A Review of Statistical Approaches to Level Set Segmentation: Integrating Color,Texture, Motion and Shape

Since their introduction as a means of front propagation and their first application to edge-based segmentation in the early 90’s, level set methods have become increasingly popular as a general framework for image segmentation. In this paper, we present a survey of a specific class of region-based level set segmentation methods and clarify how they can all be derived from a common statistical framework. Region-based segmentation schemes aim at partitioning the image domain by progressively fitting statistical models to the intensity, color, texture or motion in each of a set of regions. In contrast to edge-based schemes such as the classical Snakes, region-based methods tend to be less sensitive to noise. For typical images, the respective cost functionals tend to have less local minima which makes them particularly well-suited for local optimization methods such as the level set method. We detail a general statistical formulation for level set segmentation. Subsequently, we clarify how the integration of various low level criteria leads to a set of cost functionals. We point out relations between the different segmentation schemes. In experimental results, we demonstrate how the level set function is driven to partition the image plane into domains of coherent color, texture, dynamic texture or motion. Moreover, the Bayesian formulation allows to introduce prior shape knowledge into the level set method. We briefly review a number of advances in this domain.     

融合色彩比和梯度不变性的运动阴影检测

为解决运动前景的准确分割受运动阴影影响的问题,提出了一种融合色彩比和梯度不变性的运动阴影检测算法。该算法分析了阴影像素的色彩比和区域纹理梯度的光照不变性,利用亮度变化特性和色彩比不变性初步确定候选运动前景中的阴影像素,然后在候选阴影区域利用纹理梯度不变性进行去错处理,两者的结合弥补了单一特征或单一类型特征的阴影检测性能差的缺陷,提高了阴影检测率和阴影分辨率,能够准确地将阴影和前景区别开来。     

基于先验知识水平集方法的草莓图像分割

在实际应用中,当目标本身含有一些固有的颜色纹理特征时,可将这些特征作为一种先验信息,这样可以大大提高分割的准确性.为此,本文提出了一种基于先验信息的改进水平集图像分割方法.首先,利用传统的C-V模型能量项的构造思想构建了基于颜色信息的局部能量项,该项是用于处理彩色图像;然后将颜色分量引入到传统的结构张量中构建出新的扩展型结构张量,该项是用于处理纹理信息;最后,将上述新构造的能量项以及Li模型约束项引入到传统C-V模型中得到新的水平集模型.鉴于草莓果实所具有的颜色信息和纹理信息,本文将上述改进水平集方法应用到农业自动化应用中草莓果实分割中.对实验室环境与草莓生长环境下的草莓图像进行分别实验,结果显示该方法能够不仅能够分割出草莓果实且能够很好地处理草莓表面的纹理信息.另还与OTSU算法、传统C-V模型、改进C-V模型对草莓图像作对比实验,结果表明本文算法均比上述三种算法具有更好的分割效果.     

利用总变分最小化方法的无监督纹理图像分割

纹理图像的分割是图像处理领域中的一个典型难题。不同于传统的提取纹理特征量进行纹理分割的方法，本文将图像复原和重建中的总变分最小化方法和活动围道分割方法相结合，提出了一种简单的线性纹理模型。利用总变分最小化方法在保持图像大尺度棱边信息的基础上对纹理体现的局部小尺度周期性灰度振动细节进行平滑得到简化的图像原型。对其进行分割获得不同纹理区域之间的低定位精度的边界围道，再利用原始图像对围道进行高精度细化。在总变分最小化导致的非线性扩散方程求解过程中，运用AOS(additive operatorr splitting)数值算法以改进算法效率。实验结果表明，该方法能很快提取出纹理图像的简化图像，同时是一种无监督的纹理分割方法。     

Adaptive texture and color segmentation for tracking moving objects

Color segmentation is a very popular technique for real-time object tracking. However, even with adaptive color segmentation schemes, under varying environmental conditions in video sequences, the tracking tends to be unreliable. To overcome this problem, many multiple cue fusion techniques have been suggested. One of the cues that complements color nicely is texture. However, texture segmentation has not been used for object tracking mainly because of the computational complexity of texture segmentation. This paper presents a formulation for fusing texture and color in a manner that makes the segmentation reliable while keeping the computational cost low, with the goal of real-time target tracking. An autobinomial Gibbs Markov random field is used for modeling the texture and a 2D Gaussian distribution is used for modeling the color. This allows a probabilistic fusion of the texture and color cues and for adapting both the texture and color over time for target tracking. Experiments with both static images and dynamic image sequences establish the feasibility of the proposed approach.     

Motion segmentation based on motion/brightness integration andoscillatory correlation

A segmentation method based on the integration of motion and brightness is proposed for image sequences. The method is composed of two parallel pathways that process motion and brightness, respectively, Inspired by the visual system, the motion pathway has two stages. The first stage estimates local motion at locations with reliable information. The second stage performs segmentation based on local motion estimates. In the brightness pathway, the input scene is segmented into regions based on brightness distribution. Subsequently, segmentation results from the two pathways are integrated to refine motion estimates. The final segmentation is performed in the motion network based on refined estimates. For segmentation, locally excitatory globally inhibitory oscillator network (LEGION) architecture is employed whereby the oscillators corresponding to a region of similar motion/brightness oscillate in synchrony and different regions attain different phases. Results on synthetic and real image sequences are provided, and comparisons with other methods are made.     

基于多阈值融合的图像分割

提出了一种基于知识的多阈值融合图像分割新方法．首先利用一组多阈值分割结果建立连通域生长树．然后判断树叉对应的连通域合并是否合理，为此提出了连通体元、体元生命期、体元体积等概念，结合灰度均匀性定义出通用合并准则．最后将图像各位置的最佳连通域组合为最终图像分割结果．该算法充分利用了目标的灰度和空间属性，对灰度平稳和渐近变化的多目标图像分割非常有效．此外，该算法可以有效融合具体应用的先验知识，具有很高的智能性。     

基于局部区域信息的水平集医学图像分割方法

针对医学图像中存在的亮度分布不均匀（intensity inhomogeneity）的特点,对Chan-Vese提出的基于Mumford-Shah模型的水平集分割图像的算法进行了改进。局部区域信息是对亮度分布不均匀图像进行准确分割的关键,但是传统的基于区域信息的C-V模型没有利用到这种局部区域的图像信息,因此无法正确分割强度分布不均匀图像。利用局部区域信息构造能量函数,提出了一种基于局部区域信息的改进C-V模型。该模型无需大量计算,水平集函数可快速收敛。MR图像、血管造影图像和X线骨折图像的实验结果证明了该方法的高效性。     

High precision image segmentation algorithm using SLIC and neighborhood rough set

A high precision image segmentation algorithm using SLIC and neighborhood rough set is proposed. The algorithm mainly includes two stages: the stage of superpixel generation and the mergence stage based on neighborhood rough set. In superpixel generation stage, based on L-channel color histogram and its peak, the scheme of initial superpixel number generation is proposed according to the complexity of the image itself. For inaccuracy segmentation edge of SLIC caused by isolated pixels, the compactness factor is appropriately increased before they are generated. After that, the scheme of reclassifying each isolated pixel is proposed just relying on the color space. In superpixel mergence stage based on neighborhood rough set, the texture information using the gray level co-occurrence matrix is introduced into the feature representation of superpixel. It can reduce the dependence of color feature and improve the accuracy of the mergence. By constructing the information table, the neighborhood granule of each superpixel is acquired under the neighborhood threshold. Finally, the superpixels within the neighborhood granule are merged on the basis of the spatial adjacency between superpixels. In Berkeley segmentation data set, compared with the SLIC algorithm, the schemes of initial superpixel number generation and the isolated pixels processing are proved to be effective. Furthermore, the experiments demonstrate that the proposed algorithm can produce high-quality and high-precision image segmentation results in comparison with the SLIC-based image segmentation algorithms on three standard metrics.     

Semi-automatic computer aided lesion detection in dental X-rays using variational level set

A semi-automatic lesion detection framework is proposed to detect areas of lesions from periapical dental X-rays using level set method. In this framework, first, a new proposed competitive coupled level set method is used to segment the image into three pathologically meaningful regions using two coupled level set functions. Tailored for the dental clinical setting, a two-stage clinical segmentation acceleration scheme is used. The method uses a trained support vector machine (SVM) classifier to provide an initial contour for two coupled level sets. Then, based on the segmentation results, an analysis scheme is applied. Firstly, the scheme builds an uncertainty map from which those areas with radiolucent will be automatically emphasized by a proposed color emphasis scheme. Those radiolucent in the teeth or jaw usually suggested possible lesions. Secondly, the scheme employs a method based on the average intensity profile to isolate the teeth and locate two types of lesions: periapical lesion (PL) and bifurcation lesion (BL). Experimental results show that our proposed segmentation method is able to segment the image into pathological meaningful regions for further analysis; our proposed framework is able to automatically provide direct visual cues for the lesion detection; and when given the orientation of the teeth, it is able to automatically locate the PL and BL with a seriousness level marked for further dental diagnosis. When used in the clinical setting, the framework enables dentist to improve interpretation and to focus their attention on critical areas.     

A fast segmentation method based on constraint optimization and its applications: Intensity inhomogeneity and texture segmentation

We propose a new constraint optimization energy and an iteration scheme for image segmentation which is connected to edge-weighted centroidal Voronoi tessellation (EWCVT). We show that the characteristic functions of the edge-weighted Voronoi regions are the minimizers (may not unique) of the proposed energy at each iteration. We propose a narrow banding algorithm to accelerate the implementation, which makes the proposed method very fast. We generalize the CVT segmentation to hand intensity inhomogeneous and texture segmentation by incorporating the global and local image information into the energy functional. Compared with other approaches such as level set method, the experimental results in this paper have shown that our approach greatly improves the calculation efficiency without losing segmentation accuracy.     

一种整合单通道各向异性扩散信息的水平集图像分割方法*

在对Chan-Vese提出的基于简化Mumford-Shah模型(C-V模型)改进的基础上,针对彩色图像、多光谱图像等多通道图像,提出了一种多通道C-V模型水平集图像分割方法.首先将多通道图像分解到各单通道,使用一种新的各向异性扩散方法对各通道进行平滑滤波,然后使用能够整合各通道各向异性扩散信息的多通道C-V模型进行分割.普通彩色图像与多光谱图像数据的实验结果表明,该方法分割质量明显优于传统的C-V模型分割.     

融合颜色和CS-LBP纹理的运动阴影检测

针对现存阴影检测方法存在的实时性和精确性兼顾不周的问题, 提出加权融合颜色和纹理特征的阴影检测方法: 首先利用HSV颜色信息提取疑似阴影点; 其次, 通过阴影的亮度比计算阴影亮度隶属度, 对于高亮度隶属度的疑似阴影点, 直接判定为阴影点, 减少了纹理检测的计算量; 然后对低亮度隶属度的疑似阴影点提取高效的CS-LBP纹理, 并进行纹理匹配, 根据纹理的相似程度及阴影空间分布特点, 计算出纹理隶属度; 最后, 根据实际中纹理随亮度变化的特点, 提出了依据亮度比自适应调整纹理隶属度权重的特征融合方法, 进行有效的阴影检测. 实验表明, 本文方法实时性良好, 可去除自阴影, 分割精度较佳, 隶属度方法的使用, 使本方法对光照变化及噪声更具有鲁棒性.     

基于QPSO算法的图像颜色分割*

目前不同种类的纹理区域组成的彩色图像分割还是一个难点。当一幅图像中包含相似的和（或）非固定的纹理区域时,难以计算出精确的纹理区域和分割区域的最优数目。描述了基于量子行为的微粒群优化算法（QPSO）的图像颜色分割方法,把图像分割问题看作一个最优化问题并且采用QPSO的进化策略聚类颜色特征空间中的区域。QPSO不仅参数个数少、随机性强,并且能覆盖所有解空间,保证算法的全局收敛。给出了三幅图像的分割效果,证明了QPSO算法在自动的和无监督的纹理分割上具有很好的效果。     

Unsupervised segmentation of color-texture regions in images andvideo

A method for unsupervised segmentation of color-texture regions in images and video is presented. This method, which we refer to as JSEG, consists of two independent steps: color quantization and spatial segmentation. In the first step, colors in the image are quantized to several representative classes that can be used to differentiate regions in the image. The image pixels are then replaced by their corresponding color class labels, thus forming a class-map of the image. The focus of this work is on spatial segmentation, where a criterion for “good” segmentation using the class-map is proposed. Applying the criterion to local windows in the class-map results in the “J-image,” in which high and low values correspond to possible boundaries and interiors of color-texture regions. A region growing method is then used to segment the image based on the multiscale J-images. A similar approach is applied to video sequences. An additional region tracking scheme is embedded into the region growing process to achieve consistent segmentation and tracking results, even for scenes with nonrigid object motion. Experiments show the robustness of the JSEG algorithm on real images and video     

融合多特征的均值漂移彩色图像分割方法

针对均值漂移图像分割方法中只考虑图像颜色和空间信息,对纹理丰富的图像不能进行有效分割的情况,提出一种新的融合图像颜色、纹理和空间等低层特征信息的图像分割方法.用极性、各向异性和对比度来表示图像的纹理信息,并结合颜色和空间信息形成图像分割特征;然后用均值漂移进行图像滤波;最后,进行区域合并得到分割结果.实验结果表明,该方法对纹理丰富的自然风景图像有较好的分割效果.     

抑制纹理信息的偏置场变分图像分割模型

偏置场变分水平集图像分割模型利用原始图像的局部灰度信息,可以对灰度不均匀图像进行有效的分割,但当灰度图像中存在纹理时,分割效果往往很差。针对这一问题,提出抑制纹理信息的偏置场变分水平集图像分割模型。利用一种基于纹理几何结构的纹理描述符描述图像中不同的纹理区域,使得不同纹理区域对比更加明显,相同纹理区域更加平滑,通过抑制纹理信息使后续的图像分割在纹理部分的错分大大减少。实验结果表明,相比偏置场变分模型,所提模型对自然及人工合成纹理图像均获得更好的分割结果。     

基于简化Mumford-Shah模型的水平集红外图像分割算法

由于红外图像大多具有目标模糊,对比度低的特点,传统的分割方法容易受到噪声和边界轮廓的影响而导致分割效果不佳,提出了一种基于简化Mumford-Shah模型的水平集红外图像分割算法.该算法能够通过将初始闭合曲线嵌入水平集函数,利用函数的求解从而达到图像分割的目的.仿真实验结果表明,该分割算法与初始轮廓线位置无关,受边界轮廓线和图像噪声的影响较小,具有较强的鲁棒性,在目标与背景灰度级差别较小的红外图像的分割中取得了较好的效果.     

Segmentation of psoriasis vulgaris images using multiresolution-based orthogonal subspace techniques.

In this paper, a method is proposed for the segmentation of color images using a multiresolution-based signature subspace classifier (MSSC) with application to psoriasis images. The essential techniques consist of feature extraction and image segmentation (classification) methods. In this approach, the fuzzy texture spectrum and the two-dimensional fuzzy color histogram in the hue-saturation space are first adopted as the feature vector to locate homogeneous regions in the image. Then these regions are used to compute the signature matrices for the orthogonal subspace classifier to obtain a more accurate segmentation. To reduce the computational requirement, the MSSC has been developed. In the experiments, the method is quantitatively evaluated by using a similarity function and compared with the well-known LS-SVM method. The results show that the proposed algorithm can effectively segment psoriasis images. The proposed approach can also be applied to general color texture segmentation applications.