scholarly journals The Edge Detectors Suitable for Retinal OCT Image Segmentation

2017 ◽  
Vol 2017 ◽  
pp. 1-13 ◽  
Author(s):  
Su Luo ◽  
Jing Yang ◽  
Qian Gao ◽  
Sheng Zhou ◽  
Chang’an A. Zhan
Keyword(s):  
Image Segmentation ◽  
Edge Detection ◽  
Thickness Measurement ◽  
Retinal Layer ◽  
Detection Algorithms ◽  
Edge Detectors ◽  
Canny Edge ◽  
Segmentation Task ◽  
Effective Use ◽  
Oct Image

Retinal layer thickness measurement offers important information for reliable diagnosis of retinal diseases and for the evaluation of disease development and medical treatment responses. This task critically depends on the accurate edge detection of the retinal layers in OCT images. Here, we intended to search for the most suitable edge detectors for the retinal OCT image segmentation task. The three most promising edge detection algorithms were identified in the related literature: Canny edge detector, the two-pass method, and the EdgeFlow technique. The quantitative evaluation results show that the two-pass method outperforms consistently the Canny detector and the EdgeFlow technique in delineating the retinal layer boundaries in the OCT images. In addition, the mean localization deviation metrics show that the two-pass method caused the smallest edge shifting problem. These findings suggest that the two-pass method is the best among the three algorithms for detecting retinal layer boundaries. The overall better performance of Canny and two-pass methods over EdgeFlow technique implies that the OCT images contain more intensity gradient information than texture changes along the retinal layer boundaries. The results will guide our future efforts in the quantitative analysis of retinal OCT images for the effective use of OCT technologies in the field of ophthalmology.

Edge Detection in Natural Scenes Inspired by the Speed Drawing Challenge

2021 ◽  
Author(s):  
Marcos José Canêjo ◽  
Carlos Alexandre Barros de Mello
Keyword(s):  
Edge Detection ◽  
Recognition System ◽  
Major Elements ◽  
Contour Detection ◽  
Natural Scenes ◽  
Major Step ◽  
Detection Algorithms ◽  
Canny Edge ◽  
Short Time ◽  
Different Levels

Edge detection is a major step in several computer vision applications. Edges define the shape of objects to be used in a recognition system, for example. In this work, we introduce an approach to edge detection inspired by a challenge for artists: the Speed Drawing Challenge. In this challenge, a person is asked to draw the same figure in different times (as 10[Formula: see text]min, 1[Formula: see text]min and 10[Formula: see text]s); at each time, different levels of details are drawn by the artist. In a short time stamp, just the major elements remain. This work proposes a new approach for producing images with different amounts of edges representing different levels of relevance. Our method uses superpixel to suppress image details, followed by Globalized Probability of Boundary (gPb) and Canny edge detection algorithms to create an image containing different number of edges. After that, an edge analysis step detects whose edges are the most relevant for the scene. The results are presented for the BSDS500 dataset and they are compared to other edge and contour detection algorithms by quantitative and qualitative means with very satisfactory results.

scholarly journals Normative Data and Minimally Detectable Change for Inner Retinal Layer Thicknesses Using a Semi-automated OCT Image Segmentation Pipeline

2019 ◽  
Vol 10 ◽  
Author(s):  
Seyedamirhosein Motamedi ◽  
Kay Gawlik ◽  
Noah Ayadi ◽  
Hanna G. Zimmermann ◽  
Susanna Asseyer ◽  
...  
Keyword(s):  
Image Segmentation ◽  
Normative Data ◽  
Retinal Layer ◽  
Detectable Change ◽  
Oct Image

Early Fire Detection of Large Space Combining Thresholding with Edge Detection Techniques

2010 ◽  
Vol 44-47 ◽  
pp. 2060-2064
Author(s):  
Guo Liang Hu ◽  
Xi Jiang
Keyword(s):  
Image Segmentation ◽  
Edge Detection ◽  
Fire Detection ◽  
Image Features ◽  
Large Space ◽  
Detection Techniques ◽  
Edge Detectors ◽  
Otsu’S Method ◽  
Image Edges ◽  
Early Fire Detection

Image segmentation is a crucial step of the early fire detection in large space based on image processing technology. The image edges contain abundant feature information, and the edge detection has been a main topic of image segmentation algorithm. In this paper, several kinds of traditional edge detectors have been used to detect the edge of frame target in the fire video images, and the results have been contrasted and analyzed. Considering the influence of breaks in the edge caused by noise, nonuniform illumination and spurious intensity discontinuities, proposing the method of combining thresholding with edge detection, using Otsu’s method to compute a threshold for segmentation, extracting the flame area from the background, and then using the traditional edge detectors to detect the flame edge. At the same time, the simulation results based on the MATLAB kits indicate that this kind of method has good effectiveness and strong robustness, the detected flame edges have better effect in integrality and definition, and the relevant result can be the basis of the subsequent extraction and analysis of the fire image features as well as the space positioning of the fire.

scholarly journals Visual Sensor Based Image Segmentation by Fuzzy Classification and Subregion Merge

2017 ◽  
Vol 2017 ◽  
pp. 1-15
Author(s):  
Huidong He ◽  
Xiaoqian Mao ◽  
Wei Li ◽  
Linwei Niu ◽  
Genshe Chen
Keyword(s):  
Image Segmentation ◽  
Edge Detection ◽  
Color Image ◽  
Color Image Segmentation ◽  
Fuzzy Classification ◽  
Spatial Proximity ◽  
Visual Sensor ◽  
Detection Algorithms ◽  
Color Similarity ◽  
Different Color

The extraction and tracking of targets in an image shot by visual sensors have been studied extensively. The technology of image segmentation plays an important role in such tracking systems. This paper presents a new approach to color image segmentation based on fuzzy color extractor (FCE). Different from many existing methods, the proposed approach provides a new classification of pixels in a source color image which usually classifies an individual pixel into several subimages by fuzzy sets. This approach shows two unique features: the spatial proximity and color similarity, and it mainly consists of two algorithms: CreateSubImage and MergeSubImage. We apply the FCE to segment colors of the test images from the database at UC Berkeley in the RGB, HSV, and YUV, the three different color spaces. The comparative studies show that the FCE applied in the RGB space is superior to the HSV and YUV spaces. Finally, we compare the segmentation effect with Canny edge detection and Log edge detection algorithms. The results show that the FCE-based approach performs best in the color image segmentation.

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.

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 Identification of Virus in Microscopic Image Using Genetic Algorithm

2019 ◽  
Vol 8 (S2) ◽  
pp. 24-27
Author(s):  
N. Senthilkumaran ◽  
R. Preethi
Keyword(s):  
Genetic Algorithm ◽  
Image Segmentation ◽  
Edge Detection ◽  
Microscopic Image ◽  
Canny Edge Detection ◽  
Detection Techniques ◽  
Canny Edge ◽  
Sobel Edge Detection ◽  
The Many ◽  
Prewitt Edge Detection

In this paper describes a several techniques of effective edge detection by using image segmentation. The image segmentation provides various techniques to detect the edges on image. The paper mainly focused on edge detection using matlab parameters and solved the many problems. Edge detection techniques have a several type of techniques. We have taken microscopic image, which affects the human body by making diseases through viruses and bacteria’s. Now analyze only about the major techniques: a.) Roberts edge detection, b) sobel edge detection, c) prewitt edge detection, d) log (laplacian of gaussian) edge detection, e) genetic edge detection and f) canny edge detection. We have applied above five techniques which are used in edge detection and got a result on microscopic images. Hence, we scope this paper defines and compares the variety of techniques and demand assures the genetic algorithm provides a better performance on edge detection using microscopic image.

scholarly journals Performance comparison of segmentation algorithms for hand gesture recognition

2018 ◽  
Vol 7 (3) ◽  
pp. 1227
Author(s):  
Priyanka Parvathy D ◽  
Dr Kamalraj Subramaniam
Keyword(s):  
Image Segmentation ◽  
Edge Detection ◽  
Gesture Recognition ◽  
Noise Removal ◽  
Hand Gesture Recognition ◽  
Segmentation Algorithm ◽  
Hand Gesture ◽  
Canny Edge Detection ◽  
Canny Edge ◽  
Image Segmentation Algorithm

The gestures presented in diverse backgrounds have to be accurately processed and segmented, for it to be classified precisely by the hand gesture recognition system. This study compares performance of the proposed Image Segmentation Algorithm with a standard Canny Edge Detection Algorithm by comparing the statistical values of the features obtained from the feature extraction stage, thus validating the importance of having a robust preprocessing stage for the hand gestures. The proposed algorithm uses Non-local Mean filter for noise removal and then an improved Global Swarm Optimization based Canny edge detection for extracting the edges. Features are extracted using two dimensional Multi-resolution Discrete Wavelet Transform (2D-DWT) combined with Gray-level Co-occurrence Matrix. The efficiency of the proposed Image Segmentation Algorithm is evaluated using Radial Basis Function Neural Network as the classifier.  

Distributed Approach to Process Satellite Image Edge Detection on Hadoop Using Artificial Bee Colony

2020 ◽  
Vol 11 (2) ◽  
pp. 80-94
Author(s):  
Tapan Sharma ◽  
Vinod Shokeen ◽  
Sunil Mathur
Keyword(s):  
Edge Detection ◽  
Artificial Bee Colony ◽  
Satellite Image ◽  
Detection Algorithms ◽  
Distributed Approach ◽  
Bee Colony ◽  
Image Edge Detection ◽  
Nature Inspired Algorithm ◽  
Canny Edge ◽  
Image Edge

The remote sensing domain has witnessed tremendous growth in the past decade, due to advancement in technology. In order to store and process such a large amount of data, a platform like Hadoop is leveraged. This article proposes a MapReduce (MR) approach to perform edge detection of satellite images using a nature-inspired algorithm Artificial Bee Colony (ABC). Edge detection is one of the significant steps in the field of image processing and is being used for object detection in the image. The article also compares two edge detection approaches on Hadoop with respect to scalability parameters such as scaleup and speedup. The experiment makes use of Amazon AWS Elastic MapReduce cluster to run MR jobs. It focuses on traditional edge detection algorithms like Canny Edge (CE) and the proposed MR based Artificial Bee Colony approach. It observes that for five images, the scaleup value of CE is 1.1 whereas, for MR-ABC, it is 1.2. Similarly, speedup values come out to be 1.02 and 1.04, respectively. The algorithm proposed by authors in this article scales comparatively better when compared to Canny Edge.

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