Traditional Active Contour Model Research Articles

The line- and block-like structures extraction via ingenious snake

Active contour model (ACM) plays an important role in computer vision and medical image analysis. The traditional ACMs were employed to extract closed contours of objects. While simultaneous extraction of line- and block-like objects, such as boundary contours, centerlines, as well as their topological relationship, remains open so far. Therefore, a novel ACM named "Ingenious Snake" is proposed to adaptively extract the feature curves. The proposed ingenious snake includes the following steps: 1) In the preprocessing, the line- and block-like structures are classified with k-means clustering, following up with morphological operation and enhancement. The gradient vector flow (GVF) field is then acquired from the resultant ridge feature map. 2) For the automatic initialization, the ridge-points are extracted by using the local phase measurement of GVF field, then the two-category of object ridgelines are obtained fast. 3) Finally, the contour deformation and curves evolvement are implemented with a management strategy. The resultant contours and centerlines well characterize the objects of interest. In the experiments, we compare the existing initialization methods and the adaptive extraction with a series of phantoms and testing images. The visual and quantitative assessments of structure extraction are satisfying in terms of effectiveness and accuracy.

Ingenious Snake: An Adaptive Multi-Class Contours Extraction

Active contour model (ACM) plays an important role in computer vision and medical image application. The traditional ACMs were used to extract single-class of object contours. While, simultaneous extraction of multi-class of interesting contours (i.e., various contours with closed- or open-ended) have not been solved so far. Therefore, a novel ACM model named “Ingenious Snake” is proposed to adaptively extract these interesting contours. In the first place, the ridge-points are extracted based on the local phase measurement of gradient vector flow field; the consequential ridgelines initialization are automated with high speed. Secondly, the contours’ deformation and evolvement are implemented with the ingenious snake. In the experiments, the result from initialization, deformation and evolvement are compared with the existing methods. The quantitative evaluation of the structure extraction is satisfying with respect of effectiveness and accuracy.

Adaptive Energy Weight Based Active Contour Model for Robust Medical Image Segmentation

The performance of the traditional active contour model is subject to the energy weight parameters and initial level set functions, which significantly affect the accuracy of segmentation results. This paper proposes a new robust active contour model to eliminate the above limitations for both 2D single-object and multi-object medical image segmentation. The optimal values of energy weight parameters are defined with adaptive energy weight functions to adjust the contribution of each external energy term dynamically. Thus, the energy functional will not be controlled by the large external energy terms, otherwise boundaries leakage would occur. The initial level set functions are optimized with the coarse results obtained by fuzzy C-means clustering method. The evolution of level set functions starts from the locations near the true boundaries. Therefore, the deformable curves could converge to the true boundaries robustly. The proposed algorithm is verified using both synthetic images and medical images from different modalities. The experimental results demonstrate that the proposed algorithm could realize accurate and robust segmentation for medical images even in the presence of noises and weak boundaries.

A novel active contour model based on modified symmetric cross entropy for remote sensing river image segmentation

The traditional active contour models cannot segment the remote sensing river images accurately. To solve this problem, a novel active contour model based on modified symmetric cross entropy is proposed. In the proposed model, the external energy constraint terms are defined by the symmetric cross entropy and the region fitting centers are represented by the medians of pixel grayscale values inside the object and background regions. Moreover, the penalty energy term is incorporated into the energy functional to avoid the re-initialization. In order to improve the segmentation efficiency of the proposed model, the Chebyshev distance between the pixel grayscale values inside the region and its region fitting center is chosen as its region energy weight, which can be adaptively adjusted, instead of the constant region energy weight. The extensive experiments are performed on a large number of remote sensing river images and the results demonstrate that, compared with the CV model, the GAC model, the CEACM model, the RSF model, the LIF model, and the LGIF model, the proposed model can segment the images more accurately and rapidly, which has the clear advantages in both segmentation performance and segmentation efficiency.

Localized active contour model with background intensity compensation applied on automatic MR brain tumor segmentation

This paper presents a Localized Active Contour Model (LACM) integrating an additional step of background intensity compensation. The region-based active contour models that use statistical intensity information are more sensitive to the high mean intensity distance between consecutive regions. In Magnetic Resonance Imaging (MRI) this distance is great between the foreground and the background, hence it leads to an incorrect delineation of the target. In order to resolve this problem, an automatic process is introduced in our model for balancing the mean intensity distance between an image foreground and its background. The aim is to minimize the attraction effect of the active contour model to the undesired borderlines defined by these two mentioned image regions. By using this approach not only the obtained accuracy outperforms the traditional localized mean separation active contour model, but also it reduces the computation time of the segmentation task. In addition, this method was efficiently applied on automatic brain tumor segmentation in multimodal MRI data. The Hierarchical Centroid Shape Descriptor (HCSD) was used for detecting the region of interest i.e. abnormal tissue so as to automatically initialize the active contour. The validation of experiments was carried out on synthetic images and the quantitative evaluation was performed on the BRATS2012 database. Finally, the accuracy achieved by the proposed method was compared to the localized mean separation intensity, the localized Chan-Vese, the local Gaussian distribution fitting and the local binary fitting models by using the Dice coefficient, Sensitivity, Specificity and the Hausdorff distance. The computation time of the methods was also measured for comparison purposes. The obtained results show that the proposed model outperforms the accuracy of the selected state of the art methods. Moreover, it is also faster than the comparative methods in the medical image segmentation task.

Superpixel-based active contour model for unsupervised change detection from satellite images

ABSTRACTThis study proposes a superpixel-based active contour model (SACM) for unsupervised change detection from satellite images. The accuracy of change detection produced by the traditional active contour model suffers from the trade-off parameter. The SACM is designed to address this limitation through the incorporation of the spatial and statistical information of superpixels. The proposed method mainly consists of three steps. First, the difference image is created with change vector analysis method from two temporal satellite images. Second, statistical region merging method is applied on the difference image to produce a superpixel map. Finally, SACM is designed based on the superpixel map to detect changes from the difference image. The SACM incorporates spatial and statistical information and retains the accurate shapes and outlines of superpixels. Experiments were conducted on two data sets, namely Landsat-7 Enhanced Thematic Mapper Plus and SPOT 5, to validate the proposed method. Experimental results show that SACM reduces the effects of the trade-off parameter. The proposed method also increases the robustness of the traditional active contour model for input parameters and improves its effectiveness. In summary, SACM often outperforms some existing methods and provides an effective unsupervised change detection method.

Image segmentation using the level set and improved-variation smoothing

Traditional active contour models perform poorly on real images with inhomogeneous sub-regions. In order to overcome this limitation, this paper has proposed a novel segmentation algorithm. Firstly, analyzing the smoothing conditions for image segmentation, we construct a smoothing function with improved total variation. This function can smooth the inhomogeneous sub-regions, preserve the strong edges and enhance the weak edges. Then, the level set is employed to segment the smoothing component using the smoothing function. Lastly, according to the confidence level of segmentation sub-regions, we add a convergence condition to the smoothing to prevent the segmentation curve from vanishing. Experimental results indicate that this model is insensitive to noise and can deal with inhomogeneous intensity.

Nonlocal active contour model for texture segmentation

Texture segmentation is a very important subject in the fields of computer vision. In order to segment the textures, active contour model based on nonlocal means method and tikhonov regularization is proposed. In detail, a new nonlocal tikhonov regularization smoothness term is added. The nonlocal operator is based on the image slice similarity. So better segmentation accuracy can be achieved for the images which contain special texture features. The good matter of our method is not only nonlocal operator added but also the original tikhonov regularization smoothness item based on the pixel values retained. The nonlocal operator is time consuming, while the reserved smoothness term can save time to some extent. The traditional active contour model can only be used to segment the conventional images. The nonlocal active contour model can dispose the textures well. What's more, in order to improve the computation efficiency, this paper designs the Split-Bregman algorithm. At last, our performance is demonstrated by segmenting many real texture images.

Region-based active contours with cosine fitting energy for image segmentation.

In this paper, by employing the cosine function to express the so-called data fitting term in traditional active contour models, we propose an active contour model with the global cosine fitting energy for segmenting synthetic and real-world images. After that, in order to segment the image with intensity inhomogeneity, we extend the proposed global model to the local cosine fitting energy. In addition, we introduce level set regularization terms into the proposed models to avoid the expensive computational cost which is usually caused by the reinitialization of the evolving level set function. Experimental results indicate that the proposed models are accurate and effective when applied to segment different types of images. Moreover, our models are more efficient and robust for segmenting the images with strong noise and clutter than the Chan-Vese model and the local binary fitting model.

Active Contour Model with Edge Flow Vector for Texture Segmentation

In this paper, we propose effective method for texture segmentation using active contour model with edge flow vector. This technique was applied from previous active contour model that uses gradient vector flow as external force. It was observed that our method provided better results for texture segmentation while a traditional active contour model and active contour model with gradient vector flow were not capable to be used with texture image. Thus, texture image such as medical imaging can be identified using active contour model with edge flow vector.

Active Contour Models for Manifold Valued Image Segmentation

Image segmentation is the process of partitioning an image into different regions or groups based on some characteristics like color, texture, motion, or shape etc. Active contours are a popular variational method for object segmentation in images, in which the user initializes a contour which evolves in order to optimize an objective function designed such that the desired object boundary is the optimal solution. Recently, imaging modalities that produce Manifold-valued images are frequently used, for example, DT-MRI images, vector fields. The traditional active contour model does not work on such images. In this paper, we generalize the active contour model to work on Manifold-valued images. As expected, our algorithm detects regions with similar Manifold values in the image. Our algorithm also produces expected results on usual gray-scale images, since these are nothing but trivial examples of Manifold-valued images. As another application of our general active contour model, we perform texture segmentation on gray-scale images by first creating an appropriate Manifold-valued image. We demonstrate segmentation results for manifold-valued images and texture images.

3D Adaptive Balloon Active Contour: method of segmentation of structures in three dimensions

The Active Contour Model (ACM) was introduced in 1987 and allows the image segmentation of objects in digital images through edge detection, obtained from image information. This ACM consists in outlining an initial curve around or inside an object of interest. This curve is deformed, according to some forces which act on it, moving it to the edges of the object. This process is performed through successive minimizing iterations of a given energy associated to the curve. Recently, various applications have proposed changes in traditional ACMs, generating new methods, applications, benefits and performance improvements in certain applications. Due to that, these methods have also been expanded to three dimensions (3D), being called Active Contour Methods 3D (ACM 3D), as is the case of the proposed method, called Adaptive Balloon ACM 3D. This method is applied in the segmentation of 3D models and compared to the 3D Region Growing method (RG 3D). The results showed that the RG is superior to the 3D proposed method when applied in closed objects, but inferior in hollow objects, in which the proposed method is stably displayed in all applications. Therefore, one can conclude that the proposed method can be applied to the segmentation of 3D objects in a promising way.

A Segmentation Algorithm of Pulmonary Nodules Using Active Contour Model Based on Fuzzy Speed Function

Pulmonary nodules are potential manifestation of lung cancer. In order to detect juxta-vascular pulmonary nodules and ground glass opacity pulmonary nodules in computer-aided diagnosis (CAD) system, the above two types of pulmonary nodules need to be accurately segmented. At present, the segmentation algorithm of pulmonary nodules using traditional active contour model may cause boundary leakage. In order to avoid this phenomenon, a new segmentation algorithm of pulmonary nodules using active contour model based on fuzzy speed function is proposed in this paper. First, the fuzzy membership degree in fuzzy speed function is calculated by using the fuzzy clustering algorithm, which uses gray feature and local shape index. Second, a fuzzy speed function is incorporated into the active contour model. At the boundary of pulmonary nodules, tbe fuzzy speed function equals zero and the evolution of the contour curve stops, so that the accurate segmentation of pulmonary nodules is completed. Experimental results show that the proposed algorithm can achieve accurate segmentation of juxta-vascular pulmonary nodules and ground glass opacity pulmonary nodules.

A Morphological Multiphase Active Contour for Vascular Segmentation

This paper presents a morphological active contour ideal for vascular segmentation in biomedical images. The unenhanced images of vessels and background are successfully segmented using a two-step morphological active contour based upon Chan and Vese’s Active Contour without Edges. Using dilation and erosion as an approximation of curve evolution, the contour provides an efficient, simple, and robust alternative to solving partial differential equations used by traditional level-set Active Contour models. The proposed method is demonstrated with segmented data set images and compared to results garnered from multiphase Active Contour without Edges, morphological watershed, and Fuzzy C-means segmentations.

Elastic shapes models for improving segmentation of object boundaries in synthetic aperture sonar images

We present a variational framework for naturally incorporating prior shape knowledge in guidance of active contours for boundary extraction in images. This framework is especially suitable for images collected outside the visible spectrum, where boundary estimation is difficult due to low contrast, low resolution, and presence of noise and clutter. Accordingly, we illustrate this approach using the segmentation of various objects in synthetic aperture sonar (SAS) images of underwater terrains. We use elastic shape analysis of planar curves in which the shapes are considered as elements of a quotient space of an infinite dimensional, non-linear Riemannian manifold. Using geodesic paths under the elastic Riemannian metric, one computes sample mean and covariances of training shapes in each classes and derives statistical models for capturing class-specific shape variability. These models are then used as shape priors in a variational setting to solve for Bayesian estimation of desired contours as follows. In traditional active contour models curves are driven towards minimum of an energy composed of image and smoothing terms. We introduce an additional shape term based on shape models of relevant shape classes. The minimization of this total energy, using iterated gradient-based updates of curves, leads to an improved segmentation of object boundaries. This is demonstrated using a number of shape classes in two large SAS image datasets.

Serial slice image segmentation of digital human based on adaptive geometric active contour tracking

Segmentation is one of the crucial problems for the digital human research, as currently digital human datasets are manually segmented by experts with anatomy knowledge. Due to the thin slice thickness of digital human data, the static slices can be regarded as a sequence of temporal deformation of the same slice. This gives light to the method of object contour tracking for the segmentation task for the digital human data. In this paper, we present an adaptive geometric active contour tracking method, based on a feature image of object contour, to segment tissues in digital human data. The feature image is constructed according to the matching degree of object contour points, image variance and gradient, and statistical models of the object and background colors. Utilizing the characteristics of the feature image, the traditional edge-based geometric active contour model is improved to adaptively evolve curve in any direction instead of the single direction. Experimental results demonstrate that the proposed method is robust to automatically handle the topological changes, and is effective for the segmentation of digital human data.

Multiple Active Contours Driven by Particle Swarm Optimization for Cardiac Medical Image Segmentation

This paper presents a novel image segmentation method based on multiple active contours driven by particle swarm optimization (MACPSO). The proposed method uses particle swarm optimization over a polar coordinate system to increase the energy-minimizing capability with respect to the traditional active contour model. In the first stage, to evaluate the robustness of the proposed method, a set of synthetic images containing objects with several concavities and Gaussian noise is presented. Subsequently, MACPSO is used to segment the human heart and the human left ventricle from datasets of sequential computed tomography and magnetic resonance images, respectively. Finally, to assess the performance of the medical image segmentations with respect to regions outlined by experts and by the graph cut method objectively and quantifiably, a set of distance and similarity metrics has been adopted. The experimental results demonstrate that MACPSO outperforms the traditional active contour model in terms of segmentation accuracy and stability.

Automatic Image Segmentation Using Active Contours with Univariate Marginal Distribution

This paper presents a novel automatic image segmentation method based on the theory of active contour models and estimation of distribution algorithms. The proposed method uses the univariate marginal distribution model to infer statistical dependencies between the control points on different active contours. These contours have been generated through an alignment process of reference shape priors, in order to increase the exploration and exploitation capabilities regarding different interactive segmentation techniques. This proposed method is applied in the segmentation of the hollow core in microscopic images of photonic crystal fibers and it is also used to segment the human heart and ventricular areas from datasets of computed tomography and magnetic resonance images, respectively. Moreover, to evaluate the performance of the medical image segmentations compared to regions outlined by experts, a set of similarity measures has been adopted. The experimental results suggest that the proposed image segmentation method outperforms the traditional active contour model and the interactive Tseng method in terms of segmentation accuracy and stability.

Active contour for noisy image segmentation based on contourlet transform

Active contour is one of the most successful variational models in image segmentation, pattern analysis, and computer vision. However, traditional active contour models not only require much expensive computation but are very sensitive to noise. We propose a scheme for noisy image segmentation integrating the active contour model with the contourlet transform, an optimal sparse representation of an image. Having reconstructed all the scale maps, we downsample the last but one scale map twice. Then, we apply the active contour model on the coarsest scale map and take the segmentation results as the initial curves for the finer scale map. Experiments have demonstrated that our proposed method can yield desired segmentation results both in real and synthetic images.

Active Geodesics: Region-based Active Contour Segmentation with a Global Edge-based Constraint.

We present an active geodesic contour model in which we constrain the evolving active contour to be a geodesic with respect to a weighted edge-based energy through its entire evolution rather than just at its final state (as in the traditional geodesic active contour models). Since the contour is always a geodesic throughout the evolution, we automatically get local optimality with respect to an edge fitting criterion. This enables us to construct a purely region-based energy minimization model without having to devise arbitrary weights in the combination of our energy function to balance edge-based terms with the region-based terms. We show that this novel approach of combining edge information as the geodesic constraint in optimizing a purely region-based energy yields a new class of active contours which exhibit both local and global behaviors that are naturally responsive to intuitive types of user interaction. We also show the relationship of this new class of globally constrained active contours with traditional minimal path methods, which seek global minimizers of purely edge-based energies without incorporating region-based criteria. Finally, we present some numerical examples to illustrate the benefits of this approach over traditional active contour models.