Parametric Active Contour Model Research Articles

Evolution algorithm of parametric active contour model based on Gaussian smoothing filter

Evolution algorithm of parametric active contour model based on Gaussian smoothing filter

A New Texture Segmentation Method with Energy-driven Parametric Active Contour Model Based on Jensen-Tsallis Divergence

Texture image segmentation plays an important role in various computer vision tasks. Active contour models are one of the most efficient and popular methods for identifying the purpose and segmentation of objects in the image. This paper presents a parametric active contour model (PACM) with a robust minimization framework based on image texture energy. First, the texture features of the original image are extracted using gray level co-occurrence matrix (GLCM). Subsequently, based on the GLCM texture features inside and outside the active contour, Jensen-Tsallis divergence of energies is calculated. The Jensen-Tsallis divergence is added to the parametric active contour using the balloon equation. The divergence is maximum at the boundary between the foreground and background of the image, which results in minimizing the active contour equation at the boundary of the target object. This global minimization energy function with texture feature can avoid the existence of local minima in the PACM models. Also, as opposed to previous models, the proposed model only requires the initial contour and is not dependent on the distance of the initial contour from the target object. In terms of segmentation accuracy and efficiency, experiments with synthetic and natural images demonstrate that the proposed approach obtains more satisfactory results than the previous state-of-the-art methods.

Parametric active contour model-based tumor area segmentation from brain MRI images using minimum initial points

Accurate brain tumor segmentation from magnetic resonance imaging (MRI) images is important for proper medication. Manual segmentation may be erroneous and a computer-aided method is recommended for precise segmentation which is also challenging due to the contrast level of MRI images. This research work proposes to utilize a parametric active contour model (PACM)-based deformable snake model to segment brain tumors from MRI images. Conventional PACM model prerequisites some initial points for its initialization which may have a time-consuming issue. The main contribution of this paper is to modify the PACM algorithm, so that it can predict its initial points around the region of interest (ROI) from the given minimum (at least three) initial points. This proposed method aids PACM to find the initial contour points automatically to start the deformable mechanism. Furthermore, different parameters of the PACM algorithm are optimized for the segmentation by check and trial method. The proposed method is applied to different shapes of brain tumors inside the MRI images and found satisfactory segmentation outcomes. Furthermore, the proposed algorithm reports the number of total pixels inside the segmented area. Therefore, we hope that this proposal will help to find the area of critically shaped brain tumor in an MRI image.

Sparse Multi-Bending Snakes.

Active contour models are one of the most emblematic algorithms of computer vision. Their strong theoretical foundations and high user interoperability turned them into a reference approach for object segmentation and tracking tasks. A high number of modifications have already been proposed in order to overcome the known problems of traditional snakes, such as initialization dependence and poor convergence to concavities. In this paper, we address the scenario where the user wants to segment an object that has multiple dynamic regions but some of them do not correspond to the true object boundary. We propose a novel parametric active contour model, the Sparse Multi-Bending snake, which is capable of dividing the contour into a set of contiguous regions with different bending properties. We derive a new energy function that induces such behavior and presents a group optimization strategy that can be used to find the optimal bending resistance parameter for each point of the contour. We show the flexibility of our model in a set of synthetic images. In addition, we consider two real applications, lung segmentation in Computerized Tomography data and hand segmentation in depth images. We show how the proposed method is able to improve the segmentations obtained in both applications, when compared with other active contour models.

Direction Selective Contour Detection for Salient Objects

The active contour model is a widely used technique for automatic object contour extraction. Existing methods based on this model can perform with high accuracy, even in the case of complex contours, but challenging issues remain, like the need for precise contour initialization for high curvature boundary segments or the handling of cluttered backgrounds. To deal with such issues, this paper presents a salient object extraction method, the first step of which is the introduction of an improved edge map that incorporates edge direction as a feature. The direction information in the small neighborhoods of image feature points is extracted, and the images’ prominent orientations are defined for direction-selective edge extraction. Using such improved edge information, we provide a highly accurate shape contour representation, which we also combine with texture features. The principle of the paper is to interpret an object as the fusion of its components: its extracted contour and its inner texture. Our goal in fusing textural and structural information is twofold: it is applied for automatic contour initialization, and it is also used to establish an improved external force field. This fusion then produces highly accurate salient object extractions. We performed extensive evaluations, which confirm that the presented object extraction method outperforms parametric active contour models and achieves higher efficiency than the majority of the evaluated automatic saliency methods.

Parametric active contour based on sparse decomposition for multi-objects extraction

Active contour model has been widely used over the past decade in image segmentation. For parametric active contour model, it is always used to segment objects because of its simplicity and fast evolution. However, it could not extract multi-objects with one initial contour because of topological invariance of parametric contour and existing equilibrium points between objects in vector fields. In this paper, a method of multi-objects extraction with parametric active contour model is proposed. Firstly, after an edge map is computed, the idea of sparse representation and decomposition for edge map is introduced in order to obtain new edge maps to better describe the objects; Secondly, the obtained edge maps are used to generate vector fields; Finally, one initial contour is evolved in every vector field to extract corresponding objects. Experimental results show that the proposed model could extract multi-target objects by using one initial contour. Quantitative evaluation of the segmentation results with tested methods also shows that the proposed method is more robust to noise and gets the better effect of segmentation accuracy. Furthermore, the proposed method allows parallel computation of algorithms, hence further reducing the computational time.

A novel texture segmentation method based on co-occurrence energy-driven parametric active contour model

In this paper, a novel approach to texture segmentation based on the parametric active contour model (ACM) is proposed. At first, gray-level co-occurrence matrix and subsequently co-occurrence energy of the regions inside and outside of the dynamic contour are calculated. Difference of this energy corresponding to both the regions is used as the external energy of the proposed ACM. The contour stops and converges completely when this difference attains a maximum value. The proposed approach requires only initial contour selection and no object point selection like the other variants of parametric ACM used for texture segmentation. Experiments on a number of synthetic and real-world texture images show that in all cases, we are getting a better segmentation of the object although for few cases the execution time is bit more than that of other existing methods.

A fast texture segmentation scheme based on active contours and discrete cosine transform

This paper presents a novel and efficient parametric active contour model based on the texture describing properties derived from coefficients of the two-dimensional Discrete Cosine Transform (2-D DCT) for segmenting texture objects against complex backgrounds. Block based 2-D DCT is applied in the local neighbourhood of all the contour control points and some randomly chosen object points. DCT coefficients in the horizontal, vertical and diagonal directions which represent the dominant textures are used to construct the histograms those exhibit different structures for different texture regions. Heterogeneity of these histograms corresponding to contour and object points is measured by Wasserstein distance metric. Finally, DC coefficient which represents the energy of the corresponding block modulated by the calculated Wasserstein distance is employed as the external energy of the proposed active contour model. Experiments on synthetic and natural texture images show the efficiency of our model in terms of accuracy and speed.

Snake Energy Analysis and Result Validation for a Mobile Laser Scanning Data-Based Automated Road Edge Extraction Algorithm

The negative impact of road accidents cannot be ignored in terms of the very sizeable social and economic loss. Road infrastructure has been identified as one of the main causes of the road accidents. They are required to be recorded, located, measured, and classified in order to schedule maintenance and identify the possible risk elements of the road. Toward this, an accurate knowledge of the road edges increases the reliability and precision of extracting other road features. We have developed an automated algorithm for extracting road edges from mobile laser scanning (MLS) data based on the parametric active contour or snake model. The algorithm involves several internal and external energy parameters that need to be analyzed in order to find their optimal values. In this paper, we present a detailed analysis of the snake energy parameters involved in our road edge extraction algorithm. Their optimal values enable us to automate the process of extracting edges from MLS data for tested road sections. We present a modified external energy in our algorithm and demonstrate its utility for extracting road edges from low and nonuniform point density datasets. A novel validation approach is presented, which provides a qualitative assessment of the extracted road edges based on direct comparisons with reference road edges. This approach provides an alternative to traditional road edge validation methodologies that are based on creating buffer zones around reference road edges and then computing quality measure values for the extracted edges. We tested our road edge extraction algorithm on datasets that were acquired using multiple MLS systems along various complex road sections. The successful extraction of road edges from these datasets validates the robustness of our algorithm for use in complex route corridor environments.

An automated algorithm for extracting road edges from terrestrial mobile LiDAR data

Terrestrial mobile laser scanning systems provide rapid and cost effective 3D point cloud data which can be used for extracting features such as the road edge along a route corridor. This information can assist road authorities in carrying out safety risk assessment studies along road networks. The knowledge of the road edge is also a prerequisite for the automatic estimation of most other road features. In this paper, we present an algorithm which has been developed for extracting left and right road edges from terrestrial mobile LiDAR data. The algorithm is based on a novel combination of two modified versions of the parametric active contour or snake model. The parameters involved in the algorithm are selected empirically and are fixed for all the road sections. We have developed a novel way of initialising the snake model based on the navigation information obtained from the mobile mapping vehicle. We tested our algorithm on different types of road sections representing rural, urban and national primary road sections. The successful extraction of road edges from these multiple road section environments validates our algorithm. These findings and knowledge provide valuable insights as well as a prototype road edge extraction tool-set, for both national road authorities and survey companies.

Review of Parametric Active Contour Models in Image Processing

The parametric active contour models are widely used in computer vision and image processing. This paper reviews the research status of parametric active contour models in the past decade. The parametric active contour models have gained wide attention for a long time. First, the traditional snake and some typical modified models are introduced. Second, the parametric active contour models applications in the field of image processing are reviewed. Finally, the future work of parametric active contour models is pointed out.

Adaptive diffusion flow active contours for image segmentation

Gradient vector flow (GVF) active contour model shows good performance at concavity convergence and initialization insensitivity, yet it is susceptible to weak edges as well as deep and narrow concavity. This paper proposes a novel external force, called adaptive diffusion flow (ADF), with adaptive diffusion strategies according to the characteristics of an image region in the parametric active contour model framework for image segmentation. We exploit a harmonic hypersurface minimal functional to substitute smoothness energy term in GVF for alleviating the possible leakage. We make use of the p(x) harmonic maps, in which p(x) ranges from 1 to 2, such that the diffusion process of the flow field can be adjusted adaptively according to image characteristics. We also incorporate an infinity laplacian functional to ADF active contour model to drive the active contours onto deep and narrow concave regions of objects. The experimental results demonstrate that ADF active contour model possesses several good properties, including noise robustness, weak edge preserving and concavity convergence.

TH‐C‐137‐05: A MOrphing Technique That Projects 3D Surface Objects to a STandard Metric (MOST); Geometric Modeling of Cervix Cancer Patients for Adaptive Radiation Treatment

Purpose: Adaptive radiation therapy (ART) had been proposed based in order to correct the dosimetric discount due to geometric variations by means of feedback control. Numerical modeling of inter‐and intra‐patients' geometric variations is one of the important parts in ART. To this end, a MOrphing technique that projects 3D surface objects to a STandard metric (MOST) was developed. Methods: A total of 174 clinical target volumes (CTVs) were obtained from 32 patients and used to evaluate the MOST. The MOST consists of three main steps; 1) deforming 3D mesh of CTVs to a sphere by parametric active contour (PAC) model, 2) sampling the deformed mesh at evenly distributed girds to be the standard metric, and 3) projecting the 3D data into 2D plane for further analysis. The performance of the MOST was evaluated with respects to 1) iteration number, 2) computation time, and 3) residual deformation, the residual distance between a sphere surface and the deformed node. Result: MOST successfully transformed complex 152 meshes from 28 patients to the standard metric. Convergence was achieved with average iteration of 65 and one standard deviation (STD) of 74 for 137 cases. The computation time was 17.8 hours and this corresponds to 51.3% of the time for all 152 cases. The average±STD of residual deformation was 0.9±0.7‐mm and more than 98% nodes had less than 3‐mm residual deformation. Conclusions: MOST demonstrated its ability to transform complex 3D meshes of cervix cancer into a simple standard metric. Inter‐and intra‐patients' geometric variation could be directly compared and analyzed using the transformed results by virtue of the regularity of standard metric. Numerical modeling of tumor dynamics, implemented using this technique, may provide a systematic way for the estimation of tumor evolution and optimal correctional action for ART. This study is supported by funding from Ray Search Laboratories and Ontario Consortium for Adaptive Interventions Radiation Oncology (OCAIRO).

Segmentation of the left ventricle in cardiac cine MRI using a shape-constrained snake model

Segmentation of the left ventricle (LV) is a hot topic in cardiac magnetic resonance (MR) images analysis. In this paper, we present an automatic LV myocardial boundary segmentation method using the parametric active contour model (or snake model). By convolving the gradient map of an image, a fast external force named gradient vector convolution (GVC) is presented for the snake model. A circle-based energy is incorporated into the GVC snake model to extract the endocardium. With this prior constraint, the snake contour can conquer the unexpected local minimum stemming from artifacts and papillary muscle, etc. After the endocardium is detected, the original edge map around and within the endocardium is directly set to zero. This modified edge map is used to generate a new GVC force filed, which automatically pushes the snake contour directly to the epicardium by employing the endocardium result as initialization. Meanwhile, a novel shape-similarity based energy is proposed to prevent the snake contour from being strapped in faulty edges and to preserve weak boundaries. Both qualitative and quantitative evaluations on our dataset and the publicly available database (e.g. MICCAI 2009) demonstrate the good performance of our algorithm.

Parametric kernel-driven active contours for image segmentation

We investigated a parametric kernel-driven active contour (PKAC) model, which implicitly transfers kernel mapping and piecewise constant to modeling the image data via kernel function. The proposed model consists of curve evolution functional with three terms: global kernel-driven and local kernel-driven terms, which evaluate the deviation of the mapped image data within each region from the piecewise constant model, and a regularization term expressed as the length of the evolution curves. In the local kernel-driven term, the proposed model can effectively segment images with intensity inhomogeneity by incorporating the local image information. By balancing the weight between the global kernel-driven term and the local kernel-driven term, the proposed model can segment the images with either intensity homogeneity or intensity inhomogeneity. To ensure the smoothness of the level set function and reduce the computational cost, the distance regularizing term is applied to penalize the deviation of the level set function and eliminate the requirement of re-initialization. Compared with the local image fitting model and local binary fitting model, experimental results show the advantages of the proposed method in terms of computational efficiency and accuracy.

Comparative study on the performance of textural image features for active contour segmentation

We present a computerized method for the semi-automatic detection of contours in ultrasound images. The novelty of our study is the introduction of a fast and efficient image function relating to parametric active contour models. This new function is a combination of the gray-level information and first-order statistical features, called standard deviation parameters. In a comprehensive study, the developed algorithm and the efficiency of segmentation were first tested for synthetic images. Tests were also performed on breast and liver ultrasound images. The proposed method was compared with the watershed approach to show its efficiency. The performance of the segmentation was estimated using the area error rate. Using the standard deviation textural feature and a 5×5 kernel, our curve evolution was able to produce results close to the minimal area error rate (namely 8.88% for breast images and 10.82% for liver images). The image resolution was evaluated using the contrast-to-gradient method. The experiments showed promising segmentation results.

An automated approach for segmentation of intravascular ultrasound images based on parametric active contour models

This paper presents a fully automated approach to detect the intima and media-adventitia borders in intravascular ultrasound images based on parametric active contour models. To detect the intima border, we compute a new image feature applying a combination of short-term autocorrelations calculated for the contour pixels. These feature values are employed to define an energy function of the active contour called normalized cumulative short-term autocorrelation. Exploiting this energy function, the intima border is separated accurately from the blood region contaminated by high speckle noise. To extract media-adventitia boundary, we define a new form of energy function based on edge, texture and spring forces for the active contour. Utilizing this active contour, the media-adventitia border is identified correctly even in presence of branch openings and calcifications. Experimental results indicate accuracy of the proposed methods. In addition, statistical analysis demonstrates high conformity between manual tracing and the results obtained by the proposed approaches.

Fast texture energy based image segmentation using Directional Walsh–Hadamard Transform and parametric active contour models

Texture image segmentation is an important issue in computer vision applications. Active contour models are one of the powerful tools that are able to detect and segment textured objects against textured backgrounds. However, problems concerning the speed of the contour convergence in the texture image have limited their utility. This paper presents a fast and efficient texture energy function in the parametric active contour models. In the proposed method, we apply a novel version of the Walsh–Hadamard transform, called the Directional Walsh–Hadamard Transform or DWHT for calculating texture features of the energy function. This DWHT-based energy function is fast and easy to implement, and hence suitable for real time applications. We will show that the proposed method can reduce the execution time, while maintaining close accuracy and consequently it is more efficient than the previous active contour based methods for texture image segmentation.

Parametric active contour model by using the honey bee mating optimization

In this paper, the honey bee mating optimization (HBMO) algorithm is used to improve the detection of the concave region connected with the control points of active contour. In the traditional active contour model (ACM) method, the updating of control point is based on its local energy within a small searching window. As a result, it always results in the failure of precisely searching the boundary concavities. In order to vanquish these drawbacks, the HBMO-based snake algorithm is applied in this paper to search for the optimal position in a lager searching window around each control point. In this proposed algorithm, to each active contour there is a chromosome that includes several genes as well as the control points of active contour. These control points are moved iteratively by minimizing the total energy of the active contour. Experimental results reveal that the proposed HBMO-based snake algorithm can locate the object boundary of concavity more precisely without requiring large number of computational time.

Biomedical Image Analysis: Segmentation

Abstract The sequel to the popular lecture book entitled Biomedical Image Analysis: Tracking, this book on Biomedical Image Analysis: Segmentation tackles the challenging task of segmenting biological and medical images. The problem of partitioning multidimensional biomedical data into meaningful regions is perhaps the main roadblock in the automation of biomedical image analysis. Whether the modality of choice is MRI, PET, ultrasound, SPECT, CT, or one of a myriad of microscopy platforms, image segmentation is a vital step in analyzing the constituent biological or medical targets. This book provides a state-of-the-art, comprehensive look at biomedical image segmentation that is accessible to well-equipped undergraduates, graduate students, and research professionals in the biology, biomedical, medical, and engineering fields. Active model methods that have emerged in the last few years are a focus of the book, including parametric active contour and active surface models, active shape models, and geomet...