2D texture based classification, segmentation and 3D orientation estimation of tissues using DT-CWT feature extraction methods

Tutkimustuotos: Lehtiartikkelivertaisarvioitu

Tutkijat

Organisaatiot

  • Tampere University of Technology
  • Tampere University

Kuvaus

In this study, four different 2D dual-tree complex wavelet (DT-CWT) based texture feature extraction methods are developed and their applications are demonstrated in segmenting and classifying tissues. Two of the methods use rotation variant texture features and the other two use rotation invariant features. This paper also proposes a novel approach to estimate 3D orientations of tissues based on rotation variant DT-CWT features. The method updates the strongest structural anisotropy direction with an iterative approach and converges to a volume orientation in few steps. Although classification and segmentation results show that there is no significant difference in the performance between rotation variant and invariant features; the latter are more robust to changes in texture rotation, which is essential for classification and segmentation of objects from 3D datasets such as medical tomography images.

Yksityiskohdat

AlkuperäiskieliEnglanti
Sivut1383-1397
Sivumäärä15
JulkaisuData and Knowledge Engineering
Vuosikerta68
Numero12
TilaJulkaistu - 1 joulukuuta 2009
OKM-julkaisutyyppiA1 Julkaistu artikkeli, soviteltu

ID: 29135840