000002229 001__ 2229
000002229 005__ 20181220113733.0
000002229 022__ $$a1057-7149
000002229 0247_ $$2DOI$$a10.1109/TIP.2017.2665041
000002229 037__ $$aARTICLE
000002229 041__ $$aeng
000002229 245__ $$a3D solid texture classification using locally-oriented wavelet transforms
000002229 260__ $$c2017
000002229 269__ $$a2017-04
000002229 300__ $$a12 p.
000002229 506__ $$avisible
000002229 520__ $$9eng$$aMany image acquisition techniques used in biomedical imaging, material analysis, and structural geology are capable of acquiring 3D solid images. Computational analysis of these images is complex but necessary, since it is difficult for humans to visualize and quantify their detailed 3D content. One of the most common methods to analyze 3D data is to characterize the volumetric texture patterns. Texture analysis generally consists of encoding the local organization of image scales and directions, which can be extremely diverse in 3D. Current state-of-the-art techniques face many challenges when working with 3D solid texture, where most approaches are not able to consistently characterize both scale and directional information. 3D Riesz-wavelets can deal with both properties. One key property of Riesz filterbanks is steerability, which can be used to locally align the filters and compare textures with arbitrary (local) orientations. This paper proposes and compares three novel local alignment criteria for higher-order 3D Riesz-wavelet transforms. The estimations of local texture orientations are based on higher-order extensions of regularized structure tensors. An experimental evaluation of the proposed methods for the classification of synthetic 3D solid textures with alterations (such as rotations and noise) demonstrated the importance of local directional information for robust and accurate solid texture recognition. These alignment methods achieved an accuracy of 0.95 in the rotated data, three times more than the unaligned Riesz descriptor that achieved 0.32. The accuracy obtained is better than all other techniques that are published and tested on the same database.
000002229 546__ $$aEnglish
000002229 540__ $$acorrect
000002229 592__ $$aHEG-VS
000002229 592__ $$bInstitut Informatique de gestion
000002229 592__ $$cEconomie et Services
000002229 65017 $$aInformatique
000002229 6531_ $$9eng$$adatabases
000002229 6531_ $$9eng$$asolids
000002229 6531_ $$9eng$$awavelet transforms
000002229 6531_ $$9eng$$avisualization
000002229 6531_ $$9eng$$ashape
000002229 6531_ $$9eng$$abiomedical imaging
000002229 655__ $$ascientifique
000002229 700__ $$aDicente Cid, Yashin$$uUniversity of Applied Sciences and Arts Western Switzerland (HES-SO Valais-Wallis) ; University Hospital of Geneva, Switzerland ; University of Geneva, Switzerland
000002229 700__ $$aMüller, Henning$$uUniversity of Applied Sciences and Arts Western Switzerland (HES-SO Valais-Wallis) ; University Hospital of Geneva, Switzerland ; University of Geneva, Switzerland
000002229 700__ $$aPlaton, Alexandra$$uDepartment of Radiology and Medical Informatics, University Hospital of Geneva (HUG), Switzerland
000002229 700__ $$aPoletti, Pierre–Alexandre$$uDepartment of Radiology and Medical Informatics, University Hospital of Geneva (HUG), Switzerland
000002229 700__ $$aDepeursinge, Adrien$$uUniversity of Applied Sciences and Arts Western Switzerland (HES-SO Valais-Wallis) ; Biomedical Imaging Group, ´Ecole Polytechnique Fédérale de.Lausanne (EPFL), Switzerland
000002229 773__ $$g2017, vol. 26, no. 4, pp. 1899-1910$$tIEEE transactions on image processing
000002229 8564_ $$s4881369$$uhttps://hesso.tind.io/record/2229/files/Dicente_2017_3-D_solid_texture_classification.pdf
000002229 8564_ $$s2747369$$uhttps://hesso.tind.io/record/2229/files/Dicente_2017_3-D_solid_texture_classification.pdf?subformat=pdfa$$xpdfa
000002229 909CO $$ooai:hesso.tind.io:2229$$pGLOBAL_SET
000002229 906__ $$aGREEN
000002229 950__ $$aI2
000002229 980__ $$ascientifique