Texture Based Classification and Segmentation of Tissues Using DT-CWT Feature Extraction Methods Export

@inproceedings{citeulike:3507395, abstract = {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.}, author = {Aydogan, D. B. and Hannula, M. and Arola, T. and Hyttinen, J. and Dastidar, P.}, booktitle = {Computer-Based Medical Systems, 2008. CBMS '08. 21st IEEE International Symposium on}, citeulike-article-id = {3507395}, citeulike-linkout-0 = {http://dx.doi.org/10.1109/CBMS.2008.46}, citeulike-linkout-1 = {http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=4562069}, doi = {10.1109/CBMS.2008.46}, journal = {Computer-Based Medical Systems, 2008. CBMS '08. 21st IEEE International Symposium on}, keywords = {application, classification, med\_app, segmentation, similarity, texture}, pages = {614--619}, posted-at = {2008-11-12 22:47:08}, priority = {2}, title = {Texture Based Classification and Segmentation of Tissues Using DT-CWT Feature Extraction Methods}, url = {http://dx.doi.org/10.1109/CBMS.2008.46}, year = {2008} }

TY - CONF ID - citeulike:3507395 L3 - citeulike-article-id:3507395 N2 - 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. JF - Computer-Based Medical Systems, 2008. CBMS '08. 21st IEEE International Symposium on EP - 619 TI - Texture Based Classification and Segmentation of Tissues Using DT-CWT Feature Extraction Methods T2 - Computer-Based Medical Systems, 2008. CBMS '08. 21st IEEE International Symposium on SP - 614 KW - application KW - classification KW - med_app KW - segmentation KW - similarity KW - texture AU - Aydogan, DB AU - Hannula, M AU - Arola, T AU - Hyttinen, J AU - Dastidar, P PY - 2008/// UR - http://dx.doi.org/10.1109/CBMS.2008.46 ER -