基于图像分解的稀疏正则化多区域图像分割方法

针对图像具有不同特征的成分,提出一种基于图像分解的多区域图像分割模型和算法.首先将图像分解项引入到图像分割模型中,递减了纹理和噪声对分割的影响;其次使用稀疏正则化方法保持分割区域的边缘几何结构;最后基于增广Lagrange乘子法,给出一种由扩散流引导的小波迭代阈值图像分割算法.一系列实验结果表明,提出的方法抗干扰能力强,对噪声具有更好的鲁棒性.提出的方法不仅能够分割结构图像,并且能够分割较复杂的纹理图像.     

基于边缘吸收力场正则化的短程线主动轮廓模型

短程线主动轮廓模型是近几年提出的一种有效的多目标轮廓提取算法。本文在详细分析其动力学过程的基础上，针对该模型中存在的局限性和不足，提出对边缘吸引力场进行正则化的方法，并采用多尺度模型，有效的改善了该模型不能对存在断裂轮廓的目标进行正确提取和凹边缘搜索能力弱的缺点，增强了抗噪声和虚假边缘干扰的能力，使该算法具有更好的鲁棒性和实用性。     

基于改进多尺度形态学的带钢缺陷图像边缘检测

针对目前板带钢表面缺陷在线检测过程中无法准确地检测出所有缺陷边缘问题,根据带钢缺陷的特点,分析了结构元素的选取,提出了一种将多尺度形态学和多结构元素有机结合的边缘检测方法。该方法首先进行多尺度形态学滤波降噪,分别求取0°结构元素、45°结构元素、90°结构元素和135°结构元素带钢缺陷图像边缘;其次通过一定的运算组合,提取多结构边缘;最后对得到的带钢缺陷图像的边缘作二值化处理,再细化边缘得到缺陷图像边缘的最终结果。实验结果表明,该方法较好地解决了边缘检测精度与抗噪性能之间的协调问题,实现了在多个尺度上提取板带钢表面缺陷的边缘。同时能够较好地保留图像中缺陷的边缘细节信息,为带钢表面缺陷在线检测系统中自动分割、缺陷识别等后续处理奠定了基础。     

小波变换在医学图像边缘提取中的应用

边缘是图像的重要特征。医学图像往往较模糊．其边缘特征难以用传统方法检测。小波变换具有良好的局部化特性、多分辨特性．及检测信号局部突变的能力。对图像进行二维小波变换，其梯度模值反映了图像的边缘。介绍一种基于小波变换的图像边缘提取方法。实验证明．与传统边缘检测方法相比，该方法去噪效果好，能提取图像中较弱的边缘，且能使边缘细化。这些特点使得他特别适合于医学图像边缘的提取。     

稀疏正则化的多目标图像分割变分模型

基于隶属度函数的稀疏正则化,本文提出一个新的多目标图像分割变分模型和相应求解算法.该模型和算法有以下主要优点:首先,稀疏正则可以更好地保持分割区域的边界,克服了全变差正则导致分割边界模糊的缺点.其次,利用多尺度几何分析工具可以更好地保持图像的几何形状.最后,提出算法简单、易实现、运行速度快.一系列实验结果验证了提出方法的可行性与有效性.     

一种具有边缘增强特点的医学图像分割网络

针对传统医学图像分割网络存在边缘分割不清晰、缺失值大等问题,该文提出一种具有边缘增强特点的医学图像分割网络(AS-UNet)。利用掩膜边缘提取算法得到掩膜边缘图,在UNet扩张路径的最后3层引入结合多尺度特征图的边缘注意模块(BAB),并提出组合损失函数来提高分割精度;测试时通过舍弃BAB来减少参数。在3种不同类型的医学图像分割数据集Glas, DRIVE, ISIC2018上进行实验,与其他分割方法相比,AS-UNet分割性能较优。     

一种基于数学形态学的车牌图像分割方法

提出一种基于数学形态学的车牌图像分割提取方法.用修正后的形态学边缘检测算子对图像进行边缘检测,采用二尺度结构元素检测平均方法提高边缘检测的准确性,再用不同的结构元素对边缘图像进行形态滤波,以消除干扰.实验证明,该方法能快速准确定位分割出车牌图像,且计算量较小.     

基于增强型EM模型重叠区域图像分割算法

针对图像大规模重叠区域的有效分割一直是一个难题,传统的Log算子、Sobel算子、Canny算子以及梯度算子等算法解决大规模像素重叠问题时,模型会陷入不收敛的境地,导致分割效果较差,为了解决这样问题,提出一种增强型EM模型解决重叠区域图像分割的问题,利用curvelet变换在curvelet域内提取图像的边缘特征,并定位特征curvelet系数.通过增强特征curvelet系数,增强边沿特征对比性,分割多尺度多结构元素形态学检测的边缘图像,消除重叠带来的干扰.仿真实验结果表明:分割的边缘更为完整准确,取得了令人满意的效果.     

LCD表面液晶污染的非接触检测新算法

针对液晶器件的特点,对其表面残留液晶的非接触式检测技术进行了研究,提出了基于迭代阈值分割和数学形态学的边缘检测方法,可以从目标区域中判断出液晶残留形貌与边缘.首先将原图按坐标分成若干子图像,再对子图像进行迭代阈值分割,然后采用不同尺度的结构元素来检测图像边缘,再进行加权合成来获得边缘图像,并从理论上分析了噪声对边缘提取的影响情况.实验表明,该方法很好地抑制了噪声和保持图像边缘细节,并且能够提取出精确且封闭的残留液晶的边缘轮廓,为下一步缺陷特征量的提取与选择奠定了基础.     

一种改进的边缘停止函数图像分割方法

在基于边缘的几何活动轮廓模型中,边缘停止函数的选择会影响图像的分割效果,根据边缘停止函数的性质,提出一个新的边缘停止函数,代替GAC模型和DELSE模型中的边缘停止函数。实验结果表明,在图像背景灰度均匀和灰度不均的图像分割中,文中提出新的边缘停止函数在迭代次数和分割效果方面,都优于原来的边缘停止函数,能够实现准确分割。     

Active contour evolved by joint probability classification on Riemannian manifold

In this paper, we present an active contour model for image segmentation based on a nonparametric distribution metric without any intensity a priori of the image. A novel nonparametric distance metric, which is called joint probability classification, is established to drive the active contour avoiding the instability induced by multimodal intensity distribution. Considering an image as a Riemannian manifold with spatial and intensity information, the contour evolution is performed on the image manifold by embedding geometric image feature into the active contour model. The experimental results on medical and texture images demonstrate the advantages of the proposed method.     

Image segmentation based on multi-region multi-scale local binary fitting and Kullback–Leibler divergence

The inhomogeneity of intensity and the noise of image are the two major obstacles to accurate image segmentation by region-based level set models. To provide a more general solution to these challenges and address the difficulty of image segmentation methods to handle an arbitrary number of regions, we propose a region-based multi-phase level set method, which is based on the multi-scale local binary fitting (MLBF) and the Kullback–Leibler (KL) divergence, called KL–MMLBF. We first apply the multi-scale theory and multi-phase level set framework to the local binary fitting model to build the multi-region multi-scale local binary fitting (MMLBF). Then the energy term measured by KL divergence between regions to be segmented is incorporated into the energy function of MMLBF. KL–MMLBF utilizes the between-cluster distance and the adaptive kernel function selection strategy to formulate the energy function. Being more robust to the initial location of the contour than the classical segmentation models, KL–MMLBF can deal with blurry boundaries and noise problems. The results of experiments on synthetic and medical images have shown that KL–MMLBF can improve the effectiveness of segmentation while ensuring the accuracy by accelerating this minimization of this energy function and the model has achieved better segmentation results in terms of both accuracy and efficiency to analyze the multi-region image.     

Simultaneous vector-valued image segmentation and intensity nonuniformity correction using variational level set combined with Markov random field modeling

Object quantification requires an image segmentation to make measurements about size, material composition and morphology of the object. In vector-valued or multispectral images, each image channel has its signal characteristics and provides special information that may improve the results of image segmentation method. This paper presents a region-based active contour model for vector-valued image segmentation with a variational level set formulation. In this model, the local image intensities are characterized using Gaussian distributions with different means and variances. Furthermore, by utilizing Markov random field, the spatial correlation between neighboring pixels and voxels is modeled. With incorporation of intensity nonuniformity model, our method is able to deal with brain tissue segmentation from multispectral magnetic resonance (MR) images. Our experiments on synthetic images and multispectral cerebral MR images with different noise and bias level show the advantages of the proposed method.     

A fuzzy energy-based active contour model with adaptive contrast constraint for local segmentation

Image segmentation is to divide an image into different parts or extract some interested objects. Active contour model and fuzzy clustering are two widely used segmentation methods, which have been integrated into an effective model in recent years. Local segmentation is often needful in medical image processing. In view of local segmentation on inhomogeneous images, a new average fuzzy energy-based active contour model is proposed in this paper, in which the total fuzzy energy integrates the approximate weighted average and arithmetic average variances of the image. And an adaptive contrast constraint condition is introduced to prevent the curve from falling into local minimum, which further improves the robustness of the segmentation model to initial contour. Experimental results on synthetic and medical images demonstrate that the proposed model has considerable improvements in terms of segmentation accuracy and robustness compared to several existing local segmentation models.     

Boundary detection in medical images using edge following algorithm based on intensity gradient and texture gradient features

Finding the correct boundary in noisy images is still a difficult task. This paper introduces a new edge following technique for boundary detection in noisy images. Utilization of the proposed technique is exhibited via its application to various types of medical images. Our proposed technique can detect the boundaries of objects in noisy images using the information from the intensity gradient via the vector image model and the texture gradient via the edge map. The performance and robustness of the technique have been tested to segment objects in synthetic noisy images and medical images including prostates in ultrasound images, left ventricles in cardiac magnetic resonance (MR) images, aortas in cardiovascular MR images, and knee joints in computerized tomography images. We compare the proposed segmentation technique with the active contour models (ACM), geodesic active contour models, active contours without edges, gradient vector flow snake models, and ACMs based on vector field convolution, by using the skilled doctors' opinions as the ground truths. The results show that our technique performs very well and yields better performance than the classical contour models. The proposed method is robust and applicable on various kinds of noisy images without prior knowledge of noise properties.     

RAGS: region-aided geometric snake

An enhanced, region-aided, geometric active contour that is more tolerant toward weak edges and noise in images is introduced. The proposed method integrates gradient flow forces with region constraints, composed of image region vector flow forces obtained through the diffusion of the region segmentation map. We refer to this as the Region-aided Geometric Snake or RAGS. The diffused region forces can be generated from any reliable region segmentation technique, greylevel or color. This extra region force gives the snake a global complementary view of the boundary information within the image which, along with the local gradient flow, helps detect fuzzy boundaries and overcome noisy regions. The partial differential equation (PDE) resulting from this integration of image gradient flow and diffused region flow is implemented using a level set approach. We present various examples and also evaluate and compare the performance of RAGS on weak boundaries and noisy images.     

一种全新的两步自动化医学图像分割方案

针对现有图像分割方法存在需要手动分割,以及精确度较低的问题。采用一种全新的两步图像分割方案。该方案。以基于人工神经网络的模式识别技术,即人工神经网络的大规模培训的方法,通过对肺区不同子区域内结构进行分割处理,利用训练好的大规模人工神经网络对标准胸片中的肋骨、锁骨等骨质结构进行抑制,结合以基于区域的活动轮廓模型,即Snake模型,正确分割亮度不均匀的图像。文中选择与医护人员人工分割的图像进行对比,通过放射科医生采用等级法打分,原图的平均分为20分,而通过文中改进的分割方法平均分高达34分。     

一种具有预测能力的三维图像分割方法

在充分分析三维图像特点的基础上,提出了一种新的三维图像分割方案。该方案在改进的主动轮廓模型的基础上融入了预测算法。三维图像中物体的轮廓沿着空间轴或时间轴的位移和形变是连续的,该算法根据这一特性,利用前几幅图的分割结果来预测当前图像中物体轮廓的位置和形状,弥补了主动轮廓模型搜索范围小的缺点。对医学解剖图像的试验结果表明,这种方法能显著提高分割的准确性和速度。     

改进的分水岭算法在医学图像分割中的应用

针对传统分水岭算法对噪声敏感和易于产生过分割的问题,提出了一种将同态滤波增强与控制标记符分水岭相结合的分割策略.该方法先进行同态滤波增强预处理,再采用改进控制标记符的分水岭分割算法进行分割.仿真实验表明,提出的算法很好地抑制了过分割,实现了有意义的医学图像区域分割,同时还具有较强的区域轮廓定位能力,不需要再进行后续的合并处理,算法简单,并且能够适应医学图像分类与信息提取的需求.