Volume preserving image registration via a post-processing stage

2008 ◽  
Author(s):  
Reinhard Hameeteman ◽  
Jifke F. Veenland ◽  
Wiro J. Niessen
Keyword(s):  
Image Registration ◽  
Post Processing ◽  
Processing Stage ◽  
Volume Preserving

scholarly journals A New Design Procedure for Rotor Laminations of Synchronous Reluctance Machines with Fluid Shaped Barriers

Electronics ◽  
2022 ◽  
Vol 11 (1) ◽  
pp. 134
Author(s):  
Federica Uberti ◽  
Lucia Frosini ◽  
Loránd Szabó
Keyword(s):  
Finite Element ◽  
Design Procedure ◽  
Optimization Procedure ◽  
Torque Ripple ◽  
Stator Winding ◽  
Post Processing ◽  
Processing Stage ◽  
Synchronous Reluctance Machine ◽  
Design And Optimization ◽  
Reluctance Machine

A new procedure for the design and optimization of the rotor laminations of a synchronous reluctance machine is presented in this paper. The configuration of the laminations is symmetrical and contains fluid-shaped barriers. The parametrization principle is used, which executes variations in the lamination geometry by changing the position, thickness and shape of the flux barriers. Hence, the optimization procedure analyzes the various configurations through finite element simulations, by means of the communication between MATLAB and Flux 2D. In the post processing stage, the best geometry which optimizes mean torque, torque ripple, efficiency and power factor is selected. Once the best rotor configuration is defined, further investigations allow improving its performance by modifying the current angle, the stator winding and the thickness of the radial ribs.

Particle Manipulation Inside a Grooved Microfluidic Channel

2009 ◽  
Author(s):  
Hsiu-hung Chen ◽  
Dayong Gao
Keyword(s):  
Rapid Prototyping ◽  
Microfluidic Device ◽  
Microfluidic Channel ◽  
Lab On A Chip ◽  
Cell Suspensions ◽  
Microfluidic Channels ◽  
Particle Manipulation ◽  
Post Processing ◽  
Processing Stage ◽  
Submicron Structures

The manipulation of particles and cells in micro-fluids, such as cell suspensions, is a fundamental task in Lab-on-a-Chip applications. According to their analysis purposes in either the pre- or post-processing stage, particles/cells flowing inside a microfluidic channel are handled by means of enriching, trapping, separating or sorting. In this study, we report the use of patterning flows produced by a series of grooved surfaces with different geometrical setups integrated into a microfluidic device, to continuously manipulate the flowing particles (5 to 20 μm in diameters) of comparable sizes to the depth of the channel in ways of: 1) concentrating, 2) focusing, and 3) potential separating. The device is fabricated using soft lithographic techniques and is composed of inlets, microfluidic channels, and outlets for loading, manipulating and retrieving cell suspensions, respectively. Such fabrication methods allow rapid prototyping of micron or submicron structures with multiple layers and replica molding on those fabricated features in a clear polymer. The particles are evenly distributed in the entrance of the microchannel and illustrate the enriching, focusing, or size-selective profiles after passing through the patterning grooves. We expect that the techniques of manipulating cell suspensions from this study can facilitate the development of cell-based devices on 1) the visualization of counting, 2) the visualization of sizing, and 3) the particle separating.

Directional Search for Spiral Trajectory Extraction

2015 ◽  
Vol 713-715 ◽  
pp. 577-580
Author(s):  
Xiao Fang Shao ◽  
Hong Chen
Keyword(s):  
Tensor Voting ◽  
Post Processing ◽  
Inference Method ◽  
Production Lines ◽  
Processing Stage ◽  
Turning Angle ◽  
Spiral Trajectory

Spiral trajectory curves often occur in modern production lines. In this paper we propose a curve inference method for spiral trajectory extraction. Based on the tensor voting result of the original images, the method performs a post-processing stage and a directional neighborhood searching process which takes into account the turning angle of the pixels on a certain curve. At last the method is tested on several real images.

Interface modelling between CFD and FEM analysis: the dual-layer post-processing model

2017 ◽  
Vol 34 (4) ◽  
pp. 1166-1190 ◽  
Author(s):  
Daphne Pantousa ◽  
Euripidis Mistakidis
Keyword(s):  
Lower Layer ◽  
Fem Analysis ◽  
Cfd Analysis ◽  
Gas Temperature ◽  
Post Processing ◽  
Layer Model ◽  
Processing Stage ◽  
Discrete Function ◽  
Plan View ◽  
Content Type

Purpose The primary purpose of this paper is the development of a fire–structure interface (FSI) model, which is referred in this study as a simplified “dual-layer” model. It is oriented for design purposes, in the cases where fire-compartments exceed the “regular” dimensions, as they are defined by the guidelines of the codes (EN 1991-1-2). Design/methodology/approach The model can be used at the post-processing stage of computational fluid dynamics (CFD) analysis and it is based on the gas-temperature field (spatial and temporal) of the fire-compartment. To use the “dual-layer” model, first the gas-temperature (discrete) function along the height of the fire-compartment, at discrete plan–view points should be determined through the output of the CFD analysis. The model “compresses” the point data to (spatial) virtual zones, which are divided into two layers (with respect to the height of the fire-compartment) of uniform temperature: the upper (hot) layer and the lower (cold) layer. Findings The model calculates the temporal evolution of the gas-temperature in the fire compartment in every virtual zone which is divided in two layers (hot and cold layer). Originality/value The main advantage of this methodology is that actually only three different variables (height of interface upper-layer temperature and lower-layer temperature) are exported during the post-processing stage of the CFD analysis, for every virtual zone. Next, the gas-temperature can be used for the determination of the temperature profile of structural members using simple models that are proposed in EN 1993-1-2.

scholarly journals Segmentation of Tumour Region on Breast Histopathology Images for Assessment of Glandular Formation in Breast Cancer Grading

2021 ◽  
Vol 2071 (1) ◽  
pp. 012051
Author(s):  
P A S Nor Rahim ◽  
N Mustafa ◽  
H Yazid ◽  
T Xiao Jian ◽  
S Daud ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Median Filter ◽  
Automatic Segmentation ◽  
Post Processing ◽  
Processing Stage ◽  
Tubule Formation ◽  
Segmentation Accuracy ◽  
Three Stages ◽  
Histopathology Images ◽  
Cancer Grading

Abstract Breast cancer is the most silent killer among cancers nowadays. NHG system is widely accepted worldwide as a gold standard in providing the overall grade to breast cancer. One of the breast cancer features used in the NHG system is tubule formation. Assessment of tubule formation requires pathologist to identify tumour regions. However, colour variation on breast histopathology could influence tumour regions detection on breast histopathology images. Manual identification of tumour regions using microscope may also vary between pathologists. Thus, automatic segmentation is crucial to segment tumour regions. In this study, a simple approach of segmentation was proposed to segment tumour region on breast histopathology images. The proposed segmentation involved three stages: pre-processing, segmentation and post-processing. The proposed approach using GHE and median filter in the pre-processing stage; Otsu thresholding in the segmentation stage and; morphological operation and pixel removal in the post-processing stage was found able to segment the tumour region with average segmentation accuracy of 90.4 %.

A METHOD FOR FACE RECOGNITION USING IMAGE REGISTRATION

2011 ◽  
Vol 20 (07) ◽  
pp. 1419-1439 ◽  
Author(s):  
STEVEN GILLAN ◽  
PANAJOTIS AGATHOKLIS
Keyword(s):  
Face Recognition ◽  
Image Registration ◽  
Zernike Moments ◽  
Feature Points ◽  
Post Processing ◽  
Training Images ◽  
Mexican Hat Wavelet ◽  
Mexican Hat ◽  
The Face ◽  
Transformation Parameters

This paper presents a technique for face recognition that is based on image registration. The face recognition technique consists of three parts: a training part, an image registration part and a post-processing part. The image registration technique is based on finding a set of feature points in the two images and using these feature points for registration. This is done in four steps. In the first, images are filtered with the Mexican-hat wavelet to obtain the feature point locations. In the second, the Zernike moments of neighborhoods around the feature points are calculated and compared in the third step to establish correspondence between feature points in the two images. In the fourth, the transformation parameters between images are obtained using an iterative least squares technique to eliminate outliers.1,2 During training, a set of images are chosen as the training images and the Zernike moments for the feature points of the training images are obtained and stored. The choice of training images depends on the changes of poses and illumination that are expected. In the registration part, the transformation parameters to register the training images with the images under consideration are obtained. In the post-processing, these transformation parameters are used to determine whether a valid match is found or not. The performance of the proposed method is evaluated using various face databases3–5 and it is compared with the performance of existing techniques. Results indicate that the proposed technique gives excellent results for face recognition in conditions of varying pose, illumination, background and scale.

scholarly journals Automatic Identification and Intuitive Map Representation of the Epiretinal Membrane Presence in 3D OCT Volumes

Sensors ◽  
2019 ◽  
Vol 19 (23) ◽  
pp. 5269 ◽  
Author(s):  
Sergio Baamonde ◽  
Joaquim de Moura ◽  
Jorge Novo ◽  
Pablo Charlón ◽  
Marcos Ortega
Keyword(s):  
Epiretinal Membrane ◽  
Vision Loss ◽  
Invasive Procedure ◽  
Selection Strategy ◽  
Automatic Identification ◽  
Surgical Removal ◽  
Post Processing ◽  
Processing Stage ◽  
Cross Sectional ◽  
Training Stage

Optical Coherence Tomography (OCT) is a medical image modality providing high-resolution cross-sectional visualizations of the retinal tissues without any invasive procedure, commonly used in the analysis of retinal diseases such as diabetic retinopathy or retinal detachment. Early identification of the epiretinal membrane (ERM) facilitates ERM surgical removal operations. Moreover, presence of the ERM is linked to other retinal pathologies, such as macular edemas, being among the main causes of vision loss. In this work, we propose an automatic method for the characterization and visualization of the ERM’s presence using 3D OCT volumes. A set of 452 features is refined using the Spatial Uniform ReliefF (SURF) selection strategy to identify the most relevant ones. Afterwards, a set of representative classifiers is trained, selecting the most proficient model, generating a 2D reconstruction of the ERM’s presence. Finally, a post-processing stage using a set of morphological operators is performed to improve the quality of the generated maps. To verify the proposed methodology, we used 20 3D OCT volumes, both with and without the ERM’s presence, totalling 2428 OCT images manually labeled by a specialist. The most optimal classifier in the training stage achieved a mean accuracy of 91 . 9 % . Regarding the post-processing stage, mean specificity values of 91 . 9 % and 99 . 0 % were obtained from volumes with and without the ERM’s presence, respectively.

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