General Object Reconstruction based on Simplex Meshes

In this paper, we propose a general tridimensional reconstruction algorithm of range and volumetric images, based on deformable simplex meshes. The algorithm is able to reconstruct surfaces without any restriction on their shape or topology. The different tasks performed during the reconstruction include the segmentation of objects in the scene, the extrapolation of missing data and the control of smoothness, density and geometric quality of the reconstructed model. All surfaces are represented as simplex meshes, that are unstructured meshes whose topology is dual of triangulations. The reconstruction takes place in two stages. First, we initialize the model either manually or using an automatic initialization routine. After the first fit, the topology of the model can be modified by creating holes or increasing its genus. Finally, an iterative adaptation or refinement algorithm decrease the distance of the model from the data while preserving a high geometric and topological quality. We have applied our algorithm to several medical images or range images

Восстановление формы объемных медицинских объектов с помощью дистанционных карт поперечных сечений

Представлены новые методы для реконструкции трехмерной поверхности объекта из нескольких замкнутых, в общем случае неплоских кривых, включая контуры, которые были очерчены вручную. Для восстановления объектов разной формы используются двухмерные дистанционные карты.Представлені нові методи для реконструкції тривимірної поверхні об’єкту з декількох замкнутих, в загальному випадку неплоских кривих, включаючи контури, які були обкреслені уручну. Для відновлення об'єктів різної форми використовуються двомірні дистанційні карти.The new techniques is presented in order to reconstruct 3D object surface from several closed, in general, non-planar curves, including contours that were outlined manually. 2D distance map used to reconstruct object of a different shapes. Branching problem also discussed

Восстановление поверхности трехмерного объекта по обводкам его сечений

В статье предлагаются методы векторного восстановления поверхности и оценки объема медицинских и других трехмерных объектов по нескольким обводкам их поперечных сечений в пространстве. Обводки сечений в пространстве должны быть упорядочены, однако, в общем случае они могут быть не плоскими и не параллельными друг другу. Предложенные методы дают хорошие результаты при восстановлении выпуклых или близких к ним по форме поверхностей. Также предложен способ восстановления поверх- ности разветвляющихся объектов.У статті пропонуються методи векторного відновлення поверхні та оцінки обсягу медичних та інших тривимірних об’єктів за кількома обведеннями їх поперечних перерізів в просторі. Обведення перетинів у просторі повинні бути впорядковані, проте, в загальному випадку вони можуть бути не плоскими і не паралельними один одному. Запропоновані методи дають гарні результати при відновленні опуклих або близьких до них об’єктів. Також описані способи вирішення проблеми розгалуження для об’єктів більш складної форми.A new technique is presented in order to reconstruct 3D surface, represented in a vector form, from several closed, in general, non-planar curves, including contours that were outlined manually. Also approaches to reconstruct branching objects of complex forms are developed

Complex scene modeling and segmentation with deformable simplex meshes

In this thesis we present a system for 3D reconstruction and segmentation of complex real world scenes. The input to the system is an unstructured cloud of 3D points. The output is a 3D model for each object in the scene. The system starts with a model that encloses the input point cloud. A deformation process is applied to the initial model so it gets close to the point cloud in terms of distance, geometry and topology. Once the deformation stops the model is analyzed to check if more than one object is present in the point cloud. If necessary a segmentation process splits the model into several parts that correspond to each object in the scene. Using this segmented model the point cloud is also segmented. Each resulting sub-cloud is treated as a new input to the system. If, after the deformation process, the model is not segmented a refinement process improves the objective and subjective quality of the model by concentrating vertices around high curvature areas. The simplex mesh reconstruction algorithm was modified and extended to suit our application. A novel segmentation algorithm was designed to be applied on the simplex mesh. We test the system with synthetic and real data obtained from single objects, simple. and complex scenes. In the case of the synthetic data different levels of noise are added to examine the performance of the system. The results show that the systems performs well for either of the three cases and also in the presence of low levels of noise

ВОССТАНОВЛЕНИЕ ОБЪЕКТОВ ПО ТРЁХМЕРНЫМ УЛЬТРАЗВУКОВЫМ ИЗОБРАЖЕНИЯМ НА ОСНОВЕ СИМПЛЕКС-СЕТЕЙ

Предлагается алгоритм восстановления трёхмерных объектов по ультразвуковым изображе­ниям, основанный на симплекс-сетях. Этот полуавтоматический алгоритм использует обведённые вручную границы объекта на нескольких представительных непараллельных сечениях трёхмерного ультразвукового изображения исследуемого объекта. Границы объекта генерируют внешние силы, под влиянием которых происходит деформация начальной симплекс-сети

Semi-Automatic segmentation of multiple mouse embryos in MR images

<p>Abstract</p> <p>Background</p> <p>The motivation behind this paper is to aid the automatic phenotyping of mouse embryos, wherein multiple embryos embedded within a single tube were scanned using Magnetic Resonance Imaging (MRI).</p> <p>Results</p> <p>Our algorithm, a modified version of the simplex deformable model of Delingette, addresses various issues with deformable models including initialization and inability to adapt to boundary concavities. In addition, it proposes a novel technique for automatic collision detection of multiple objects which are being segmented simultaneously, hence avoiding major leaks into adjacent neighbouring structures. We address the initialization problem by introducing balloon forces which expand the initial spherical models close to the true boundaries of the embryos. This results in models which are less sensitive to initial minimum of two fold after each stage of deformation. To determine collision during segmentation, our unique collision detection algorithm finds the intersection between binary masks created from the deformed models after every few iterations of the deformation and modifies the segmentation parameters accordingly hence avoiding collision.</p> <p>We have segmented six tubes of three dimensional MR images of multiple mouse embryos using our modified deformable model algorithm. We have then validated the results of the our semi-automatic segmentation versus manual segmentation of the same embryos. Our Validation shows that except paws and tails we have been able to segment the mouse embryos with minor error.</p> <p>Conclusions</p> <p>This paper describes our novel multiple object segmentation technique with collision detection using a modified deformable model algorithm. Further, it presents the results of segmenting magnetic resonance images of up to 32 mouse embryos stacked in one gel filled test tube and creating 32 individual masks.</p

Anatomical Modelling of the Musculoskeletal System from MRI

Abstract. This paper presents a novel approach for multi-organ (mus-culoskeletal system) automatic registration and segmentation from clini-cal MRI datasets, based on discrete deformable models (simplex meshes). We reduce the computational complexity using multi-resolution forces, multi-resolution hierarchical collision handling and large simulation time steps (implicit integration scheme), allowing real-time user control and cost-efficient segmentation. Radial forces and topological constraints (at-tachments) are applied to regularize the segmentation process. Based on a medial axis constrained approximation, we efficiently characterize shapes and deformations. We validate our methods for the hip joint and the thigh (20 muscles, 4 bones) on 4 datasets: average error=1.5mm, computation time=15min.

3D nonlinear PET-CT image registration algorithm with constrained Free-Form Deformations

International audienceThis paper presents a 3D nonlinear PET-CT image registration method guided by a B-Spline Free-Form Deformations (FFD) model, dedicated to thoracic and abdominal regions. It is divided into two stages: one FFD-based registration of structures that can be identified in both images; and a whole-image intensity registration step constrained by the FFD computed during the first step. Different similarity criteria have been adopted for both stages: Root Mean Square (RMS) to register recognized structures and Normalized Mutual Information (NMI) for optimizing the whole-image intensity stage. Structure segmentation is performed according to a hierarchical procedure, where the extraction of a given structure is driven by information derived from a simpler one. This information is composed of spatial constraints and expressed by the means of regions of interest, in which a 3D simplex mesh deformable model based method is applied. The results have been very positively evaluated by three medical experts