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Segmentation of shallow scratches image using an improved multi-scale line detection approach

multiMEDIA TOOLS AND APPLICATIONS 2019年第1期78卷 1053-1066页

作者： Jiang, Xiaoliang Yang, Xiaojun Ying, Zhengen Zhang, Liwen Pan, Jie Chen, Shaojie Quzhou Univ Coll Mech Engn Quzhou 324000 Zhejiang Peoples R China Southwest Jiaotong Univ Coll Mech Engn Chengdu 610031 Sichuan Peoples R China

Along with developing modern technology, the demands for optical element surface develop towards the characteristics of large scale and high precision. However, it is challenging to evaluate the surface defects since some shallow scratches in optical element surface images are usually characterized by low contrast and blurry outlines. This property makes the machine vision inspection extremely difficult. So, this paper proposes a novel multi-scale line detection method that can efficiently extract shallow scratches. Firstly, to decrease the influence of the surrounding region, a new multi-scale line detector combines all the responses at different scales by setting different weights for each scale. Then, based on the scratches features, we utilize morphological operations to get the full continuum of the scratches area. Experimental results show that our model can ideally extract the contours of shallow scratches that are very close to the optical microscope results observed by specialists.

关键词： Optical element Shallow scratches multi-scale line detection Morphological operations

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Retinal Vessel Segmentation Based on multi-scale line detection and Morphological Transformation

Retinal Vessel Segmentation Based on Multi-scale Line Detect...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Meng, Lin Liu, Jing Feng, Yibo Liu, Shuxuan Cao, Hui Shandong Univ Tradit Chinese Med Sch Technol Jinan Peoples R China

ISBN: (纸本)9781728118673

Retina is an important part of human eyes, analyses of retinal vessels are preferable to the early prevention, detection and treatment of cardiovascular diseases. In this paper, we propose an effective unsupervised segmentation method. Firstly, the image is preprocessed to reduce interference of external factors;Secondly, the preprocessed image is enhanced to suppress the optic disc and background interference;Finally, based on the method of multi-scale line detection, the blood vessels images are obtained. We evaluate our algorithm on the public datasets DRIVE and STARE, the experimental results show that the accuracy is about 95.90% and 96.11%, the sensitivity is 76.70% and 75.86%, the specificity is 95.20% and 95.56% respectively. Compared with other unsupervised methods, the proposed algorithm has a better segmentation effect.

关键词： morphological transformation multi-scale line detection retinal vessels unsupervised learning

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Retinal Vessel Segmentation Based on multi-scale line detection and Morphological Transformation

Retinal Vessel Segmentation Based on Multi-scale Line Detect...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Lin Meng Jing Liu Yibo Feng Shuxuan Liu Hui Cao School of Technology Shandong University of Traditional Chinese Medicine Jinan China

ISBN: (纸本)9781728118680

Retina is an important part of human eyes, analyses of retinal vessels are preferable to the early prevention, detection and treatment of cardiovascular diseases. In this paper, we propose an effective unsupervised segmentation method. Firstly, the image is preprocessed to reduce interference of external factors; Secondly, the preprocessed image is enhanced to suppress the optic disc and background interference; Finally, based on the method of multi-scale line detection, the blood vessels images are obtained. We evaluate our algorithm on the public datasets DRIVE and STARE, the experimental results show that the accuracy is about 95.90% and 96.11%, the sensitivity is 76.70% and 75.86%, the specificity is 95.20% and 95.56% respectively. Compared with other unsupervised methods, the proposed algorithm has a better segmentation effect.

关键词： morphological transformation multi-scale line detection retinal vessels unsupervised learning Retinal Vessels Unsupervised learning on line detection multi-scale optic disk

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A retinal vessel boundary tracking method based on Bayesian theory and multi-scale line detection

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COMPUTERIZED MEDICAL IMAGING AND GRAPHICS 2014年第6期38卷 517-525页

作者： Zhang, Jia Li, Huiqi Nie, Qing Cheng, Li Beijing Inst Technol Beijing 100081 Peoples R China ASTAR Bioinformat Inst Singapore Singapore

A retinal vessel tracking method based on Bayesian theory and multi-scale line detection is proposed in this paper. The optic disk is located by a PCA method and the initial points of tracking are identified. In each step, candidate points for vessel edges are selected on a semi-ellipse. Three types of vessel structure are considered in the tracking: normal vessel, branching, and crossing. To determine the new pair of edge points, the characteristics of the vessel intensity profiles along both the cross section and the longitudinal direction are considered in the tracking. A Gaussian model is assumed in the cross section and multi-scale line detection is employed in the longitudinal direction. The advantage of the proposed method is that two dimensional vessel information is employed, which makes it work better than methods using one dimensional information only. Our method is tested on the REVIEW database and a comparison study is performed. Experimental results show that the proposed method is precise and robust in tracking vessel edges. (c) 2014 Elsevier Ltd. All rights reserved.

关键词： Retinal image Vessel tracking Bayesian theory multi-scale line detection

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AUTOMATED SYSTEM FOR RETINAL VESSEL SEGMENTATION 2

AUTOMATED SYSTEM FOR RETINAL VESSEL SEGMENTATION

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International Conference on Inventive Communication and Computational Technologies (ICICCT)

作者： Ahamed, Akyas T. U. Jothish, Abhin Johnson, Geo Krishna, Santhosh B., V Velammal Inst Technol Dept Elect & Commun Engn Chennai Tamil Nadu India

ISBN: (纸本)9781538619742

Retinal blood vessels play a significant role in the diagnosis and treatment of various retinal diseases such as diabetic retinopathy, glaucoma, arteriosclerosis and hypertension. Retinal blood vessel extraction is primary step for detecting the above eye diseases. Due to the reason that manual extraction is long and tedious tasks, many automated systems are proposed by various researchers. Automatic segmentation of the retinal vasculature is the major task in the automatic calculation of the features of retinal blood vessels. Screening larger populations for blood vessel abnormalities by eye care specialists can be done using computer based automatic segmentation of blood vessels. In the proposed method we have developed an automated system for the extraction of blood vessels with promising result. In our method we introduced enhanced preprocessing with multi scale line detection for the extraction of blood vessels. The proposed method is evaluated both quantitatively and qualitatively on publicly available DRIVE database. Our proposed method is better than many existing methods by achieving average accuracy, sensitivity and specificity as 0.7421, 0.9803, and 0.9592 respectively.

关键词： Retinal blood Vessels multi-scale line detection Extraction

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Particle Swarm Optimization Approach for the Segmentation of Retinal Vessels from Fundus Images 14th

Particle Swarm Optimization Approach for the Segmentation of...

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14th International Conference on Image Analysis and Recognition (ICIAR)

作者： Khomri, Bilal Christodoulidis, Argyrios Djerou, Leila Babahenini, Mohamed Chaouki Cheriet, Farida Polytech Montreal Lab LIV4D Montreal PQ Canada Univ Biskra LESIA Lab Biskra Algeria

ISBN: (纸本)9783319598765;9783319598758

In this paper, we propose to use the Particle Swarm Optimization (PSO) algorithm to improve the multi-scale line detection (MSLD) method for the retinal blood vessel segmentation problem. The PSO algorithm is applied to find the best arrangement of scales in the basic line detector method. The segmentation performance was validated using a public high-resolution fundus images database containing healthy subjects. The optimized MSLD method demonstrates fast convergence to the optimal solution reducing the execution time by approximately 35%. For the same level of specificity, the proposed approach improves the sensitivity rate by 3.1% compared to the original MSLD method. The proposed method will allow to reduce the amount of missing vessels segments that might lead to false positives of red lesions detection in CAD systems used for diabetic retinopathy diagnosis.

关键词： Retinal blood vessel segmentation Optimization Particle Swarm Optimization Algorithm multi-scale line detection

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A multi-scale tensor voting approach for small retinal vessel segmentation in high resolution fundus images

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COMPUTERIZED MEDICAL IMAGING AND GRAPHICS 2016年 52卷 28-43页

作者： Christodoulidis, Argyrios Hurtut, Thomas Ben Tahar, Houssem Cheriet, Farida Polytech Montreal Montreal PQ H3C 3A7 Canada Diagnos Inc Brossard PQ J4Z 1A7 Canada

Segmenting the retinal vessels from fundus images is a prerequisite for many CAD systems for the automatic detection of diabetic retinopathy lesions. So far, research efforts have concentrated mainly on the accurate localization of the large to medium diameter vessels. However, failure to detect the smallest vessels at the segmentation step can lead to false positive lesion detection counts in a subsequent lesion analysis stage. In this study, a new hybrid method for the segmentation of the smallest vessels is proposed. line detection and perceptual organization techniques are combined in a multi-scale scheme. Small vessels are reconstructed from the perceptual-based approach via tracking and pixel painting. The segmentation was validated in a high resolution fundus image database including healthy and diabetic subjects using pixel-based as well as perceptual-based measures. The proposed method achieves 85.06% sensitivity rate, while the original multi-scale line detection method achieves 81.06% sensitivity rate for the corresponding images (p < 0.05). The improvement in the sensitivity rate for the database is 6.47% when only the smallest vessels are considered (p < 0.05). For the perceptual-based measure, the proposed method improves the detection of the vasculature by 7.8% against the original multi-scale line detection method (p < 0.05). (C) 2016 Elsevier Ltd. All rights reserved.

关键词： Diabetic retinopathy Fundus imaging Retinal blood vessel segmentation multi-scale line detection Perceptual organization

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An efficient and robust line segment matching approach based on LBD descriptor and pairwise geometric consistency

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JOURNAL OF VISUAL COMMUNICATION AND IMAGE REPRESENTATION 2013年第7期24卷 794-805页

作者： Zhang, Lilian Koch, Reinhard Univ Kiel Inst Comp Sci D-24098 Kiel Germany

We present a line matching algorithm which utilizes both the local appearance of lines and their geometric attributes. To overcome the problem of segment fragmentation and geometric variation, we extract lines in the scale space. To depict the local appearance of lines, we design a novel line descriptor called line Band Descriptor (LBD). To evaluate the pairwise geometric consistency, we define the pairwise geometric attributes between line pairs. Then we built a relational graph for candidate line matches and employ a spectral technique to solve this matching problem efficiently. The advantages of the proposed algorithm are as follows: (1) it is robust to image transformations because of the multi-scale line detection strategy;(2) it is efficient because the designed LBD descriptor is fast to compute and the appearance similarities reduce the dimension of the graph matching problem;(3) it is accurate even for low-texture images because of the pairwise geometric consistency evaluation. (C) 2013 Elsevier Inc. All rights reserved.

关键词： line segment matching multi-scale line detection line band descriptor Unary geometric attribute Pairwise geometric consistency Relational graph Graph matching Spectral method

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