@MASTERSTHESIS\{IMM2006-04569,
    author       = "M. S. Hansen",
    title        = "Detection of Connective Tissue Disorders from {4D} Aortic {MR} images using Independent Component Analysis",
    year         = "2006",
    school       = "Informatics and Mathematical Modelling, Technical University of Denmark, {DTU}",
    address      = "Richard Petersens Plads, Building 321, {DK-}2800 Kgs. Lyngby",
    type         = "",
    note         = "Supervised by Bjarne Kj{\ae}r Ersb{\o}ll, {IMM}.",
    url          = "http://www2.compute.dtu.dk/pubdb/pubs/4569-full.html",
    abstract     = "The current report concerns methods of early detection of connective tissue disorders leading to aortic aneurysms and dissections. Automated and accurate segmentation of the aorta in {4D} ({3D} + time) {MR} image data is reviewed, and a computer-aided diagnosis (CAD) method using independent component analysis is reported. This admits the objective identification of subjects with connective tissue disorders from {4D} aortic {MR} images.

The majority of the presented work is concentrated on independent component analysis(ICA), estimating sources to be used for the diagnosis task. Prior knowledge of the source distribution is utilized using an ordering of the components. Two new ordering measures are introduced in current work. A novel approach to constrained dimensionality reduction in {ICA} is developed. A new idea of time-invariant independent components is introduced, and assists in the disease detection in the presence of sparse data.

{4D} {MR} image data sets acquired from 21 normal and 10 diseased subjects are used to evaluate the efficiency of the method. The automated {4D} segmentation result produces accurate aortic surfaces. The {ICA} results are validated by a leave-one-out classification test, and are further substantiated by visual inspec-tion of the components. Using a single phase of the cardiac cycle, 8 out of 10 diseased subjects are identified and the specificity is 100 \%, classifying all 21 healthy subjects correctly. These results are obtained using components showing correspondence to clinical observations. With {4D} information included, the {CAD} method classifies 9 out of 10 diseased correctly, and still the specificity is 100 \%."
}