Multirresolución adaptativa de mallas triangulares basado en criterios de textura

AbstractUsually, 3D models are composed by thousands of polygons. Some times, those representations can be obtained with the same visual quality but with a smaller number of polygons. In this paper, we present a method that reduces the size of 3D textured images based on triangular meshes, keeping the visual quality of the model. We introduced a texture criterion that controls the triangle decimation process. We used a polygonal algorithm of decimation that permits the structured point elimination without carrying out a new triangulation on the point cloud. In order to determine which points must to be removed, we used a 2D Sobel filter on the texture. We show that the algorithm can be used for reducing the load, rendering, transfer and storage times of 3D textured images.Los modelos 3D están generalmente compuestos por miles de polígonos. En ocasiones estas representaciones pueden obtenerse con la misma calidad visual pero con un menor número de polígonos. En este artículo se propone un método para reducir el tamaño de imágenes 3D texturadas basadas en mallas triangulares, conservando la calidad visual del modelo. Se introduce un criterio de textura que controla el proceso de decimación triangular. Para eliminar puntos sin necesidad de realizar una nueva triangulación, sobre la nube de puntos se usa un algoritmo poligonal de decimación. Para definir cuáles puntos deben ser removidos, se usa un filtro de Sobel 2D sobre la textura correspondiente. Se muestra que se puede usar el algoritmo para reducir los tiempos de carga, de renderización, de transferencia y de almacenamiento de una imagen 3D texturada

Statistical shape analysis of neuroanatomical structures based on spherical wavelet transformation

Thesis (Ph. D.)--Harvard-MIT Division of Health Sciences and Technology, 2008.Includes bibliographical references.Evidence suggests that morphological changes of neuroanatomical structures may reflect abnormalities in neurodevelopment, or relate to a variety of disorders, such as schizophrenia and Alzheimer's disease (AD). Advances in high-resolution Magnetic Resonance Imaging (MRI) techniques allow us to study these alterations of brain structures in vivo. Previous work in studying the shape variations of brain structures has provided additional localized information compared with traditional volume-based study. However, challenges remain in finding an accurate shape presentation and conducting shape analysis with sound statistical principles. In this work, we develop methods for automatically extracting localized and multi-scale shape features and conducting statistical shape analysis of neuroanatomical structures obtained from MR images. We first develop a procedure to extract multi-scale shape features of brain structures using biorthogonal spherical wavelets. Using this wavelet-based shape representation, we build multi-scale shape models and study the localized cortical folding variations in a normal population using Principal Component Analysis (PCA). We then build a shape-based classification framework for detecting pathological changes of cortical surfaces using advanced classification methods, such as predictive Automatic Relevance Determination (pred-ARD), and demonstrate promising results in patient/control group comparison studies. Thirdly, we develop a nonlinear temporal model for studying the temporal order and regional difference of cortical folding development based on this shape representation. Furthermore, we develop a shape-guided segmentation method to improve the segmentation of sub-cortical structures, such as hippocampus, by using shape constraints obtained in the wavelet domain.(cont.) Finally, we improve upon the proposed wavelet-based shape representation by adopting a newly developed over-complete spherical wavelet transformation and demonstrate its utility in improving the accuracy and stability of shape representations. By using these shape representations and statistical analysis methods, we have demonstrated promising results in localizing shape changes of neuroanatomical structures related to aging, neurological diseases, and neurodevelopment at multiple spatial scales. Identification of these shape changes could potentially lead to more accurate diagnoses and improved understanding of neurodevelopment and neurological diseases.by Peng Yu.Ph.D