The first order absolute central moment as an edge-detector in cardiovascular imaging: a comparison with two well-known edge-detectors

2005 ◽  
Author(s):  
F. Faita ◽  
V. Gemignani ◽  
M. Giannoni ◽  
A. Benassi ◽  
E.M. Ferdeghini ◽  
...  
Keyword(s):  
Cardiovascular Imaging ◽  
Central Moment ◽  
Edge Detector ◽  
First Order ◽  
Edge Detectors

A Novel Edge Detection Algorithm Based on Texture Feature Coding

2015 ◽  
Vol 24 (2) ◽  
pp. 235-248 ◽  
Author(s):  
Abdulkadir Sengur ◽  
Yanhui Guo ◽  
Mehmet Ustundag ◽  
Ömer Faruk Alcin
Keyword(s):  
Edge Detection ◽  
Texture Feature ◽  
Detection Algorithm ◽  
Input Image ◽  
Edge Detector ◽  
Edge Image ◽  
Hardware System ◽  
Analysis Scheme ◽  
Edge Detectors ◽  
Field Programmable

AbstractA new edge detection technique based on the texture feature coding method (TFCM) is proposed. The TFCM is a texture analysis scheme that is generally used in texture-based image segmentation and classification applications. The TFCM transforms an input image into a texture feature image whose pixel values represent the texture information of the pixel in the original image. Then, on the basis of the transformed image, several features are calculated as texture descriptors. In this article, the TFCM is employed differently to construct an edge detector. In particular, the texture feature number (TFN) of the TFCM is considered. In other words, the TFN image is used for subsequent processes. After obtaining the TFN image, a simple thresholding scheme is employed for obtaining the coarse edge image. Finally, an edge-thinning procedure is used to obtain the tuned edges. We conducted several experiments on a variety of images and compared the results with the popular existing methods such as the Sobel, Prewitt, Canny, and Canny–Deriche edge detectors. The obtained results were evaluated quantitatively with the Figure of Merit criterion. The experimental results demonstrated that our proposed method improved the edge detection performance greatly. We further implemented the proposed edge detector with a hardware system. To this end, a field programmable gate array chip was used. The related simulations were carried out with the MATLAB Simulink tool. Both software and hardware implementations demonstrated the efficiency of the proposed edge detector.

A NEW EDGE DETECTION METHOD THROUGH TEMPLATE MATCHING

1994 ◽  
Vol 08 (04) ◽  
pp. 899-917 ◽  
Author(s):  
MAO-JIUN J. WANG ◽  
SHIAU-CHYI CHANG ◽  
CHIH-MING LIU ◽  
WEN-YEN WU
Keyword(s):  
Edge Detection ◽  
Template Matching ◽  
Pattern Analysis ◽  
Detection Method ◽  
Performance Criteria ◽  
Detection Methods ◽  
Edge Detector ◽  
Edge Detectors ◽  
Edge Detection Method ◽  
Single Pixel

This paper reviews some gradient edge detection methods and proposes a new detector — the template matching edge detector (TMED). This detector utilizes the concepts of pattern analysis and the template matching of 3×3 masks. A set of performance criteria was used to evaluate the gradient edge detectors as well as the template matching edge detector. The results indicate that the new method is superior to the other gradient edge detectors. In addition, the template matching edge detector has also demonstrated good performance on noisy images. It can obtain very precise edge detection of single pixel width.

A step edge detector based on bilinear transformation

2021 ◽  
pp. 1-12
Author(s):  
Koorosh Dabighi ◽  
Akbar Nazari ◽  
Saeid Saryazdi
Keyword(s):  
Edge Detection ◽  
Signal To Noise Ratio ◽  
Quantitative Comparison ◽  
Discrete Version ◽  
Edge Detector ◽  
Bilinear Transformation ◽  
Proposed Model ◽  
Edge Detectors ◽  
Canny Edge ◽  
Error Metric

Nowadays, Canny edge detector is considered to be one of the best edge detection approaches for the images with step form. Various overgeneralized versions of these edge detectors have been offered up to now, e.g. Saryazdi edge detector. This paper proposes a new discrete version of edge detection which is obtained from Shen-Castan and Saryazdi filters by using bilinear transformation. Different experimentations are conducted to decide the suitable parameters of the proposed edge detector and to examine its validity. To evaluate the strength of the proposed model, the results are compared to Canny, Sobel, Prewitt, LOG and Saryazdi methods. Finally, by calculation of mean square error (MSE) and peak signal-to-noise ratio (PSNR), the value of PSNR is always equal to or greater than the PSNR value of suggested methods. Moreover, by calculation of Baddeley’s error metric (BEM) on ten test images from the Berkeley Segmentation DataSet (BSDS), we show that the proposed method outperforms the other methods. Therefore, visual and quantitative comparison shows the efficiency and strength of proposed method.

scholarly journals Hyperanalytic Wavelet-Based Robust Edge Detection

2021 ◽  
Vol 13 (15) ◽  
pp. 2888
Author(s):  
Alexandru Isar ◽  
Corina Nafornita ◽  
Georgiana Magu
Keyword(s):  
Edge Detection ◽  
Detection Method ◽  
State Of The Art ◽  
Speckle Noise ◽  
Transform Domain ◽  
Edge Detector ◽  
Edge Detectors ◽  
Gradient Based ◽  
New Type ◽  
Edge Detection Method

The imperfections of image acquisition systems produce noise. The majority of edge detectors, including gradient-based edge detectors, are sensitive to noise. To reduce this sensitivity, the first step of some edge detectors’ algorithms, such as the Canny’s edge detector, is the filtering of acquired images with a Gaussian filter. We show experimentally that this filtering is not sufficient in case of strong Additive White Gaussian or multiplicative speckle noise, because the remaining grains of noise produce false edges. The aim of this paper is to improve edge detection robustness against Gaussian and speckle noise by preceding the Canny’s edge detector with a new type of denoising system. We propose a two-stage denoising system acting in the Hyperanalytic Wavelet Transform Domain. The results obtained in applying the proposed edge detection method outperform state-of-the-art edge detection results from the literature.

An Edge Detection Method Based on Adjacent Dispersion

2016 ◽  
Vol 30 (10) ◽  
pp. 1655026
Author(s):  
Qindong Sun ◽  
Yimin Qiao ◽  
Hua Wu ◽  
Jiamin Wang
Keyword(s):  
Image Segmentation ◽  
Edge Detection ◽  
Detection Method ◽  
Superior Performance ◽  
Gray Level ◽  
Edge Detector ◽  
Edge Detectors ◽  
Gray Level Image ◽  
Novel Method ◽  
Edge Detection Method

Edge detection is a vital part in image segmentation. In this paper, a novel method based on adjacent dispersion for edge detection is proposed. This method utilizes adjacent dispersion to detect edges, avoiding thresholds selection, anisotropy in convolution computation and discontinuity in edges, and it is composed of two modules, namely the dispersion operator and the refinement. The dispersion is to obtain a matrix of discrete coefficient of a gray level image and the refinement is to thin edges to one-pixel-point and ensure it logically continuous. The performance of the proposed edge detector is evaluated on different test images and compared with popular edge detectors, Canny and Sobel. Experiment results indicate that the proposed method performs well without thresholds and offers superior performance in continuity in edge detection in digital images.

scholarly journals Robust Edge Detection Method for Segmentation of Diabetic Foot Ulcer Images

2020 ◽  
Vol 10 (4) ◽  
pp. 6034-6040
Author(s):  
R. H. Mwawado ◽  
B. J. Maiseli ◽  
M. A. Dida
Keyword(s):  
Edge Detection ◽  
Diabetic Foot ◽  
Skin Color ◽  
Foot Ulcer ◽  
Foot Ulcers ◽  
Semantic Features ◽  
Diabetic Foot Ulcers ◽  
Edge Detector ◽  
Comparative Performance ◽  
Edge Detectors

Segmentation is an open-ended research problem in various computer vision and image processing tasks. This pre-processing operation requires a robust edge detector to generate appealing results. However, the available approaches for edge detection underperform when applied to images corrupted by noise or impacted by poor imaging conditions. The problem becomes significant for images containing diabetic foot ulcers, which originate from people with varied skin color. Comparative performance evaluation of the edge detectors facilitates the process of deciding an appropriate method for image segmentation of diabetic foot ulcers. Our research discovered that the classical edge detectors cannot clearly locate ulcers in images with black-skin feet. In addition, these methods collapse for degraded input images. Therefore, the current research proposes a robust edge detector that can address some limitations of the previous attempts. The proposed method incorporates a hybrid diffusion-steered functional derived from the total variation and the Perona-Malik diffusivities, which have been reported to can effectively capture semantic features in images. The empirical results show that our method generates clearer and stronger edge maps with higher perceptual and objective qualities. More importantly, the proposed method offers lower computational times—an advantage that gives more insights into the possible application of the method in time-sensitive tasks.

scholarly journals Bayesian Edge Detector Using Deformable Directivity-Aware Sampling Window

Entropy ◽  
2020 ◽  
Vol 22 (10) ◽  
pp. 1080
Author(s):  
Ren-Jie Huang ◽  
Jung-Hua Wang ◽  
Chun-Shun Tseng ◽  
Zhe-Wei Tu ◽  
Kai-Chun Chiang
Keyword(s):  
Vision System ◽  
Principal Direction ◽  
Edge Detector ◽  
Pixel Intensity ◽  
Edge Detectors ◽  
Gestalt Grouping ◽  
Sampling Window ◽  
Research Questions ◽  
New Research ◽  
Human Eyes

Conventional image entropy merely involves the overall pixel intensity statistics which cannot respond to intensity patterns over spatial domain. However, spatial distribution of pixel intensity is definitely crucial to any biological or computer vision system, and that is why gestalt grouping rules involve using features of both aspects. Recently, the increasing integration of knowledge from gestalt research into visualization-related techniques has fundamentally altered both fields, offering not only new research questions, but also new ways of solving existing issues. This paper presents a Bayesian edge detector called GestEdge, which is effective in detecting gestalt edges, especially useful for forming object boundaries as perceived by human eyes. GestEdge is characterized by employing a directivity-aware sampling window or mask that iteratively deforms to probe or explore the existence of principal direction of sampling pixels; when convergence is reached, the window covers pixels best representing the directivity in compliance with the similarity and proximity laws in gestalt theory. During the iterative process based on the unsupervised Expectation-Minimization (EM) algorithm, the shape of the sampling window is optimally adjusted. Such a deformable window allows us to exploit the similarity and proximity among the sampled pixels. Comparisons between GestEdge and other edge detectors are shown to justify the effectiveness of GestEdge in extracting the gestalt edges.

Comparison of Crack Extraction Performance According to Different Edge Detectors

2014 ◽  
Vol 607 ◽  
pp. 669-672 ◽  
Author(s):  
Hyun Woo Cho ◽  
Hyuk Jin Yoon
Keyword(s):  
Image Processing ◽  
Edge Detection ◽  
Concrete Structures ◽  
Edge Detector ◽  
Concrete Walls ◽  
Edge Detectors ◽  
Extraction Performance

Edges of images contain important information such as form, size, and location of objects. Thus, edge detection is an extremely important factor in image processing. In order to detect such edges, much research has been carried out, and edge detectors such as Sobel, Prewitt, Robert, Canny, and Laplacian of Gaussian (“LoG”) are universally used. This Research compares and analyzes the crack extraction function of each edge detector to automatically detect cracks occurring in concrete structures. In order to do this, simulated crack samples of various widths and contrasts were produced and attached to concrete walls, and crack images were taken from different distances. Cracks were extracted from the images taken using edge detectors like Sobel, Prewitt, Robert, Canny, and LoG, and the crack extraction performance of the edge detectors used were compared and analyzed based on the photographed distance.

scholarly journals Skin Lesion Border Detection Based on Best Statistical Model Using Optimal Colour Channel

2020 ◽  
Vol 3 (1) ◽  
pp. 18
Author(s):  
Alaa Ahmed Abbas Al-abayechi ◽  
Fadeheela Sabri Abu-Almash
Keyword(s):  
Skin Lesion ◽  
Median Filter ◽  
Bilateral Filter ◽  
Polynomial Model ◽  
Morphological Operations ◽  
Edge Detector ◽  
Border Detection ◽  
Canny Edge Detector ◽  
Edge Detectors ◽  
Canny Edge

This paper proposes an effective way to segment melanoma skin lesion in colour dermoscopic images, using an edge-based approach. The proposed method, different methods were combined to improve the segmentation performance. These methods are morphological operations, bilateral filter, spline, polynomial model and canny edge detector. Different methods were tested to select the best method that was produced the best outcome. These testing methods, bilateral filter provided the highest PSNR amongst other filters such as median filter, Gaussian and average filter. Two statistical models were implemented polynomial model and linear regression and selected the best performance as polynomial model. Four edge detectors were applied to detect the edge of skin lesion and select the best segmentation accuracy.  Manual border selection was used as the benchmark to evaluation the accuracy of the automatic border. The proposed method was able to achieve a good average accuracy of 96.69% based on canny edge detector. Our dataset consists of (70) dermoscopic images that includes melanoma and nevus.

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