一种基于Graph Cuts的多尺度乳腺肿块分割方法

提出一种基于Graph Cuts的多尺度乳腺肿块自动分割方法.首先,应用区域统计融合方法对图像进行粗分割,将得到的粗轮廓作为后续Graph Cuts分割的初始轮廓.在迭代优化阶段,引入多尺度分析方法,以高斯金字塔分解得到的多尺度图像序列代替固定尺度的原始图像序列估计高斯混合模型(GMM)参数,将粗糙尺度的易分割性与精细...     

Interactive image segmentation by maximal similarity based region merging

Efficient and effective image segmentation is an important task in computer vision and object recognition. Since fully automatic image segmentation is usually very hard for natural images, interactive schemes with a few simple user inputs are good solutions. This paper presents a new region merging based interactive image segmentation method. The users only need to roughly indicate the location and region of the object and background by using strokes, which are called markers. A novel maximal-similarity based region merging mechanism is proposed to guide the merging process with the help of markers. A region R is merged with its adjacent region Q if Q has the highest similarity with Q among all Q's adjacent regions. The proposed method automatically merges the regions that are initially segmented by mean shift segmentation, and then effectively extracts the object contour by labeling all the non-marker regions as either background or object. The region merging process is adaptive to the image content and it does not need to set the similarity threshold in advance. Extensive experiments are performed and the results show that the proposed scheme can reliably extract the object contour from the complex background.     

岩矿薄片偏光序列图像的矿物颗粒分割方法

岩矿薄片图像中矿物颗粒的提取是岩矿薄片粒度分析、成分识别的基础,为了进一步提升矿物颗粒提取的准确性,提出一种新的矿物颗粒分割提取方法.该方法以相同视域、不同角度下的正交偏光序列图像为基础,对序列图像进行融合后,利用熵率超像素算法提取矿物颗粒目标.为了减少矿物颗粒的过分割现象,采用快速区域合并算法合并具有相似性的区域,最后,依据矿物颗粒在正交偏光下的变化规律对分割区域筛选,再次进行颗粒目标融合实现颗粒自动分割.该方法提取岩矿薄片图像中的矿物颗粒,可以获得较好的效果.     

一种基于细菌觅食优化算法的舌体分割算法

针对医学舌体数字图像的准确分割,提出了一种基于细菌觅食优化算法(BFOA) 和 Snake 活动轮廓模型相组合的舌体分割算法。首先,以信息熵与 Kapur 算法相结合作为自适 应函数来改进 BFOA 算法,通过改进的 BFOA 算法计算舌体图像的最佳图像二值化阈值,并将 舌体图像二值化;然后,利用舌体图像的对称性提取舌体的关键边缘点,并基于 B-样条插值算 法由关键点集合插值得到闭合的 B-样条曲线,作为 Snake 模型的初始轮廓;最后,通过 Snake 模型计算求解,即可准确提取舌体的轮廓曲线。实验结果表明,改进算法能够高精度地分割出 舌体图像,并能消除基本 Snake 模型在初始轮廓曲线选取中存在的人机交互难题,实现了舌体 图像的自动分割。     

边界先验和自适应区域合并的显著性检测

为了对图像中的显著目标进行更精确的识别,提出了一种基于边界先验和自适应区域合并的显著性检测算法。采用超像素分割算法对图像进行过分割,把超像素看做图的一个顶点来进行构图;定位和消除错误边界,使背景基准集中存在很少的噪声,减小目标接触图像边界时造成的误检;采用单通道索引颜色直方图度量区域相似度并进行区域合并得到显著图。对比实验表明该算法相比其他算法取得了较高的查准率,说明了算法的有效性。     

面向舌体分割的两阶段卷积神经网络设计

目的 由于舌体与周围组织颜色相似,轮廓模糊,传统的分割方法难以精准分割舌体,为此提出一种基于两阶段卷积神经网络的舌体分割方法。方法 首先,在粗分割阶段,将卷积层和全连接层相结合构建网络Rsnet,采用区域建议策略得到舌体候选框,从候选框中进一步确定舌体,从而实现对舌体的定位,去除大量的干扰信息;然后,在精分割阶段,将卷积层与反卷积层相结合构建网络Fsnet,对粗分割舌象中的每一个像素点进行分类进而实现精分割;最后,采用形态学相关算法对精分割后的舌体图像进行后续处理,进一步消除噪点和边缘粗糙点。结果 本文构建了包含2 764张舌象的数据集,在该数据集上进行五折交叉实验。实验结果表明,本文算法能够取得较为理想的分割结果且具有较快的处理速度。选取了精确度、召回率及F值作为评价标准,与3种常用的传统分割方法相比,在综合指标F值上分别提高了0.58、0.34、0.12,效率上至少提高6倍,与同样基于深度学习思想的MNC（multi-task network cascades）算法相比,在F值上提高0.17,效率上提高1.9倍。结论 将基于深度学习的方法应用到舌体分割中,有利于实现舌象的准确、鲁棒、快速分割。在分割之前,先对舌体进行定位,有助于进一步减少分割中的错分与漏分。实验结果表明,本文算法有效提升了舌体分割的准确性,能够为后续的舌象自动识别和分析奠定坚实的基础。     

Brain MR images segmentation using statistical ratio: mapping between watershed and competitive Hopfield clustering network algorithms

Conventional watershed segmentation methods invariably produce over-segmented images due to the presence of noise or local irregularities in the source images. In this paper, a robust medical image segmentation technique is proposed, which combines watershed segmentation and the competitive Hopfield clustering network (CHCN) algorithm to minimize undesirable over-segmentation. In the proposed method, a region merging method is presented, which is based on employing the region adjacency graph (RAG) to improve the quality of watershed segmentation. The relation of inter-region similarities is then investigated using image mapping in the watershed and CHCN images to determine more appropriate region merging. The performance of the proposed technique is presented through quantitative and qualitative validation experiments on benchmark images. Significant and promising segmentation results were achieved on brain phantom simulated data.     

基于改进的GAC模型的医学图像序列分割算法

为了快速有效地提取出图像序列的边缘,提出了一种基于改进的测地线活动轮廓（GAC）模型的图像分割算法。在该方法中,只需在第一幅图像中感兴趣区域的内部给出大致的初始轮廓。在后续图像中,首先采用运动估计与区域统计特征结合的方法得到轮廓模型的初始轮廓,然后利用结合先验信息的测地线活动轮廓模型进行分割。此外,为了有效地减少算法运算时间,采用手工办法在第一张图像上选定模型演化的区域,该区域在后续图像上将依据分割结果自动调整大小和位置。实验结果表明：方法能够快速有效地提取目标物体的边缘。     

基于多尺度形态学融合的分水岭图像分割方法

提出了一种基于多尺度形态学融合的分水岭图像分割方法，采用多尺度结构元素对输入图像并行滤波，并对结果图像进行基于小波变换的图像信息融合。针对小波分解的不同频率域，选择不同的融合规则，既抑制噪声又保持目标轮廓信息。最后，采用最大熵法自适应确定算法的初始阈值，并给出一种有效的区域合并方法来优化分割结果。实验证明，采用此分割方法可以获得较好的分割结果。     

基于多尺度形态滤波的分水岭图像分割方法

采用多尺度形态滤波器对输入噪声图像及滤波后图像的梯度图像进行平滑，实现了消除噪声、简化图像、保持物体重要轮廓信息的作用．最后，给出一种改进的快速区域合并算法优化分割结果．实验证明，采用文中分割方法可以获得很好的分割结果。     

一种有效的舌体自动化分割算法*

舌体轮廓正确分割是实现中医舌诊信息化的重要前提.目前主流方法是用阈值方法或先验知识得到舌体的初始轮廓线,然后再用Snake模型使得曲线收敛到舌体边缘.但它们都存在两个问题:对比度较小的舌像,如舌面和脸部有相似的对比度的舌象,基本上没法处理;得到的初始化轮廓线无法克服嘴唇的影响,因此用Snake方法曲线常收敛于嘴唇边缘.通过对舌象的低层信息的研究,发现使用简单而有效的直方图均衡化能扩大图像的颜色分辨力,使得现有的阈值处理方法能在原有基础上很好地处理那些原来无法处理的舌体图像;然后根据舌体形状、位置等先验信息,用几何方法修正受嘴角和嘴唇影响得到的边缘.实验结果表明,该方法取得了很好的分割效果.     

一种基于区域分级合并的彩色图像分割方法

在区域合并过程中,手工设置颜色相似性和边界距离的权重极大地影响了分割的精度和自动化.针对这一问题,提出了一种新的基于区域分级合并的彩色图像分割算法.该方法能够根据邻接区域的边界特点设置权重因子,从而自适应地融合区域的颜色相似性和边界距离.使用均值漂移算法对图像进行初始分割,将原图像分割为具有较好边界的同质区域;通过计算区域相似度对区域进行分级合并.多幅彩色图像的分割实验结果证明,所提算法优于传统的基于区域合并的方法.     

Multiresolution-based watersheds for efficient image segmentation

This paper presents an efficient method for image segmentation based on a multiresolution application of a wavelet transform and watershed segmentation algorithm. The procedure toward complete segmentation consists of four steps: pyramid representation, image segmentation, region merging and region projection. First, pyramid representation creates multiresolution images using a wavelet transform. Second, image segmentation segments the lowest-resolution image of the pyramid using a watershed segmentation algorithm. Third, region merging merges the segmented regions using the third-order moment values of the wavelet coefficients. Finally, the segmented low-resolution image with label is projected into a full-resolution image (original image) by inverse wavelet transform. Experimental results of the presented method can be applied to the segmentation of noise or degraded images as well as reduce over-segmentation. In addition, we applied our method to human face detection with accurate and closed boundaries.     

形状约束下活动轮廓模型冠脉血管图像多尺度分割

目的 由于计算机断层血管造影(CTA)图像的复杂性,临床诊断冠脉疾病往往需要经验丰富的医师对冠状动脉进行手动分割,快速、准确自动分割出冠状动脉对提高冠脉疾病诊断效率具有重要意义。针对双源CT图像特点以及传统单一基于区域或边界的活动轮廓模型的不足,研究了心脏冠脉3维分割算法,提出一种基于血管形状约束的活动轮廓模型分割方法。方法 首先,利用改进的FCM(fuzzy C-means)对心脏CT图像感兴趣区域初分割,其结果用于初始化C-V模型水平集演化曲线及控制参数,提取感兴趣区域轮廓。接着,由3维心脏图像数据获取多尺度梯度矢量信息构造边界型能量泛函,然后利用基于Hessian矩阵的多尺度血管函数对心脏感兴趣区域3维体数据增强滤波,获取血管先验形状信息用于约束能量泛函。最后融合边界、区域能量泛函并利用变分原理及水平集方法得到适合冠脉血管分割的水平集演化方程。结果 由于血管图像的灰度不均匀,血管末端区域更为细小,所以上述算法的实施是面向被划分多个子区域的血管,在缩小的范围内进行轮廓的演化。相比于传统的血管分割方法,该方法充分融合血管图像的先验信息及梯度场信息,能够从灰度及造影剂分布不均匀的冠脉血管图像中准确分割出冠状动脉,对于细小的血管结构亦能获得较好的分割效果。实验结果表明,该方法只需在给定初始轮廓前提下,有效提取3维冠脉血管。结论 对多组心脏CT图像进行分割,本文基于血管先验形状约束的活动轮廓模型可以准确分割出冠脉结构完整轮廓,并且人工交互简单。该方法在双源CT冠脉图像自动分割方面具有较好的正确率与优越性。     

融合mean shift和区域显著性的彩色图像分割算法

提出了一种无监督的提取图像中显著区域的彩色图像分割算法。首先,运用mean shift算法对图像进行分割,得到初始的分割结果;然后,根据给出的区域显著性的定义和区域合并策略,对初始分割结果进行合并,得到最终的分割结果。仿真结果表明,对于大多数测试图像,该算法都能获得很好的分割结果,并且具有较高的运行效率。     

Extraction of diffuse objects from images by means of watershed and region merging: example of solar images.

This paper presents a new method for extraction of diffuse objects from images, which was developed for segmentation of solar images obtained from extreme-UV imaging telescope (EIT) experiments of the satellite SOHO mission. As a particular type of objects to be extracted coronal holes in EIT images have been chosen. The method described is based on the use of a watershed algorithm. The result of the watershed segmentation is a partition of the whole domain of the image into a large number of small regions. These regions are then combined in a region merging process. The proposed region merging algorithm iteratively adds the darkest regions and maximizes the average contrast between a current mask and a set of its neighboring regions. We show that the maximization of the average contrast gives segmentation results that are visually acceptable. Furthermore, this approach allows us to conduct the segmentation of EIT images independently of any explicit fine-tuning parameters. The proposed method was extensively tested on EIT images obtained at various times and various levels of solar activity, and we will show that it can be used independently of the local brightness level and the extent of coronal holes.     

SAR imagery segmentation by statistical region growing and hierarchical merging

This paper presents an algorithm to segment synthetic aperture radar (SAR) images, corrupted by speckle noise. Most standard segmentation techniques may require speckle filtering previously. Our approach performs radar image segmentation using the original noisy pixels as input data, i.e. without any preprocessing step. The algorithm includes a statistical region growing procedure combined with hierarchical region merging. The region growing step oversegments the input radar image, thus enabling region aggregation by employing a combination of the Kolmogorov–Smirnov (KS) test with a hierarchical stepwise optimization (HSWO) algorithm for performance improvement. We have tested and assessed the proposed technique on artificially speckled image and real SAR data.     

结合显著性的主动轮廓图像分割

传统的主动轮廓方法无法突出分割区域的显著性,同时在由显著性检测算法所得到的显著图中目标具有较高的信噪比,因此提出结合显著性的主动轮廓图像分割。通过线性光谱聚类分割得到超像素,以超像素为处理单位利用基于图论的流形排序算法获得较好的显著图;将高斯混合模型引入到主动轮廓的曲线演化过程中,计算曲线内外的平均灰度值,从而通过高斯混合模型和显著性信息得到了新的主动轮廓能量方程,并运用水平集方法指导分割,获得最终的分割结果。实验结果表明,提出的图像分割方法可以对图像进行快速和有效的分割。     

结合位图切割和区域合并的彩色图像分割

就经典分水岭图像分割算法中存在的过分割问题，提出一种结合位图切割和区域合并的彩色图像分割算法。对原始彩色图像通过空域梯度算子求其梯度图像，并利用位图切割重建梯度图像；对新梯度图像进行分水岭预分割；对预分割图像基于异质性最小原则进行区域合并，并获得最终分割结果。相比于现有的同类方法，该算法引入位图切割，抑制噪声对分割结果的影响，在边缘模糊处分割准确，得到符合人类视觉的较小分割区域数目，同时在运行效率上提高。     

Color-Based Image Salient Region Segmentation Using Novel Region Merging Strategy

In this paper, we propose a novel unsupervised algorithm for the segmentation of salient regions in color images. There are three phases in this algorithm. In the first phase, we use nonparametric density estimation to extract candidates of dominant colors in an image, which are then used for the quantization of the image. The label map of the quantized image forms initial regions of segmentation. In the second phase, we define salient region with two properties; i.e., conspicuous; compact and complete. According to the definition, two new parameters are proposed. One is called “Importance index”, which is used to measure the importance of a region, and the other is called “Merging likelihood”, which is utilized to measure the suitability of region merging. Initial regions are merged based on the two new parameters. In the third phase, a similarity check is performed to further merge the surviving regions. Experimental results show that the proposed method achieves excellent segmentation performance for most of our test images. In addition, the computation is very efficient.