Local Active Contour Research Articles

Virtual M-Mode for Echocardiography: A New Approach for the Segmentation of the Anterior Mitral Leaflet

Rheumatic heart disease can result from repeated episodes of acute rheumatic fever, which damages the heart valves and reduces their functionality. Early manifestations of heart valve damage are visible in echocardiography in the form of valve thickening, shape changing and mobility reduction. The quantification of these features is important for a precise diagnosis and it is the main motivation for this work. The first step to make this quantification is to accurately identify and track the anterior mitral leaflet throughout the cardiac cycle. An accurate segmentation and tracking with minimum user interaction is still an open problem in literature due to low image quality, speckle noise, signal dropout and nonrigid deformations. In this work, we propose a novel approach for the identification of the anterior mitral valve leaflet in all frames. The method requires a single user-specified point on the posterior wall of the aorta as input, in the first frame. The echocardiography videos are converted into a new image space, the Virtual M-mode, which samples the original echocardiography image over automatically estimated scanning lines. This new image space not only provides the motion pattern of the posterior wall of the aorta, the anterior wall of the aorta and the posterior wall of the left atrium, but also provides the location of the structures in each frame. The location information is then used to initialize the localized active contours, followed by segmenting the anterior mitral leaflet. Results shown that the new image space has robustly identified the anterior mitral valve leaflet, without any failure. The median modified Hausdorff distance error of the proposed method was 2.3 mm, with a recall of 0.94.

Local and Global Active Contour Model for Image Segmentation With Intensity Inhomogeneity

The active contour model (ACM) is a popular approach for image segmentation. Many existing ACMs perform poorly in severe inhomogeneous images. To address this issue, a novel local and global ACM (LaG_ACM) is proposed in this paper. First, we define a global fitting image formulation that encodes the global property of an image and a global energy term using the relative entropy between the original image and the proposed global fitting image formulation. Then, a local image bias field formulation is defined to extract the local image information and to estimate the bias field. By integrating the proposed local image bias field formulation with the ACM, we specify a local energy term using the mean squared error to accommodate severe inhomogeneous images. More importantly, we define an adaptive weighting function using image entropy, which can automatically adjust the weight between the local and global energy terms according to the degree of intensity inhomogeneity. Finally, the experimental results on images with different degrees of intensity inhomogeneity validate the favorable performance of the LaG_ACM.

A 3D Hermite-based multiscale local active contour method with elliptical shape constraints for segmentation of cardiac MR and CT volumes.

Analysis of cardiac images is a fundamental task to diagnose heart problems. Left ventricle (LV) is one of the most important heart structures used for cardiac evaluation. In this work, we propose a novel 3D hierarchical multiscale segmentation method based on a local active contour (AC) model and the Hermite transform (HT) for LV analysis in cardiac magnetic resonance (MR) and computed tomography (CT) volumes in short axis view. Features such as directional edges, texture, and intensities are analyzed using the multiscale HT space. A local AC model is configured using the HT coefficients and geometrical constraints. The endocardial and epicardial boundaries are used for evaluation. Segmentation of the endocardium is controlled using elliptical shape constraints. The final endocardial shape is used to define the geometrical constraints for segmentation of the epicardium. We follow the assumption that epicardial and endocardial shapes are similar in volumes with short axis view. An initialization scheme based on a fuzzy C-means algorithm and mathematical morphology was designed. The algorithm performance was evaluated using cardiac MR and CT volumes in short axis view demonstrating the feasibility of the proposed method.

Automatic selection of localized region-based active contour models using image content analysis applied to brain tumor segmentation

Brain tumor segmentation is a routine process in a clinical setting and provides useful information for diagnosis and treatment planning. Manual segmentation, performed by physicians or radiologists, is a time-consuming task due to the large quantity of medical data generated presently. Hence, automatic segmentation methods are needed, and several approaches have been introduced in recent years including the Localized Region-based Active Contour Model (LRACM). There are many popular LRACM, but each of them presents strong and weak points. In this paper, the automatic selection of LRACM based on image content and its application on brain tumor segmentation is presented. Thereby, a framework to select one of three LRACM, i.e., Local Gaussian Distribution Fitting (LGDF), localized Chan-Vese (C-V) and Localized Active Contour Model with Background Intensity Compensation (LACM-BIC), is proposed. Twelve visual features are extracted to properly select the method that may process a given input image. The system is based on a supervised approach. Applied specifically to Magnetic Resonance Imaging (MRI) images, the experiments showed that the proposed system is able to correctly select the suitable LRACM to handle a specific image. Consequently, the selection framework achieves better accuracy performance than the three LRACM separately.

A global and local active contour model based on dual algorithm for image segmentation

Most of local region-based active contour models in terms of the level set approach are able to segment images with intensity inhomogeneity. However, these models do not utilize global statistical information and are quite sensitive to the initial placement of the contour. This paper presents a new global and local region-based active contour model to segment images with intensity inhomogeneity. First, in order to reduce number of iterations, the global energy functional of the Chan–Vese (C–V) model is used as the global term. Then, the local term is proposed to incorporate both local spatial information and local intensity information to handle intensity inhomogeneity. Moreover, to increase robustness of the initialization of the contour and reduce number of iterations, a convex energy functional and the dual algorithm are designed in the numerical implementation. Experimental results for synthetic and medical images have shown the efficiency and robustness of the proposed method.

RETRACTED ARTICLE: Lip segmentation using localized active contour model with automatic initial contour

Lip-reading is one of important approaches for human–computer interaction (HCI). Its development would have a large range of applications, especially in augmented reality. Lip segmentation is the first and foremost step in the lip-reading system. Conventional method of region-based active contour model adopts the global information of image and is unable to perform well. In this paper, from a localized perspective, we introduce the methodology of localized active contour model (LACM) and, meanwhile, propose the method that using LACM to perform the lip segmentation with the initial contour automatically generated. The scope for active contour model is reduced to the local region that reduces the disturbances of unrelated factors. The experimental results demonstrate the method adopts this model would dramatically improve the robustness for lip segmentation. On this basis, we analyze the influence of initial contours and local radiuses, study the efficiencies under different initial contours and compare it with the conventional active contour model which adopts the global information.

A Study of Multilevel Banded Graph Cuts for Three-Dimensional Colon Tissue Segmentation

Graph cuts is an image segmentation method by which the region and boundary information of objects can be revolved comprehensively. Because of the complex spatial characteristics of high-dimensional images, time complexity and segmentation accuracy of graph cuts methods for high-dimensional images need to be improved. This paper proposes a new three-dimensional multilevel banded graph cuts model to increase its accuracy and reduce its complexity. Firstly, three-dimensional image is viewed as a high-dimensional space to construct three-dimensional network graphs. A pyramid image sequence is created by Gaussian pyramid downsampling procedure. Then, a new energy function is built according to the spatial characteristics of the three-dimensional image, in which the adjacent points are expressed by using a 26-connected system. At last, the banded graph is constructed on a narrow band around the object/background. The graph cuts method is performed on the banded graph layer by layer to obtain the object region sequentially. In order to verify the proposed method, we have performed an experiment on a set of three-dimensional colon CT images, and compared the results with local region active contour and Chan–Vese model. The experimental results demonstrate that the proposed method can segment colon tissues from three-dimensional abdominal CT images accurately. The segmentation accuracy can be increased to 95.1% and the time complexity is reduced by about 30% of the other two methods.

Detection of pulmonary nodules in low-dose computed tomography using localized active contours and shape features

Pulmonary nodules are symptoms of lung cancer. The shape and size of these nodules are used to diagnose lung cancer in computed tomography (CT) images. In the early stages, nodules are very small, and radiologist has to refer to many CT images to diagnose the disease, causing operator mistakes. Image processing algorithms are used as an aid to detect and localize nodules. In this paper, a novel lung nodules detection scheme is proposed. First, in the preprocessing stage, our algorithm segments two lung lobes to increase processing speed and accuracy. Second, template-matching is applied to detect the suspicious nodule candidates, including both nodules and some blood vessels. Third, the suspicious nodule candidates are segmented by localized active contours. Finally, the false-positive errors produced by vessels are reduced using some two-/three-dimensional geometrical features in three steps. In these steps, the size, long and short diameters and sphericity are used to decrease the false-positive rate. In the first step, some vessels that are parallel to CT cross-plane are identified. In the second step, oblique vessels are detected using shift of center of gravity in two successive slices. In step three, vessels vertical to CT cross-plane are identified. Using these steps, vessels are separated from nodules. Early Lung Cancer Action Project is used as a popular dataset in this work. Our algorithm achieved a sensitivity of 90.1% and a specificity of 92.8%, quite acceptable in comparison to other related works.

A Multi-Classifier System for Automatic Mitosis Detection in Breast Histopathology Images Using Deep Belief Networks.

Mitotic count is an important diagnostic factor in breast cancer grading and prognosis. Detection of mitosis in breast histopathology images is very challenging mainly due to diffused intensities along object boundary and shape variation in different stages of mitosis. This paper demonstrates an accurate technique for detecting the mitotic cells in Hematoxyline and Eosin stained images by step by step refinement of segmentation and classification stages. Krill Herd Algorithm-based localized active contour model precisely segments cell nuclei from background stroma. A deep belief network based multi-classifier system classifies the labeled cells into mitotic and nonmitotic groups. The proposed method has been evaluated on MITOS data set provided for MITOS-ATYPIA contest 2014 and also on clinical images obtained from Regional Cancer Centre (RCC), Thiruvananthapuram, which is a pioneer institute specifically for cancer diagnosis and research in India. The algorithm provides improved performance compared with other state–of–the-art techniques with average F-score of 84.29% for the MITOS data set and 75% for the clinical data set from RCC.

Segmentation of Brain Tumor Based on Gaussian Probability Localizing Local Region Active Contour Model

Segmentation of Brain Tumor Based on Gaussian Probability Localizing Local Region Active Contour Model

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.

A local Gaussian distribution fitting energy-based active contour model for image segmentation

Intensity inhomogeneity and the bias field often occur in real-world images, which cause considerable difficulties in image segmentation. This paper presents a local region-based active contour model for segmentation of images with intensity inhomogeneity and simultaneous estimation of the bias field. In our model, the local image intensities and the bias field are described by the Gaussian distributions with different means and variances. A local Gaussian distribution fitting energy functional is defined on the image region, which combines the level set function and the bias field. Then, gradient flow equations and the bias field are derived for energy minimization. Due to the definition of local image intensities and the bias field, the proposed model is able to deal with intensity inhomogeneity and estimate the bias field. Experimental results on real images demonstrate that the proposed model has advantages over the other classical methods.

SU‐C‐BRA‐06: Automatic Brain Tumor Segmentation for Stereotactic Radiosurgery Applications

Purpose:Stereotactic radiosurgery (SRS), which delivers a potent dose of highly conformal radiation to the target in a single fraction, requires accurate tumor delineation for treatment planning. We present an automatic segmentation strategy, that synergizes intensity histogram thresholding, super‐voxel clustering, and level‐set based contour evolving methods to efficiently and accurately delineate SRS brain tumors on contrast‐enhance T1‐weighted (T1c) Magnetic Resonance Images (MRI).Methods:The developed auto‐segmentation strategy consists of three major steps. Firstly, tumor sites are localized through 2D slice intensity histogram scanning. Then, super voxels are obtained through clustering the corresponding voxels in 3D with reference to the similarity metrics composited from spatial distance and intensity difference. The combination of the above two could generate the initial contour surface. Finally, a localized region active contour model is utilized to evolve the surface to achieve the accurate delineation of the tumors. The developed method was evaluated on numerical phantom data, synthetic BRATS (Multimodal Brain Tumor Image Segmentation challenge) data, and clinical patients’ data. The auto‐segmentation results were quantitatively evaluated by comparing to ground truths with both volume and surface similarity metrics.Results:DICE coefficient (DC) was performed as a quantitative metric to evaluate the auto‐segmentation in the numerical phantom with 8 tumors. DCs are 0.999±0.001 without noise, 0.969±0.065 with Rician noise and 0.976±0.038 with Gaussian noise. DC, NMI (Normalized Mutual Information), SSIM (Structural Similarity) and Hausdorff distance (HD) were calculated as the metrics for the BRATS and patients’ data. Assessment of BRATS data across 25 tumor segmentation yield DC 0.886±0.078, NMI 0.817±0.108, SSIM 0.997±0.002, and HD 6.483±4.079mm. Evaluation on 8 patients with total 14 tumor sites yield DC 0.872±0.070, NMI 0.824±0.078, SSIM 0.999±0.001, and HD 5.926±6.141mm.Conclusion:The developed automatic segmentation strategy, which yields accurate brain tumor delineation in evaluation cases, is promising for its application in SRS treatment planning.

A two-stage image segmentation via global and local region active contours

Based on popular active contours, this paper proposes a novel two-stage image segmentation method, which incorporates the global and local image region fitting energies. In the first stage, according to the global region active contour, we preliminarily segment the image by globally using the Gaussian distribution, which can rapidly get a coarse segmentation result. Subsequently, by employing a window function, we further segment the image by using the local region active contour, where we use the final active contour of the first stage as the initialization. Compared with the first stage, the local object details are accurately segmented in the second stage, which can be considered as an accurate segmentation result. Due to the suitable initialization from the first stage, the second stage works well in accurately segmenting the image, especially in local details. To regularize the level set function, we introduce a Laplace operator, which efficiently eliminates the expensive re-initialization process of traditional level set methods. Compared with the state-of-the-art methods, experiment results demonstrate the effectiveness and performance of the proposed method with applications to synthetical and real-world images, which usually contain noise, blurry boundaries, and intensity inhomogeneities.

Efficient quantitative assessment of facial paralysis using iris segmentation and active contour-based key points detection with hybrid classifier.

BackgroundFacial palsy or paralysis (FP) is a symptom that loses voluntary muscles movement in one side of the human face, which could be very devastating in the part of the patients. Traditional methods are solely dependent to clinician’s judgment and therefore time consuming and subjective in nature. Hence, a quantitative assessment system becomes apparently invaluable for physicians to begin the rehabilitation process; and to produce a reliable and robust method is challenging and still underway.MethodsWe introduce a novel approach for a quantitative assessment of facial paralysis that tackles classification problem for FP type and degree of severity. Specifically, a novel method of quantitative assessment is presented: an algorithm that extracts the human iris and detects facial landmarks; and a hybrid approach combining the rule-based and machine learning algorithm to analyze and prognosticate facial paralysis using the captured images. A method combining the optimized Daugman’s algorithm and Localized Active Contour (LAC) model is proposed to efficiently extract the iris and facial landmark or key points. To improve the performance of LAC, appropriate parameters of initial evolving curve for facial features’ segmentation are automatically selected. The symmetry score is measured by the ratio between features extracted from the two sides of the face. Hybrid classifiers (i.e. rule-based with regularized logistic regression) were employed for discriminating healthy and unhealthy subjects, FP type classification, and for facial paralysis grading based on House-Brackmann (H-B) scale.ResultsQuantitative analysis was performed to evaluate the performance of the proposed approach. Experiments show that the proposed method demonstrates its efficiency.ConclusionsFacial movement feature extraction on facial images based on iris segmentation and LAC-based key point detection along with a hybrid classifier provides a more efficient way of addressing classification problem on facial palsy type and degree of severity. Combining iris segmentation and key point-based method has several merits that are essential for our real application. Aside from the facial key points, iris segmentation provides significant contribution as it describes the changes of the iris exposure while performing some facial expressions. It reveals the significant difference between the healthy side and the severe palsy side when raising eyebrows with both eyes directed upward, and can model the typical changes in the iris region.

2D-3D Pose Estimation of Heterogeneous Objects Using a Region Based Approach

Recently, region based methods for estimating the 3D pose of an object from a 2D image have gained increasing popularity. They do not require prior knowledge of the object's texture, making them particularity attractive when the object's texture is unknown a priori. Region based methods estimate the 3D pose of an object by finding the pose which maximizes the image segmentation in to foreground and background regions. Typically the foreground and background regions are described using global appearance models, and an energy function measuring their fit quality is optimized with respect to the pose parameters. Applying a region based approach on standard 2D-3D pose estimation databases shows its performance is strongly dependent on the scene complexity. In simple scenes, where the statistical properties of the foreground and background do not spatially vary, it performs well. However, in more complex scenes, where the statistical properties of the foreground or background vary, the performance strongly degrades. The global appearance models used to segment the image do not sufficiently capture the spatial variation. Inspired by ideas from local active contours, we propose a framework for simultaneous image segmentation and pose estimation using multiple local appearance models. The local appearance models are capable of capturing spatial variation in statistical properties, where global appearance models are limited. We derive an energy function, measuring the image segmentation, using multiple local regions and optimize it with respect to the pose parameters. Our experiments show a substantially higher probability of estimating the correct pose for heterogeneous objects, whereas for homogeneous objects there is minor improvement.

An improved active contours model for image segmentation by level set method

The variational level set approaches are widely used in image segmentation. To extract the objects from the inhomogeneity image, an improved local and global binary fitting active contours model is presented in this paper. The energy functional has been defined by a local intensity fitting term and a global intensity fitting term. The generalized Gaussian distribution has been used as the kernel function of the local binary fitting information. The experiment results of the homogeneity images, artificial and real inhomogeneous images have shown the advantages of LGBF model in terms of accuracy and robustness.

Adaptive Active Contour Model: a Localized Mutual Information Approach for Medical Image Segmentation

Troubles are often met when traditional active contours extract boundaries of medical images with inhomogeneous bias and various noises. Focusing on such a circumstance, a localized mutual information active contour model is discussed in the paper. By defining neighborhood of each point on the level set, mutual information is introduced to describe the relationship between the zero level set and image field. A driving energy term is then generated by integrating all the information. In addition, an expanding energy and internal energy are designed to regularize the driving energy. Contrary to piecewise constant model, new model has a better command of driving the contours without initialization.

Local average fitting active contour model with thresholding for noisy image segmentation

In this paper, an active contours model using neighborhood average fitting with thresholding is proposed for noisy images segmentation. Energy of the proposed model is formulated according to the difference between the local average and global region information. For images corrupted by noise, the neighborhood average method is capable of denoising at the expense of blurring images to some extent. However, problems that appear with average method can be settled by thresholding in this work. Minimization of the energy associated with the active contour model is then implemented in a variational level set framework. Moreover, to eliminate the need for costly re-initialization procedure, a reaction-diffusion method is adopted to regularize the level set function for stability. Experimental results on synthetic and real images validate the effectiveness of the proposed approach.

Retinal vessel segmentation: an efficient graph cut approach with retinex and local phase.

Our application concerns the automated detection of vessels in retinal images to improve understanding of the disease mechanism, diagnosis and treatment of retinal and a number of systemic diseases. We propose a new framework for segmenting retinal vasculatures with much improved accuracy and efficiency. The proposed framework consists of three technical components: Retinex-based image inhomogeneity correction, local phase-based vessel enhancement and graph cut-based active contour segmentation. These procedures are applied in the following order. Underpinned by the Retinex theory, the inhomogeneity correction step aims to address challenges presented by the image intensity inhomogeneities, and the relatively low contrast of thin vessels compared to the background. The local phase enhancement technique is employed to enhance vessels for its superiority in preserving the vessel edges. The graph cut-based active contour method is used for its efficiency and effectiveness in segmenting the vessels from the enhanced images using the local phase filter. We have demonstrated its performance by applying it to four public retinal image datasets (3 datasets of color fundus photography and 1 of fluorescein angiography). Statistical analysis demonstrates that each component of the framework can provide the level of performance expected. The proposed framework is compared with widely used unsupervised and supervised methods, showing that the overall framework outperforms its competitors. For example, the achieved sensitivity (0:744), specificity (0:978) and accuracy (0:953) for the DRIVE dataset are very close to those of the manual annotations obtained by the second observer.