Masters Theses

Keywords and Phrases

Artificial neural network; Computer Aided Detection (CAD); Image processing; Medical image analysis; Oral lesions

Abstract

"Oral lesions are important findings on computed tomography images. They are difficult to detect on CT images because of low contrast, arbitrary orientation of objects, complicated topology and lack of clear lines indicating lesions. In this thesis, a fully automatic method to detect oral lesions from dental CT images is proposed to identify (1) Closed boundary lesions and (2) Bone deformation lesions. Two algorithms were developed to recognize these two types of lesions, which cover most of the lesion types that can be found on CT images. The results were validated using a dataset of 52 patients. Using non training dataset, closed boundary lesion detection algorithm yielded 71% sensitivity with 0.31 false positives per patient. Moreover, bone deformation lesion detection algorithm achieved 100% sensitivity with 0.13 false positives per patient. Results suggest that, the proposed framework has the potential to be used in clinical context, and assist radiologists for better diagnosis."--Abstract, page iv.

Advisor(s)

Lee, Hyoung-Koo

Committee Member(s)

Usman, Shoaib
Alajo, Ayodeji Babatunde

Department(s)

Nuclear Engineering and Radiation Science

Degree Name

M.S. in Nuclear Engineering

Sponsor(s)

Vatech Co., Ltd, South Korea

Publisher

Missouri University of Science and Technology

Publication Date

Spring 2015

Pagination

ix, 40 pages

Note about bibliography

Includes bibliographical references (pages 37-39).

Rights

© 2015 Shaikat Mahmood Galib, All rights reserved.

Document Type

Thesis - Open Access

File Type

text

Language

English

Subject Headings

Mouth -- Cancer -- Diagnosis
Tomography -- Digital techniques
Diagnostic imaging -- Data processing

Thesis Number

T 10673

Electronic OCLC #

913485202

Download

Share

 
COinS