@CONFERENCE\{IMM2006-04818,
    author       = "M. S. Hansen and F. Zhao and H. Zhang and N. E. Walker and A. Wahle and T. Scholz and M. Sonka",
    title        = "Detection of Connective Tissue Disorders from {3D} Aortic {MR} Images Using Independent Component Analysis",
    year         = "2006",
    month        = "may",
    booktitle    = "2nd international workshop on Computer Vision Approaches to Medical Image Analysis, {CVAMIA'}06",
    volume       = "",
    series       = "Lecture notes in computer science (LNCS)",
    editor       = "Reinhard Beichel and Milan Sonka",
    publisher    = "Springer",
    organization = "",
    address      = "",
    url          = "http://www2.compute.dtu.dk/pubdb/pubs/4818-full.html",
    abstract     = "A computer-aided diagnosis (CAD) method is reported that allows the objective identification of subjects with connective tissue disorders from {3D} aortic {MR} images using segmentation and independent component analysis (ICA). The first step to extend the model to {4D} ({3D} + time) has also been taken. {ICA} is an effective tool for connective tissue disease detection in the presence of sparse data using prior knowledge to order the components, and the components can be inspected visually.

 {3D}+time {MR} image data sets acquired from 31 normal and connective tissue disorder subjects at end-diastole (R-wave peak) and at 45\$\backslash\$\% of the {R-R} interval were used to evaluate the performance of our method. The automated {3D} segmentation result produced accurate aortic surfaces covering the aorta. The {CAD} method distinguished between normal and connective tissue disorder subjects with a classification accuracy of 93.5\verb+~+\$\backslash\$\%."
}