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

Tutkimustuotos: Artikkeli kirjassa/konferenssijulkaisussavertaisarvioitu

Tutkijat

Organisaatiot

  • Tampere University of Technology
  • Tampere University

Kuvaus

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.

Yksityiskohdat

AlkuperäiskieliEnglanti
OtsikkoProceedings of the 21st IEEE International Symposium on Computer-Based Medical Systems, CBMS 2008
TilaJulkaistu - 22 syyskuuta 2008
OKM-julkaisutyyppiA4 Artikkeli konferenssijulkaisuussa
TapahtumaIEEE International Symposium on Computer-Based Medical Systems - Jyväskylä, Suomi
Kesto: 17 kesäkuuta 200819 kesäkuuta 2008
Konferenssinumero: 21

Conference

ConferenceIEEE International Symposium on Computer-Based Medical Systems
LyhennettäCBMS
MaaSuomi
KaupunkiJyväskylä
Ajanjakso17/06/200819/06/2008

ID: 29135672