scholarly journals Edge Location Method for Multidimensional Image Based on Edge Symmetry Algorithm

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Chen Li
Keyword(s):  
Vertical Direction ◽  
Region Of Interest ◽  
Basic Feature ◽  
Vertical Gradient ◽  
Edge Region ◽  
Image Region ◽  
Location Method ◽  
Candidate Image ◽  
Positioning Method ◽  
Edge Location

The most basic feature of an image is edge, which is the junction of one attribute area and another attribute area in the image. It is the most uncertain place in the image and the place where the image information is most concentrated. The edge of an image contains rich information. So, the edge location plays an important role in image processing, and its positioning method directly affects the image effect. In order to further improve the accuracy of edge location for multidimensional image, an edge location method for multidimensional image based on edge symmetry is proposed. The method first detects and counts the edges of multidimensional image, sets the region of interest, preprocesses the image with the Gauss filter, detects the vertical edges of the filtered image, and superposes the vertical gradient values of each pixel in the vertical direction to obtain candidate image regions. The symmetry axis position of the candidate image region is analyzed, and its symmetry intensity is measured. Then, the symmetry of vertical gradient projection in the candidate image region is analyzed to verify whether the candidate region is a real edge region. The multidimensional pulse coupled neural network (PCNN) model is used to synthesize the real edge region after edge symmetry processing, and the result of edge location of the multidimensional image is obtained. The results show that the method has strong antinoise ability, clear edge contour, and precise location.

ROI Based Post Image Quality Assessment Technique on Multiple Localized Filtering Method On Kinect HD Sensor Mobile Robot

2018 ◽  
Vol 3 (2) ◽  
pp. 145
Author(s):  
Kholilatul Wardani ◽  
Aditya Kurniawan
Keyword(s):  
Image Quality ◽  
Quality Assessment ◽  
Image Quality Assessment ◽  
Similarity Index ◽  
Assessment Model ◽  
Sensor Data ◽  
Edge Region ◽  
Kinect Sensor ◽  
Image Region ◽  
Filtering Technique

 The ROI (Region of Interest) Image Quality Assessment is an image quality assessment model based on the SSI (Structural Similarity Index) index used in the specific image region desired to be assessed. Output assessmen value used by this image assessment model is 1 which means identical and -1 which means not identical. Assessment model of ROI Quality Assessment in this research is used to measure image quality on Kinect sensor capture result used in Mobile HD Robot after applied Multiple Localized Filtering Technique. The filter is applied to each capture sensor depth result on Kinect, with the aim to eliminate structural noise that occurs in the Kinect sensor. Assessment is done by comparing image quality before filter and after filter applied to certain region. The kinect sensor will be conditioned to capture a square black object measuring 10cm x 10cm perpendicular to a homogeneous background (white with RGB code 255,255,255). The results of kinect sensor data will be taken through EWRF 3022 by visual basic 6.0 program periodically 10 times each session with frequency 1 time per minute. The results of this trial show the same similar index (value 1: identical) in the luminance, contrast, and structural section of the edge region or edge region of the specimen. The value indicates that the Multiple Localized Filtering Technique applied to the noise generated by the Kinect sensor, based on the ROI Image Quality Assessment model has no effect on the image quality generated by the sensor.

A Palmprint Recognition Approach Based on Image Segmentation of Region of Interest

2016 ◽  
Vol 30 (02) ◽  
pp. 1656002 ◽  
Author(s):  
Qing E Wu ◽  
Zhiwu Chen ◽  
Ruijie Han ◽  
Cunxiang Yang ◽  
Yuhao Du ◽  
...  
Keyword(s):  
Image Segmentation ◽  
Wavelet Transform ◽  
Recognition Rate ◽  
Region Of Interest ◽  
Palmprint Recognition ◽  
Characteristic Points ◽  
Positioning Method ◽  
Roi Extraction ◽  
Energy Feature

To carry out an effective recognition for palmprint, this paper presents an algorithm of image segmentation of region of interest (ROI), extracts the ROI of a palmprint image and studies the composing features of palmprint. This paper constructs a coordinate by making use of characteristic points in the palm geometric contour, improves the algorithm of ROI extraction and provides a positioning method of ROI. Moreover, this paper uses the wavelet transform to divide up ROI, extracts the energy feature of wavelet, gives an approach of matching and recognition to improve the correctness and efficiency of existing main recognition approaches, and compares it with existing main approaches of palmprint recognition by experiments. The experiment results show that the approach in this paper has the better recognition effect, the faster matching speed, and the higher recognition rate which is improved averagely by 2.69% than those of the main recognition approaches.

Research on Single-Phase Grounding Fault in Distribution Networks with a DC Power Positioning Method

2014 ◽  
Vol 530-531 ◽  
pp. 353-356
Author(s):  
Run Sheng Li
Keyword(s):  
Power Distribution ◽  
Single Phase ◽  
Distribution Network ◽  
Distribution Networks ◽  
Power Distribution Network ◽  
Power Outage ◽  
Location Method ◽  
Fault Resistance ◽  
Ground Fault ◽  
Positioning Method

Due to the high ground fault resistance and the complexity of power distribution network structure (such as too many nodes, branches and too long lines), adopting common traveling wave method and ac injection method can not effectively locate the single-phase grounding fault in the distribution network system.To solve above problems and determine the position of the point of failure prisely, this paper adopted the dc location method of injecting the dc signal from the point of failure under the power outage offline. This paper introduces the single phase dc method and the method of three phase dc, and the simulation shows that the dc location method is effective and feasible.

Palmprint Recognition Based on Image Segmentation of Region of Interest

2017 ◽  
pp. 73-90
Author(s):  
QingE Wu ◽  
Weidong Yang
Keyword(s):  
Image Segmentation ◽  
Wavelet Transform ◽  
Recognition Rate ◽  
Region Of Interest ◽  
Palmprint Recognition ◽  
Characteristic Points ◽  
Positioning Method ◽  
Roi Extraction ◽  
Energy Feature

To carry out an effective recognition for palmprint, this paper presents an algorithm of image segmentation of region of interest (ROI), extracts the ROI of a palmprint image and studies the composing features of palmprint. This paper constructs coordinates by making use of characteristic points in the palm geometric contour, improves the algorithm of ROI extraction, and provides a positioning method of ROI. Moreover, this paper uses the wavelet transform to divide up ROI, extracts the energy feature of wavelet, gives an approach of matching and recognition to improve the correctness and efficiency of existing main recognition approaches, and compares it with existing main approaches of palmprint recognition by experiments. The experiment results show that the approach in this paper has the better recognition effect, the faster matching speed, and the higher recognition rate which is improved averagely by 2.69% than those of the main recognition approaches.

Perfusion parameters of intravoxel incoherent motion based on tumor edge region of interest in cervical cancer: evaluation of differentiation and correlation with dynamic contrast-enhanced MRI

2019 ◽  
Vol 61 (8) ◽  
pp. 1087-1095
Author(s):  
Hongyan Wang ◽  
Lixia Zhu ◽  
Guohua Li ◽  
Menzhe Zuo ◽  
Xi Ma ◽  
...  
Keyword(s):  
Cervical Cancer ◽  
Region Of Interest ◽  
Roc Curves ◽  
Edge Region ◽  
Dce Mri ◽  
Contrast Enhanced ◽  
Poorly Differentiated ◽  
Positive Correlations ◽  
Tumor Edge ◽  
Contrast Enhanced Mri

Background Intravoxel incoherent motion (IVIM) diffusion-weighted imaging (DWI) is a functional magnetic resonance imaging (MRI) sequence. Purpose To evaluate the value of perfusion parameters derived from IVIM-DWI based on tumor edge region of interest (ROI) in differentiation in cervical cancer and investigate the relationship between IVIM and dynamic contrast-enhanced MRI (DCE-MRI). Material and Methods Thirty-three patients with pathologically diagnosed squamous cell carcinoma who underwent IVIM-DWI (nine b-values: 1–1000 s/mm2) and DCE-MRI were retrospectively assessed in this study. Parameters of IVIM (D, f, D*, fD*) and quantitative parameters of DCE-MRI (Ktrans, Kep, Ve) were derived using tumor edge ROI. Mann–Whitney U test was used to compare parameters between pathological grades and receiver operating characteristic (ROC) curves were used. Pearson’s correlation coefficient (r) evaluated the correlation between perfusion parameters derived from IVIM and DCE-MRI. Results The poorly differentiated group showed the significantly lower D value and the higher f, Ktrans and Kep values than the well-to-moderately differentiated group ( P < 0.05). ROC curves indicated that f < 26%, Ktrans <0.38/min, and Kep <1.62/min could differentiate the poorly differentiated group from the well-to-moderately differentiated group (AUC 0.753–0.808). Significantly positive correlations were found between f and Ktrans (r = 0.422, P = 0.014) and between fD* and Ktrans (r = 0.448, P = 0.009). Conclusion Perfusion parameters derived from IVIM based on tumor edge ROI may offer additional value in differentiation in cervical cancer, and the IVIM perfusion parameters showed moderate positive correlations with quantitative perfusion parameters from DCE-MRI, while f and fD* showed promising significance.

Intelligent Computer Aided Detection of Tumor in MRI Brain Images Using Cascaded Correlation Neural Network Classifier

2014 ◽  
Vol 626 ◽  
pp. 65-71
Author(s):  
V. Amsaveni ◽  
N. Albert Singh ◽  
J. Dheeba
Keyword(s):  
Neural Network ◽  
Region Of Interest ◽  
Texture Features ◽  
Neural Network Classifier ◽  
Image Region ◽  
Brain Images ◽  
Gabor Features ◽  
Computer Aided ◽  
Pixel Location ◽  
Nonlinear Classifier

In this paper, a Computer aided classification approach using Cascaded Correlation Neural Network for detection of brain tumor from MRI is proposed. Cascaded Correlation Neural Network is a nonlinear classifier which is formulated as a supervised learning problem and the classifier was applied to determine at each pixel location in the MRI if the tumor is present or not. Gabor texture features are taken from the image Region of interest (ROI). The extracted Gabor features from MRI is given as input to the proposed classifier. The method was applied to real time images from the collected from diagnostic centers. Based on the analysis the performance of the proposed cascaded correlation neural network classifier is superior when compared with other classification approaches.

Relationships between regional ventilation and vascular and extravascular volume in supine humans

1994 ◽  
Vol 76 (3) ◽  
pp. 1195-1204 ◽  
Author(s):  
L. H. Brudin ◽  
C. G. Rhodes ◽  
S. O. Valind ◽  
T. Jones ◽  
B. Jonson ◽  
...  
Keyword(s):  
Left Lung ◽  
Vertical Direction ◽  
Vertical Gradient ◽  
Quiet Breathing ◽  
Alveolar Volume ◽  
Positron Emission ◽  
Significant Linear Correlation ◽  
Horizontal Variation ◽  
Gravitational Gradients ◽  
Extravascular Volume

With the use of positron emission tomography, alveolar ventilation (VA), lung density, and pulmonary blood volume (VB) were measured regionally in eight nonsmokers in the supine posture and one nonsmoker in the prone posture during quiet breathing in a transaxial thoracic section at midheart level. Regional values of alveolar volume (VA) and extravascular tissue volume (VEV) were derived from the inherent relationships between different compartments in the lung. Ratios proportional to gas volume (VA/VEV) and ventilation (VA/VEV) per alveolar unit, respectively, were calculated. No differences between right and left lung were found. Variations in the vertical direction could explain approximately 65% of the total within-group variation in VA, VB, and ln (VA), whereas the corresponding value for horizontal variation was only 3–9% (right lung, supine subjects). Similar gravitational gradients were found in the single prone subject. There was a significant linear correlation between VA and ln (VA). When VA and VA are related to a given number of alveolar units (VEV), the data are consistent with a linear relationship between VA/VEV and VA/VEV, indicating that ventilation might be explained by the elastic properties of lung tissue according to Salazar and Knowles (J. Appl. Physiol. 19: 97–104, 1964). Regional VB was closely associated with the gradient of regional alveolar volume (VA/VEV) (by virtue of weight of blood and competition for space) and therefore, indirectly, closely associated with the vertical gradient of ventilation.

scholarly journals Eye-tracking and learning experience: gaze trajectories to better understand the behavior of memorial visitors

2020 ◽  
Vol 13 (2) ◽  
Author(s):  
Salma Mesmoudi ◽  
Stanislas Hommet ◽  
Denis Peschanski
Keyword(s):  
Focus Group ◽  
Eye Tracking ◽  
Learning Experience ◽  
Region Of Interest ◽  
Viewing Time ◽  
Image Region ◽  
Mass Violence ◽  
The Gaze ◽  
Before And After ◽  
First Results

Eye-tracking technology is increasingly introduced in museums to assess their role in learning and knowledge transfer. However, their use provide limited quantitative and/or qualitative measures such as viewing time and/or gaze trajectory on an isolated object or image (Region of Interest "ROI"). The aim of this work is to evaluate the potential of the mobile eye-tracking to quantify the students’ experience and behaviors through their visit of the "Genocide and mass violence" area of the Caen memorial. In this study, we collected eye-tracking data from 17 students during their visit to the memorial. In addition, all visitors filled out a questionnaire before the visit, and a focus group was conducted before and after the visit. The first results of this study allowed us to analyze the viewing time spent by each visitor in front of 19-selected ROIs, and some of their specific sub-parts. The other important result was the reconstruction of the gaze trajectory through these ROIs. Our global trajectory approach allowed to complete the information obtained from an isolated ROI, and to identify some behaviors such as avoidance. Clustering analysis revealed some typical trajectories performed by specific sub-groups. The eye-tracking results were consolidated by the participants' answers during the focus group.  

Morphology Preserving Segmentation Method for Occluded Cell Nuclei from Medical Microscopy Image

2021 ◽  
pp. 2250019
Author(s):  
Rafflesia Khan ◽  
Rameswar Debnath
Keyword(s):  
Prior Knowledge ◽  
Single Cells ◽  
Region Of Interest ◽  
Automatic Measurement ◽  
Cell Counting ◽  
Manual Annotation ◽  
Cell Nuclei ◽  
Image Region ◽  
Microscopy Image ◽  
Optimal Approach

Nowadays, image segmentation techniques are being used in many medical applications such as tissue culture monitoring, cell counting, automatic measurement of organs, etc., for assisting doctors. However, high-level segmentation results cannot be obtained without manual annotation or prior knowledge for high variability, noise and other imaging artifacts in medical images. Furthermore, unstable and continuously changing characteristics of all human cells, tissues and organs manipulate training-based segmentation methods. Detecting appropriate contour of a region of interest and single cells from overlapping condition are extremely challenging. In this paper, we aim for a model that can detect biological structure (e.g. cell nuclei and lung contour) with their proper morphology even in overlapping or occluded condition without manual annotation or prior knowledge. We have introduced a new optimal approach for automatic medical image region segmentation. The method first clearly focuses the boundaries of all object regions in a microscopy image. Then it detects the areas by following their contours. Our model is capable of detecting and segmenting object regions from medial image using less computation effort. Our experimental results prove that our model provides better detection on several datasets of different types of medical data and ensures more than 98% segmentation rate in the case of densely connected regions.

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