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.