Document Type

Thesis

Date of Degree Completion

Spring 2020

Degree Name

Master of Science (MS)

Department

Computational Science

Committee Chair

Szilard Vajda

Second Committee Member

Donald Davendra

Third Committee Member

Razvan Andonie

Abstract

Tuberculosis (TB) is a respiratory disease which affects millions of people each year, accounting for the tenth leading cause of death worldwide, and is especially prevalent in underdeveloped regions where access to adequate medical care may be limited. Analysis of digital chest radiographs (CXRs) is a common and inexpensive method for the diagnosis of TB; however, a trained radiologist is required to interpret the results, and is subject to human error. Computer-Aided Detection (CAD) systems are a promising machine-learning based solution to automate the diagnosis of TB from CXR images. As the dimensionality of a high-resolution CXR image is very large, image features are used to describe the CXR image in a lower dimension while preserving the elements in the CXR necessary for the detection of TB. In this thesis, I present a set of image features using Pyramid Histogram of Oriented Gradients, Local Binary Patterns, and Principal Component Analysis which provides high classifier performance on two publicly available CXR datasets, and compare my results to current state-of-the-art research.

Download

Share

COinS