scholarly journals An Application of Multi-functional Morphological Operations to River Edge Detection for Landsat TM Data.

1996 ◽  
Vol 35 (5) ◽  
pp. 36-45
Author(s):  
Makoto KAWAMURA ◽  
Yuji TSUJIKO
Keyword(s):  
Edge Detection ◽  
Landsat Tm ◽  
Morphological Operations

scholarly journals Optic nerve disc mathematical parameterization and the statistical comparison with measurements

2010 ◽  
Vol 51 ◽  
Author(s):  
Vytautas Tiešis ◽  
Povilas Treigys
Keyword(s):  
Optic Nerve ◽  
Edge Detection ◽  
Statistical Tests ◽  
Automatic Measurement ◽  
Least Square Method ◽  
Least Square ◽  
Morphological Operations ◽  
Statistical Comparison ◽  
Diagnostic Parameters ◽  
Significant Difference

The paper deals with the automatic measurement of the optic nerve disc diagnostic parameters from eye fundus digital images. The automation gives objective measurements, in contrast to measurements by physicians that have large variance. The disc shape has been detected by the use of mathematical morphological operations, of Canny’s edge detection, of geometric Hough’s transformation and of the approximation by the least square method. The statistical tests confirm that there is no significant difference between measurements performed by physicians and automatic measurements.

scholarly journals Edge Detection to Indication Brain Tumor Using Sobel and Morphological Operations Methods Based on Image Magnetic Resonance Imaging

2018 ◽  
Vol 3 (2) ◽  
pp. 179
Author(s):  
Oscar Adriyanto ◽  
Halim Agung
Keyword(s):  
Magnetic Resonance Imaging ◽  
Brain Tumor ◽  
Magnetic Resonance ◽  
Brain Tumors ◽  
Edge Detection ◽  
Morphological Operations ◽  
Resonance Imaging ◽  
Edge Detection Method ◽  
Tumor Edge ◽  
The Brain

Brain tumors are the second leading cause of death in the world in children under 20, scientists and researchers are developing applications to react brain tumors based on magnetic resonance imaging images. In this application the method used is sobel and morphological operations. Based on research conducted on brain tumor edge detection based on magnetic resonance imaging image, sobel method can reduce the noise contained in the image mri and can localize the edge of the image of Magnetic Resonance Imaging well. This research can conclude that the sobel method is suitable for edge detection but there is still some unprocessed noise, with the results of the brain imaging of 30 test images have 60% percentage, while for the use of edge detection method of 62.11%.

A Novel Improved Edge Detection Method

2011 ◽  
Vol 225-226 ◽  
pp. 1096-1099
Author(s):  
Yan Ying Guo ◽  
Yan Ying Guo
Keyword(s):  
Edge Detection ◽  
Detection Method ◽  
Adaptive Algorithms ◽  
Morphological Operations ◽  
Training Method ◽  
Morphological Operators ◽  
Structuring Element ◽  
Edge Strength ◽  
Addition And Subtraction ◽  
Edge Detection Method

In this paper, a novel morphological edge detection using adaptive weighted morphological operators is presented. The newly introduced operators employ weighted structuring element (SE) and apply multiplication or division in place of addition and subtraction in classical morphological operations. It judges its edge and its direction by means of training method and differentiable equivalent representations for the operators, efficient adaptive algorithms to optimize SEs are derived. The gradient of the adaptive weighted morphology utilizes a set of SEs to detect the edge strength with a view to decrease the spurious detail edge and suppressed the noise. Results will be presenting for images in comparison with the others edging detectors.

A Novel Edge Detection Approach Based on Soft Morphological Operations

2012 ◽  
Vol 220-223 ◽  
pp. 2828-2832
Author(s):  
Bo Chen ◽  
Meng Jia
Keyword(s):  
Edge Detection ◽  
Edge Effect ◽  
Detection Method ◽  
Experimental Results ◽  
Morphological Operations ◽  
The Novel ◽  
Target Segmentation ◽  
Detection Approach ◽  
Edge Detection Method ◽  
Image Details

Edge detection and target segmentation is difficult due to noise existing in an image. A novel edge detection method is proposed based on soft morphological operations in this paper. Because soft morphological operations can remove noise while preserving image details, which can be used to construct morphological edge detection operators with high robustness and better edge effect. Experimental results show that, comparing with the existing edge detection operators, the novel edge detection method can get better edge effect while removing pseudo edges.

scholarly journals Edge Enhancement Based Active Contour Model for Segmentation of Brain Tumor in MRI Images

2021 ◽  
Vol 19 (3) ◽  
Author(s):  
Mustafa Rashid Ismael
Keyword(s):  
Edge Detection ◽  
Active Contour ◽  
Active Contour Model ◽  
Initial Contour ◽  
Morphological Operations ◽  
Significant Information ◽  
Automated Method ◽  
Contour Model ◽  
Tumor Region ◽  
Edge Sharpening

Tumor segmentation is one of the most significant tasks in brain image analysis due to the significant information obtained by the tumor region. Therefore, many methods have been proposed during the last two decades for segmenting the tumor in MRI images. In this paper, an automated method is proposed using an active contour model with an initial contour creation using edge sharpening, thresholding, and morphological operations. Four methods of edge detection are utilized in the edge sharpening process (Sobel, Roberts, Prewitt, and Canny) and their performance was investigated in terms of Dice, Jaccard, and F1 score. The experiments were implemented on BRATS datasets with both HGG and LGG images. The study indicates that sharpening the edges using edge detection is essential to improve the segmentation of the tumor region especially when it is used with an active contour model. The achieved results show the effectiveness of the proposed method and it outperformed some recent existing methods.

scholarly journals Tunnel Lining Crack Recognition Based on Improved Multiscale Retinex and Sobel Edge Detection

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Quanlei Wang ◽  
Ning Zhang ◽  
Kun Jiang ◽  
Chao Ma ◽  
Zhaochen Zhou ◽  
...  
Keyword(s):  
Edge Detection ◽  
Engineering Application ◽  
Objective Evaluation ◽  
Recognition Algorithm ◽  
Tunnel Lining ◽  
Automatic Identification ◽  
Main Body ◽  
Morphological Operations ◽  
Crack Recognition ◽  
Sobel Edge Detection

China is gradually transitioning from the “tunnel construction era” to the “tunnel maintenance era,” and more and more operating tunnels need to be inspected for diseases. With the continuous development of computer vision, the automatic identification of tunnel lining cracks with computers has gradually been applied in engineering. On the basis of summarizing the weaknesses and strengths of previous studies, this paper first uses the improved multiscale Retinex algorithm to filter the collected tunnel crack images and introduces the eight-direction Sobel edge detection operator to extract the edges of the cracks. Perform mathematical morphological operations on the image after edge extraction, and use the principle of the smallest enclosing rectangle to remove the isolated points of the image. Finally, the performance of the algorithm is judged by the objective evaluation index of the image, the accuracy of crack recognition, and the running time of the algorithm. The image filtering algorithm proposed in this paper can better preserve the edges of the image while enhancing the image. The objective evaluation indexes of the image have been improved significantly, and the main body of the crack can be accurately identified. The overall crack recognition accuracy rate can reach 97.5%, which is higher than the existing tunnel lining crack recognition algorithm, and the average calculation time for each image is shorter. This algorithm has high research significance and engineering application value.

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