Texture based classification and segmentation of tissues using DT-CWT feature extraction methods

Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review


Research units

  • Tampere University of Technology
  • Tampere University


In this study, four different dual-tree complex wavelet (DT-CWT) based texture feature extraction methods are developed and compared to segment and classify tissues. Methods that are proposed in this study are based on local energy calculations of sub-bands. Two of the methods use rotation variant texture features and the other two use rotation invariant features. The methods are tested on two texture compositions from the Brodatz texture database and two actual magnetic resonance (MR) images. Results show that there is not a significant difference between using rotation variant or invariant features. On the other hand, for the same Brodatz textures, all DT-CWT based feature extraction methods are competitive with other filtering approaches.


Original languageEnglish
Title of host publicationProceedings of the 21st IEEE International Symposium on Computer-Based Medical Systems, CBMS 2008
Publication statusPublished - 22 Sep 2008
MoE publication typeA4 Article in a conference publication
EventIEEE International Symposium on Computer-Based Medical Systems - Jyväskylä, Finland
Duration: 17 Jun 200819 Jun 2008
Conference number: 21


ConferenceIEEE International Symposium on Computer-Based Medical Systems
Abbreviated titleCBMS

ID: 29135672