Systolic architectures for parallel Fourier transform - Baradaran-Seyed, T.; Johnson, L.G.; Karimi, B. (Institute of Electrical and Electronics Engineers Inc., 1991)
New systolic architectures are proposed for the computation of the Fourier transform based on the generation of the coefficients of the transform during the computation. These architectures require less input/output pins on the chip. The new architectures are also extremely modular and cascadeable, thus, amenable for efficient VLSI implementation. VLSI complexity of the architectures are compared with the existing parallel architectures. © 1992 IEEE.
Keywords: Input/output; Parallel architectures; Systolic architecture; VLSI circuits; VLSI implementation; dept.csc

Detection of liver metastisis using the backpropagation algorithm and linear discriminant analysis - Daponte, John S; Parikh, Jo Ann; Katz, David A (SPIE, 1991)
The purpose of this study was to compare the classification capabilities of the backpropagation algorithm and linear discriminant analysis for detecting liver metastisis using image texture features obtained from ultrasonic images of the liver. Twenty-one quantitative parameters were obtained from 134 regions of interest of equal size. The images were collected by the same radiologist on the same imager with the controls adjusted for variations in patient body size so as to produce images of consistent quality. Quantitative features were divided so that 13 were first-order statistics, 6 were second-order statistics, and 2 were image gradient parameters. The same features were processed by both the backpropagation algorithm and linear discriminant analysis using `jack-knife'' testing and the results of each computer- generated classification was compared to the supplied diagnosis in an effort to determine which method could best identify patterns. For this particular application, the backpropagation neural network was found to have slightly superior classification results (87) than linear discriminant analysis (83).
Keywords: Backpropagation; Biomedical Engineering; Linear Discriminant Analysis; Medical Imaging; Neural Networks - Medical Applications; Pattern Classification; Pattern Recognition - Medical Applications; dept.csc

Classification of ultrasonic image texture by statistical discriminant analysis and neural networks - Daponte, John S; Sherman, Porter (Computerized Medical Imaging and Graphics, 1991)
In this paper the ability of two common statistical discriminant analysis procedures are compared with two commercial neural network software packages. The major objective of this study was to determine which of the procedures could best discriminate between normal and abnormal ultrasonic liver textures. The same set of features were input into both statistical discriminant analysis procedures and both neural network models. Preliminary results have found the restricted Coulomb Energy (RCE) neural network model to have a testing accuracy of 90.6% which is approximately 10% better than any of the other techniques investigated. © 1991.
Keywords: Algorithms; Artificial Intelligence; Computer Programming - Algorithms; Discriminant Analysis; Human; Image Processing, Computer-Assisted; Image Texture; Image texture; Imaging Techniques; Linear Discriminant Analysis; Linear discriminant analysis; Liver; Liver Diseases; Models, Biological; Nearest Neighbor Analysis; Nearest neighbor analysis; Neural Networks - Medical Applications; Neural networks; Pattern Recognition; Reference Values; Software; Support, Non-U.S. Gov't; Ultrasonic Applications - Medical Applications; Ultrasonic Image Texture; Ultrasound; article; artificial intelligence; dept.csc; echography; image analysis; priority journal; tissue characterization

Comparison of backpropagation neural networks and statistical techniques for analysis of geological features in Landsat imagery - Parikh, Jo Ann; Daponte, John S; Damodaran, Meledath; Karageorgiou, Angelos; Podaras, Petros (Publ by Int Soc for Optical Engineering, 1991)
Backpropagation neural networks have been developed for detection of geological lineaments in the Landsat Thematic Mapper (TM) imagery of the Canadian Shield using edge images as input and digitized lineament maps as the desired output. Lineament detection is a challenging problem for traditional image processing and pattern recognition techniques. Many linear features observable in geological image data do not represent lineaments, and the presence and extent of lineaments must be inferred from contextual information. In order to compare the ability of neural networks and conventional classifiers to recognize lineaments prior to performing edge/line element grouping operations, various gradient and curvature features are extracted from the image data set. Selected features from this group formed the inputs to backpropagation neural networks, linear discriminant classifiers, and nearest-neighbor classifiers. The neural network results were compared with the results obtained using conventional classifiers for sample training and test sets. The trained neural network was then applied to the edge image to mask out those edge points which had been classified as non- lineament points.
Keywords: Artificial Neural Networks; Backpropagation; Feature Extraction; Geological Features; Geological Surveys; Image Processing - Image Analysis; Landsat Imagery; Learning Systems; Neural Networks - Applications; Pattern Recognition; Statistical Analysis; dept.csc