Методика выделения топологических признаков на трехмерных изображениях

Представлена новая методика получения и анализа топологических характеристик для последующей классификации на трехмерных изображениях. Для получения топологических структур использовались трехмерные дистанционные карты, анализ применялся с помощью рассмотрения совместных свойств скелета, границы и выпуклой оболочки объекта.Представлена нова методика отримання та аналізу топологічних характеристик для подальшої класифікації на тривимірних зображеннях. Для отримання топологічних структур використовувалися тривимірні дистанційні карти, аналіз застосовувався з допомогою розгляду спільних властивостей скелета, межі та опуклої оболонки об’єкту.The paper is devoted to the problem of obtaining and analyzing the topological features for the subsequent classification of three-dimensional images. To obtain the topological structures, we used three-dimensional distance maps, the analysis applied by considering the joint properties of the skeleton, the boundary and the convex hul of the object

Методика выделения топологических признаков на трехмерных изображениях

Представлена новая методика получения и анализа топологических характеристик для последующей классификации на трехмерных изображениях. Для получения топологических структур использовались трехмерные дистанционные карты, анализ применялся с помощью рассмотрения совместных свойств скелета, границы и выпуклой оболочки объекта.Представлена нова методика отримання та аналізу топологічних характеристик для подальшої класифікації на тривимірних зображеннях. Для отримання топологічних структур використовувалися тривимірні дистанційні карти, аналіз застосовувався з допомогою розгляду спільних властивостей скелета, межі та опуклої оболонки об’єкту.The paper is devoted to the problem of obtaining and analyzing the topological features for the subsequent classification of three-dimensional images. To obtain the topological structures, we used three-dimensional distance maps, the analysis applied by considering the joint properties of the skeleton, the boundary and the convex hul of the object

Extracción Automática de la Línea Central de Estructuras Tubulares: Implementación Matricial

Se propone una nueva implementación matricial de un algoritmo para la extracción automática de la línea central de estructuras tubulares. El algoritmo seleccionado calcula la línea central de estructuras complejas sin la necesidad de interacción con el usuario. En el trabajo se explica detalladamente cómo llevar a cabo la implementación matricial utilizando el lenguaje de computación de Matlab. La implementación matricial permite el cálculo de la línea central en pocos segundos, mejorando en varios grados de magnitud la implementación disponible en ITK

Fast colon centreline calculation using optimised 3D topological thinning

Topological thinning can be used to accurately identify the central path through a computer model of the colon generated using computed tomography colonography. The central path can subsequently be used to simplify the task of navigation within the colon model. Unfortunately standard topological thinning is an extremely inefficient process. We present an optimised version of topological thinning that significantly improves the performance of centreline calculation without compromising the accuracy of the result. This is achieved by using lookup tables to reduce the computational burden associated with the thinning process

Correcting curvature-density effects in the Hamilton-Jacobi skeleton

The Hainilton-Jacobi approach has proven to be a powerful and elegant method for extracting the skeleton of two-dimensional (2-D) shapes. The approach is based on the observation that the normalized flux associated with the inward evolution of the object boundary at nonskeletal points tends to zero as the size of the integration area tends to zero, while the flux is negative at the locations of skeletal points. Nonetheless, the error in calculating the flux on the image lattice is both limited by the pixel resolution and also proportional to the curvature of the boundary evolution front and, hence, unbounded near endpoints. This makes the exact location of endpoints difficult and renders the performance of the skeleton extraction algorithm dependent on a threshold parameter. This problem can be overcome by using interpolation techniques to calculate the flux with subpixel precision. However, here, we develop a method for 2-D skeleton extraction that circumvents the problem by eliminating the curvature contribution to the error. This is done by taking into account variations of density due to boundary curvature. This yields a skeletonization algorithm that gives both better localization and less susceptibility to boundary noise and parameter choice than the Hamilton-Jacobi method

Aquatics reconstruction software: the design of a diagnostic tool based on computer vision algorithms

Computer vision methods can be applied to a variety of medical and surgical applications, and many techniques and algorithms are available that can be used to recover 3D shapes and information from images range and volume data. Complex practical applications, however, are rarely approachable with a single technique, and require detailed analysis on how they can be subdivided in subtasks that are computationally treatable and that, at the same time, allow for the appropriate level of user-interaction. In this paper we show an example of a complex application where, following criteria of efficiency, reliability and user friendliness, several computer vision techniques have been selected and customized to build a system able to support diagnosis and endovascular treatment of Abdominal Aortic Aneurysms. The system reconstructs the geometrical representation of four different structures related to the aorta (vessel lumen, thrombus, calcifications and skeleton) from CT angiography data. In this way it supports the three dimensional measurements required for a careful geometrical evaluation of the vessel, that is fundamental to decide if the treatment is necessary and to perform, in this case, its planning. The system has been realized within the European trial AQUATICS (IST-1999-20226 EUTIST-M WP 12), and it has been widely tested on clinical data

High accuracy capillary network representation in digital rock reveals permeability scaling functions

Permeability is the key parameter for quantifying fluid flow in porous rocks. Knowledge of the spatial distribution of the connected pore space allows, in principle, to predict the permeability of a rock sample. However, limitations in feature resolution and approximations at microscopic scales have so far precluded systematic upscaling of permeability predictions. Here, we report fluid flow simulations in capillary network representations designed to overcome such limitations. Performed with an unprecedented level of accuracy in geometric approximation at microscale, the pore scale flow simulations predict experimental permeabilities measured at lab scale in the same rock sample without the need for calibration or correction. By applying the method to a broader class of representative geological samples, with permeability values covering two orders of magnitude, we obtain scaling relationships that reveal how mesoscale permeability emerges from microscopic capillary diameter and fluid velocity distributions.Comment: Main article: 11 pages and 4 figures. Supplementary Information: 6 pages and 4 figures. Version 2 includes DOI for microCT datase

A Novel Skeleton Extraction Algorithm for 3d Wireless Sensor Networks

Wireless sensor network design is critical and resource allocation is a major problem which remains to be solved satisfactorily. The discrete nature of sensor networks renders the existing skeleton extraction algorithms inapplicable. 3D topologies of sensor networks for practical scenarios are considered in this paper and the research carried out in the field of skeleton extraction for three dimensional wireless sensor networks. A skeleton extraction algorithm applicable to complex 3D spaces of sensor networks is introduced in this paper and is represented in the form of a graph. The skeletal links are identified on the basis of a novel energy utilization function computed for the transmissions carried out through the network. The frequency based weight assignment function is introduced to identify the root node of the skeleton graph. Topological clustering is used to construct the layered topological sets to preserve the nature of the topology in the skeleton graph. The skeleton graph is constructed with the help of the layered topological sets and the experimental results prove the robustness of the skeleton extraction algorithm introduced. Provisioning of additional resources to skeletal nodes enhances the sensor network performance by 20% as proved by the results presented in this paper

Skeleton Extraction from Polygonal Meshes

Tato bakalářská práce se zabývá extrakcí kostry z modelu třírozměrného tělesa. Z existujících metod byla vybrána extrakce kostry pomocí os lokální válcové symetrie (ROSA - rotational symmetry axis), která je velmi odolná ke ztrátám vstupních dat a je vhodná pro tělesa složená z obecně válcových částí a jejich spojů (jako jsou například lidská a zvířecí těla). Bylo optimalizováno vyhledávání os lokální válcové symetrie. V experimentech byl zkoumán vliv vlastností tělesa (jako např. ztráta vstupních dat, zvrásnění povrchu, tvar) a vliv parametrů metody na kostru.This bachelor's thesis deals with skeleton extraction from 3D objects. The presented technique for skeleton extraction from incomplete points cloud is based on ROSA (Rotational Symmetry Axis) method, while the searching for rotational symmetry axis was optimized. The method is resistant to missing data and is suitable for objects made from cylindrical parts and joints of that parts (for example human and animals bodies). In experiments, both the influence of object properties (for example missing data, chill mark, shape) and the influence of method's parameters were examined.

Computer-aided detection of colonic polyps with level set-based adaptive convolution in volumetric mucosa to advance CT colonography toward a screening modality

As a promising second reader of computed tomographic colonography (CTC) screening, the computer-aided detection (CAD) of colonic polyps has earned fast growing research interest. In this paper, we present a CAD scheme to automatically detect colonic polyps in CTC images. First, a thick colon wall representation, ie, a volumetric mucosa (VM) with several voxels wide in general, was segmented from CTC images by a partial-volume image segmentation algorithm. Based on the VM, we employed a level set-based adaptive convolution method for calculating the first- and second-order spatial derivatives more accurately to start the geometric analysis. Furthermore, to emphasize the correspondence among different layers in the VM, we introduced a middle-layer enhanced integration along the image gradient direction inside the VM to improve the operation of extracting the geometric information, like the principal curvatures. Initial polyp candidates (IPCs) were then determined by thresholding the geometric measurements. Based on IPCs, several features were extracted for each IPC, and fed into a support vector machine to reduce false positives (FPs). The final detections were displayed in a commercial system to provide second opinions for radiologists. The CAD scheme was applied to 26 patient CTC studies with 32 confirmed polyps by both optical and virtual colonoscopies. Compared to our previous work, all the polyps can be detected successfully with less FPs. At the 100% by polyp sensitivity, the new method yielded 3.5 FPs/dataset