Structural characterization of liver fibrosis in magnetic resonance images

The overall clinical motivation of this thesis is to differentiate between the different stages of liver disease stratifying into: no disease, mild disease, and severe fibrosis using Magnetic Resonance Imaging (MRI). As a related aim, we seek to differentiate as much as possible pericellular and nonpericellular fibrosis. This latter is clinically important, but currently no method exists that is able to perform this. Quickly, we realised that these aims push low level image analysis beyond their current bounds and so a great deal of the thesis is dedicated to extending such techniques before they can be applied. To work on the most fundamental low level image analysis concepts and algorithms we choose one of the most recent developments, namely continuous intrinsic dimensionality (ciD), which allows the continuous classification of homogeneous patches from 1D structures to intrinsically 2D structures. We show that the current formalism has several fundamental limitations and we propose a number of developments to improve on these. We re-evaluated feature energy statistics that were originally proposed in ciD, and additionally we examined the confidence one may have in stateof- the-art methods to estimate the orientation of features. We show that new statistical methods are required for feature energy, and that orientation predictability is more important than correctness of the estimation. This evaluation led us to the monogenic signal local orientation. Analysis of feature or texture energy is also a main contribution of this thesis. Within this framework we propose the Riesz-weighted phase congruency model. This is able to detect internal texture structures but it is not capable of delineating boundaries. Nevertheless, it proves an appropriate basis for texture quantification. Finally, we show that in contrast to using the standard established Kovesi approach, the developed texture measure leads to good results on the suboptimal T1w MRI liver image staging images. We show that we are able to differentiate automatically between the separate disease scores and between pericellular and non-pericellular fibrosis.