Award Date

1-1-1996

Degree Type

Thesis

Degree Name

Master of Science (MS)

Department

Computer Science

Number of Pages

112

Abstract

A statistical pattern recognition system for ultrasound medical images of prostatic tissue for cancer has been proposed. Using the autocorrelation method, the correct size of a statistical sliding window for feature extraction was defined. Known texture discrimination features have been tested for effectiveness. Another set of discriminating features, based on edge value distribution, Fourier power spectrum and wavelet transform has been derived and investigated. The set can be used as an input to a neural net classifier.

Keywords

Cancer; Classification; Feature; Human; Images; Medical; Prostate; Prostate Cancer; Recognition; Selection; Tissue

Controlled Subject

Computer science; Diagnostic imaging; Oncology; Artificial intelligence

File Format

pdf

File Size

1669.12 KB

Degree Grantor

University of Nevada, Las Vegas

Language

English

Permissions

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Identifier

https://doi.org/10.25669/uoxr-jz6q


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