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

Research output: Contribution to journalArticleScientificpeer-review


Research units

  • Tampere University of Technology
  • Tampere University


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.


Original languageEnglish
Pages (from-to)1383-1397
Number of pages15
JournalData and Knowledge Engineering
Issue number12
Publication statusPublished - 1 Dec 2009
MoE publication typeA1 Journal article-refereed

    Research areas

  • 3D orientation estimation, Image DB, Machine learning, Texture analysis

ID: 29135840