Stefan Ovidiu Oancea
Variance Orientation Transform Detection of Early Osteoarthritis in Knee Trabecular Bone
[
Thesis]
Information
- Publication Type: Bachelor Thesis
- Workgroup(s)/Project(s):
- Date: March 2016
- Matrikelnummer: 1227706
- First Supervisor: Eduard Gröller
Abstract
Since the fractal properties of the knee trabecular bone were discovered, fractal methods for analyzing bone surface radiographic projections have gained more attention. This is partly due to the fact that radiography is the cheapest imaging technique in routine clinical screening and partly due to the fact that it was shown that the trabecular bones of osteoarthritic patients indicate early deformations, even long before the characteristic join loss occurs. The ultimate goal of such an algorithm would be to differentiate healthy from unhealthy trabecular bone.This paper presents a report of our implementation of the Variance Orientation Transform (VOT) algorithm, a fractal method, which unlike other similar methods, is able to quantify bone texture in different directions and over different scales of measurement.
It is based on the idea that a single fractal dimension value is not enough to describe such a complex structure as the trabecular bone and thus, VOT calculates more descriptive fractal dimensions called fractal signatures (FSs).
In Chapters 1 and 2 we introduce the notion of fractals and the theoretical background behind them and the VOT algorithm. In Chapter 3 similar techniques for analyzing trabecular bone are presented and in Chapter 4 our particular attempt at implementing VOT is described in detail; moreover, in the same Chapter VOT is validated using some artificially generated fractal surfaces and the ability of differentiating healthy and affected bone is also investigated. The last Chapter, Chapter 5, covers further possible ideas of improving and testing of the algorithm.
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No further information available.BibTeX
@bachelorsthesis{Oancea_Stefan_2016_VOT,
title = "Variance Orientation Transform Detection of Early
Osteoarthritis in Knee Trabecular Bone",
author = "Stefan Ovidiu Oancea",
year = "2016",
abstract = "Since the fractal properties of the knee trabecular bone
were discovered, fractal methods for analyzing bone surface
radiographic projections have gained more attention. This is
partly due to the fact that radiography is the cheapest
imaging technique in routine clinical screening and partly
due to the fact that it was shown that the trabecular bones
of osteoarthritic patients indicate early deformations, even
long before the characteristic join loss occurs. The
ultimate goal of such an algorithm would be to differentiate
healthy from unhealthy trabecular bone. This paper presents
a report of our implementation of the Variance Orientation
Transform (VOT) algorithm, a fractal method, which unlike
other similar methods, is able to quantify bone texture in
different directions and over different scales of
measurement. It is based on the idea that a single fractal
dimension value is not enough to describe such a complex
structure as the trabecular bone and thus, VOT calculates
more descriptive fractal dimensions called fractal
signatures (FSs). In Chapters 1 and 2 we introduce the
notion of fractals and the theoretical background behind
them and the VOT algorithm. In Chapter 3 similar techniques
for analyzing trabecular bone are presented and in Chapter 4
our particular attempt at implementing VOT is described in
detail; moreover, in the same Chapter VOT is validated using
some artificially generated fractal surfaces and the ability
of differentiating healthy and affected bone is also
investigated. The last Chapter, Chapter 5, covers further
possible ideas of improving and testing of the algorithm.",
month = mar,
address = "Favoritenstrasse 9-11/E193-02, A-1040 Vienna, Austria",
school = "Institute of Computer Graphics and Algorithms, Vienna
University of Technology ",
URL = "https://www.cg.tuwien.ac.at/research/publications/2016/Oancea_Stefan_2016_VOT/",
}