. "Anal\u00FDza trojrozm\u011Brn\u00FDch CT medic\u00EDnsk\u00FDch obr\u00E1zk\u016F pomoc\u00ED roz\u0161\u00ED\u0159en\u00ED Haralickov\u00FDch texturn\u00EDch p\u0159\u00EDznak\u016F"@cs . "RIV/67985556:_____/08:00313710" . "Medical image analysis of 3D CT images based on extensions of Haralick texture features"@en . . "Medical image analysis of 3D CT images based on extensions of Haralick texture features" . "378361" . "Nawano, S." . . . "[4178A17676FD]" . "Computerized Medical Imaging and Graphics" . "Tesa\u0159, Ludv\u00EDk" . "P(1ET101050403), P(1M0572), Z(AV0Z10750506)" . "5"^^ . . "image segmentation; Gaussian mixture model; 3D image analysis"@en . . . . "Kobatake, H." . "8"^^ . . . "Anal\u00FDza trojrozm\u011Brn\u00FDch CT medic\u00EDnsk\u00FDch obr\u00E1zk\u016F pomoc\u00ED roz\u0161\u00ED\u0159en\u00ED Haralickov\u00FDch texturn\u00EDch p\u0159\u00EDznak\u016F"@cs . "Medical image analysis of 3D CT images based on extensions of Haralick texture features" . "RIV/67985556:_____/08:00313710!RIV09-MSM-67985556" . "1"^^ . . "000258739700009" . . "Medical image analysis of 3D CT images based on extensions of Haralick texture features"@en . "US - Spojen\u00E9 st\u00E1ty americk\u00E9" . "6" . "Texture-based segmentation of 3D CT images is adressed. The extension of Haralick 2D texture features to the 3D domain was studied. The co-occurrence matrix was calculated separately for each voxel in the image, using the co-occurrences of all voxels in a small cubic region around the voxel. The segmentation method used was model-based with a Gaussian Mixture Model. Evaluation of the proposed approach was performed using a set of 3D abdominal CT images. Statistical improvement of segmentation with 3D texture features was observed as opposed to the case without those features." . . . "Smutek, D." . "0895-6111" . "Shimizu, A." . "Texture-based segmentation of 3D CT images is adressed. The extension of Haralick 2D texture features to the 3D domain was studied. The co-occurrence matrix was calculated separately for each voxel in the image, using the co-occurrences of all voxels in a small cubic region around the voxel. The segmentation method used was model-based with a Gaussian Mixture Model. Evaluation of the proposed approach was performed using a set of 3D abdominal CT images. Statistical improvement of segmentation with 3D texture features was observed as opposed to the case without those features."@en . . . "32" . "Tato pr\u00E1ce se zab\u00FDv\u00E1 segmentac\u00ED 3D CT medic\u00EDnsk\u00FDch obr\u00E1zk\u016F. Byla pou\u017Eita extenze Haralickov\u00FDch dvourozm\u011Brn\u00FDch texturn\u00EDch p\u0159\u00EDznak\u016F na t\u0159et\u00ED dimenzi. Ko-okuren\u010Dn\u00ED matice se po\u010D\u00EDt\u00E1 pro ka\u017Ed\u00ED voxel zvl\u00E1\u0161t, na z\u00E1klad\u011B ko-okurenc\u00ED v jeho okol\u00ED. Segmentace se prov\u00E1d\u00ED pomoc\u00ED Gaussovsk\u00E9ho sm\u011Bsov\u00E9ho modelu. Vyhodnocen\u00ED metody bylo provedeno s pou\u017Eit\u00EDm abdomin\u00E1ln\u00EDch 3D CT obr\u00E1zk\u016F. Bylo pozorov\u00E1no zlep\u0161en\u00ED segmentace pomoc\u00ED 3D texturn\u00EDch p\u0159\u00EDznak\u016F oproti segmenaci bez nich."@cs . . .