Nicolae Goga

3D dental reconstruction from CBCT data

We will present in this paper a method for the reconstruction of the oral 3D structure using Computed Tomography (CT) sections obtained from Cone Beam Computed Tomography (CBCT). A high-quality visualization of the dental tissues, based nowadays on fine 3D reconstruction, is essential for accurate diagnosis and treatments. Dental CBCT images present several major problems because of the noisiness and of the configuration of the system. We tried several algorithms in order to overcome these issues and to obtain a feasible and efficient method that can be used directly in the clinical procedures. Our approach includes novel procedures like the delimitation of the region of interest and the combination of registration and segmentation steps, which grant faster results and open the route to better representations.

Automatic segmentation for 3D dental reconstruction

Cone Beam Computed Tomography (CBCT) is one of the favorite imaging technologies in dentistry because if offers 3D images in conditions of reduced irradiation. High quality 3D imaging is essential for first-rate diagnostic and treatment, despite the rather low quality images, with low contrast and high noise. The paper presents an automatic method for 3D reconstruction of the oral cavity. We analyze the existing state of the art in the field and propose a segmentation method that uses the domain knowledge related to mouth and teeth to reduce the computational load and to provide good results in an automatic procedure. The method is based on a Canny type edge detector, that was found to offer better control than active contour methods.

Teeth labeling from CBCT data using the Circular Hough Transform

In this paper, we explore the possibilities to detect and label straightforward the teeth in Cone Beam Computed Tomography images, without performing complex segmentation procedures on teeth. We are using the Circular Hough Transform (CHT) to find the teeth positions, segmentation using intensity level to determine the mandible and deformable templates to find the best fit position for the teeth on the mandible. The dental arch is split into 16 regions and the teeth are labeled by their position in a given region.

Improved GROMACS algorithms using the MPI parallelization

Molecular dynamics (MD) studies the structure of molecular systems which are subject to certain constraints and forces. The simulation of particles is a tool for examining atomic systems during a period of nanoseconds in which the trajectory of atoms and the state of the system is analyzed. GROMACS is a package which supports molecular dynamics simulations and energy minimization, being started the University of Groningen. Usually molecular dynamics simulations are time consuming, sometimes taking weeks and even months. In order to obtain the output of the simulation in less time, parallelization is used through the use of MPI (Message Passing Interface). The article presents the MPI parallelization of a novel thermostat algorithm for molecular dynamics and experimental results.

Neural network based edge detection for CBCT segmentation

Edge detection is an important task in image processing, many times as part of the segmentation process. When segmentation is performed in medical imaging, one of the preferred tools is neural networks, because of their capabilities of adaptive learning and non-linear mapping. We present in this paper the neural network tools used for edge detection and we propose one that is able to perform edge detection in dental Cone Beam Computer Tomography (CBCT) images, a necessary step for the teeth 3D reconstruction.

Improved OpenMM algorithms for CUDA and OpenCL using new stochastic dynamics and the Berendsen thermostat

The simulations done in molecular dynamics (MD) are used to learn about the behavior of macromolecular systems. While analyzing large macromolecular systems, the computation can last long, many times for days, weeks and months, due to which the need of parallelization occurs for shortening the time. The graphics processing unit (GPU) yields to multithread computational capacity by the use of CUDA high-level language which suits the molecular dynamics algorithms. The OpenCL platform allows its reckoning kernels to run without difficulty on GPUs, as well as on multicore computers. We developed a new thermostat that combines Langevin dynamics with the global Berendsen thermostat which is parallelized in CUDA, as well as in OpenCL. The GPU parallelization is presented in this paper by applying stochastic dynamics, respectively the Langevin integrator, together with the Berendsen thermostat. A system of atoms is tested by taking into consideration the friction coefficient and the coupling parameter.

Automatic contour detection from dental CBCT DICOM data

The algorithm described in this paper is part of a program supporting the process of teeth contour detection, segmentation and 3D reconstruction. The data is taken from dental conical tomographies. Automatic contour detection starts by filtering the image according to an adaptive threshold. The region of interest is established and the tooth contour is determined using the possible edge directions. The applied method is innovative for the contour detection, because it exploits the domain knowledge, where each tooth (incisor, canine, premolar, molar) has a certain shape. For touching teeth, the delimitation is automatically performed by drawing a line using the Bresenham algorithm. The algorithm will further contribute to the determination and treatment of teeth pathologies.

Integrated platform for extracting Dental knowledge imaging and 3D Modeling

Today digital medical imaging starts to be more introduced in the daily practice for clinical, educational, research uses. The work reported in this paper is done within the context of the Research Project UEFISCDI No. 31/2014, “AIMMS - Application for Using Image Data Mining and 3D Modeling in Dental Screening”. AIMMS provides an integrated platform to support dental treatment methods using the most recent digital image processing technology, image mapping technology and 3D modeling. DICOM files are used for detecting anatomical information and pathological dentistry problems. The platform aims to encourage the progress of computer aided dentistry detection, focusing on building and exploiting knowledge about models that include discriminating elements between normal and dental pathologies scans. This is done through semantic annotations which store information about the structural and anatomical elements, regions and points of interest, as well as information related to analyzed teeth. In this article we provide a general overview of the AIMMS integrated platform and the AIMMS research work and main obtained results.

Semantic report search engine - Questor

As ever more data is collected in the business processes of large enterprises, the decision makers need new business intelligence tools. Enterprise reporting tools have been available for some time, however, while reports aggregate business data, the large number of reports can become unmanageable. The European funded project Questor aims to create a revolutionary product that will eliminate the complexities inherent in the report management workflow. The final purpose is to make querying the report database as simple as addressing a question in natural language. This paper gives an overview over the concept of the Questor project, its software architecture, algorithms and results.