Segmentation Toolbox for Tomographic Image Data

Motivation: Image acquisition has vastly improved over the past years, introducing techniques such as X-ray computed tomography (CT). CT images provide the means to probe a sample non-invasively to investigate its inner structure. Given the wide usage of this technique and massive data amounts, techniques to automatically analyze such data becomes ever more important.

Most segmentation methods for large datasets, such as CT images, deal with simple thresholding techniques, where intensity values cut offs are predetermined and hard coded. For data where the intensity difference is not sufficient, and partial volume voxels occur frequently, thresholding methods do not suffice and more advanced methods are required.

Contribution: To meet these requirements a toolbox has been developed, combining well known methods within the image analysis field. The toolbox includes cluster-based methods to automatically determine parameters of the different classes present in the data, and edge weighted smoothing of the final segmentation based on Markov Random Fields (MRF).

The toolbox is developed for Matlab users and requires only minimal background knowledge of Matlab.