Journal article
Sparse Decomposition and Modeling of Anatomical Shape Variation
Recent advances in statistics have spawned powerful methods for regression and data decomposition that promote sparsity, a property that facilitates interpretation of the results. Sparse models use a small subset of the available variables and may perform as well or better than their full counterparts if constructed carefully.
In most medical applications, models are required to have both good statistical performance and a relevant clinical interpretation to be of value. Morphometry of the corpus callosum is one illustrative example. This paper presents a method for relating spatial features to clinical outcome data. A set of parsimonious variables is extracted using sparse principal component analysis, producing simple yet characteristic features.
The relation of these variables with clinical data is then established using a regression model. The result may be visualized as patterns of anatomical variation related to clinical outcome. In the present application, landmark-based shape data of the corpus callosum is analyzed in relation to age, gender, and clinical tests of walking speed and verbal fluency.
To put the data-driven sparse principal component method into perspective, we consider two alternative techniques, one where features are derived using a model-based wavelet approach, and one where the original variables are regressed directly on the outcome.
Language: | English |
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Publisher: | IEEE |
Year: | 2007 |
Pages: | 1625-1635 |
ISSN: | 1558254x and 02780062 |
Types: | Journal article |
DOI: | 10.1109/TMI.2007.898808 |
ORCIDs: | Larsen, Rasmus |
Age Factors Anatomy Biomedical imaging Corpus Callosum Corpus callosum (CC) Data mining Hospitals Humans Image Interpretation, Computer-Assisted Image Processing, Computer-Assisted Informatics Leukoaraiosis And DISability in the elderly (LADIS) Magnetic resonance Mathematical model Models, Biological Models, Statistical Nervous system Numerical Analysis, Computer-Assisted Pattern Recognition, Automated Principal Component Analysis Principal component analysis Psychomotor Performance Regression Analysis Sex Factors Shape Speech anatomical shape variation modeling brain corpus callosum data decomposition decomposition feature extraction geriatrics medical image processing model-based wavelet approach neurophysiology parsimonious variables principal component analysis principal component analysis (PCA) regression analysis regression model shape analysis sparse sparse decomposition spatial feature extraction wavelet transforms