一种新的边缘保持分水岭的图像分割算法

为了达到抑制分水岭过分割和保持物体边缘信息不受破坏的双重目的,提出了一种新的边缘保持水岭(Watershed)算法.首先,根据K-均值聚类将图像分成多块;然后利用噪声标准差构造相对应的双边滤波器平滑每块图像;接着计算形态学梯度,对梯度图像进行H-minima标记;最后对标记图像进行分水岭分割.该算法将双边滤波和分水岭算法相结合,有效地抑制了过分割并且较好得保持了物体边缘信息.     

基于改进分水岭算法的苹果识别方法

目的 针对自然生长环境下重叠苹果的识别问题,提出一种基于距离变换机制的改进分水岭算法.方法 首先,通过对比实验优选出苹果在RGB、HSI和Lab颜色空间的图像处理模型;然后,采用距离变换和归一化处理方法对苹果的位置特征进行内部标记;进而基于锐化方法提高原图像的边缘强度,并结合内部标记符改进分水岭算法,实现苹果轮廓的分离;最后,基于最小外接圆法提取苹果的单连通区域,实现重叠苹果的识别.结果 简化了目标与背景的分割过程,通过内部标记法改进了分水岭算法,实现重叠目标的轮廓重建,解决重叠苹果的过度分割问题.结论 实验结果表明,该算法对重叠苹果的准确识别率高于92％,验证了所提方法的有效性.     

基于标记的多尺度分水岭视频目标分割算法

针对视频目标提取的问题,提出了基于标记的多尺度分水岭视频目标分割算法.该算法以帧间变化检测为基础,通过改进的最小Tsallis交叉熵进行去噪、滤波,经形态学处理后得到运动目标初始二值掩模,并利用初始二值掩模得到用于分水岭算法的前景与背景标记,用该标记修正当前帧的多尺度形态学梯度图像,最后进行分水岭分割,得到具有精确边界的视频对象.实验结果表明,该算法能有效地分割和提取视频序列中的单个、多个以及快速运动的目标,继承了变化检测和分水岭算法速度快的优点,克服了分水岭容易产生过分割的缺点,具有较强的适用性.     

基于区域MRF的SAR图像快速分割算法

针对受相干斑噪声影响较严重的合成孔径雷达(SAR)图像,提出了一种基于边缘保持(EPR)的区域MRF快速分割算法.基于EPR的SAR图像表示方法包括各向异性扩散的相干斑降噪算法和分水岭变换两部分,该方法在存在相干斑噪声的情况下,能够有效地抑制过分割和在区域边界进行目标边缘的准确定位.将基于EPR的表示方法和区域MRF相结合,能够大幅减少优化过程的搜索空间,获得准确的分类结果和统计特性,同时减少了计算量和分割错误.将提出的算法用于一幅添加了各种不同噪声水平的合成图像和SAR海冰影像的分割中,实验结果证明了该算法的有效性.该算法与现有的区域MRF相比,实验结果证明新算法能够节约计算时间50%,同时提高了分割准确性,尤其是在相干斑噪声较强的区域.     

基于FCM和图割的交互式图像分割方法

为了提高在前景和背景颜色相似情况下图像的分割效果,提出了一种基于模糊C均值聚类(FCM)和图割的交互式图像分割方法。首先,利用分水岭算法对图像进行预处理,将图像分成多个小区域,用区域代替像素点进行分析。然后,采用模糊C均值算法对用户标记的前景区域和背景区域分别进行聚类分析,挖掘用户交互所提供的隐藏信息。用未标记区域的颜色分量到前景区域及背景区域类心的最小距离表示相似能量,用未标记区域与其相邻区域的相关性表示先验能量。最后,利用最大流/最小割算法求能量函数的全局最优解。与其他方法相比,该文方法具有较好的分割性能,能从前景背景相似的图像中较精确地提取感兴趣的物体,且用户操作简单。     

基于改进FAST与分水岭算法的颗粒图像分割

为准确、高效分割粘连颗粒,提出一种改进FAST特征点检测算法,并与基于h-maxima变换的分水岭算法相结合实现粘连颗粒的分割。该算法通过h-maxima分水岭算法得到候选边缘分割点和候选分割线;采用改进FAST算法遍历二值图像边缘获得图像边缘特征点;利用边缘特征点对所有候选边缘分割点进行非极大值抑制得到待边缘分割点;通过待边缘分割点从候选分割线中提取待分割线;利用边缘特征点识别待分割线中的伪边缘分割点,并去除与其相连的分割线;获得正确分割线进行图像分割;对典型颗粒进行分割实验,并与现有3种图像分割算法进行比较。结果表明:该算法相对运算时间最短,分割正确率最高且均大于95%。     

基于边缘探测的密集颗粒图像分割方法

针对密集的颗粒图像提出了一种基于边缘探测的颗粒分割方法.该方法采用分块自适应的边缘检测算法实现对图像的边缘检测,并采用形态学方法去除边缘噪音;采用分水岭算法对图像进行初步分割,根据分割区域自动选取种子点,并利用区域面积对种子点进行修正,然后从种子点发射探测线探测边缘位置,记录边界点,通过判断机制判别出由于边缘不完整或噪...     

基于多分辨率分析和分水岭的图像分割方法

提出了一种基于小波多分辨率分析和分水岭算法的图像分割方法.在小波分解后的低分辨率图像上进行分水岭分割,提高了分割的速度;由低分辨率图像返回到高分辨率图像时,采用了一种基于边缘信息的合并函数,避免了边缘信息的丢失,保证了分割的准确性.此外预处理过程中,在梯度图像上基于Rayleigh分布采用阈值处理的方法,有效抑制了高斯噪声对梯度图像的影响,避免了过分割.实验结果证明,本文所提出的基于小波多分辨率分析的图像分水岭分割算法能够很好地兼顾算法的效率和分割的准确性.     

增强图像细节和去噪能力的改进形态学分水岭算法

为了抑制分水岭算法过分割和滤波后保持图像细节,论文提出一种改进的形态学分水岭分割算法.首先,对图像进行多尺度小波分解得到低频系数和高频系数;对低频系数进行基于Perona-Malik扩散模型各向异性扩散滤波;对高频系数,引入神经网络中的sigrnoid函数改进自适应遗传算法的变异和交叉概率生成,并用父代的最优个体替换子代中最差的个体来保护最优个体不被破坏,克服遗传算法的局部最优现象,利用改进的自适应遗传算法增强和去噪.然后,对梯度图像做锐化处理以突出边缘,再做形态学运算并进行H-minima标记.最后,执行分水岭分割,实现改进的算法.实验结果表明,改进算法能够有效地抑制噪声的干扰,减轻过分割,分割精度也有所提高.     

基于数学形态学的显微图像分割算法

分水岭算法是一种图像分割的强有力的工具,而其分割性能和待分割图像梯度的计算方法相关,LucVincent所提出的分水岭算法主要存在过分割的问题。文章提出用形态学混合开闭重建运算的尺度空间平滑算法对图像进行平滑滤波处理,以消除图像细节和噪声而保留重要的边缘轮廓,再对平滑后的图像进行梯度修正,并进行梯度极小值抑制,进一步消除造成过分割区域,然后对修正后的梯度边缘图像进行分水岭分割。实验结果表明,采用本文的算法对显微动物精细胞图像分割能较好的解决过分割问题。     

Development of morphological technique for segmentation of anatomical objects in abdominal MRI

Abstract

In this paper, a morphological technique for the segmentation of abdominal organs in magnetic resonance imaging (MRI) images is proposed based on watershed segmentation. New morphological based preprocessing and post-processing techniques are developed to reduce oversegmentation by means of removing and merging spurious segments. The preprocessing aims at removing trivial regions as well as background noise by combining thresholding, morphological smoothing, Gaussian smoothing and morphological edge detection. To obtain a more concise region representation, the watershed segmented image is post-processed, where a region adjacency list is built for the region merging process that produces the final segments. To control the merging process, a similarity function is defined, whence the most similar neighbouring regions are merged. The proposed technique produces effective and significant results in successfully segmenting various anatomical objects in axial MRI images of the abdomen, as it is shown in this paper.     

一种新颖的分割算法在果业机器人视觉中的应用

传统流域变换主要依赖图像梯度,该方法并不完善。在噪声图像中,边缘提取也不能够达 到很好的效果。提出的多尺度流域变换算法主要利用一组结构元素按照一定顺序,进行腐蚀膨胀的迭代来分割灰度图像。在多尺度测地重建滤波下,不同尺度的梯度流域分割线存在严格的因果关系。实验结果证实多尺度流域分割的性能远比传统方法优越。     

Object-of-interest image segmentation based on human attention and semantic region clustering

We propose a novel object-of-interest (OOI) segmentation algorithm for various images that is based on human attention and semantic region clustering. As object-based image segmentation is beyond current computer vision techniques, the proposed method segments an image into regions, which are then merged as a semantic object. At the same time, an attention window (AW) is created based on the saliency map and saliency points from an image. Within the AW, a support vector machine is used to select the salient regions, which are then clustered into the OOI using the proposed region merging. Unlike other algorithms, the proposed method allows multiple OOIs to be segmented according to the saliency map.     

An efficient and automatic glioblastoma brain tumor detection using shift‐invariant shearlet transform and neural networks

The detection and segmentation of tumor region in brain image is a critical task due to the similarity between abnormal and normal region. In this article, a computer‐aided automatic detection and segmentation of brain tumor is proposed. The proposed system consists of enhancement, transformation, feature extraction, and classification. The shift‐invariant shearlet transform (SIST) is used to enhance the brain image. Further, nonsubsampled contourlet transform (NSCT) is used as multiresolution transform which transforms the spatial domain enhanced image into multiresolution image. The texture features from grey level co‐occurrence matrix (GLCM), Gabor, and discrete wavelet transform (DWT) are extracted with the approximate subband of the NSCT transformed image. These extracted features are trained and classified into either normal or glioblastoma brain image using feed forward back propagation neural networks. Further, K‐means clustering algorithm is used to segment the tumor region in classified glioblastoma brain image. The proposed method achieves 89.7% of sensitivity, 99.9% of specificity, and 99.8% of accuracy.     

Brain image segmentation using a combination of expectation‐maximization algorithm and watershed transform

Watershed transformation is an effective segmentation algorithm that originates from the mathematical morphology field. This algorithm is widely used in medical image segmentation because it produces complete division even under poor contrast. However, over‐segmentation is its most significant limitation. Therefore, this article proposes a combination of watershed transformation and the expectation‐maximization (EM) algorithm to segment MR brain images efficiently. The EM algorithm is used to form clusters. Then, the brightest cluster is considered and converted into a binary image. A Sobel operator applied on the binary image generates the initial gradient image. Morphological reconstruction is applied to find the foreground and background markers. The final gradient image is obtained using the minima imposition technique on the initial gradient magnitude along with markers. In addition, watershed segmentation applied on the final gradient magnitude generates effective gray matter and cerebrospinal fluid segmentation. The results are compared with simple marker controlled watershed segmentation, watershed segmentation combined with Otsu multilevel thresholding, and local binary fitting energy model for validation. © 2016 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 26, 225–232, 2016     

Airplane extraction and identification by improved PCNN with wavelet transform and modified Zernike moments

At an airport, the information of the number and positions of airplanes is very important for the applications of air navigation. Especially, the information from airplane extraction and identification is significant in both civil and military remote sensing. In this paper, according to the characteristics of airplanes and airport in satellite remote sensing images, a new airplane image segmentation algorithm is proposed based on improved pulse-coupled neural network (PCNN) with wavelet transform, and airplane identification algorithm is carried out by using modified Zernike moments. Firstly, for an original image, a PCNN model is improved and then used to do image segmentation by combining the wavelet transform. Then, in order to reduce the number of irrespective targets in the image and increase the processing speed, the airplanes in the original image are roughly detected on the characteristics of the segmented object contour geometries. Finally, the Zernike moments are modified and then applied to identify the roughly detected airplanes accurately. By comparing to the five traditional image segmentation algorithms for the same airplane images, the testing results show that the improved PCNN image segmentation algorithm can segment and detect airplane regions at an airport accurately at a high recognising rate and with high recognising stability, and it is not affected by the image shadows and rotations.     

Segmentation of glioma tumors using convolutional neural networks

The abnormal development of cells in brain leads to the formation of tumors in brain. In this article, image fusion based brain tumor detection and segmentation methodology is proposed using convolutional neural networks (CNN). This proposed methodology consists of image fusion, feature extraction, classification, and segmentation. Discrete wavelet transform (DWT) is used for image fusion and enhanced brain image is obtained by fusing the coefficients of the DWT transform. Further, Grey Level Co‐occurrence Matrix features are extracted and fed to the CNN classifier for glioma image classifications. Then, morphological operations with closing and opening functions are used to segment the tumor region in classified glioma brain image.     

基于双正交小波变换的资源二号卫星与多光谱影像融合方法研究

针对基于Darbechies等正交小波函数的遥感影像融合时引起失真的现象,引入双正交小波函数,提出了基于双正交小波变换的融合方法,对资源二号卫星和TM的多光谱影像的融合进行研究,并把融合结果与主成分、IHS和Brovey等融合结果进行比较。通过对光谱特征曲线、相关系数、光谱扭曲程度等分析和空间细节的对比,说明双正交小波融合方法除了提高空间分辨率外,最好地保持了多光谱影像的光谱特征,有利于专题信息的提取。     

Optoelectronic parallel watershed implementation for segmentation of magnetic resonance brain images

An optoelectronic implementation for the morphological watershed transform is proposed. Fiber-optic programmable logic arrays are used in the implementation because of their high fan factors at high clock speeds. Image segmentation is one of the main applications of the watershed transform. Based on the optoelectronic implementation, an algorithm for the segmentation of axial magnetic resonance (MR) head images to extract information on brain matter is presented. Simulation results for the different steps of the segmentation process are presented.     

A Method of Obtaining Catchment Basins with Contour Lines for Foam Image Segmentation

Foam image segmentation, represented by watershed algorithm, is wildly used in the extraction of bubble morphology features. H-minima transformation was proved to be effective in locating the catchment basins in the traditional watershed segmentation method. To further improve the accuracy of watershed segmentation, method of top-bottom-cap filters and method of morphological reconstruction were implied to marking the catchment basins. In this paper, instead of H-minima transformation, a method of contour lines is specially proposed to obtain the catchment basins for foam image segmentation by using top-bottom-cap filters and less morphological reconstruction. Experimental results in foam segmentation show that the proposed method is equally accurate but more efficient than the method of H-minima plus morphological reconstruction, and equally efficient but more accurate than the method of H-minima plus top-bottom-cap filters.