/mhj In Vivo Quantitative Assessment of Carotid Plaque component with multi-contrast MRI Jannie Wijnen 22 may 2003 using clustering algorithms, implemented in Mathematica

/mhj Question from the clinic If the highly thrombogenic plaque content is exposed to the blood, thrombo-embolic incidents as myocardial infarction, stroke, and peripheral vascular disease might occur Design a program that automatically detects the various components of the atherosclerotic plaque The program should be accurate and easy to use

/mhj Solution to the question 5 different MR images of the plaque -> notebook combining these images in a 5-dimensional feature space search for clusters in feature space that represent a tissue (hemorrhage, fibrous tissue, calcium and lipid) show these clusters in the original image

/mhj Feature space Feature space: An n dimensional plot showing the combination of grey values in each of the n images for all corresponding points Example:

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Clustering Example of 3D feature space of the images Kmeans cluster algorithm : Place K points into the space represented by the objects that are being clustered. These points represent initial group centroids. Assign each object to the group that has the closest centroid Example of clusters in 3D space

/mhj Problems For the best classification the clusters should be small and well- separated from each other background intensity image miss-registration noise

/mhj Background intensities Example of clusters in images with a large intensity gradient Background correction by minimisation of entropy of image with a known gradient. Conclusion: no background correction needed in these images.

/mhj Image Registration Pixels that are compared to each other must represent exactly the same anatomical positions. Example of non- registered images Minimisation of the mutual information (entropy) leads to the displacement needed to register the images. Example of registered images

/mhj Noise Reduction The less noise the smaller the clusters Euclideanshortening: preserves the edges example Variance of the clusters after euclideanshortening is smaller

/mhj Starting Values Difference between the clustering with randomly chosen starting values and starting values chosen by user

/mhj Starting values The clustering is not reproducible when starting values are chosen by hand It is better to extract starting values form the information in the 5D space try to find starting values from the maximum intensities in the 5D space

/mhj R esults Calcium is too small in lower serie information loss by euclideans shortening

/mhj Results The algorithm does not find hemorrhage and lipid core, the two dangerous components

/mhj Discussion /Conclusion Cluster techniques can be used for tissue detection in atherosclerotic plaque A lot of progress can be made: starting values, cluster technique, non-linear registration, smoothing.