Parametric Active Contour 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.

Automatic brain extraction and hemisphere segmentation in rat brain MR images after stroke using deformable models.

Experimental ischemic stroke models play an essential role in understanding the mechanisms of cerebral ischemia and evaluating the development of pathological extent. An important precursor to the investigation of ischemic strokes associated with rodents is the brain extraction and hemisphere segmentation in rat brain diffusion-weighted imaging (DWI) and T2-weighted MRI (T2WI) images. Accurate and reliable image segmentation tools for extracting the rat brain and hemispheres in the MR images are critical in subsequent processes, such as lesion identification and injury analysis. This study is an attempt to investigate rat brain extraction and hemisphere segmentation algorithms that are practicable in both DWI and T2WI images. To automatically perform brain extraction, the proposed framework is based on an efficient geometric deformable model. By introducing an additional image force in response to the rat brain characteristics into the skull stripping model, we establish a unique rat brain extraction scheme in DWI and T2WI images. For the subsequent hemisphere segmentation, we develop an efficient brain feature detection algorithm to approximately separate the rat brain. A refinement process is enforced by constructing a gradient vector flow in the proximity of the midsurface, where a parametric active contour is attracted to achieve hemisphere segmentation. Extensive experiments with 55 DWI and T2WI subjects were executed in comparison with the state-of-the-art methods. Experimental results indicated that our rat brain extraction and hemisphere segmentation schemes outperformed the competitive methods and exhibited high performance both qualitatively and quantitatively. For rat brain extraction, the average Dice scores were 97.13% and 97.42% in DWI and T2WI image volumes, respectively. Rat hemisphere segmentation results based on the Hausdorff distance metric revealed average values of 0.17 and 0.15mm for DWI and T2WI subjects, respectively. We believe that the established frameworks are advantageous to facilitate preclinical stroke investigation and relevant neuroscience research that requires accurate brain extraction and hemisphere segmentation using rat DWI and T2WI images.

Piecewise constant level-set enhanced active shape reconstruction for electrical impedance tomography

To accurately access the quantitative information of industrial and biomedical processes, a piecewise constant level-set enhanced inclusion boundary reconstruction (PLBR) method is proposed for EIT. The target inclusion boundaries are parameterized by a parametric active contour joint piecewise constant level set model. An augmented least square approach is presented to simultaneously estimate the unknown boundary and conductivity parameters from the EIT voltage data. Compared with the other active shape reconstruction methods of EIT, the PLBR does not need to know the number of target inclusion and the number of conductivity phases, thus owns wider applications. The numerical and experiential results from a tank model with both single- and multi-phase conductivity further proved the topological robustness and shape reconstruction accuracy of the proposed method.

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.

Deep Parametric Active Contour Model for Neurofibromatosis Segmentation

Locating the neurofibromatosis boundaries is one of the most challenging tasks due to the properties of low contrast and irregular blurred boundaries. To handle this problem, we propose Deep Parametric Active Contours (DPAC), a novel end-to-end framework that integrates the precise boundary localization abilities of the Active Contour Models (ACMs) with the robust nonlinear priori feature extraction abilities of the Convolutional Neural Networks (CNN). DPAC employs CNN to predict the parameter maps of the ACM per instance and incorporates them into the energy functional of the ACM to locate the neurofibromatosis boundaries, which makes DPAC end-to-end trainable. We evaluate the proposed method on three neurofibromatosis datasets that we created, and experimental results show that our DPAC method can improve the boundary similarity and precision of the classic ACMs. Even in the case with a small amount of data, our method can achieve good performance outperforming similar methods.

Optimising adjacent membrane segmentation and parameterisation in multicellular aggregates by piecewise active contours

In fluorescence microscopy imaging, the segmentation of adjacent cell membranes within cell aggregates, multicellular samples, tissue, organs, or whole organisms remains a challenging task. The lipid bilayer is a very thin membrane when compared to the wavelength of photons in the visual spectra. Fluorescent molecules or proteins used for labelling membranes provide a limited signal intensity, and light scattering in combination with sample dynamics during in vivo imaging lead to poor or ambivalent signal patterns that hinder precise localisation of the membrane sheets. In the proximity of cells, membranes approach and distance each other. Here, the presence of membrane protrusions such as blebs; filopodia and lamellipodia; microvilli; or membrane vesicle trafficking, lead to a plurality of signal patterns, and the accurate localisation of two adjacent membranes becomes difficult. Several computational methods for membrane segmentation have been introduced. However, few of them specifically consider the accurate detection of adjacent membranes. In this article we present ALPACA (ALgorithm for Piecewise Adjacent Contour Adjustment), a novel method based on 2D piecewise parametric active contours that allows: (i) a definition of proximity for adjacent contours, (ii) a precise detection of adjacent, nonadjacent, and overlapping contour sections, (iii) the definition of a polyline for an optimised shared contour within adjacent sections and (iv) a solution for connecting adjacent and nonadjacent sections under the constraint of preserving the inherent cell morphology. We show that ALPACA leads to a precise quantification of adjacent and nonadjacent membrane zones in regular hexagons and live image sequences of cells of the parapineal organ during zebrafish embryo development. The algorithm detects and corrects adjacent, nonadjacent, and overlapping contour sections within a selected adjacency distance d, calculates shared contour sections for neighbouring cells with minimum alterations of the contour characteristics, and presents piecewise active contour solutions, preserving the contour shape and the overall cell morphology. ALPACA quantifies adjacent contours and can improve the meshing of 3D surfaces, the determination of forces, or tracking of contours in combination with previously published algorithms. We discuss pitfalls, strengths, and limits of our approach, and present a guideline to take the best decision for varying experimental conditions for in vivo microscopy.

Toward an automatic tool for oligoclonal band detection in cerebrospinal fluid and tears for multiple sclerosis diagnosis: lane segmentation based on a ribbon univariate open active contour.

The latest revision of multiple sclerosis diagnosis guidelines emphasizes the role of oligoclonal band detection in isoelectric focusing images of cerebrospinal fluid. Recent studies suggest tears as a promising noninvasive alternative to cerebrospinal fluid. We are developing the first automatic method for isoelectric focusing image analysis and oligoclonal band detection in cerebrospinal fluid and tear samples. The automatic analysis would provide an accurate, fast analysis and would reduce the expert-dependent variability and errors of the current visual analysis. In this paper, we describe a new effective model for the fully automated segmentation of highly distorted lanes in isoelectric focusing images. This approach is a new formulation of the classic parametric active contour problem, in which an open active contour is constrained to move from the top to the bottom of the image, and the x-axis coordinate is expressed as a function of the y-axis coordinate. The left and right edges of the lane evolved together in a ribbon-like shape so that the full width of the lane was captured reliably. The segmentation algorithm was implemented using a multiresolution approach in which the scale factor and the active contour control points were progressively increased. The lane segmentation algorithm was tested on a database of 51 isoelectric focusing images containing 419 analyzable lanes. The new model gave robust results for highly curved lanes, weak edges, and low-contrast lanes. A total of 98.8% of the lanes were perfectly segmented, and the remaining 1.2% had only minor errors. The computation time (1s per membrane) is negligible. This method precisely defines the region of interest in each lane and thus is a major step toward the first fully automatic tool for oligoclonal band detection in isoelectric focusing images. Graphical abstract.

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.

Cage Active Contours for image warping and morphing

Cage Active Contours (CACs) have shown to be a framework for segmenting connected objects using a new class of parametric region-based active contours. The CAC approach deforms the contour locally by moving cage’s points through affine transformations. The method has shown good performance for image segmentation, but other applications have not been studied. In this paper, we extend the method with new energy functions based on Gaussian mixture models to capture multiple color components per region and extend their applicability to RGB color space. In addition, we provide an extended mathematical formalization of the CAC framework with the purpose of showing its good properties for segmentation, warping, and morphing. Thus, we propose a multiple-step combined method for segmenting images, warping the correspondences of the object cage points, and morphing the objects to create new images. For validation, both quantitative and qualitative tests are used on different datasets. The results show that the new energies produce improvements over the previously developed energies for the CAC. Moreover, we provide examples of the application of the CAC in image segmentation, warping, and morphing supported by our theoretical conclusions.

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.

Automated segmentation of retinal layers from optical coherence tomography images using geodesic distance

Optical coherence tomography (OCT) is a noninvasive imaging technique that can produce images of the eye at the microscopic level. OCT image segmentation to detect retinal layer boundaries is a fundamental procedure for diagnosing and monitoring the progression of retinal and optical nerve diseases. In this paper, we introduce a novel and accurate segmentation method based on geodesic distance for both two and three dimensional OCT images. The geodesic distance is weighted by an exponential function, which takes into account both horizontal and vertical intensity variations in the image. The weighted geodesic distance is efficiently calculated from an Eikonal equation via the fast sweeping method. Segmentation then proceeds by solving an ordinary differential equation of the geodesic distance. The performance of the proposed method is compared with manual segmentation. Extensive experiments demonstrate that the proposed method is robust to complex retinal structures with large curvature variations and irregularities and it outperforms the parametric active contour algorithm as well as graph based approaches for segmenting retinal layers in both healthy and pathological images.

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.

CAD for demarcation of malignant and benign nodules in CT lung images of spiculated nodules

This research work intents to remove the intricacies involved in demarcating the malignant and benign of the spiculated Solitary Pulmonary Nodules (SPN). Edges can be classified as irregular edge with corona radiata, lobulation, notching signs and a distinct soft, uncloudy contour edge. These edges are hardly spotted in bronchial carcinoma. This paper develops an algorithm for automatically detecting stipulated nodules using BPN algorithm, from the given computed tomography (CT) lung image. Here, to automate the detection of lung nodule, parametric active contours are used for manual segmentation. Features are extracted from gray level co-occurrence matrix (GLCM) derived from manually segmented lung nodule and used for further classification as nodule and non-nodule/normal image. This paper further classifies spiculated nodule into malignant or benign by fixing a threshold for the average image intensity after administering contrast.

CAD for demarcation of malignant and benign nodules in CT lung images of spiculated nodules

This research work intents to remove the intricacies involved in demarcating the malignant and benign of the spiculated Solitary Pulmonary Nodules (SPN). Edges can be classified as irregular edge with corona radiata, lobulation, notching signs and a distinct soft, uncloudy contour edge. These edges are hardly spotted in bronchial carcinoma. This paper develops an algorithm for automatically detecting stipulated nodules using BPN algorithm, from the given computed tomography (CT) lung image. Here, to automate the detection of lung nodule, parametric active contours are used for manual segmentation. Features are extracted from gray level co-occurrence matrix (GLCM) derived from manually segmented lung nodule and used for further classification as nodule and non-nodule/normal image. This paper further classifies spiculated nodule into malignant or benign by fixing a threshold for the average image intensity after administering contrast.

Improving parametric active contours by using attracting point distance map

In this paper, we propose an improvement of the classical parametric active contours. The method, presented here, consists in adding a new energy term based on attraction point distance map chosen on the object. This additional term acts as attraction forces that constrain the contour to remain in the vicinity of the object. The distance map introduced here differs from the classical one since it is not based on the whole binary image, but rather constitutes a simplified and very fast version that relates only to one point. The additional forces, so introduced, act as a kind of balloon method. The attracting point is selected on an image based the shape of the object of interest. To improve convergence, we also propose the use of weighting factors for the externals forces as dependent on snake points. The method is evaluated for object segmentation in images, and is also tested for multi-object segmentation. Compared to the conventional balloon method, the presented approach admits a faster convergence and provides better results in particular at object concavities.

Segmentation and Restoration of Images on Surfaces by Parametric Active Contours with Topology Changes

In this article, a new method for segmentation and restoration of images on two-dimensional surfaces is given. Active contour models for image segmentation are extended to images on surfaces. The evolving curves on the surfaces are mathematically described using a parametric approach. For image restoration, a diffusion equation with Neumann boundary conditions is solved in a postprocessing step in the individual regions. Numerical schemes are presented which allow to efficiently compute segmentations and denoised versions of images on surfaces. Also topology changes of the evolving curves are detected and performed using a fast sub-routine. Finally, several experiments are presented where the developed methods are applied on different artificial and real images defined on different surfaces.