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.     

基于形变模型的图像分割技术综述

基于形变模型的图像分割技术是近年来兴起的一种新型图像分割方法,已有相当广泛的研究。该技术为如何有效地从图像中分割出不规则对象及自然对象指出了一条佳径。该文简要介绍基于形变模型图像分割技术的基本原理和发展历程。按技术发展的线索介绍各种典型的形变模型表示形式,提出各种表示形式的优缺点,分析基于形变模型的图像分割的各种技术所存在的缺点,并建议了可能的研究方向。     

Image segmentation using fuzzy energy-based active contour with shape prior

This paper presents a fuzzy energy-based active contour model with shape prior for image segmentation. The paper proposes a fuzzy energy functional including a data term and a shape prior term. The data term, inspired from the region-based active contour approach proposed by Chan and Vese, evolves the contour relied on image information. The shape term inspired from Chan and Zhu’s work, defined as the distance between the evolving shape and a reference one, constrains the evolving contour with respect to the reference shape. To align the shapes, we exploit the shape normalization procedure which takes into account the affine transformation. In addition, to minimize the energy functional, we utilize a direct method to calculate the energy alterations. The proposed model therefore can deal with images with background clutter and object occlusion, improves the computational speed, and avoids difficulties associated with time step selection issue in gradient descent-based approaches.     

A semi-automated system based on level sets and invariant spatial interrelation shape features for Caenorhabditis elegans phenotypes

Caenorhabditis elegans shares several molecular and physiological homologies with humans and thus plays a key role in studying biological processes. As a consequence, much progress has been made in automating the analysis of C. elegans. However, there is still a strong need to achieve more progress in automating the analysis of static images of adult worms. In this paper, a three-phase semi-automated system has been proposed. As a first phase, a novel segmentation framework, based on variational level sets and local pressure force function, has been introduced to handle effectively images corrupted with intensity inhomogeneity. Then, a set of robust invariant symbolic features for high-throughput screening of image-based C. elegans phenotypes are extracted. Finally, a classification model is applied to discriminate between the different subsets. The proposed system demonstrates its effectiveness in measuring morphological phenotypes in individual worms of C. elegans.     

Parametric shape modeling using deformable superellipses for prostate segmentation

Automatic prostate segmentation in ultrasound images is a challenging task due to speckle noise, missing boundary segments, and complex prostate anatomy. One popular approach has been the use of deformable models. For such techniques, prior knowledge of the prostate shape plays an important role in automating model initialization and constraining model evolution. In this paper, we have modeled the prostate shape using deformable superellipses. This model was fitted to 594 manual prostate contours outlined by five experts. We found that the superellipse with simple parametric deformations can efficiently model the prostate shape with the Hausdorff distance error (model versus manual outline) of 1.32 +/- 0.62 mm and mean absolute distance error of 0.54 +/- 0.20 mm. The variability between the manual outlinings and their corresponding fitted deformable superellipses was significantly less than the variability between human experts with p-value being less than 0.0001. Based on this deformable superellipse model, we have developed an efficient and robust Bayesian segmentation algorithm. This algorithm was applied to 125 prostate ultrasound images collected from 16 patients. The mean error between the computer-generated boundaries and the manual outlinings was 1.36 +/- 0.58 mm, which is significantly less than the manual interobserver distances. The algorithm was also shown to be fairly insensitive to the choice of the initial curve.     

High speed VLSI architecture for improved region based active contour segmentation technique

Active contour segmentation is an important stage in image analysis applications. In this article, an improved region based active contour segmentation is proposed. The proposed active contour model speeds up the contour convergence by up to 40% while maintaining the advantages of a local region based active contour model by reducing the number of iterations. Moreover, we propose a low-complexity pipelined VLSI architecture for improved region based active contour model targeting FPGA and 90 nm ASIC platforms. The proposed pipelined design offers an increased speed of operation. Its complexity is independent of the size of image.     

基于SPKFCM快速双循环水平集非均匀图像分割

针对传统快速双循环水平集对初始演化曲线过于依赖的问题,提出一种基于空间惩罚核模糊C-means (SPKFCM)算法的初始演化曲线自动选取快速双循环水平集算法.首先,对模糊均值聚类算法进行改进,通过增加空间惩罚函数提出SPKFCM算法,用于对快速双循环水平集算法的自动初始化;其次,基于SPKFCM并结合快速双循环水平集算法,设计基于SPKFCM快速双循环水平集算法框架,并给出相应速度参量Fd和Fint模糊化形式;最后,通过与已有算法在仿真图像上的对比结果显示,所提算法在随机初始化条件下,具有更高的分割精度和计算效率.     

Image segmentation and inpainting using hierarchical level set and texture mapping

Image inpainting is an artistic procedure to recover a damaged painting or picture. We propose a novel approach for image inpainting by using the Mumford-Shah (MS) model and the level set method to estimate image structure of the damaged regions. This approach has been successfully used in image segmentation problem. Compared to some other inpainting methods, the MS model approach detects and preserves edges in the inpainting areas. We propose a fast and efficient algorithm that achieves both inpainting and segmentation. In previous works on the MS model, only one or two level set functions are used to segment an image. While this approach works well on simple cases, detailed edges cannot be detected in complicated image structures. Although multi-level set functions can be used to segment an image into many regions, the traditional approach causes extensive computations and the solutions depend on the location of initial curves. Our proposed approach utilizes faster hierarchical level set method and guarantees convergence independent of initial conditions. Because we detect both the main structure and the detailed edges, our approach preserves edges in the inpainting area. Also, exemplar-based approach for filling textured regions is employed. Experimental results demonstrate the advantage of our method.     

基于改进C-V模型的图像分割算法

提出一种新的模型——Chan-Vese模型,该模型是基于曲线演化、水平集方法、局部的统计信息,新模型包括两个方面:局部核心函数和惩罚项.引入局部统计信息后的新模型可以对非同质图像进行有效的分割.另外,核心函数中加入惩罚项,可以有效避免水平集函数初始化,缩短模型演化时间.通过实验的仿真结果发现,新模型在对非同质图像进行分割时得到了良好的结果.     

基于先验形状的CV模型肝脏CT图像分割

针对目标被部分遮挡或部分信息丢失情况下CV模型不能正确识别的问题,提出一种新的分割算法。首先,利用数学形态学对原肝脏图像进行滤波,并结合其他算法建立肝脏先验形状;然后,采用边缘查找和区域标定等算法,对肝脏先验性状的边缘以及边缘内外区域进行赋值,构建执行效率高的符号函数距离函数,将其通过形状比较函数嵌入到CV模型的能量泛函中,形成新的基于先验形状的CV模型,并将此模型用于分割存在干扰或者被部分遮挡的肝脏CT图像。与CV模型分割结果相比,本文算法能在目标周围存在干扰信息或者被部分遮挡的情形下,成功地正确识别出目标区域。     

Depth extraction of partially occluded objects using deformable net

Estimating depth using a single camera moving along its optical axis has great benefits to autonomous robot navigation. In Shaaban and Omar (2012) we estimated depth information for non-occluded regions using the change of their seen area. The proposed work extends this technique to handle regions with partial occlusion. As the robot moves, the seen area of a partially occluded region is different from the two points of view. Therefore, at least one of the edges is deceptive and is not a real object boundary. We propose a technique to detect deceptive-edges at the boundary of occlusion then use triangulation to detect the structure of occlusion. With this knowledge, we estimate the percentage change in the actual area seen as the camera moves. This percentage is used to correct the results of the original Area-based method. Experimentally the algorithm provides an average depth with good accuracy for both far and near objects.     

基于改进主动轮廓模型的图像分割算法

针对C-V模型对灰度不均匀的图像分割效果不理想的情况,提出一种改进的C-V模型.该模型在C-V模型的基础上,引入非加权的邻域平均和局部窗口方差概念,加快并精确了C-V模型的演化效果,同时在C-V模型的能量函数中加入惩罚项,使得C-V模型在演化过程中无须重新初始化,进一步提高了分割速度.仿真实验结果表明改进的C-V模型较原模型对灰度不均匀图像分割具有较好的分割效果.     

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.     

一种基于二维拉格朗日连续水平集的图像分割方法

在图像分割领域中,基于离散水平集的图像分割方法不但对低信噪比图像难以实现正确地分割,而且分割速度较慢。针对这一问题,该文提出了一个基于连续水平集的图像分割方法。利用2维拉格朗日基函数的线性组合把水平集函数表示成连续函数。最小化实现图像分割的能量函数,建立基函数系数演化的微分方程。因此能量函数的最小值可直接根据拉格朗日的系数值获得。利用简单有限差分法对系数演化微分方程求解,实现了低信噪比图像的快速分割。实验结果表明该方法具有理想的分割结果。     

A spatially constrained generative asymmetric Gaussian mixture model for image segmentation

Accurate image segmentation is an essential step in image processing, where Gaussian mixture models with spatial constraint play an important role. Nevertheless, most methods suffer from one or more challenges such as limited robustness to noise, over-smoothness for segmentations, and lack of flexibility to fit the observed data. To address these issues, in this paper, we propose a generative asymmetric Gaussian mixture model with spatial constraint for image segmentation. The asymmetric distribution is modified to be easily incorporated the spatial information. Then our asymmetric model can be constructed based on the posterior and prior probabilities of within-cluster and between-cluster. Based on the Kullback-Leibler divergence, we introduce two pseudo-likelihood quantities which consider the neighboring priors of within-cluster and between-cluster. Finally, we derive an expectation maximization algorithm to maximize the approximation of the data log-likelihood. We compare our algorithm with state-of-the-art segmentation approaches to demonstrate the superior performance of the proposed algorithm.     

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.