Novel Active Contour Model for Image Segmentation Based on Local Fuzzy Gaussian Distribution Fitting

A novel active contour model is proposed, which incorporates local information distributions in a fuzzy energy function to effectively deal with the intensity inhomogeneity. Moreover, the proposed model is convex with respect to the variable which is used for extracting the contour. This makes the model independent on the initial condition and suitable for an automatic segmentation. Furthermore, the energy function is minimized in a computationally efficient way by calculating the fuzzy energy alterations directly. Experiments are carried out to prove the performance of the proposed model over some existing methods. The obtained results confirm the efficiency of the method.

Liver Hydatid CT Image Segmentation Using Smoothed Bayesian Classification Method and Modified Parametric Active Contour Model

Liver hydatid disease is a common parasitic disease in farm and pastoral areas, which seriously influences people's health. Based on CT imaging features of this disease, an iterative approach for liver segmentation and hydatid lesion extraction simultaneously is proposed. In each iteration, our algorithm consists of two main steps: 1) according to the user-defined pixel seeds in the liver and hydatid lesion, Gaussian probability model fitting and smoothed Bayesian classification are applied to get initial segmentation of liver and lesion; 2) the parametric active contour model using priori shape force field is adopted to refine initial segmentation. We make subjective and objective evaluation on the proposed algorithm validity by the experiments of liver and hydatid lesion segmentation on different patients' CT slices. In comparison with ground-truth manual segmentation results, the experimental results show the effectiveness of our method to segment liver and hydatid lesion.

Automatic Tracing and Segmentation of Rat Mammary Fat Pads in MRI Image Sequences Based on Cartoon-Texture Model

The growth patterns of mammary fat pads and glandular tissues inside the fat pads may be related with the risk factors of breast cancer.Quantitative measurements of this relationship are available after segmentation of mammary pads and glandular tissues.Rat fat pads may lose continuity along image sequences or adjoin similar intensity areas like epidermis and subcutaneous regions.A new approach for automatic tracing and segmentation of fat pads in magnetic resonance imaging(MRI) image sequences is presented,which does not require that the number of pads be constant or the spatial location of pads be adjacent among image slices.First,each image is decomposed into cartoon image and texture image based on cartoon-texture model.They will be used as smooth image and feature image for segmentation and for targeting pad seeds,respectively.Then,two-phase direct energy segmentation based on Chan-Vese active contour model is applied to partitioning the cartoon image into a set of regions,from which the pad boundary is traced iteratively from the pad seed.A tracing algorithm based on scanning order is proposed to accurately trace the pad boundary,which effectively removes the epidermis attached to the pad without any post processing as well as solves the problem of over-segmentation of some small holes inside the pad.The experimental results demonstrate the utility of this approach in accurate delineation of various numbers of mammary pads from several sets of MRI images.

Hybrid Active Contour Mammographic Mass Segmentation and Classification

This research implements a novel segmentation of mammographic mass.Three methods are proposed,namely,segmentation of mass based on iterative active contour,automatic region growing,and fully automatic mask selectionbased active contour techniques.In the first method,iterative threshold is performed for manual cropped preprocessed image,and active contour is applied thereafter.To overcome manual cropping in the second method,an automatic seed selection followed by region growing is performed.Given that the result is only a few images owing to over segmentation,the third method uses a fully automatic active contour.Results of the segmentation techniques are compared with the manual markup by experts,specifically by taking the difference in their mean values.Accordingly,the difference in the mean value of the third method is 1.0853,which indicates the closeness of the segmentation.Moreover,the proposed method is compared with the existing fuzzy C means and level set methods.The automatic mass segmentation based on active contour technique results in segmentation with high accuracy.By using adaptive neuro fuzzy inference system,classification is done and results in a sensitivity of 94.73%,accuracy of 93.93%,and Mathew’s correlation coefficient(MCC)of 0.876.

Jacquard image segmentation using Mumford-Shah model

Jacquard image segmentation is one of the primary steps in image analysis for jacquard pattern identification. The main aim is to recognize homogeneous regions within a jacquard image as distinct, which belongs to different patterns. Active contour models have become popular for finding the contours of a pattern with a complex shape. However, the performance of active contour models is often inadequate under noisy environment. In this paper, a robust algorithm based on the Mumford-Shah model is proposed for the segmentation of noisy jacquard images. First, the Mumford-Shah model is discretized on piecewise linear finite element spaces to yield greater stability. Then, an iterative relaxation algorithm for numerically solving the discrete version of the model is presented. In this algorithm, an adaptive triangular mesh is refined to generate Delaunay type triangular mesh defined on structured triangulations, and then a quasi-Newton numerical method is applied to find the absolute minimum of the discrete model. Experimental results on noisy jacquard images demonstrated the efficacy of the proposed algorithm.

Color Cell Image Segmentation Based on Chan-Vese Model for Vector-Valued Images

In this paper, we propose a color cell image segmentation method based on the modified Chan-Vese model for vectorvalued images. In this method, both the cell nuclei and cytoplasm can be served simultaneously from the color cervical cell image. Color image could be regarded as vector-valued images because there are three channels, red, green and blue in color image. In the proposed color cell image segmentation method, to segment the cell nuclei and cytoplasm precisely in color cell image, we should use the coarse-fine segmentation which combined the auto dual-threshold method to separate the single cell connection region from the original image, and the modified C-V model for vectorvalued images which use two independent level set functions to separate the cell nuclei and cytoplasm from the cell body. From the result we can see that by using the proposed method we can get the nuclei and cytoplasm region more accurately than traditional model.

The Method of Flotation Froth Image Segmentation Based on Threshold Level Set

A novel flotation froth image segmentation based on threshold level set method is put forward in view of the problem of over-segmentation and under-segmentation which occurs when the existing method segmented the flotation froth images. Firstly, the proposed method adopts histogram equalization to improve the contrast of the image, and then chooses the upper threshold and lower threshold from grey value of histogram of the image equalization, and complete image segmentation using the level set method. In this paper, the model which integrates edge with region level set model is utilized, and the speed energy term is introduced to segment the target. Experimental results show that the proposed method has better segmentation results and higher segmentation efficiency on the images with under-segmentation and incorrect segmentation, and it is meaningful for ore dressing industrial.

Two-stage image segmentation based on edge and region information

A two-stage method for image segmentation based on edge and region information is proposed. Different deformation schemes are used at two stages for segmenting the object correctly in image plane. At the first stage, the contour of the model is divided into several segments hierarchically that deform respectively using affine transformation. After the contour is deformed to the approximate boundary of object, a fine match mechanism using statistical information of local region to redefine the external energy of the model is used to make the contour fit the object's boundary exactly. The algorithm is effective, as the hierarchical segmental deformation makes use of the globe and local information of the image, the affine transformation keeps the consistency of the model, and the reformative approaches of computing the internal energy and external energy are proposed to reduce the algorithm complexity. The adaptive method of defining the search area at the second stage makes the model converge quickly. The experimental results indicate that the proposed model is effective and robust to local minima and able to search for concave objects.

Fully Automatic Segmentation of Gynaecological Abnormality Using a New Viola–Jones Model

One of the most complex tasks for computer-aided diagnosis(Intelligent decision support system)is the segmentation of lesions.Thus,this study proposes a new fully automated method for the segmentation of ovarian and breast ultrasound images.The main contributions of this research is the development of a novel Viola–James model capable of segmenting the ultrasound images of breast and ovarian cancer cases.In addition,proposed an approach that can efficiently generate region-of-interest(ROI)and new features that can be used in characterizing lesion boundaries.This study uses two databases in training and testing the proposed segmentation approach.The breast cancer database contains 250 images,while that of the ovarian tumor has 100 images obtained from several hospitals in Iraq.Results of the experiments showed that the proposed approach demonstrates better performance compared with those of other segmentation methods used for segmenting breast and ovarian ultrasound images.The segmentation result of the proposed system compared with the other existing techniques in the breast cancer data set was 78.8%.By contrast,the segmentation result of the proposed system in the ovarian tumor data set was 79.2%.In the classification results,we achieved 95.43%accuracy,92.20%sensitivity,and 97.5%specificity when we used the breast cancer data set.For the ovarian tumor data set,we achieved 94.84%accuracy,96.96%sensitivity,and 90.32%specificity.

A distribution prior model for airplane segmentation without exact template

In many practical applications of image segmentation problems,employing prior information can greatly improve segmentation results.This paper continues to study one kind of prior information,called prior distribution.Within this research,there is no exact template of the object;instead only several samples are given.The proposed method,called the parametric distribution prior model,extends our previous model by adding the training procedure to learn the prior distribution of the objects.Then this paper establishes the energy function of the active contour model(ACM)with consideration of this parametric form of prior distribution.Therefore,during the process of segmenting,the template can update itself while the contour evolves.Experiments are performed on the airplane data set.Experimental results demonstrate the potential of the proposed method that with the information of prior distribution,the segmentation effect and speed can be both improved efficaciously.

An Efficient Liver-Segmentation System Based on a Level-Set Method and Consequent Processes

This paper presents an efficient liver-segmentation system developed by combining three ideas under the operations of a level-set method and consequent processes. First, an effective initial process creates mask and seed regions. The mask regions assist in prevention of leakage regions due to an overlap of gray-intensities between liver and another soft-tissue around ribs and verte-brae. The seed regions are allocated inside the liver to measure statistical values of its gray-intensities. Second, we introduce liver-corrective images to represent statistical regions of the liver and preserve edge information. These images help a geodesic active contour (GAC) to move without obstruction from high level of image noises. Lastly, the computation time in a level-set based on reaction-diffusion evolution and the GAC method is reduced by using a concept of multi-resolution. We applied the proposed system to 40 sets of 3D CT-liver data, which were acquired from four patients (10 different sets per patient) by a 4D-CT imaging system. The segmentation results showed 86.38% ± 4.26% (DSC: 91.38% ± 2.99%) of similarities to outlines of manual delineation provided by a radiologist. Meanwhile, the results of liver segmentation only using edge images presented 79.17% ± 5.15% or statistical regions showed 74.04% ± 9.77% of similarities.

Cell Segmentation and Tracking in Microfluidic Platform

In this research, we have concentrated on trajectory extraction based on image segmentation and data association in order to provide an economic and complete solution for rapid microfluidic cell migration experiments. We applied region scalable active contour model to segment the individual cells and then employed the ellipse fitting technique to process touching cells. Subsequently, we have also introduced a topology based technique to associate the cells between consecutive frames. This scheme achieves satisfactory segmentation and tracking results on the datasets acquired by our microfluidic platform.

基于窄带成像及放大内镜的脂质分割方法

一种主要成分是脂质的白色不透明物质(WOS)会覆盖与癌症诊断有关的微观结构,但WOS的形态特征又与肿瘤分级有密切关系。为了给医生提供更多与脂质相关的可用信息,本文对脂质图像的分割方法进行了研究。首先,介绍了基于Retinex框架的脂质图像增强算法,并介绍了反光去除算法。然后,介绍了基于活动轮廓模型的脂质分割方法,该方法从校正后的色调值中提取局部信息,从强度值中提取全局信息,自适应地获得权重因子,并基于初始轮廓来分割脂质区域。最后,基于自研细胞内镜成像系统,设计了仿体实验来验证了该方法的有效性。实验结果表明,该分割方法的像素准确度、灵敏度、Dice系数均高于90%。该方法能够克服照明不均匀、反光等的影响,很好地反映脂质的形状,为医生提供可用的信息。

活动轮廓模型和Contourlet多分辨率分析分割血管内超声图像

针对传统图像分割方法中初始化和鲁棒性两个问题,研究了基于活动轮廓模型和Contourlet多分辨率分析分割血管内超声斑块图像的新方法。该方法运用Contourlet变换将原图像分解为多分辨率低通分量和多分辨率带通分量方向性子带。对低通分量进行模板匹配,确定血管内腔边界和中-外膜边界的初始轮廓;对带通分量方向性子带进行扩散滤波,抑制噪声的同时尽可能保留有用边缘,并结合边界矢量场使轮廓演化得到最终分割结果,从而提高了分割算法的鲁棒性。对100幅仿真图像和120幅实际图像的分割结果表明,相对于传统活动轮廓模型,该方法分割实际图像的平均距离误差提高了3.04 pixel,面积差异百分比提高了6.30%。表明该方法能自动、精确地提取血管的两条边界。

基于变分水平集的多尺度纹理图像分割方法

为了更有效地对纹理图像进行多尺度分割,我们提出了一种混合模型。该模型使用全变分模型来控制纹理图像的多尺度表示,并且引入局部自相似性以及局部高斯分布拟合函数作为边界提取项,最终得到一个混合的多尺度纹理图像分割模型。实验结果表明,所提模型能够在不同尺度下对纹理图像进行较好地分割,使分割过程更加符合人类视觉的感知机制,并且当图像受到不同程度的高斯噪声污染时,该模型也能够对目标边界进行精确地提取。

边缘检测和Snake Model结合的轮廓识别

基于主动轮廓识别和被动轮廓识别方法各自的优缺点,提出结合主动和被动的方法,先对图像进行被动轮廓的预处理,然后再应用主动轮廓的方法,通过比较选出Canny算子进行预处理,再通过基于概率的方法分割图像,最后再应用Snake模型进行轮廓提取。并取得较好的应用。

联合自然梯度和AdamW算法的RSF图像分割模型

关键零件内部复杂结构的精密测量是高端制造领域攻克的难题。当采用工业CT技术实现对象内部结构精密测量时,面临目标图像灰度不均匀性、边缘模糊、伪影等问题。有鉴于此,本文研究了局部能量最小化模型(RSF)的图像分割方法,引入自然梯度和AdamW算法分别提高了RSF模型的收敛速度和参数自适应性。首先,在统计流形上计算自然梯度,提高梯度下降效率和RSF模型收敛速度;其次,采用AdamW算法实现RSF模型的高斯核函数尺度大小自适应控制。与经典RSF模型相比,改进后的RSF模型迭代次数减少了1353次,迭代次数降低约76.79%,迭代时间减少约43.61%,测针球面半径和航空燃油喷嘴圆柱直径测量误差均较小,既保持了原模型亚像素分割精度,又大幅提高了模型收敛速度和鲁棒性。

基于局部熵的区域活动轮廓图像分割模型

为解决区域活动轮廓模型不能有效分割灰度不均图像的问题,提出了局部熵约束的区域活动轮廓模型应用于图像分割。首先基于局部熵信息将图像划分为两个特征区域,然后利用局部熵特征信息构造二值拟合能量,并与区域可放缩拟合(Region⁃scalable fitting,RSF)模型相结合,最后得到水平集演化方程。该模型考虑了图像灰度分布的聚集特征和局部区域统计信息,能有效处理灰度不均匀、弱边缘等图像分割问题,且对轮廓初始位置更具鲁棒性,医学图像实验结果验证了模型的有效性。

融合显著性特征的自适应主动轮廓模型

主动轮廓模型存在演化速度慢、对初始轮廓和噪声敏感、弱边缘泄漏及目标过分割等问题。对以上问题进行了研究,提出了融合显著性特征的自适应主动轮廓模型。提出基于去雾算法的显著性映射作为正则项提升模型对初始轮廓位置的鲁棒性,防止轮廓演化过程过早陷入局部最优解,同时缩短轮廓演化时间。为了防止模型在演化过程中出现弱边界泄漏,模型中引入边缘检测函数作为能量泛函的权重。该模型利用最大面积稀疏约束,提出自适应目标提取方法来消除目标过分割影响。与多种主动轮廓模型在数据集MRSA500(500张)上进行实验对比,表明了提出的模型对初始轮廓和噪声的鲁棒性,而且提出模型的平均分割效率提升约5.6倍,平均Jaccard相似度系数提升约22%。

利用改进的对称活动轮廓模型分割非匀质图像

现有活动轮廓模型很难较好地处理非匀质图像。针对上述难题,提出了一种对称全局局部混合活动轮廓模型。在研究全局局部信息活动轮廓模型的基础上,提出了该模型的对称形式,能很好抑制模型对初始轮廓的敏感性;将全局局部信息活动轮廓模型和其对称形式相结合,并赋予归一化的加权系数,以确保模型的通用性并提高模型的时间性能。利用提出模型和现有活动轮廓模型分割合成以及实际非匀质图像,结果表明,提出模型能实现非匀质图像的快速、准确分割,且对初始轮廓具有良好的鲁棒性。