13,635 research outputs found
Data transfer between Cad system and RP system: a report
Rapid Prototyping (RP) is a technology that transform a design generated in Computer Aided Design (CAD) to a 3D model parts. CAD models are usually done on a CAD system and then transported into the RP system. A good interface between the CAD and the RP system is one of the key factors of producing a good quality prototype. This paper reports on the results of an experimentation carried out to identify the problems in transferring data between a CAD system (UNIGRAPHICS) and an RP system (QUICKSLICE). Based on the experimentation’s results and analysis, a basic guideline is proposed for a safer data transfer between the CAD system (UNIGRAPHICS) and an RP system (QUICKSLICE)
A Robotic CAD System using a Bayesian Framework
We present in this paper a Bayesian CAD system
for robotic applications. We address the problem of the
propagation of geometric uncertainties and how esian
CAD system for robotic applications. We address the
problem of the propagation of geometric uncertainties
and how to take this propagation into account when
solving inverse problems. We describe the methodology
we use to represent and handle uncertainties using
probability distributions on the system's parameters
and sensor measurements. It may be seen as a
generalization of constraint-based approaches where we
express a constraint as a probability distribution instead
of a simple equality or inequality. Appropriate
numerical algorithms used to apply this methodology
are also described. Using an example, we show how
to apply our approach by providing simulation results
using our CAD system
Multi-scale analysis of lung computed tomography images
A computer-aided detection (CAD) system for the identification of lung
internal nodules in low-dose multi-detector helical Computed Tomography (CT)
images was developed in the framework of the MAGIC-5 project. The three modules
of our lung CAD system, a segmentation algorithm for lung internal region
identification, a multi-scale dot-enhancement filter for nodule candidate
selection and a multi-scale neural technique for false positive finding
reduction, are described. The results obtained on a dataset of low-dose and
thin-slice CT scans are shown in terms of free response receiver operating
characteristic (FROC) curves and discussed.Comment: 18 pages, 12 low-resolution figure
Computer-aided detection of pulmonary nodules in low-dose CT
A computer-aided detection (CAD) system for the identification of pulmonary
nodules in low-dose multi-detector helical CT images with 1.25 mm slice
thickness is being developed in the framework of the INFN-supported MAGIC-5
Italian project. The basic modules of our lung-CAD system, a dot enhancement
filter for nodule candidate selection and a voxel-based neural classifier for
false-positive finding reduction, are described. Preliminary results obtained
on the so-far collected database of lung CT scans are discussed.Comment: 3 pages, 4 figures; Proceedings of the CompIMAGE - International
Symposium on Computational Modelling of Objects Represented in Images:
Fundamentals, Methods and Applications, 20-21 Oct. 2006, Coimbra, Portuga
Gray-level co-occurrence matrix bone fracture detection
Problem statement: Currently doctors in orthopedic wards inspect the bone x-ray images according to their experience and knowledge in bone fracture analysis. Manual examination of x-rays has multitude drawbacks. The process is time-consuming and subjective. Approach: Since detection of fractures is an important orthopedics and radiologic problem and therefore a Computer Aided Detection(CAD) system should be developed to improve the scenario. In this study, a fracture detection CAD based on GLCM recognition could improve the current manual inspection of x-ray images system. The GLCM for fracture and non-fracture bone is computed and analysis is made. Features of Homogeneity, contrast, energy, correlation are calculated to classify the fractured bone. Results: 30 images of femur fractures have been tested, the result shows that the CAD system can differentiate the x-ray bone into fractured and nonfractured femur. The accuracy obtained from the system is 86.67. Conclusion: The CAD system is proved to be effective in classifying the digital radiograph of bone fracture. However the accuracy rate is not perfect, the performance of this system can be further improved using multiple features of GLCM and future works can be done on classifying the bone into different degree of fracture specifically
An automated system for lung nodule detection in low-dose computed tomography
A computer-aided detection (CAD) system for the identification of pulmonary
nodules in low-dose multi-detector helical Computed Tomography (CT) images was
developed in the framework of the MAGIC-5 Italian project. One of the main
goals of this project is to build a distributed database of lung CT scans in
order to enable automated image analysis through a data and cpu GRID
infrastructure. The basic modules of our lung-CAD system, a dot-enhancement
filter for nodule candidate selection and a neural classifier for
false-positive finding reduction, are described. The system was designed and
tested for both internal and sub-pleural nodules. The results obtained on the
collected database of low-dose thin-slice CT scans are shown in terms of free
response receiver operating characteristic (FROC) curves and discussed.Comment: 9 pages, 9 figures; Proceedings of the SPIE Medical Imaging
Conference, 17-22 February 2007, San Diego, California, USA, Vol. 6514,
65143
Fully automatic binary glioma grading based on pre-therapy MRI using 3D Convolutional Neural Networks
The optimal treatment strategy of newly diagnosed glioma is strongly influenced by tumour malignancy. Manual non-invasive grading based on MRI is not always accurate and biopsies to verify diagnosis negatively impact overall survival. In this paper, we propose a fully automatic 3D computer-aided diagnosis (CAD) system to non-invasively differentiate high-grade glioblastoma from lower-grade glioma. The approach consists of an automatic segmentation step to extract the tumour ROI followed by classification using a 3D convolutional neural network. Segmentation was performed using a 3D U-Net achieving a dice score of 88.53% which matches top performing algorithms in the BraTS 2018 challenge. The classification network was trained and evaluated on a large heterogeneous dataset of 549 patients reaching an accuracy of 91%. Additionally, the CAD system was evaluated on data from the Ghent University Hospital and achieved an accuracy of 92% which shows that the algorithm is robust to data from different centres
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