Decomposition of Three-Dimensional Medical Images Into Visual Patterns

Abstract

In this paper, we present a method for the decomposition of a volumetric image into its most relevant visual patterns, which we define as features associated to local energy maxima of the image. The method involves the clustering of a set of predefined bandpass energy filters according to their ability to segregate the different features in the image, thus generating a set of composite-feature detectors tuned to the specific visual patterns present in the data. Clustering is based on a measure of statistical dependence between pairs of frequency features. We will illustrate the applicability of the method to the initialization of a three-dimensional geodesic active model.

Data-driven synthesis of composite-feature detectors for 3D image analysis

“…In this work we have presented a low level representation model for volumetric data, based on composite low level features, which we have named visual patterns (Dosil et al, , 2005a) -further work under revision in (Dosil et al, 2005d). The method for the unsupervised isolation of those patterns is founded in the notion of Phase Congruence, which allows to identify and integrate relevant composite features by multiresolution analysis.…”

Energy Feature Integration for Motion Segmentation

“…This measure has proved to produce good results in visual pattern extraction from volumetric data Dosil et al, 2005b).…”

“…The composite feature representation will be applied directly, without estimating motion parameters. Composite energy features have proved to be a powerful tool for the representation of visually independent spatial patterns in 2D data (Rodriguez-Sánchez et al, 1999), volumetric data Dosil et al, 2005b) and video sequences (Chamorro-Martínez et al, 2003). To identify relevant composite features in a sequence, it is necessary to define an integration criterion able to relate elementary energy features contributing to the same motion pattern.…”

“…To identify relevant composite features in a sequence, it is necessary to define an integration criterion able to relate elementary energy features contributing to the same motion pattern. In previous works Dosil et al, 2005a;Dosil et al, 2005b), we have introduced an integration criterion inspired in biological vision that improves the computational cost and performance of earlier approaches (Rodriguez-Sánchez et al, 1999;Chamorro-Martínez et al, 2003). It is based on the hypothesis of Morrone and Owens (1987) that the Human Visual System (HVS) perceives features at points of locally maximal Phase Congruence (PC).…”

“…The bank of spatio-temporal filters applied here Dosil et al, 2005b) uses an extension to 3D of the log Gabor function (Field, 1994). The filter is designed in the frequency domain, since it has no analytical expression in the spatial domain.…”