3D ultrasound image reconstruction based on VTK

Three dimensional (3D) ultrasound image reconstruction based on two dimensional (2D) images has become a famous method for analyzing some anatomy related to abnormalities. 3D ultrasound image reconstruction system is required in order to view the specific part of the object and so that it can be used for analysis purpose. In this paper, 2D images were taken by using untracked free-hand system. Few sets of 2D images were taken with different number of slices and after some 2D image processing, 3D reconstruction is done by using surface rendering techniques by implementing marching cubes algorithm in Visual C++ 6.0 with Visualization Toolkit (VTK) toolbox. From the experiment, we can conclude that in order to reconstruct a better 3D image, the aid of tracking sensor is important. Besides, another parameter such as the number of slices of the images and image processing technique will affect the smoothness of the reconstructed 3D image

3D Reconstruction of CT Scans For Visualization in Virtual Reality

Computed tomography allows analyzing the internal structure of an object, which is useful especially in medicine. The standard visualization displays scans in the 2D plane. 3D reconstruction of scans provides a complex image of the morphology of the scanned object. Matlab is a software commonly used for image processing and analysis. It includes Medical Image Processing Toolbox for displaying data from CT scan in DICOM format. However, it is not possible with this toolbox to export the dataset of the image as a 3D object. Therefore, the aim of the paper is the implementation of a toolbox for loading and displaying data as a 3D reconstruction. This toolbox allows the user to export the data in OBJ or STL format. That allows the user (i) to visualize the 3D models in virtual reality and (ii) to prepare the model for 3D printing. The OBJ model is imported to Blender and then exported out with a texture as an object file. In Unity, we created a 3D scene and imported model. The advantage of displaying the 3D model in virtual reality is a more realistic view of the shape and dimension of an object.Výpočetní tomografie umožňuje studovat vnitřní strukturu objektu, což je využíváno především v medicíně. Standardní zobrazovací techniky promítají snímky ve 2D rovině. 3D rekonstrukce snímků přináší komplexní pohled na morfologii snímané tkáně. Matlab je software běžně užívaný v oblasti zpracování a analýze obrazových dat. Zároveň obsahuje nástroj “Image Procesessing Toolbox”, který umožňuje zobrazit CT snímky uchované ve formátu DICOM. Tento nástroj však neumožňuje vyexportovat zobrazený model jako 3D objekt. Cílem tohoto projektu bylo vytvoření nástroje pro načítání a zobrazení zrekonstruovaných 3D modelů. Tento nástroj umožňuje uživateli vyexportovat data v OBJ nebo STL formátu, který umožňuje (i) vizualizovat 3D model ve virtuální realitě a (ii) připravit model vhodný pro 3D tisk. V editor Unity byla vytvořena 3D scéna a do ní byl importován vygenerovaný model. Výhodou zobrazení 3D modelu ve virtuální realitě je přirozený pohled na prostorové uspořádání objektu

The 3D model control of image processing

Telerobotics studies remote control of distant robots by a human operator using supervisory or direct control. Even if the robot manipulators has vision or other senses, problems arise involving control, communications, and delay. The communication delays that may be expected with telerobots working in space stations while being controlled from an Earth lab have led to a number of experiments attempting to circumvent the problem. This delay in communication is a main motivating factor in moving from well understood instantaneous hands-on manual control to less well understood supervisory control; the ultimate step would be the realization of a fully autonomous robot. The 3-D model control plays a crucial role in resolving many conflicting image processing problems that are inherent in resolving in the bottom-up approach of most current machine vision processes. The 3-D model control approach is also capable of providing the necessary visual feedback information for both the control algorithms and for the human operator

Extended whole mesh deformation model: Full 3D processing

Processing medical data has always been an interesting field that has shown the need for effective image segmentation methods. Modern medical image segmentation solutions are focused on 3D image volumes, which originate at advanced acquisition devices. Operating on such data in a 3D envi- ronment is essential in order to take the full advantage of the available information. In this paper we present an extended version of our 3D image segmentation and reconstruction model that belongs to the family of Deformable Models and is capable of processing large image volumes in competitive times and in fully 3D environment, offering a big level of automation of the process and a high precision of results. It is also capable of handling topology changes and offers a very good scalability on multi-processing unit architectures. We present a description of the model and show its capabilities in the field of medical image processing

3D Geometric Analysis of Tubular Objects based on Surface Normal Accumulation

This paper proposes a simple and efficient method for the reconstruction and extraction of geometric parameters from 3D tubular objects. Our method constructs an image that accumulates surface normal information, then peaks within this image are located by tracking. Finally, the positions of these are optimized to lie precisely on the tubular shape centerline. This method is very versatile, and is able to process various input data types like full or partial mesh acquired from 3D laser scans, 3D height map or discrete volumetric images. The proposed algorithm is simple to implement, contains few parameters and can be computed in linear time with respect to the number of surface faces. Since the extracted tube centerline is accurate, we are able to decompose the tube into rectilinear parts and torus-like parts. This is done with a new linear time 3D torus detection algorithm, which follows the same principle of a previous work on 2D arc circle recognition. Detailed experiments show the versatility, accuracy and robustness of our new method.Comment: in 18th International Conference on Image Analysis and Processing, Sep 2015, Genova, Italy. 201