title: Classification of ultrasonic image texture by statistical discriminant analysis of neutral networks.
abstract: 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.
authors.affiliation: Computer Science Department, Southern Connecticut State University, New Haven 06515.
authors.forename: J S
authors.initials: JS
authors.lastname: DaPonte
authors.forename: P
authors.initials: P
authors.lastname: Sherman
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D016002:Discriminant Analysis; D006801:Humans; D007091:Image Processing, Computer-Assisted; D008099:Liver / Q000000981:diagnostic imaging*; D008107:Liver Diseases / Q000000981:diagnostic imaging*; D008954:Models, Biological; D010363:Pattern Recognition, Automated; D012016:Reference Values; D012984:Software; D014463:Ultrasonography