A robust hybrid active contour model based on pre-fitting bias field correction for fast image segmentation

An array of existing active contour models is prone to suffering from the deficiencies of poor anti-noise ability, initialization sensitivity, and slow convergence. In order to handle these problems, a robust hybrid active contour method based on bias correction is proposed in this research paper The energy functional is formulated through incorporating the adaptive edge indicator function and level set formulation driven by bias field correction. The adaptive edge indicator function, which is formulated based on image gradient information, is utilized to detect object boundaries and accelerate the segmentation in the homogeneous region. The level set formulation is constructed based on an improved criterion function, in which bias field information is considered. Specifically, the bias field distribution is approximated through the local mean gray value algorithm as a prior. Moreover, a new regularized function is proposed so as to maintain the stability of curve evolution. The segmentation process is implemented by the optimized energy function and the novel regularized term. Compared to previous active contour models, the modified active contour method can yield more precise, stable, and efficient segmentation results on some challenging images.     

Improving image segmentation by using energy function based on mixture of Gaussian pre-processing

In this paper, by proposing a two-stage segmentation method based on active contour model, we improve the procedure of former image segmentation methods. The first stage of our method is computing weights, means and variances of image by utilizing Mixture of Gaussian distribution which parameters are obtained from EM-algorithm. Once they are obtained, in the second stage, by incorporating level set method for minimizing energy function, the segmentation is achieved. We use an adaptive direction function to make the curve evolution robust against the curves initial position and a nonlinear adaptive velocity to speed up the process of curve evolution and also a probability-weighted edge and region indicator function to implement a robust segmentation for objects with weak boundaries. The paper consists of minimizing a functional containing a penalty term in an attempt to maintain the signed distance property in the entire domain and an external energy term such that it achieves a minimum when the zero level set of the function is located at desired position.     

Level set formulation for automatic medical image segmentation based on fuzzy clustering

The level set method is widely used in medical image segmentation, in which the performance is seriously subject to the initialization and parameters configuration. An automatic segmentation method was proposed in this paper, which integrates fuzzy clustering with level set method through a dynamic constrained term in the new energy functional. It is able to use the results of fuzzy clustering directly, which can control the level set evolution. Moreover, the added constrained term is changing continuously until getting the final results. Such algorithm eliminates the manual operation a lot and leads to more robust segmentation results. With the split Bregman method, the minimization of the new energy functional is fast. The proposed algorithm was tested on some medical images and also compared with other level set models and the state-of-the-art method such as U-Net. The quantitative and qualitative experimental results show its effectiveness and obvious improvement for medical image segmentation.     

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.     

Medical image segmentation using minimal path deformable models with implicit shape priors.

This paper presents a new method for segmentation of medical images by extracting organ contours, using minimal path deformable models incorporated with statistical shape priors. In our approach, boundaries of structures are considered as minimal paths, i.e., paths associated with the minimal energy, on weighted graphs. Starting from the theory of minimal path deformable models, an intelligent "worm" algorithm is proposed for segmentation, which is used to evaluate the paths and finally find the minimal path. Prior shape knowledge is incorporated into the segmentation process to achieve more robust segmentation. The shape priors are implicitly represented and the estimated shapes of the structures can be conveniently obtained. The worm evolves under the joint influence of the image features, its internal energy, and the shape priors. The contour of the structure is then extracted as the worm trail. The proposed segmentation framework overcomes the short-comings of existing deformable models and has been successfully applied to segmenting various medical images.     

基于改进的变分水平集和区域生长的图像分割方法的研究

针对水平集和区域生长方法都存在对噪声和初始边界敏感以及容易从弱边缘处泄露等不稳定的问题,提出了结合待分割目标灰度统计信息和图像梯度信息的水平集演化函数对水平集方法进行改进,并利用区域生长方法解决水平集方法对初始边界敏感的问题.分别用传统区域生长方法、阈值方法、GAC模型、C-V模型、Snake模型以及本文方法进行从腹部CT图像分割肝脏区域的实验比较,实验结果表明:本文方法不仅可以减少图像分割的时间,而且显著地提高了分割质量.     

Local morphology fitting active contour for automatic vascular segmentation

In this paper, we propose an active contour model using local morphology fitting for automatic vascular segmentation on 2-D angiogram. The vessel and background are fitted to fuzzy morphology maximum and minimum opening, separately, using linear structuring element with adaptive scale and orientation. The minimization of the energy associated with the active contour model is implemented within a level set framework. As in the current local model, fitting the image to local region information makes the model robust against the inhomogeneous background. Moreover, selective local estimations for fitting that are precomputed instead of updated in each contour evolution makes the evolution of level set robust again initial location compared to the current local model. The results on synthetic image and real angiogram compared with other methods are presented. It is shown that the proposed method can achieve automatic and accurate segmentation of vascular angiogram.     

基于模糊阈值和水平集的红外图像分割方法

针对阈值法分割红外图像易产生误分割和水平集分割方法受初始曲线限制大,提出了一种结合模糊阈值与水平集的自适应红外图像分割方法。该方法首先采用二维Otsu方法计算阈值,利用该阈值获取模糊阈值分割法中的窗口宽度,使模糊阈值分割法具有自适应性;然后采用此自适应模糊阈值分割法预分割红外图像,利用预分割结果自动获取水平集初始曲线;最后将Chan-Vese方法与Shi方法结合提出改进的水平集方法,并用此方法分割红外图像。实验结果表明,本文方法具有较好的分割效果和较强的鲁棒性。     

Zernike moment and local distribution fitting fuzzy energy-based active contours for image segmentation

This paper presents a new region-based active contour model for extracting the object boundaries in an image, based on techniques of curve evolution. The proposed model introduces an energy functional that involves intensity distributions in local image regions and fuzzy membership functions. The local image intensity distribution information used to guide the motion of the contour, in the paper, is derived by Hueckel operator in the neighborhood of each image point. The parameters of Hueckel operator are estimated by a set of orthogonal Zernike moments before curve evolution. Meanwhile, the fuzzy membership functions are used to measure the association degree of each image pixel to the region outside and inside the contour. To minimize the energy functional, instead of solving the Euler–Lagrange equation of the underlying problem, the paper employs a direct method to compute the energy alterations. As a result, the model can deal with images with intensity inhomogeneity. In addition, the model effectively alleviates the sensitivity to contour initialization. Moreover, the model reduces computational cost, avoids problems associated with choosing time steps as well as allows fast convergence to the segmentation solutions. Experimental results on synthetic, real images and comparisons with other models show the desired performances of the proposed model.     

QoS路由选择:问题与解决方法综述

保证服务质量的QoS路由(Quality of Service Routing)是网络中解决QoS问题的一项关键技术.QoS路由的主要目标是为接入的业务选择满足服务质量要求的传输路径,同时保证整个网络资源的有效利用.度量参数选择问题、寻路问题和路由信息不准确问题是QoS路由中的几个主要研究内容.本文围绕这三个方面,介绍了QoS路由中的主要问题及相关的解决办法,并探讨了今后QoS路由可能的研究方向.     

甲状腺结节超声图像分割算法研究

超声甲状腺结节分割是发现与识别甲状腺良恶性肿瘤的关键技术之一.针对模糊聚类法无法准确分割超声图像甲状腺结节边缘,而局部拟合(RSF)模型法对手动初始化轮廓敏感的问题,提出一种融合空间约束模糊C均值聚类和局部拟合RSF模型的分割结节方法.用空间约束模糊C均值聚类法(SKFCM)对图像进行聚类并二值化聚类结果作为RSF模型法初始轮廓,克服了RSF模型法对初始轮廓敏感问题,水平集演化参数也将通过聚类结果自动给出,不再需要人为设定.同时改进了RSF模型法拟合项,并利用高斯正则化规则RSF模型水平集,提高了RSF模型演化效率,缩短了收敛时间.仿真实验结果表明,提出的甲状腺结节超声图像分割方法能够快速准确地分割出结节区域.     

A NEW DEFORMABLE MODEL USING LEVEL SETS FOR SHAPE SEGMENTALTION

In this paper,we present a new deformable model for shape segmentation,which makes two modifications to the original level set implementation of deformable models.The modifications are motivated by difficulties that we have encountered in applying deformable models to segmentation of medical images.The level set algorithm has some advantages over the classical snake deformable models.However,it could develop large gaps in the boundary and holes within the objects.Such boundary gaps and holes of objects can cause inaccurate segmentation that requires manual correction.The proposed method in this paper possesses an inherent property to detect gaps and holes within the object with a single initial contour and also does not require specific initialization.The first modification is to replace the edge detector by some area constraint,and the second modification utilizes weighted length constraint to regularize the curve under evolution.The proposed method has been applied to both synthetic and real images with promising results.     

结合局部能量与改进的符号距离正则项的图像目标分割算法

针对传统C-V模型对颜色不均匀图像分割失败并且对初始轮廓和位置敏感问题,以及现有符号距离正则项存在周期性振荡和局部极值问题。该文提出结合局部能量信息和改进的符号距离正则项的图像目标分割算法。首先,将全局图像信息扩展到HSV空间,并使用局部能量项信息分析每个像素及其领域内的统计特性,从而在较少的迭代次数内有效分割颜色分布不均匀图像。其次,改进现有符号距离正则项,改进后的符号距离正则项在避免水平集函数的重新初始化的同时,提高了计算效率,保证了水平集函数演化过程的稳定性。然后,定义阈值判断法的水平集函数演化的终止准则,使曲线准确演化到目标轮廓。该算法与同类模型的对比实验表明该模型具有较高的分割精度和对初始轮廓的鲁棒性。     

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.     

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.     

An integrated region-, boundary-, shape-based active contour for multiple object overlap resolution in histological imagery

Active contours and active shape models (ASM) have been widely employed in image segmentation. A major limitation of active contours, however, is in their 1) inability to resolve boundaries of intersecting objects and to 2) handle occlusion. Multiple overlapping objects are typically segmented out as a single object. On the other hand, ASMs are limited by point correspondence issues since object landmarks need to be identified across multiple objects for initial object alignment. ASMs are also are constrained in that they can usually only segment a single object in an image. In this paper, we present a novel synergistic boundary and region-based active contour model that incorporates shape priors in a level set formulation with automated initialization based on watershed. We demonstrate an application of these synergistic active contour models using multiple level sets to segment nuclear and glandular structures on digitized histopathology images of breast and prostate biopsy specimens. Unlike previous related approaches, our model is able to resolve object overlap and separate occluded boundaries of multiple objects simultaneously. The energy functional of the active contour is comprised of three terms. The first term is the prior shape term, modeled on the object of interest, thereby constraining the deformation achievable by the active contour. The second term, a boundary-based term detects object boundaries from image gradients. The third term drives the shape prior and the contour towards the object boundary based on region statistics. The results of qualitative and quantitative evaluation on 100 prostate and 14 breast cancer histology images for the task of detecting and segmenting nuclei and lymphocytes reveals that the model easily outperforms two state of the art segmentation schemes (geodesic active contour and Rousson shape-based model) and on average is able to resolve up to 91% of overlapping/occluded structures in the images.     

A new deformable model using level sets for shape segmentation

In this paper, we present a new deformable model for shape segmentation, which makes two modifications to the original level set implementation of deformable models. The modifications are motivated by difficulties that we have encountered in applying deformable models to segmentation of medical images. The level set algorithm has some advantages over the classical snake deformable models. However, it could develop large gaps in the boundary and holes within the objects. Such boundary gaps and holes of objects can cause inaccurate segmentation that requires manual correction. The proposed method in this paper possesses an inherent property to detect gaps and holes within the object with a single initial contour and also does not require specific initialization. The first modification is to replace the edge detector by some area constraint, and the second modification utilizes weighted length constraint to regularize the curve under evolution. The proposed method has been applied to both synthetic and real images with promising results. Communication author: He Ning, born in 1970, female, Ph.D. candidate.     

利用局部离散度活动轮廓模型的强度非均匀图像分割

强度非均匀现象在真实图像中普遍存在,采用常规基于强度的分割算法会导致严重的误分割。针对强度非均匀图像分割,提出了基于局部离散度的活动轮廓模型分割算法。首先定义基于类内类间距离的离散度,然后利用核函数提取局部区域信息,同时加入边缘指示函数加权的轮廓线长度项能量,建立基于局部离散度的活动轮廓模型。最后引入水平集函数惩罚项,避免水平集方法在演化求解时需要不断初始化的问题。合成图像和真实图像实验结果证明本文算法性能稳定,适应于强度非均匀图像的分割。      

基于C-V模型的红外图像自动分割方法研究

为解决红外图像分割中背景噪声及边界轮廓的影响,引入了基于曲线演化理论、水平集方法和M-S分割函数的C-V模型。通过将图像表达为分段常量函数来建立适当的能量函数模型,引入水平集的表示方法,在整个图像域中依据最小化分割寻找全局极小值,可令活动轮廓最终到达目标边缘。由MATLAB实现的仿真结果表明采用C-V模型对红外图像进行自动分割不受边界轮廓线连续性限制,对初始轮廓线位置不敏感,对图像噪声具有很强的鲁棒性,对均匀灰度目标分割效果良好。     

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.