An evaluation of image segmentation techniques for MRI scans

An evaluation of image segmentation techniques for MRI scans

Wagenaar, Claire (TU Delft Electrical Engineering, Mathematics and Computer Science; TU Delft Numerical Analysis)

van Gijzen, Martin (mentor)
van Gennip, Yves (graduation committee)

Delft University of Technology

Applied Mathematics

2020-07-02

In this thesis, several image segmentation techniques will be tested that eventually will be applied to MRI brain scans in order to detect hydrocephalus. The meth- ods include Sobel edge detection, Canny edge detection, active contour model (also known as snakes), k-means clustering and region growing. Furthermore two exten- sions are discussed. We propose a method to complete disconnected edges and, as an extension to the k-means clustering algorithm, we suggest a manner to reassign pixels to different clusters. The focus of the first part of the research lies on explaining and illustrating these methods and extensions. Two test images are used, namely the Shepp-Logan Phan- tom and an MRI scan of a recreated Shepp-Logan Phantom. In the second part, we evaluate each method by applying the methods to two independent images, that is an MRI scan of an apple and a CT scan of a brain with hydrocephalus. We discuss whether methods are useful and behave as expected. Additionally, we investigate ways to combine methods.

image segmentation
k-means clustering
region growing
mri
active contour model

http://resolver.tudelft.nl/uuid:436e147b-7b1e-4653-8624-76f7bdceb7a9

Student theses

bachelor thesis

© 2020 Claire Wagenaar