基于改进Lazy Snapping算法的红外图像分割方法研究

针对红外图像含大量噪声以及对比度低等特点,提出一种结合快速模糊C均值聚类的改进Lazy Snapping分割方法.对红外图像使用快速模糊C均值聚类算法进行预分割,通过形态学骨架提取的方法在图像中标记出目标和背景种子点,将Lazy Snapping算法由全局分割转化为聚类区域分割,并构造能量函数,通过最小割算法求解能量函...     

基于FCM的分块自适应图像分割方法研究

基于模糊C均值聚类（FCM）的图像分割是应用较为广泛的图像分割方法之一,但是传统的模糊C均值聚类算法都是基于欧氏距离的,对于图像中的噪声是十分敏感的。针对这一局限性,提出一种基于FCM的分块自适应图像分割方法。该方法不仅考虑了噪声不均匀分布对分割结果的影响,而且充分考虑了图像像素的灰度信息和空间信息。通过对含有噪声的自然图像和合成图像的分割试验,我们可以得到,与传统的FCM图像分割算法相比,本文方法能显著提高含有噪声图像的分割质量。     

A MAP approach for joint motion estimation, segmentation, and super resolution.

Super resolution image reconstruction allows the recovery of a high-resolution (HR) image from several low-resolution images that are noisy, blurred, and down sampled. In this paper, we present a joint formulation for a complex super-resolution problem in which the scenes contain multiple independently moving objects. This formulation is built upon the maximum a posteriori (MAP) framework, which judiciously combines motion estimation, segmentation, and super resolution together. A cyclic coordinate descent optimization procedure is used to solve the MAP formulation, in which the motion fields, segmentation fields, and HR images are found in an alternate manner given the two others, respectively. Specifically, the gradient-based methods are employed to solve the HR image and motion fields, and an iterated conditional mode optimization method to obtain the segmentation fields. The proposed algorithm has been tested using a synthetic image sequence, the "Mobile and Calendar" sequence, and the original "Motorcycle and Car" sequence. The experiment results and error analyses verify the efficacy of this algorithm.     

基于多参数配准模型的脑核磁影像分割算法

配准技术在基于多图谱的分割方法中能有效地将医学图谱的先验知识融入分割过程,再结合以高效的标记融合算法,最终实现精确地自动分割.针对图谱配准的较大误差及其对标记融合的重要影响,本文建立了一种新的概率图模型框架并以此提出了基于多参数配准模型的分割算法,将此方法与高效的标记融合算法相结合,可以提高目标图像中特定组织区域的分割精度,更使其在少量图谱分割的情形下具有重要应用.首先,使用多种配准参数对所有目标图像进行配准;然后,分别采用不同的算法对配准图像进行灰度融合和标记融合,实现训练图像的重构过程;最后,利用高效的标记融合算法对重构后的图像进行融合得到最终精确的分割结果.实验结果表明该方法均优于本文其他分割算法,能够有效提升脑部组织分割精度.     

多分类CNN的胶质母细胞瘤多模态MR图像分割

为提高胶质母细胞瘤（GBM）多模态磁共振（MR）图像中各肿瘤子区域分割的准确性,提出一种多分类卷积神经网络（CNN）的GBM多模态MR图像自动分割算法.首先在98%缩尾处理和配准GBM多模态MR图像后,利用N4ITK法校正偏移场;其次构建一个主要由4个卷积层、2个池化层和2个全连接层组成的多分类CNN模型,训练后预分割GBM多模态MR图像,将体素分为5类不同的标签;最后移除所有小于200体素的假阳性区域,中值滤波后获得最终分割结果.以Dice相似性系数DSC、阳性预测值PPV和平均Hausdorff距离AHD为评价指标,利用所提出的算法对F-C-GBM数据集中整个肿瘤组织进行分割,获得的DSC、PPV、AHD分别为0.889±0.087、0.859±0.127和1.923.结果表明,该算法能有效提高GBM多模态MR图像分割的性能,可望有临床应用前景.     

基于互补空间信息的多目标进化聚类图像分割

现有的多目标进化聚类算法应用于图像分割时,没有考虑图像的任何空间信息,使得该类算法在含噪图像上的分割性能不理想。该文鉴于图像的局部空间信息和非局部空间信息的互补性,试图将这两种空间信息同时引入到聚类有效性函数中,构造了融合互补空间信息的目标函数,进而提出了应用于图像分割的基于互补空间信息的多目标进化聚类算法。该算法采用染色体可变长编码策略在进化过程中自动确定图像分割数目,减少了人为干预。自然图像的分割实验表明,该算法不但能在含噪图像上取得较为满意的分割性能,而且适用于多种类型的含噪图像。     

基于分形理论的改进型二维最大熵红外图像分割算法

提出了一种基于分形理论的改进型二维最大熵红外图像阈值分割算法。该算法利用图像分形维数挖掘像素的空间分布信息,然后将原图像灰度及其分形维数映射图像灰度相结合组成二维随机向量,并构造出联合离散概率分布。在此基础上,以二维最大熵原则来确定一个最佳二维分割阈值,进而取得分割结果。实验结果表明,该算法在分割效果上优于传统的二维最大熵分割算法。     

Segmentation methodology for automated classification and differentiation of soft tissues in multiband images of high-resolution ultrasonic transmission tomography

This paper presents a novel segmentation methodology for automated classification and differentiation of soft tissues using multiband data obtained with the newly developed system of high-resolution ultrasonic transmission tomography (HUTT) for imaging biological organs. This methodology extends and combines two existing approaches: the L-level set active contour (AC) segmentation approach and the agglomerative hierarchical kappa-means approach for unsupervised clustering (UC). To prevent the trapping of the current iterative minimization AC algorithm in a local minimum, we introduce a multiresolution approach that applies the level set functions at successively increasing resolutions of the image data. The resulting AC clusters are subsequently rearranged by the UC algorithm that seeks the optimal set of clusters yielding the minimum within-cluster distances in the feature space. The presented results from Monte Carlo simulations and experimental animal-tissue data demonstrate that the proposed methodology outperforms other existing methods without depending on heuristic parameters and provides a reliable means for soft tissue differentiation in HUTT images.     

一种三维分割算法在超声序列图像分割中的应用

图像分割在医学超声图像的定量分析和定性分析中具有重要的作用,它直接影响到后续的分析和处理工作。对于具有复杂特性的医学超声图像,传统的图像分割算法难以获得满意的效果。文中提出了基于一维Otsu方法的三维分割算法,具有快速性和准确性的优点。该方法应用于B超图像,实现了序列图像的自动分割,与普通的一维Otsu法相比,具有更好的分割效果。     

An evaluation of contrast enhancement techniques for mammographic breast masses.

The main aim of this paper is to propose a novel set of metrics that measure the quality of the image enhancement of mammographic images in a computer-aided detection framework aimed at automatically finding masses using machine learning techniques. Our methodology includes a novel mechanism for the combination of the metrics proposed into a single quantitative measure. We have evaluated our methodology on 200 images from the publicly available digital database for screening mammograms. We show that the quantitative measures help us select the best suited image enhancement on a per mammogram basis, which improves the quality of subsequent image segmentation much better than using the same enhancement method for all mammograms.     

Automatic watershed segmentation of randomly textured color images

A new method is proposed for processing randomly textured color images. The method is based on a bottom-up segmentation algorithm that takes into consideration both color and texture properties of the image. An LUV gradient is introduced, which provides both a color similarity measure and a basis for applying the watershed transform. The patches of watershed mosaic are merged according to their color contrast until a termination criterion is met. This criterion is based on the topology of the typical processed image. The resulting algorithm does not require any additional information, be it various thresholds, marker extraction rules, and suchlike, thus being suitable for automatic processing of color images. The algorithm is demonstrated within the framework of the problem of automatic granite inspection. The segmentation procedure has been found to be very robust, producing good results not only on granite images, but on the wide range of other noisy color images as well, subject to the termination criterion.     

Gray-level image denoising with an improved weighted sparse coding

The nonlocal self-similarity of images means that groups of similar patches have low-dimensional property. The property has been previously used for image denoising, with particularly notable success via sparse coding. However, only a few studies have focused on the varying statistics of noise in different similar patches during the iterative denoising process. This has motivated us to introduce an improved weighted sparse coding for gray-level image denoising in this paper. On the basis of traditional sparse coding, we introduce a weight matrix to account for the noise variation characteristics of different similar patches, while introduce another weight matrix to make full use of the sparsity priors of natural images. The Maximum A-Posterior estimation (MAP) is used to obtain the closed-form solution of the proposed method. Experimental results demonstrate the competitiveness of the proposed method compared with that of state-of-the-art methods in both the objective and perceptual quality.     

Single face hallucination via local neighbor patches

Based on learning neighborhood patches a new single face hallucination method is proposed in this paper. In the proposed method, each input low-resolution (LR) position-patch and all patches in a local window centered at the same position of training images are used to hallucinate a high-resolution (HR) face patch, meanwhile two local similarity measurements between each input LR patch and all local LR and HR neighborhood patches of training images are computed to constrain the hallucination. Additionally, a residue image is estimated for the further improvement of the reconstructed result. Experimental results show that the proposed method can obtain superior or competitive results.     

多源影像融合与分割的协同方法

针对现有影像融合与分割方法之间缺乏协同的问题,借鉴数据同化系统能够协同其模型算子和观测算子,并且能够自适应地优化其本身的思想,提出一个多源影像融合与分割的协同框架.在该框架下,以基于对比度金字塔变换和基于非下采样的Contourlet变换的两种融合方法分别模拟模型算子和观测算子,以评价分割效果的概率随机系数为目标函数,以带交叉算子的粒子群算法作为数据同化系统的优化算法.该框架可根据融合结果影像来调整分割算法的参数,利用分割结果来指导融合结果的优化,从而使得影像融合与分割协同工作.二组实验验证了该框架的有效性.     

基于级联网络的复杂大场景SAR图像舰船目标快速检测

合成孔径雷达(synthetic aperture radar,SAR)图像舰船目标检测紧贴军事和民用需求,为海洋监视提供重要信息支撑.针对复杂大场景SAR图像,本文设计了一种基于级联网络的舰船目标检测框架,该网络框架主要由D-BiSeNet海陆分割、分块区域筛选和CP-FCOS目标检测三部分组成.通过改进双边网络(D...     

Hole‐Filling Methods Using Depth and Color Information for Generating Multiview Images

This paper presents new hole‐filling methods for generating multiview images by using depth image based rendering (DIBR). Holes appear in a depth image captured from 3D sensors and in the multiview images rendered by DIBR. The holes are often found around the background regions of the images because the background is prone to occlusions by the foreground objects. Background‐oriented priority and gradient‐oriented priority are also introduced to find the order of hole‐filling after the DIBR process. In addition, to obtain a sample to fill the hole region, we propose the fusing of depth and color information to obtain a weighted sum of two patches for the depth (or rendered depth) images and a new distance measure to find the best‐matched patch for the rendered color images. The conventional method produces jagged edges and a blurry phenomenon in the final results, whereas the proposed method can minimize them, which is quite important for high fidelity in stereo imaging. The experimental results show that, by reducing these errors, the proposed methods can significantly improve the hole‐filling quality in the multiview images generated.     

A unified framework for MAP estimation in remote sensing image segmentation

A complete framework is proposed for applying the maximum a posteriori (MAP) estimation principle in remote sensing image segmentation. The MAP principle provides an estimate for the segmented image by maximizing the posterior probabilities of the classes defined in the image. The posterior probability can be represented as the product of the class conditional probability (CCP) and the class prior probability (CPP). In this paper, novel supervised algorithms for the CCP and the CPP estimations are proposed which are appropriate for remote sensing images where the estimation process might to be done in high-dimensional spaces. For the CCP, a supervised algorithm which uses the support vector machines (SVM) density estimation approach is proposed. This algorithm uses a novel learning procedure, derived from the main field theory, which avoids the (hard) quadratic optimization problem arising from the traditional formulation of the SVM density estimation. For the CPP estimation, Markov random field (MRF) is a common choice which incorporates contextual and geometrical information in the estimation process. Instead of using predefined values for the parameters of the MRF, an analytical algorithm is proposed which automatically identifies the values of the MRF parameters. The proposed framework is built in an iterative setup which refines the estimated image to get the optimum solution. Experiments using both synthetic and real remote sensing data (multispectral and hyperspectral) show the powerful performance of the proposed framework. The results show that the proposed density estimation algorithm outperforms other algorithms for remote sensing data over a wide range of spectral dimensions. The MRF modeling raises the segmentation accuracy by up to 10% in remote sensing images.     

Image Segmentation Based on GrabCut Framework Integrating Multiscale Nonlinear Structure Tensor

In this paper, we propose an interactive color natural image segmentation method. The method integrates color feature with multiscale nonlinear structure tensor texture (MSNST) feature and then uses GrabCut method to obtain the segmentations. The MSNST feature is used to describe the texture feature of an image and integrated into GrabCut framework to overcome the problem of the scale difference of textured images. In addition, we extend the Gaussian Mixture Model (GMM) to MSNST feature and GMM based on MSNST is constructed to describe the energy function so that the texture feature can be suitably integrated into GrabCut framework and fused with the color feature to achieve the more superior image segmentation performance than the original GrabCut method. For easier implementation and more efficient computation, the symmetric KL divergence is chosen to produce the estimates of the tensor statistics instead of the Riemannian structure of the space of tensor. The Conjugate norm was employed using Locality Preserving Projections (LPP) technique as the distance measure in the color space for more discriminating power. An adaptive fusing strategy is presented to effectively adjust the mixing factor so that the color and MSNST texture features are efficiently integrated to achieve more robust segmentation performance. Last, an iteration convergence criterion is proposed to reduce the time of the iteration of GrabCut algorithm dramatically with satisfied segmentation accuracy. Experiments using synthesis texture images and real natural scene images demonstrate the superior performance of our proposed method.     

结合Voronoi划分HMRF模型的模糊ISODATA图像分割

为了解决传统模糊聚类图像分割方法对噪声敏感及无法自动准确确定聚类数的问题,提出结合Voronoi划分HMRF模型的模糊ISODATA图像分割方法。利用Voronoi划分将图像域划分为若干子区域,以划分子区域为基本单元定义基于隐马尔科夫随机场(HMRF)模型的模糊聚类目标函数,以解决噪声敏感问题;通过迭代自组织数据分析技术算法(ISODATA)中聚类分裂、合并技术改变聚类数,以实现聚类数的自动确定。对模拟、合成图像和真实图像分割结果的定性和定量分析表明:提出算法不仅可以有效克服噪声和像素异常值对分割结果的影响,而且还能自动准确确定聚类数,实现高精度的自动变类图像分割。      

Entropy-based dual-portal-to-3-DCT registration incorporating pixel correlation

For patient setup verification in external beam radiotherapy (EBRT) of prostate cancer, we developed an information theoretic registration framework, called the minimax entropy registration framework, to simultaneously and iteratively segment portal images and register them to three-dimensional (3-D) computed tomography (CT) image data. The registration framework has two steps, the max step and the min step, and evaluates appropriate entropies to estimate segmentations of the portal images and to find the transformation parameters. In the initial version of the algorithm (Bansal et al. 1999), we assumed image pixels to be independently distributed, an assumption not true in general. Thus, to better segment the portal images and to improve the accuracy of the estimated registration parameters, in this initial formulation of the problem, the correlation among pixel intensities is modeled using a one-dimensional Markov random process. Line processes are incorporated into the model to improve the estimation of segmentation of the portal images. In the max step, the principle of maximum entropy is invoked to estimate the probability distribution on the segmentations. The estimated distribution is then incorporated into the min step to estimate the registration parameters. Performance of the proposed framework is evaluated and compared to that of a mutual information-based registration algorithm using both simulated and real patient data. In the proposed registration framework, registration of the 3-D CT image and the portal images is guided by an estimated segmentation of the pelvic bone. However, as the prostate can move with respect to the pelvic structure, further localization of the prostate using ultrasound image data is required, an issue to be further explored in future.