Search results for " Enhancement"
showing 10 items of 521 documents
An automatic L1-based regularization method for the analysis of FFC dispersion profiles with quadrupolar peaks
2023
Fast Field-Cycling Nuclear Magnetic Resonance relaxometry is a non-destructive technique to investigate molecular dynamics and structure of systems having a wide range of ap- plications such as environment, biology, and food. Besides a considerable amount of liter- ature about modeling and application of such technique in specific areas, an algorithmic approach to the related parameter identification problem is still lacking. We believe that a robust algorithmic approach will allow a unified treatment of different samples in several application areas. In this paper, we model the parameters identification problem as a con- strained L 1 -regularized non-linear least squares problem. Following…
Simple computation of the approximated modulation transfer function (MTF) using spreadsheet-software: method and evaluation in five maxillofacial CBC…
2019
OBJECTIVES: To develop a simple way to compute the approximated modulation transfer function (MTF) manually using conventional spreadsheet software. METHODS: Basing on an edge-image a method was developed, facilitating computation of the edge spread and line spread function in open-source spreadsheet software (Gnumeric; http://projects.gnome.org/gnumeric/downloads.shtml). By means of the integrated fast Fourier transformation Fourier coefficients are obtained from the line spread function which can then be plotted vs spatial frequency to obtain MTF-plots. For the experimental evaluation an edge test object was exposed in five commercial CBCT devices for maxillofacial applications. RESULTS: …
Nanomaterials and new biorecognition molecules based surface plasmon resonance biosensors for mycotoxin detection
2019
Mycotoxins are highly toxic secondary metabolites, which may contaminate many types of food and feeds. These toxins have serious health risks for both human and animals. One of the effective ways to prevent food contamination and protect people against mycotoxins is based on timely detection. Several methods like enzyme-linked immunosorbent assay and affinity chromatography are commercially available for this purpose. Nevertheless, sensitive, fast, simple, low-cost, and portable devices are absolutely required for a fast point-of care information and making decisions. Application of biosensors appears to be a possible technique to meet this need for mycotoxins analyze. The present study has…
Power estimation for non-standardized multisite studies
2016
A concern for researchers planning multisite studies is that scanner and T1-weighted sequence-related biases on regional volumes could overshadow true effects, especially for studies with a heterogeneous set of scanners and sequences. Current approaches attempt to harmonize data by standardizing hardware, pulse sequences, and protocols, or by calibrating across sites using phantom-based corrections to ensure the same raw image intensities. We propose to avoid harmonization and phantom-based correction entirely. We hypothesized that the bias of estimated regional volumes is scaled between sites due to the contrast and gradient distortion differences between scanners and sequences. Given this…
Automatic skull stripping in MRI based on morphological filters and fuzzy c-means segmentation
2012
In this paper a new automatic skull stripping method for T1-weighted MR image of human brain is presented. Skull stripping is a process that allows to separate the brain from the rest of tissues. The proposed method is based on a 2D brain extraction making use of fuzzy c-means segmentation and morphological operators applied on transversal slices. The approach is extended to the 3D case, taking into account the result obtained from the preceding slice to solve the organ splitting problem. The proposed approach is compared with BET (Brain Extraction Tool) implemented in MRIcro software.
Bias artifact suppression on MR volumes.
2007
RF-Inhomogeneity correction is a relevant research topic in the field of Magnetic Resonance Imaging (MRI). A volume corrupted by this artifact exhibits nonuni- form illumination both inside a single slice and between adjacent ones. In this work a bias correction technique is presented, which suppresses this artifact on MR vol- umes scanned from different body parts without any a-priori hypothesis on the artifact model. Theoretical foundations of the method are reported together with experimental results and a comparison is presented with both the 2D version of the algorithm and other techniques that are widely used in MRI literature.
Robust Resolution-Enhanced Prostate Segmentation in Magnetic Resonance and Ultrasound Images through Convolutional Neural Networks
2021
[EN] Prostate segmentations are required for an ever-increasing number of medical applications, such as image-based lesion detection, fusion-guided biopsy and focal therapies. However, obtaining accurate segmentations is laborious, requires expertise and, even then, the inter-observer variability remains high. In this paper, a robust, accurate and generalizable model for Magnetic Resonance (MR) and three-dimensional (3D) Ultrasound (US) prostate image segmentation is proposed. It uses a densenet-resnet-based Convolutional Neural Network (CNN) combined with techniques such as deep supervision, checkpoint ensembling and Neural Resolution Enhancement. The MR prostate segmentation model was tra…
Dynamic 3D Scene Reconstruction and Enhancement
2017
International audience; In this paper, we present a 3D reconstruction and enhancement approach for high quality dynamic city scene reconstructions. We first detect and segment the moving objects using 3D Motion Segmenta-tion approach by exploiting the feature trajectories' behaviours. Getting the segmentations of both the dynamic scene parts and the static scene parts, we propose an efficient point cloud registration approach which takes the advantages of 3-point RANSAC and Iterative Closest Points algorithms to produce precise point cloud alignment. Furthermore, we proposed a point cloud smoothing and texture mapping framework to enhance the results of reconstructions for both the static a…
A 3D Network Based Shape Prior for Automatic Myocardial Disease Segmentation in Delayed-Enhancement MRI
2021
Abstract Objectives: In this work, a new deep learning model for relevant myocardial infarction segmentation from Late Gadolinium Enhancement (LGE)-MRI is proposed. Moreover, our novel segmentation method aims to detect microvascular-obstructed regions accurately. Material and methods: We first segment the anatomical structures, i.e., the left ventricular cavity and the myocardium, to achieve a preliminary segmentation. Then, a shape prior based framework that fuses the 3D U-Net architecture with 3D Autoencoder segmentation framework to constrain the segmentation process of pathological tissues is applied. Results: The proposed network reached outstanding myocardial segmentation compared wi…
Divisive normalization image quality metric revisited.
2010
Structural similarity metrics and information-theory-based metrics have been proposed as completely different alternatives to the traditional metrics based on error visibility and human vision models. Three basic criticisms were raised against the traditional error visibility approach: (1) it is based on near-threshold performance, (2) its geometric meaning may be limited, and (3) stationary pooling strategies may not be statistically justified. These criticisms and the good performance of structural and information-theory-based metrics have popularized the idea of their superiority over the error visibility approach. In this work we experimentally or analytically show that the above critic…