pattern recognition procedure for classification of brain-stem auditory evoked potential (BSAEP) is presented. A pre-processing stage of zero-phase bandpass filtering enhances the peaks and suppresses the noise. A finite-state grammar was designed to identify the peaks. Attributes of the peaks (latencies and amplitudes) that are identified are(More)