TWave: high-order analysis of functional MRI.

The traditional approach to functional image analysis models images as matrices of raw voxel intensity values. Although such a representation is widely utilized and heavily entrenched both within neuroimaging and in the wider data mining community, the strong interactions among space, time, and categorical modes such as subject and experimental task inherent in functional imaging yield a dataset with "high-order" structure, which matrix models are incapable of exploiting. Reasoning across all of these modes of data concurrently requires a high-order model capable of representing relationships between all modes of the data in tandem.

Spatial feature extraction techniques for the analysis of ductal tree structures.

The objective of this study is the classification of the breast ductal tree structures appearing in galactograms in order to investigate the relation between topological properties of the tree-like parenchymal networks and radiological findings regarding breast cancer. We present two different methods to characterize the spatial distribution of branching points; a variation of Sholl's analysis and a sectoring technique. Similarity searches and k-nearest neighbor classification of the trees are performed using the cosine similarity metric.

A representation and classification scheme for tree-like structures in medical images: analyzing the branching pattern of ductal trees in X-ray galactograms.

We propose a multistep approach for representing and classifying tree-like structures in medical images. Tree-like structures are frequently encountered in biomedical contexts; examples are the bronchial system, the vascular topology, and the breast ductal network. We use tree encoding techniques, such as the depth-first string encoding and the PrUfer encoding, to obtain a symbolic string representation of the tree's branching topology; the problem of classifying trees is then reduced to string classification.