Search results for "InGaN"
showing 10 items of 1214 documents
A2Ba: Adaptive Background Modelling for Visual Aerial Surveillance Conditions
2015
International audience; Background modelling algorithms are widely used to define a part of an image that most time remains stationary in a video. In surveillance tasks, this model helps to recognize those outlier objects in an area under monitoring. Set up a background model on mobile platforms (UAVs, intelligent cars, etc.) is a challenging task due camera motion when images are acquired. In this paper, we propose A2Ba, a robust method to support instabilities caused by aerial images fusing different information about image motion. We used frame difference as first approximation, then age of pixels is estimated. This latter gives us an invariability level of a pixel over time. Gradient di…
Analysis and Performance Evaluation of the IEEE 802.16 ARQ Mechanism
2008
The IEEE 802.16 standard defines the ARQ mechanismas a part of the MAC layer. The functioning of the ARQmechanism depends on a number of parameters. The IEEE802.16 specification defines them but it does not provide concrete values and solutions. This paper studies the key features and parameters of the 802.16 ARQ mechanism. In particular, we consider a choice for the ARQ feedback type, an algorithm to build block sequences, the ARQ feedback intensity, a scheduling of the ARQ feedbacks and retransmissions, the ARQ block rearrangement, ARQ transmission window and the ARQ block size. We ran simulation scenarios to study these parameters and how they impact the performance of application protoc…
High Performance 3D PET Reconstruction Using Spherical Basis Functions on a Polar Grid
2011
Statistical iterative methods are a widely used method of image reconstruction in emission tomography. Traditionally, the image space is modelled as a combination of cubic voxels as a matter of simplicity. After reconstruction, images are routinely filtered to reduce statistical noise at the cost of spatial resolution degradation. An alternative to produce lower noise during reconstruction is to model the image space with spherical basis functions. These basis functions overlap in space producing a significantly large number of non-zero elements in the system response matrix (SRM) to store, which additionally leads to long reconstruction times. These two problems are partly overcome by expl…
A web-based collection of genotype-phenotype associations in hereditary recurrent fevers from the Eurofever registry
2017
PubMed ID: 29047407
Artificial Neural Network Based Abdominal Organ Segmentations: A Review
2015
There are many neural network based abdominal organ segmentation approaches from medical images. Computed tomography images were mostly used in these approaches. Applied techniques are usually based on prior information regarding position, shape, and size of organs in these methods. In the literature, there are only a few neural network based techniques that were implemented to segment abdominal organs from magnetic resonance based images. In this paper, we present these methods and their results.
Hybrid segmentation and virtual bronchoscopy based on CT images1
2004
Rationale and objectives Introduction of combination of the segmentation tool SegoMeTex and the virtual endoscopy system VIVENDI to perform virtual endoscopic inspections of the human lung. This virtual bronchoscopy system enables visualization of the tracheobronchial tree down to seventh generation. Furthermore, the modified virtual system visualizes hidden structures such as segmented vascular system or tumors. Materials and methods The segmentation is based on image data acquired by a multislice computed tomography scanner. SegoMeTex is used to segment the tracheobronchial tree by a hybrid system with minimal user action. Similarly, the complementary pulmonary arterial can be segmented, …
GPCALMA: An Italian mammographic database of digitized images for research
2006
In this work the implementation of a database of digitized mammograms is described. The digitized images were collected since 1999 by a community of physicists in collaboration with radiologists in several Italian hospitals, as a first step in order to develop and implement a Computer Aided Detection (CAD) system. 3369 mammograms were collected from 967 patients; they were classified according to the type and the morphology of the lesions, the type of the breast tissue and the type of pathologies. A dedicated Graphical User Interface was developed for mammography visualization and processing, in order to support the medical diagnosis directly on a high-resolution screen. The database has be…
SIFT Texture Description for Understanding Breast Ultrasound Images
2014
Texture is a powerful cue for describing structures that show a high degree of similarity in their image intensity patterns. This paper describes the use of Self-Invariant Feature Transform (SIFT), both as low-level and high-level descriptors, applied to differentiate the tissues present in breast US images. For the low-level texture descriptors case, SIFT descriptors are extracted from a regular grid. The high-level texture descriptor is build as a Bag-of-Features (BoF) of SIFT descriptors. Experimental results are provided showing the validity of the proposed approach for describing the tissues in breast US images.
Kinetic analysis of functional images: The case for a practical approach to performance prediction
1999
We present the first parallel medical application for the analysis of dynamic positron emission tomography (PET) images together with a practical performance model. The parallel application may improve the diagnosis for a patient (e. g. in epilepsy surgery) because it enables the fast computation of parametric images on a pixed level as opposed to the traditionally used region of interest (ROI) approach which is applied to determine an average parametric value for a particular anatomic region of the brain. We derive the performance model from the application context and show its relation to abstract machine models. We demonstrate the accuracy of the model to predict the runtime of the appli…
Clustering Algorithms for MRI
1991
Magnetic Resonance Imaging (MRI) plays a relevant role in the design of systems for computer assisted diagnosis. MR-images are multi-dimensional in nature; physicians have to combine several perceptual information images to perform the tissue classification needed for diagnosis. Automatic clustering methods help to discriminate relevant features and to perform a preliminary segmentation of the image; it can guide the final manual classification of body-tissues. Three clustering techniques and their integration in a MRI-system are described. Their performance and accuracy was evaluated on synthetic and real image-data. A comparison of our approach with the tissue-classification done by a rad…