Multiresolution Volumetric Texture Segmentation

This thesis investigates the segmentation of data in 2D and 3D by texture analysis using Fourier domain filtering. The field of texture analysis is a well-trodden one in 2D, but many applications, such as Medical Imaging, Stratigraphy or Crystallography, would benefit from 3D analysis instead of the traditional, slice-by-slice approach. With the intention of contributing to texture analysis and segmentation in 3D, a multiresolution volumetric texture segmentation (M-VTS) algorithm is presented. The method extracts textural measurements from the Fourier domain of the data via sub-band filtering using a Second Orientation Pyramid. A novel Bhattacharyya space, based on the Bhattacharyya distance is proposed for selecting of the most discriminant measurements and produces a compact feature space. Each dimension of the feature space is used to form a Quad Tree. At the highest level of the tree, new positional features are added to improve the contiguity of the classification. The classified space is then projected to lower levels of the tree where a boundary refinement procedure is performed with a 3D equivalent of butterfly filters. The performance of M-VTS is tested in 2D by classifying a set of standard texture images. The figures contain different textures that are visually stationary. M-VTS yields lower misclassification rates than reported elsewhere ([104, 111, 124]). The algorithm was tested in 3D with artificial isotropic data and three Magnetic Resonance Imaging sets of human knees with satisfactory results. The regions segmented from the knees correspond to anatomical structures that could be used as a starting point for other measurements. By way of example, we demonstrate successful cartilage extraction using our approach

Application of Texture Analysis to Study Small Vessel Disease and Blood–Brain Barrier Integrity

artículo 327Evaluamos el uso alternativo del análisis de textura para evaluar el papel de la barrera hematoencefálica (BBB) en la enfermedad de pequeños vasos (SVD). Utilizamos imágenes de resonancia magnética cerebral de 204 pacientes con accidente cerebrovascular, adquiridas antes y 20 minutos después de la administración intravenosa de gadolinio. Segmentamos tejidos, hiperintensidades de la materia blanca (WMH) y aplicamos puntuaciones visuales validadas. Medimos las características de la textura en todos los tejidos antes y después del contraste y utilizamos ANCOVA para evalúe el efecto de los indicadores de SVD en el cambio anterior / posterior al contraste, Kruskal-Wallis para determinar la importancia entre los grupos de pacientes y los modelos lineales mixtos para las variaciones anteriores / posteriores al contraste en el líquido cefalorraquídeo (LCR) con puntuaciones Fazekas. El aumento de la "homogeneidad" textural en los tejidos normales con mayor presencia de indicadores de la EVP fue consecuentemente más evidente que en los tejidos anormales. La “homogeneidad” textural aumentó con la edad, las puntuaciones de los espacios perivasculares de los ganglios basales (p <0,01) y las puntuaciones de la EVP (p <0,05) y fue significativamente mayor en los pacientes hipertensos (p <0,002) y el ictus lacunar (p = 0,04). La hipertensión (74% de los pacientes), la carga de WMH (mediana = 1.5 ± 1.6% del volumen intracraneal) y la edad (media = 65.6 años, SD = 11.3) predijeron el cambio pre / post-contraste en la sustancia blanca normal, WMH e índice Lesión de trazo. Señal CSF Incremento con el aumento de SVD post-contraste. Un patrón general consistente de aumento de la homogeneidad de la textura "con el aumento de la SVD y el cambio posterior al contraste en el LCR con el aumento de WMH sugiere que el análisis de textura puede ser útil para el estudio de la integridad de BBB.S

Multiresolution volumetric texture segmentation

EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Multiresolution volumetric texture segmentation

This thesis investigates the segmentation of data in 2D and 3D by texture analysis using Fourier domain filtering. The field of texture analysis is a well-trodden one in 2D, but many applications, such as Medical Imaging, Stratigraphy or Crystallography, would benefit from 3D analysis instead of the traditional, slice-by-slice approach. With the intention of contributing to texture analysis and segmentation in 3D, a multiresolution volumetric texture segmentation (M-VTS) algorithm is presented. The method extracts textural measurements from the Fourier domain of the data via sub-band filtering using a Second Orientation Pyramid. A novel Bhattacharyya space, based on the Bhattacharyya distance is proposed for selecting of the most discriminant measurements and produces a compact feature space. Each dimension of the feature space is used to form a Quad Tree. At the highest level of the tree, new positional features are added to improve the contiguity of the classification. The classified space is then projected to lower levels of the tree where a boundary refinement procedure is performed with a 3D equivalent of butterfly filters. The performance of M-VTS is tested in 2D by classifying a set of standard texture images. The figures contain different textures that are visually stationary. M-VTS yields lower misclassification rates than reported elsewhere ([104, 111, 124]). The algorithm was tested in 3D with artificial isotropic data and three Magnetic Resonance Imaging sets of human knees with satisfactory results. The regions segmented from the knees correspond to anatomical structures that could be used as a starting point for other measurements. By way of example, we demonstrate successful cartilage extraction using our approach

Volumetric texture segmentation by discriminant feature selection and multiresolution classification

In this paper, a multiresolution volumetric texture segmentation (M-VTS) algorithm is presented. The method extracts textural measurements from the Fourier domain of the data via subband filtering using an orientation pyramid (Wilson and Spann, 1988). A novel Bhattacharyya space, based on the Bhattacharyya distance, is proposed for selecting the most discriminant measurements and producing a compact feature space. An oct tree is built of the multivariate features space and a chosen level at a lower spatial resolution is first classified. The classified voxel labels are then projected to lower levels of the tree where a boundary refinement procedure is performed with a three-dimensional (3-D) equivalent of butterfly filters. The algorithm was tested with 3-D artificial data and three magnetic resonance imaging sets of human knees with encouraging results. The regions segmented from the knees correspond to anatomical structures that can be used as a starting point for other measurements such as cartilage extraction