1. 8
Abstract
Nowadays everybody has high expectations for technological and technical
improvements that can be used for making everyday life easier, saving time or
increasing our sense of security. Think of the increase of the reliability of safety
systems, the high degree of computerization of transport, or the development of medical
imaging systems. In these fields there are a number of algorithms that are based on
computerized imaging techniques.
In my thesis I focus on a small part of dental treatments supported by medical
image processing methods and algorithms. One of the most complicated dental
procedure is the root canal treatment. In this case, unfortunately, dentists cannot
examine root canals and collect information about their shapes and other features
(number of them, thicknesses) before opening them manually. They need some methods
and ways to make plans of an intervention easier before opening root canals for example
by using CT machines and images or other medical devices. In this paper I describe a
new helper procedure. Based on 25 cone-beam CT (CBCT) and 25 noisy micro-CT
(MCT) image slices of some teeth I implemented a user interface where we can load
source data, calculate and analyse results in point clouds. After some pre-processing
steps on them I determined binary images of every slices, identified the root canals and
their centerlines. On CBCT images I used fuzzy c-means, on MCT slices I used
histograms to calculate thresholds to create binary slices. Results are put into 3D point
clouds where an opportunity is built-in to determine and calculate differences between
centerlines from CBCT and MCT slices based on distances. This method can show that
part of a medial line from MCT where the distance between it and a given CBCT line is
the smallest.
In the first part of my thesis I describe tried and applied image processing
methods which could help me to planned, created and implemented a new helper
procedure.
Key words: root canal treatment, error function, medical image processing,
micro-CT, cone-beam CT, matching curves in 3D, distance between curves, filtering,
segmentation, binary pictures, point clouds, histogram, fuzzy c-means