Traditional Active Contour Model Research Articles

A novel active contour model based on features for image segmentation

Active contour model is an extraordinarily valuable technique in image segmentation, which is essential for image analysis and understanding. Active contour model has been widely studied because it delineates closed and smooth contours or surfaces of target objects. However, traditional active contour models underperform on complex natural images. To tackle this problem, we propose a novel active contour model framework, called FeaACM. We introduce the feature energy function into the conventional energy functional to minimize the energy functional to maintain the consistency of the object region and account for different distributions of objects and backgrounds in the feature space. To demonstrate the advantages of our method, we compare our method with the state-of-the-art methods, and show that our method achieves competitive performance. In addition, we utilize AutoEncoder technology to extract the feature of the image verifying the generality of our framework. Extensive and numerous experiments indicate that our method can segment complex natural images effectively. Our code is available at https://github.com/xuepeng1234/FeaACM.

A Laplace operator-based active contour model with improved image edge detection performance

Active contour model (ACM) is an important branch in the field of image segmentation, since it adapts to different image types and scenes. However, traditional ACMs rely on local image information, which makes them sensitive to initial contours and time-consuming. In this paper, a Laplace operator based ACM (LOACM) with improved image edge detection performance is proposed. LOACM combines the global pre-fitting function which is constructed by using the image's second-order gradient information with the adaptive boundary indicator function to build a hybrid model. Its compact code size allows it to be executed on a wide range of edge devices. To substantiate the segmentation capability of the LOACM model, the BSD500 dataset was utilized for experimentation. The results indicate that LOACM exhibits excellent performance, with average IOU and DSC scores of 0.861 and 0.924 respectively. Additionally, it consistently handles processes within an average time of less than 0.3 s, significantly less than that of comparative models, providing compelling evidence of LOACM's exceptional efficiency and precision.

An optimized denoised bias correction model with local pre-fitting function for weak boundary image segmentation

The active contour model (ACM) plays a paramount part in grasping visual properties of images and exacting targets of interest. It is overwhelming hardship for traditional ACMs to segment images with noise, intensity inhomogeneity or low contrast and consider computation speed for practical applicability. Therefore, an optimized denoised bias correction (ODBC) model incorporating the pre-piecewise fitting function and the variational denoised term into the energy function is proposed for images with low contrast and intensity inhomogeneity. An optimized gradient descent equation and an upgraded regularization term are produced to enhance the robustness and sensitivity of this model. Experiments are conducted to validate that ODBC model possesses the superiority of reliability and speed in segmenting images with inhomogeneous intensity and strengthens the robustness to initial contours. The results manifest that mIOU of ODBC model exceeds 0.9, superior to prime ACMs and deep learning schemes, in segmenting images associated and unrelated with the dataset.

An effective and robust lattice Boltzmann model guided by atlas for hippocampal subregions segmentation.

Given the varying vulnerability of the rostral and caudal regions of the hippocampus to neuropathology in the Alzheimer's disease (AD) continuum, accurately assessing structural changes in these subregions is crucial for early AD detection. The development of reliable and robust automatic segmentation methods for hippocampal subregions (HS) is of utmostimportance. Our aim is to propose and validate a HS segmentation model that is both training-free and highly generalizable. This method should exhibit comparable accuracy and efficiency to state-of-the-art techniques. The segmented HS can serve as a biomarker for studying the progression ofAD. We utilized the functional magnetic resonance imaging of the Brain's Integrated Registration and Segmentation Tool (FIRST) to segment the entire hippocampus. By intersecting the segmentation results with the Brainnetome (BN) atlas, we obtained coarse segmentation of the four HS regions. This coarse segmentation was then employed as a shape prior term in the lattice Boltzmann (LB) model, as well as for initializing contours. Additionally, image gradients and local gray levels were integrated into the external force terms of the LB model to refine the coarse segmentation results. We assessed the segmentation accuracy of the model using the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset and evaluated the potential of the segmentation results as AD biomarkers on both the ADNI and Xuanwudatasets. The median Dice similarity coefficients (DSC) for the left caudal, right caudal, left rostral, and right rostral hippocampus were 0.87, 0.88, 0.88, and 0.89, respectively. The proportion of segmentation results with a DSC exceeding 0.8 was 77%, 78%, 77%, and 94% for the respective regions. In terms of volume, the correlation coefficients between the segmentation results of the four HS regions and the gold standard were 0.95, 0.93, 0.96, and 0.96, respectively. Regarding asymmetry, the correlation coefficient between the segmentation result's right caudal minus left caudal and the corresponding gold standard was 0.91, while for right rostral minus left rostral, it was 0.93. Over time, we observed a decline in the volumes of the four HS regions and the total hippocampal volume of mild cognitive impairment (MCI) converters. Analysis of inter-group differences revealed that, except for the right rostral region in the ADNI dataset, the p-values for the four HS regions in the normal controls (NC), MCI, and AD groups from both datasets were all below 0.05. The right caudal hippocampal volume demonstrated correlation coefficients of 0.47 and 0.43 with the mini-mental state examination (MMSE) and Montreal cognitive assessment (MoCA), respectively. Similarly, the left rostral hippocampal volume showed correlation coefficients of 0.50 and 0.58 with MMSE and MoCA,respectively. Our framework allows for direct application to different brain magnetic resonance (MR) datasets without the need for training. It eliminates the requirement for complex image preprocessing steps while achieving segmentation accuracy comparable to deep learning (DL) methods even with small sample sizes. Compared to traditional active contour models (ACM) and atlas-based methods, our approach exhibits significant speed advantages. The segmented HS regions hold promise as potential biomarkers for studying the progression ofAD.

A Novel Adaptive Fractional Differential Active Contour Image Segmentation Method

When the image is affected by strong noise and uneven intensity, the traditional active contour models often cannot obtain accurate results. In this paper, a novel adaptive fractional differential active contour image segmentation method is proposed to solve the above problem. At first, in order to extract more texture parts of the image, an adaptively fractional order matrix is constructed according to the gradient information of the image, varying the fractional order of each pixel. Then, the traditional edge-stopping function in the regularization term is susceptible to noise, and a new fractional-order edge-stopping function is designed to improve noise resistance. In this paper, a fitting term based on adaptive fractional differentiation is introduced to solve the problem of improper selection of the initial contour position leading to inaccurate segmentation results so that the initial contour position can be selected arbitrarily. Finally, the experimental results show that the proposed method can effectively improve the segmentation accuracy of noise images and weak-edge images and can arbitrarily select the position selection of the initial contour.

Online Education Classroom Intelligent Management System Based on Tensor CS Reconstruction Model

To study a high-efficiency online classroom intelligent management system, this article builds an artificial intelligence classroom management system based on the tensor CS reconstruction model. Moreover, this study uses the cosine function to represent the data energy fitting of the traditional active contour model and proposes a local cosine fitting energy active contour model based on partial image restoration, which is used for image and composite image segmentation. Simultaneously, this study proposes a new type of super-resolution algorithm. This algorithm performs Fourier transform of a low-resolution image into a frequency range and then performs an inverse Fourier transform on the image expanded in the frequency range to obtain the initial high-resolution image and finally reconstructs a new super-resolution image using the frequency-domain compressed data of the high-resolution image. Finally, this study verifies and analyzes the performance of the model through experiments. The research results are basically consistent with the expectations of the model.

Active contour model based on local absolute difference energy and fractional-order penalty term

The active contour model (ACM) is one of the popular image segmentation methods. In practice, however, images are often corrupted by severe noise and intensity inhomogeneity, making traditional ACMs unavailable. In this paper, a novel ACM is presented to effectively cope with noise and intensity inhomogeneity. To better discriminate the object and background regions in inhomogeneous images, a new saliency fitting image (SFI), defined as the difference between the original image and the local-weighted image (LWI), is first constructed. In addition, the local mean absolute deviation (LMAD) based weight factor can reflect the degree of the intensity variation in a local region, and the LWI represents the bias component accounting for intensity inhomogeneity. Then, a local fitting energy term is defined as the absolute difference between two local fitting functions, which represent the local averages of image intensity inside/outside the contour in two windows with different sizes. And a threshold strategy is utilized to improve the robustness of the proposed model to noise. Finally, a fractional-order penalty term is defined to penalize the deviation between the level set function (LSF) and the signed distance function (SDF), which makes the proposed model avoid the side effect of the penalty energy term in traditional ACMs. Experimental results show the efficiency and accuracy of the proposed ACM.

An active contour model algorithm combined with anisotropic diffusion filtering and global pre-fitting energy

Traditional local active contour models mainly depend on the data fitting term to obtain image edge information, which are sensitive to the position of initial contour. In fact, setting an unreasonable initial contour will result in a time-consuming image segmentation process and difficult evolution at weak boundary. To solve this problem, we come up with a second-order edge detection operator which is based on the global gradient image information to replace the traditional data fitting term, and to obtain edge information with higher speed and accuracy. In addition, the anisotropic edge enhancement method has been introduced to improve the robustness to the weak boundary. In this way, an active contour model which is insensitive to initial contour position is proposed to achieve a faster and more accurate image segmentation result.

Artificial Intelligence Segmentation Model-Based Computed Tomography Angiography Image in the Diagnosis of Congenital Aortic Constriction

This study was to analyze the impacts of the image segmentation model and computed tomography angiography (CTA) on the clinical diagnosis of aortic constriction under the background of artificial intelligence. In this study, 126 patients with congenital aortic constriction (CAC) diagnosed by surgery were selected as the research objects and routine digital subtraction angiography (DSA) and CTA were performed. Then, the traditional active contour model (AC model) was optimized based on the local area information to construct a new image segmentation model for intelligent segmentation and reconstruction of the CTA images of patients. The results revealed that compared with the AC model and the image segmentation model based on region growth (RG model) obtained from angiography segmentation, the algorithm constructed in this study showed a smaller segmentation range for angiography images and more accurate segmentation results. The quantitative data results suggested that the evolution times and running time of the constructed model were less than those of the AC and RG models P < 0.05 . Based on the gold standard of DSA examination results, there were 122 correctly diagnosed cases, 3 missed diagnosed cases, and 1 misdiagnosed by CTA, so the diagnosis coincidence rate was 96.83%. Compared with DSA, the average inner diameter and average pressure difference of patients with precatheter, paracatheter, and postcatheter type were not greatly different in CTA P > 0.05 . The CTA examination suggested there were 154 cases with intracardiac structural abnormalities, with a detection rate of 86.52%; there were 32 cases of cardiac-vascular connection abnormalities, with a detection rate of 100%; and there were 79 extracardiac vascular abnormalities, with the detection rate of 95.18%. It indicated that the optimized image segmentation model based on local area information proposed in this paper has excellent segmentation performance for CT angiography images and has good segmentation effect and efficiency. The CTA based on the artificial intelligence image segmentation model showed a better diagnostic effect on abnormal heart-vascular connection and abnormal extracardiac blood vessels and can be used as an effective examination method for clinical diagnosis of CAC.

Fuzzy Active Contour Model With Markov Random Field for Change Detection

The traditional active contour models are sensitive to the speckle noise in the synthetic aperture radar (SAR) images. In this paper, the Markov random field (MRF) theory is incorporated into the fuzzy active contour model to detect the changes of multitemporal SAR images. In the proposed method, neighboring information is considered to modify the pointwise prior probability for exploiting the mutual and spatial information. In addition, we incorporate MRF into the fuzzy active contour model and get the resulting MRF-based energy function. Finally, we drive the associated first variation of the energy function to compute the fuzzy membership. Due to the introduction of MRF, the proposed MRF-based fuzzy active contour model is robust to the speckle noise in the SAR images and can achieve accurate change detection results. Experiments on four SAR image datasets demonstrate that the proposed MRF-based fuzzy active contour model is able to accurately segment the difference image and has better performance in comparison with other change detection techniques.

Application of Video Processing Technology Based on Diffusion Equation Model in Basketball Analysis

Video event detection and annotation work is an important content of video analysis and the basis of video content retrieval. Basketball is one of the most popular types of sports. Event detection and labeling of basketball videos can help viewers quickly locate events of interest and meet retrieval needs. This paper studies the application of anisotropic diffusion in video image smoothing, denoising, and enhancement. An improved form of anisotropic diffusion that can be used for video image enhancement is analyzed. This paper studies the anisotropic diffusion method for coherent speckle noise removal and proposes a video image denoising method that combines anisotropic diffusion and stationary wavelet transform. This paper proposes an anisotropic diffusion method based on visual characteristics, which adds a factor of video image detail while smoothing, and improves the visual effect of diffusion. This article discusses how to apply anisotropic diffusion methods and ideas to video image segmentation. We introduced the classic watershed segmentation algorithm and used forward-backward diffusion to process video images to reduce oversegmentation, introduced the active contour model and its improved GVF Snake, and analyzed the idea of how to use anisotropic diffusion and improve the GVF Snake model to get a new GGVF Snake model. In the study of basketball segmentation of close-up shots, we propose an improved Hough transform method based on a variable direction filter, which can effectively extract the center and radius of the basketball. The algorithm has good robustness to basketball partial occlusion and motion blur. In the basketball segmentation research of the perspective shot, the commonly used object segmentation method based on the change area detection is very sensitive to noise and requires the object not to move too fast. In order to correct the basketball segmentation deviation caused by the video noise and the fast basketball movement, we make corrections based on the peak characteristics of the edge gradient. At the same time, the internal and external energy calculation methods of the traditional active contour model are improved, and the judgment standard of the regional optimal solution and segmentation validity is further established. In the basketball tracking research, an improved block matching method is proposed. On the one hand, in order to overcome the influence of basketball’s own rotation, this article establishes a matching criterion that has nothing to do with the location of the area. On the other hand, this article improves the diamond motion search path based on the basketball’s motion correlation and center offset characteristics to reduce the number of searches and improve the tracking speed.

Coastline Extraction from GF-3 SAR Images Using LKDACM and GMM Algorithms

Coastline detection using a Gaussian Mixture Model (GMM) applied to synthetic aperture radar (SAR) imagery is usually inaccurate due to the inherent noise of SAR data. In addition, the traditional active counter model is sensitive to the initial position of the contour line and requires a large number of iterations to converge to a solution. In this study, we first used the GMM algorithm to segment the SAR images and obtain a coarse land and sea segmentation map. This map is then used as the initial position for a subsequent active contour model. The K distribution was introduced into the local statistical active contour model to better model the SAR image. The Gaussian distribution-based local active contour model and the algorithm detailed in this paper were used to perform coastline extraction experiments on four SAR images. Four GF-3 SAR images with different modes were collected to validate the efficiency of the proposed method. The experimental results show that the coastline extraction methods from SAR images based on the GMM algorithm and the K distribution-based local statistical active contour model (LKDACM) overcame the shortcomings of the traditional active contour model to accurately and quickly detect coastlines, thus enabling the detection of coastline changes.

Multi-Scale Coal Fire Detection Based on an Improved Active Contour Model from Landsat-8 Satellite and UAV Images

Underground coal fires can increase surface temperature, cause surface cracks and collapse, and release poisonous and harmful gases, which significantly harm the ecological environment and humans. Traditional methods of extracting coal fires, such as global threshold, K-mean and active contour model, usually produce many false alarms. Therefore, this paper proposes an improved active contour model by introducing the distinguishing energies of coal fires and others into the traditional active contour model. Taking Urumqi, Xinjiang, China as the research area, coal fires are detected from Landsat-8 satellite and unmanned aerial vehicle (UAV) data. The results show that the proposed method can eliminate many false alarms compared with some traditional methods, and achieve detection of small-area coal fires by referring field survey data. More importantly, the results obtained from UAV data can help identify not only burning coal fires but also potential underground coal fires. This paper provides an efficient method for high-precision coal fire detection and strong technical support for reducing environmental pollution and coal energy use.

A morphological approach to piecewise constant active contour model incorporated with the geodesic edge term

Traditional level set-based image segmentation method has to solve the level set evolution equation which is the Euler–Lagrange equation of the energy functional defined on the image domain. Solving level set evolution equation is very time-consuming, and reinitialization is usually needed. The level set evolution equation can also be solved by mathematical morphology. The morphological implementation is very simple, fast and stable. The piecewise constant active contour model incorporated with the geodesic edge term is a hybrid active contour model which combines two active contour models which are active contour model without edges and geodesic active contour model. In this paper, the mathematical morphology-based level set evolution method is applied to the piecewise constant active contour model incorporated with the geodesic edge term. The curvature morphological operator is also improved. Experimental results show that, compared with the original piecewise constant active contour model incorporated the geodesic edge term, the new mathematical morphology-based model can segment images more accurately and there are significant gains in simplicity, speed and stability. The new mathematical morphology-based model is also compared to morphological piecewise constant active contour model, morphological geodesic active contour model, traditional piecewise constant active contour model and two other active contour models. Results show that the proposed method gets the segmentation result with faster speed and higher accuracy.

Prior distribution-based statistical active contour model

Employing prior information can greatly improve the segmentation result of many image segmentation problems. For example, a commonly used prior information is the shape of the object. In this paper, we introduce a different kind of prior information called the prior distribution. On the basis of non-parametric statistical active contour model, we add prior distribution energy to build a novel prior active contour model. During the convergence of contour curve, distribution difference between the inside and outside of the active contour is maximized while the distribution difference between the inside/outside of contour and the prior object/background is minimized. Furthermore, in order to improve the computation speed, a method to accelerate the computation speed is also proposed, which significantly relieves the burden of estimating probability density functions. As the experimental results suggest, satisfactory effects can be achieved in the segmentation of synthetic images and natural images via the our algorithm. Compared with the traditional non-parametric statistical active contour model without prior information, our method achieves a distinct improvement in both accuracy and computation efficiency.

An adaptable active contour model for medical image segmentation based on region and edge information

Due to the complexity of the internal structure of human body and the physiological movement of the illuminated tissue, the digital medical image exists low contrast, high noise intensity, complex internal structure and edge blur phenomenon, which will limit the segmentation accuracy of traditional active contour models. To solve this problem, this paper proposes a novel active contour model based on the combination of regional information and the edge information of the image. The new approach has four key characteristics. First the local information fitting of the image is incorporated into the pressure force function (SPF) of the SBGFRLS model, which improves the ability of dealing with medical images with low contrast and complex structure. Second, the adaptive balance of local information and global information is realized by adding a novel weighting function, which accelerates the evolution speed and enhances the adaptability of the model; Third, in the numerical implementation process of the proposed model, the divergence operator is replaced by the Gaussian filter, in this way, the level set function is smoothed and the computation is simplified. Last, a penalty term of symbolic function is introduced to reduce the computational complexity of the level set function due to re-initialization and regularization process. In order to verify the effectiveness of the model, we use different kinds of medical images for simulation experiments. Experimental results show that compared with the traditional active contour models, the proposed method can achieve an satisfactory both in segmentation speed and accuracy.

Non‐local weighted fuzzy energy‐based active contour model with level set evolution starting with a constant function

In the traditional active contour models, global region-based methods fail to segment images with intensity inhomogeneity, and local region-based methods are sensitive to initial contour. In this study, a novel fuzzy energy-based active contour model is proposed to segment medical images, which are always corrupted by intensity inhomogeneity. In order to deal with intensity inhomogeneity, a local energy term is first constructed by substituting a non-local weight for Gaussian kernel widely used in traditional local region-based models. Second, the defined adaptive force can drive the level set function to adaptively increase or decrease according to image intensity information. Therefore, the initial contour can be initialised as a constant function, which eliminates the problem caused by contour initialisation. Moreover, the proposed active contour model is a convex function. Thus, the problem, resulting from optimising a non-convex functional in the traditional active contour models, can be avoided. Experimental results validate the superiorities and effectiveness of the proposed model for image segmentation with comparisons of those yielded by several state-of-the-art techniques.

An Efficient MRI Brain Tumor Segmentation by the Fusion of Active Contour Model and Self-Organizing-Map

Accurate detection of tumors in brain MR images is very important for the medical image analysis and interpretation. Tumors which are detected and treated in the early stage gives better long-term survival than those detected lately. This paper proposes a combined method of Self-Organizing –Map (SOM) and Active Contour Model (ACM) for the effective segmentation of the brain tumor from MR images. ACMs are energy-based image segmentation methods and they treat the segmentation as an optimization problem. The optimization function is formulated in terms of appropriate parameters and is designed such that the minimum value of its correspondence to a contour which is a near approximation of the real object boundary. The traditional ACMs depend on pixel intensity as well as very susceptible to parameter tuning and it turns out to be a challenge for these ACMs to deal the image objects of distinct intensities. Conversely, Neural Networks (NNs) are very effective in dealing inhomogeneities but usually results in noise due to the misclassification of pixels. Additionally, NNs deal the segmentation problems without objective function. Hence we proposed a framework for the brain tumor segmentation which integrates SOM with ACM and is termed as SOMACM. This works by exactly integrating the global information derived from the weights or prototypes of the trained SOM neurons to aid choosing whether to shrink or enlarge the present contour during the optimization process and is performed in an iterative way. The proposed method can deal with the images of complex intensity distributions, even in the presence of noise. Exploratory outcomes demonstrate the high accuracy in the segmentation results of SOMACM on different tumor images, compared to the ACM as well as the general SOM segmentation methods. Furthermore, the proposed framework is not highly sensitive to parameter tuning.

An Improved Active Contour Method for Medical Image Segmentation using Singular Value Decomposition

An image segmentation method uses dynamic shape models (ACM) executed by methods for level set systems have been successfully used as piece of picture division. essential idea of ACM is to undeniably address shapes zero level game plan of higher dimensional level set limit, & figure progression of frame through improvement of level set.since they always produce sub-regions with continuous boundaries. However, traditional edge-based active contour models have been applicable to only relatively simple images whose sub-regions are uniform without internal edges. A partial solution to the problem of internal edges is to partition an image based on the statistical approach i.e. Information of image intensity measured and active contour model based on level set and Singular Value Decomposition may be applied to improve the efficiency and accuracy in poor quality images. In this paper an Improved Active Contour Method for medical image segmentation is based on active appearance models, active shape models, level set,PCA and Linear Discriminant Analysis are analyzed with intent to produce an inhomogeneous environment using SVD for segmentation of real world images in the presence of intensity in-homogeneity and noise.

K-means++ clustering-based active contour model for fast image segmentation

Intensity inhomogeneity often occurs in real-life images because of nonuniform illumination, device operating, and technical limitation. We propose a K-means++ clustering-based active contour model for fast image segmentation. The key point is two fitting functions whose value computed by K-means++ clustering algorithm before level set function evolution. At first, we set up a rectangular local window. Two fitting functions represent the center points of brighter and darker subregions in the moving rectangular local windows. The method avoids repeating calculation of the fitting functions during curve evolution compared with the traditional region-based active contour models. Therefore, the proposed model has lower computational costs, and we can obtain correct segmentation results in less time and fewer iterations. The proposed model can efficiently segment images with intensity inhomogeneity. In addition, the experiments have proved that the proposed model has strong robustness to initialization.