Phase Based 3D Texture Features

In this paper, we present a novel method for the voxel-wise extraction of rotation and gray-scale invariant features. These features are used for simultaneous segmentation and classification of anisotropic textured objects in 3D volume data. The proposed new class of phase based voxel-wise features...

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Hauptverfasser: Fehr, Janis, Burkhardt, Hans
Format: Buchkapitel
Sprache:eng
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Beschreibung
Zusammenfassung:In this paper, we present a novel method for the voxel-wise extraction of rotation and gray-scale invariant features. These features are used for simultaneous segmentation and classification of anisotropic textured objects in 3D volume data. The proposed new class of phase based voxel-wise features achieves two major properties which can not be achieved by the previously known Haar-Integral based gray-scale features [1]: invariance towards non-linear gray-scale changes and a easy to handle data driven feature selection. In addition, the phase based features are specialized to encode 3D textures, while texture and shape information interfere in the Haar-Integral approach. Analog to the Haar-Integral features, the phase based approach uses convolution methods in the spherical harmonic domain in order to achieve a fast feature extraction. The proposed features were evaluated and compared to existing methods on a database of volumetric data sets containing cell nuclei recorded in tissue by use of a 3D laser scanning microscope.
ISSN:0302-9743
1611-3349
DOI:10.1007/11861898_27