Improved quality using newly designed algorithms in gamma- and X-ray fusion images with a photon counting CZT detector: combining the median modified Wiener filter and edge detection method

Optik ◽  
2021 ◽  
pp. 167681
Author(s):  
Youngjin Lee
Keyword(s):  
Edge Detection ◽  
Detection Method ◽  
Photon Counting ◽  
Wiener Filter ◽  
X Ray ◽  
Fusion Images ◽  
Edge Detection Method

Edge Detection Using the Bhattacharyya Distance with Adjustable Block Space

2020 ◽  
Vol 2020 (10) ◽  
pp. 133-1-133-7
Author(s):  
Jiho Yoon ◽  
Chulhee Lee
Keyword(s):  
Edge Detection ◽  
Detection Method ◽  
Wiener Filter ◽  
Detection Methods ◽  
Bhattacharyya Distance ◽  
Block Space ◽  
Mean Vectors ◽  
The Mean ◽  
Edge Detection Method ◽  
Mean Vector

In this paper, we propose a new edge detection method for color images, based on the Bhattacharyya distance with adjustable block space. First, the Wiener filter was used to remove the noise as pre-processing. To calculate the Bhattacharyya distance, a pair of blocks were extracted for each pixel. To detect subtle edges, we adjusted the block space. The mean vector and covariance matrix were computed from each block. Using the mean vectors and covariance matrices, we computed the Bhattacharyya distance, which was used to detect edges. By adjusting the block space, we were able to detect weak edges, which other edge detections failed to detect. Experimental results show promising results compared to some existing edge detection methods.

Panoramic X-Ray Analysis Using Edge Detection Method on Mandibular Cortex

2018 ◽  
Author(s):  
Darmastuti ◽  
Eri Prasetyo Wibowo ◽  
Metty Mustikasari ◽  
Johan Harlan ◽  
Sigit Widiyanto ◽  
...  
Keyword(s):  
Edge Detection ◽  
Detection Method ◽  
X Ray ◽  
Edge Detection Method

scholarly journals Deteksi Tepi pada Citra Rontgen Penyakit COVID-19 Menggunakan Metode Sobel

2020 ◽  
Vol 6 (2) ◽  
pp. 51-59
Author(s):  
Muhammad Ghozali ◽  
Heni Sumarti
Keyword(s):  
Edge Detection ◽  
Detection Method ◽  
World Health ◽  
X Ray ◽  
Global Pandemic ◽  
Lung Area ◽  
Edge Detection Method ◽  
Health Organization ◽  
Processing Techniques ◽  
High Pass

Background: Coronavirus Disease 2019 (COVID-19) discovered at the end of 2019 occasioned by coronavirus 2 (SARS-CoV-2) causing severe acute respiratory syndrome and expanded globally so that World Health Organization (WHO) declare a global pandemic. There was a delay of socialization and delivering information to society about this disease. The doctors did a method to detect COVID-19 by reading the correct X-ray images of patients who affected by a coronavirus.Methods: With advances in the field of computers in the application of image processing techniques method of this research use application to get better digital image results for COVID-19 X-ray images, so make it easier to analyze the X-ray images. There are 13 samples of X-ray images that are processed through the clean the stage with high-pass filtering, then segmented with thresholding technique in the lung area, then the edge detection method is used to mark the area that makes the image detail.Results: The result of this detection form the pattern of objects and regions of the spread of coronavirus, then there is a limit on the image looks clear enough, with the Sobel method producing white pixels that are so visible as well.Conclusions: This study to make a simulation of x-ray thorax COVID-19 and know the region of virus infection using Sobel method with thresholding technique that can see the spread of coronavirus and shown that edge detection use Sobel method as one of diagnosing for COVID-19 disease.

Edge detection method based on Facet model for flash X-ray radiographs

2010 ◽  
Vol 22 (7) ◽  
pp. 1640-1644
Author(s):  
张小琳 Zhang Xiaolin ◽  
景越峰 Jing Yuefeng ◽  
刘军 Liu Jun
Keyword(s):  
Edge Detection ◽  
Detection Method ◽  
X Ray ◽  
Edge Detection Method ◽  
Facet Model

scholarly journals Cut-Edge Detection Method for Rice Harvesting Based on Machine Vision

Agronomy ◽  
2020 ◽  
Vol 10 (4) ◽  
pp. 590
Author(s):  
Zhenqian Zhang ◽  
Ruyue Cao ◽  
Cheng Peng ◽  
Renjie Liu ◽  
Yifan Sun ◽  
...  
Keyword(s):  
Machine Vision ◽  
Edge Detection ◽  
Detection Method ◽  
Region Of Interest ◽  
Average Error ◽  
Vertical Projection ◽  
Cut Edge ◽  
Fitting Method ◽  
Combine Harvester ◽  
Edge Detection Method

A cut-edge detection method based on machine vision was developed for obtaining the navigation path of a combine harvester. First, the Cr component in the YCbCr color model was selected as the grayscale feature factor. Then, by detecting the end of the crop row, judging the target demarcation and getting the feature points, the region of interest (ROI) was automatically gained. Subsequently, the vertical projection was applied to reduce the noise. All the points in the ROI were calculated, and a dividing point was found in each row. The hierarchical clustering method was used to extract the outliers. At last, the polynomial fitting method was used to acquire the straight or curved cut-edge. The results gained from the samples showed that the average error for locating the cut-edge was 2.84 cm. The method was capable of providing support for the automatic navigation of a combine harvester.

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