## Information

- Publication Type: Bachelor Thesis
- Workgroup(s)/Project(s):
- Date: March 2016
- Matrikelnummer: 1227706
- First Supervisor: Eduard GrÃ¶ller

## 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.

## Additional Files and Images

## Weblinks

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/", }