An Implementation of Multiscale Line Detection and Mathematical Morphology for Efficient and Precise Blood Vessel Segmentation in Fundus Images

Diagnosing various diseases such as glaucoma,age-related macular degeneration,cardiovascular conditions,and diabetic retinopathy involves segmenting retinal blood vessels.The task is particularly challenging when dealing with color fundus images due to issues like non-uniformillumination,low contrast,and variations in vessel appearance,especially in the presence of different pathologies.Furthermore,the speed of the retinal vessel segmentation system is of utmost importance.With the surge of now available big data,the speed of the algorithm becomes increasingly important,carrying almost equivalent weightage to the accuracy of the algorithm.To address these challenges,we present a novel approach for retinal vessel segmentation,leveraging efficient and robust techniques based on multiscale line detection and mathematical morphology.Our algorithm’s performance is evaluated on two publicly available datasets,namely the Digital Retinal Images for Vessel Extraction dataset(DRIVE)and the Structure Analysis of Retina(STARE)dataset.The experimental results demonstrate the effectiveness of our method,withmean accuracy values of 0.9467 forDRIVE and 0.9535 for STARE datasets,aswell as sensitivity values of 0.6952 forDRIVE and 0.6809 for STARE datasets.Notably,our algorithmexhibits competitive performance with state-of-the-art methods.Importantly,it operates at an average speed of 3.73 s per image for DRIVE and 3.75 s for STARE datasets.It is worth noting that these results were achieved using Matlab scripts containing multiple loops.This suggests that the processing time can be further reduced by replacing loops with vectorization.Thus the proposed algorithm can be deployed in real time applications.In summary,our proposed system strikes a fine balance between swift computation and accuracy that is on par with the best available methods in the field.

EDGE DETECTION METHOD OF REMOTE SENSING IMAGES BASED ON MATHEMATICAL MORPHOLOGY OF MULTI-STRUCTURE ELEMENTS

This paper puts forward an effective, specific algorithm for edge detection. Based on multi-structure elements of gray mathematics morphology, in the light of difference between noise and edge shape of RS images, the paper establishes multi-structure elements to detect edge by utilizing the grey form transformation principle. Compared with some classical edge detection operators, such as Sobel Edge Detection Operator, LOG Edge Detection Operator, and Canny Edge Detection Operator, the experiment indicates that this new algorithm possesses very good edge detection ability, which can detect edges more effectively, but its noise-resisting ability is relatively low. Because of the bigger noise & remote sensing image, the authors probe into putting forward other edge detection method based on combination of wavelet directivity checkout technology and small-scale Mathematical Morphology finally. So, position at the edge can be accurately located, the noise can be inhibited to a certain extent and the effect of edge detection is obvious.

Image edge detection based on nonsubsampled contourlet transform and mathematical morphology

A novel algorithm for image edge detection is presented. This algorithm combines the nonsubsampled contourlet transform and the mathematical morphology. First, the source image is decomposed by the nonsubsampled contourlet transform into multi-scale and multi-directional subbands. Then the edges in the high-frequency and low-frequency sub-bands are respectively extracted by the dualthreshold modulus maxima method and the mathematical morphology operator. Finally, the edges from the high- frequency and low-frequency sub-bands are integrated to the edges of the source image, which are refined, and isolated points are excluded to achieve the edges of the source image. The simulation results show that the proposed algorithm can effectively suppress noise, eliminate pseudo-edges and overcome the adverse effects caused by uneven illumination to a certain extent. Compared with the traditional methods such as LoG, Sobel, and Carmy operators and the modulus maxima algorithm, the proposed method can maintain sufficient positioning accuracy and edge details, and it can also make an improvement in the completeness, smoothness and clearness of the outline.

A method of remote sensing image water segmentation based on adaptive morphological elliptical structuring elements

The use of visible and infrared remote sensing images to calculate the water area is an effective means to grasp the basic situation of water resources,and water segmentation is the premise of statistics.Generally,the edge features of the water in the remote sensing images are complex.When the traditional morphology is used for image segmentation,it is easy to change the image edge and affect the accuracy of image segmentation because the fixed structuring elements are used to perform morphological operations on the image.To segment water in the remote sensing image accurately,a remote sensing image water segmentation method based on adaptive morphological elliptical structuring elements is proposed.Firstly,the eigenvalue and eigenvector of the image are estimated by linear structure tensor,and the elliptical structuring elements are constructed by the eigenvalue and eigenvector.Then adaptive morphological operations are defined,combining the close operation to eliminate the influence of dark detail noise on water without overstretching the water edge,so that the water edge can be maintained more accurately.Finally,on this basis,the water area can be segmented by gray slice.The experimental results show that the proposed method has higher segmentation accuracy and the average segmentation error is less than 1.43%.

列车滚子轴承表面缺陷图像的边缘提取

为准确提取滚动轴承内圈外表面缺陷图像的边缘特征信息,设计了一种对轴承内圈外表面缺陷图像自动获取的系统装置,并提出了改进形态学的边缘检测算法。首先,通过工业内窥镜采集轴承表面缺陷图像,采用两种不同类型结构元素对采集的轴承缺陷图像进行形态学滤波处理;其次,通过不同方向的线型结构元素对轴承缺陷图像进行边缘检测,得到四个边缘缺陷子图;最后,根据每个子图的信息熵权值系数进行图像融合得到最终的边缘图像。通过仿真结果表明,在含有噪声干扰的情况下,该算法具有较强的抗噪性能和较好的边缘检测效果。

RESEARCH ON SATELLITE IMAGE PROCESSING AND RECOGNITION WITH PARALLEL ALGORITHM

Using the method of mathematical morphology,this paper fulfills filtration,segmentation and extraction of morphological features of the satellite cloud image.It also gives out the relative algorithms,which is realized by parallel C programming based on Transputer networks.It has been successfully used to process the typhoon and the low tornado cloud image.And it will be used in weather forecast.

影院票务辅助系统中座位信息的自动提取

在电影院的票务系统中,一般分为线上售票和线下售票两种方式。目前线上售票有成熟的软件工具,用户可以通过手机或电脑在线上自动选择各种类型的座位信息。但在线下售票中,操作人员只能通过人工观察票务系统软件的屏幕界面,人工获取某个场次的不同类型的空余座位信息,然后再手工输入空余的座位信息到出票系统,打印纸质电影票后,然后进行售票。针对线下售票需要人工操作这种不方便不快捷的情况,作者利用C++语言,基于OpenCV图像处理库,首先快速读取票务系统软件的界面图片的彩色图像,将三通道的彩色图像转化为单通道的灰度图像。利用findContours()函数获取图像的轮廓集,处理掉缺失和过小的轮廓后,通过外接矩形来获取轮廓的位置,将轮廓和外接矩形作为属性初始化对应的每个座位的实例化对象。通过读取各个对象的矩形内的像素点信息,可以确定对象的颜色以及位置。实现了对屏幕中各种不同类型的座位信息的自动识别与定位,准确率为100%;然后,自动输入到出票系统,自动打印出纸质电影票,达到了自动线下售票的目的。开发此系统使用的技术方法,对于需要识别第三方软件界面的屏幕信息及其变化的场景,如:监控系统屏幕的变化,有一定的借鉴意义。

Application of multi-scaled morphology in denoising seismic data

In this paper, multi-scaled morphology is introduced into the digital processing domain for land seismic data. First, we describe the basic theory of multi-scaled morphology image decomposition of exploration seismic waves; second, we illustrate how to use multi-scaled morphology for seismic data processing using two real examples. The first example demonstrates suppressing the surface waves in pre-stack seismic records using multi-scaled morphology decomposition and reconstitution and the other example demonstrates filtering different interference waves on the seismic record. Multi-scaled morphology filtering separates signal from noise by the detailed differences of the wave shapes. The successful applications suggest that multi-scaled morphology has a promising application in seismic data processing.

Processing of Laser Scanner Data and Extraction of Structure Lines Using Methods of the Image Processing

激光扫描数据提供了一种新的手段用于获取高精度的数字地形表面模型。原始的航空激光扫描数据表达的是一些非规则分布的“点云” ,这些非规则分布的点需要进行有效的事后处理。这种事后处理有 2个目的 :一是将那些分布在地表面上的点 (即地面点 )与分布在非地表面上的点 (譬如树木、房屋或汽车上的点 ,即非地面点 )进行有效的分离 ;二是从分离后的地面点中提取结构线 ,用于建立高精度的数字地面模型。作者发展了一系列的基于数字形态学理论和稳健参数估计理论的方法用于分离和探测地面点。这里所介绍和开发的提取结构线的算法建立在数字图像处理和表面曲率理论的基础上。这些算法同样可以扩展地用于其他领域。所介绍的基于数字图像处理理论处理原始的航空激光扫瞄数据和提取结构线的方法取得了很好的结果。

A Novel Adaptive Approach to Process Binary Fingerprint Images Using Directional Morphological Operations

An adaptive algorithm for removing false ridges, bridges and filling gaps in binary fingerprint images based on morphological operations is presented. A novel procedure for structuring elements design based on the specific fingerprint characteristic is described. Using the images from FVC2000 database, we have compared our method proposed here with the approach proposed by other ones. The Experimental results have demonstrated the efficiency of our method.

Hepatic vessel segmentation on contrast enhanced CT image sequence for liver transplantation planning

The structure and morphology of the hepatic vessels and their relationship between tumors and liver segments are major interests to surgeons for liver surgical planning. In case of living donor liver transplantation (LDLT), the most important step in determining donor suitability is an accurate assessment of the liver volume available for transplantation. In addition, the mutual principles of the procedures include dissection in the appropriate anatomic plane without portal occlusion, minimization of blood loss, and avoidance of injury to the remaining liver. It is essential first step to identify and evaluate the major hepatic vascular structure for liver surgical planning. In this paper, the threshold was determined to segment the liver region automatically based on the distribution ratio of intensity value;and the hepatic vessels were extracted with mathematical morphology transformation, which called hit operation, that is slightly modified version of hit-and-miss operation on contrast enhanced CT image sequence. We identified the vein using the preserved voxel connectivity between two consecutive transverse image sequences, followed by resection into right lobe including right hepatic vein, middle hepatic vein branches andleft lobe including left hepatic vein. An automated hepatic vessel segmentation scheme is recommended for liver surgical planning such as tumor resection and transplantation. These vessel extraction method combined with liver region segmentation technique could be applicable to extract tree-like organ structures such as carotid, renal, coronary artery, and airway path from various medical imaging modalities.

Salience adaptive morphological structuring element construction method based on minimum spanning tree

Classical mathematical morphology operations use a fixed size and shape structuring element to process the whole image.Due to the diversity of image content and the complexity of target structure,for processed image,its shape may be changed and part of the information may be lost.Therefore,we propose a method for constructing salience adaptive morphological structuring elements based on minimum spanning tree(MST).First,the gradient image of the input image is calculated,the edge image is obtained by non-maximum suppression(NMS)of the gradient image,and then chamfer distance transformation is performed on the edge image to obtain a salience map(SM).Second,the radius of structuring element is determined by calculating the maximum and minimum values of SM and then the minimum spanning tree is calculated on the SM.Finally,the radius is used to construct a structuring element whose shape and size adaptively change with the local features of the input image.In addition,the basic morphological operators such as erosion,dilation,opening and closing are redefined using the adaptive structuring elements and then compared with the classical morphological operators.The simulation results show that the proposed method can make full use of the local features of the image and has better processing results in image structure preservation and image filtering.

基于自适应结构元素的改进分水岭图像分割方法

图像分割是按照一定的规则,将图像中具有特殊意义的区域划分为若干个互不相交的子区域,是从图像处理到图像分析的关键环节,传统分水岭图像分割方法是一种应用较为广泛的技术,具有快速、简单的优点,但该方法易受噪声干扰,分割结果易丢失边缘重要信息,出现过分割现象。为改善传统分水岭图像分割方法存在的过分割问题,提出了一种基于自适应结构元素的改进分水岭图像分割方法。首先,利用图像像素点邻域的局部密度、对称度及边缘特征构造形状可变的自适应结构元素,确保其与图像目标几何结构具有较强的一致性;其次,利用该结构元素获取图像形态学梯度,提高目标边缘的定位精度;将L_(0)范数梯度最小化和形态学开闭混合重建相结合修正梯度图像,减少梯度图像中的局部无效最小值点,抑制过分割现象的产生;最后对修正后的梯度图像进行分水岭分割,实现图像目标区域的精确分割。实验结果表明,该方法能够有效抑制过分割现象,提高目标边缘定位的准确性,具有较高的分割精度。

数学形态学在数字图像处理中的应用

阐述数学形态学的特点,数字图像的表达方式,数学形态学在数字图像处理中的应用,包括二值数学形态学、数字图像处理中的消除噪声、边缘检测、提取形态骨架、图像分割。

连通区域拓扑结构约束的甲骨拓片图像分割

甲骨拓片图像往往遭受点状噪声、人工纹理和固有纹理等的严重影响,导致典型的图像分割方法尚无法得到令人满意的文字分割结果.为此,提出一种连通区域拓扑结构约束的甲骨拓片图像分割方法.该方法采用逐步求精的层次分割策略,首先,利用超像素分割和最大类间方差法进行初始分割;其次,采用最小凸包算法分离断裂的甲骨文字与粘连的拓片背景,得到粗分割结果;最后,利用形态学运算、欧拉数、方向投影变换对连通区域的拓扑结构和形状特征进行分析,得到精细分割结果.实验结果表明,在多种典型的图像分割方法中,该方法在正确分割率、敏感度、特异度3个指标上较最优方法分别提升了2.03%、0.23%、1.54%.并且,建立连通区域的形态学先验是克服背景粘连、大面积片状斑纹和盾纹的影响及改善甲骨拓片图像分割效率的有效手段.

基于形态学和改进管道滤波的红外小目标检测

红外小目标的正确检测是图像处理领域中的一个重要课题,现有方法没有充分挖掘小目标的特征导致目标容易淹没在复杂的背景中.此外,目标的运动轨迹信息没有完全利用也使得目标和孤立噪声不易区分.为了解决该问题,设计了一种基于多属性形态学和改进管道滤波相结合的算法.对红外图像构建最大树,并通过面积、高度和对角线属性综合提取小目标的多种特征达到目标增强背景抑制的目的,融合多属性形态学得到的结果确定候选目标,通过改进的管道滤波利用目标运动轨迹的规律性进一步去除类目标噪声.将所提出的方法在4个数据集与4种方法进行比较,实验结果证明所提出的方法能在最大程度上抑制背景杂波的同时增强目标,对多种复杂场景下的不同类型、尺寸不一和亮度差异大的目标检测效果鲁棒.

A Signal Based “W” Structural Elements for Multi-scale Mathematical Morphology Analysis and Application to Fault Diagnosis of Rolling Bearings of Wind Turbines

Working conditions of rolling bearings of wind turbine generators are complicated, and their vibration signals often show non-linear and non-stationary characteristics. In order to improve the efficiency of feature extraction of wind turbine rolling bearings and to strengthen the feature information, a new structural element and an adaptive algorithm based on the peak energy are proposed,which are combined with spectral correlation analysis to form a fault diagnosis algorithm for wind turbine rolling bearings. The proposed method firstly addresses the problem of impulsive signal omissions that are prone to occur in the process of fault feature extraction of traditional structural elements and proposes a "W" structural element to capture more characteristic information. Then, the proposed method selects the scale of multi-scale mathematical morphology, aiming at the problem of multi-scale mathematical morphology scale selection and structural element expansion law. An adaptive algorithm based on peak energy is proposed to carry out morphological scale selection and structural element expansion by improving the computing efficiency and enhancing the feature extraction effect.Finally, the proposed method performs spectral correlation analysis in the frequency domain for an unknown signal of the extracted feature and identifies the fault based on the correlation coefficient. The method is verified by numerical examples using experimental rig bearing data and actual wind field acquisition data and compared with traditional triangular and flat structural elements. The experimental results show that the new structural elements can more effectively extract the pulses in the signal and reduce noise interference,and the fault-diagnosis algorithm can accurately identify the fault category and improve the reliability of the results.

基于数学形态学的路面裂纹图像分割方法

已有裂纹图像分割方法无法对较小裂纹特征进行具体描述,导致路面图像中的裂纹提取结果不全面等问题,为此提出基于数学形态学的路面裂纹图像分割方法。采用空间域处理方法对原始图像像素进行增强处理,通过正变换及逆变换得到新的路面裂纹图像,并划分出图像中的噪声类型;使用灰度值表示噪声,使用均值滤波法中的3×3模板表示离散图像的裂纹元素与噪声元素,并对路面裂纹图像进行去噪处理;利用经典数学形态学理论,对路面裂纹图像结构元素进行表达,在膨胀和腐蚀处理过程中判断裂纹的形式变化,实现裂纹分割。单一裂纹图像和复杂裂纹图像的实验结果表明:所提方法可以有效提取裂纹特征值,特征提取率为99.5%,并在不同噪声条件下具有较高的分割效率,可以满足路面裂纹检测需求。

A novel mathematical morphology based algorithm for shoreline extraction from satellite images

Shoreline extraction is fundamental and inevitable for several studies.Ascertaining the precise spatial location of the shoreline is crucial.Recently,the need for using remote sensing data to accomplish the complex task of automatic extraction of features,such as shoreline,has considerably increased.Automated feature extraction can drastically minimize the time and cost of data acquisition and database updating.Effective and fast approaches are essential to monitor coastline retreat and update shoreline maps.Here,we present a flexible mathematical morphology-driven approach for shoreline extraction algorithm from satellite imageries.The salient features of this work are the preservation of actual size and shape of the shorelines,run-time structuring element definition,semi-automation,faster processing,and single band adaptability.The proposed approach is tested with various sensor-driven images with low to high resolutions.Accuracy of the developed methodology has been assessed with manually prepared ground truths of the study area and compared with an existing shoreline classification approach.The proposed approach is found successful in shoreline extraction from the wide variety of satellite images based on the results drawn from visual and quantitative assessments.