Chan Vese Research Articles

Intelligent bite marking analysis and classification using deep convolutional neural network based Xception model

Bite mark analysis and classification play a vital role in forensics. The recent advances in computer vision and deep learning models paves a way for the design of automated bite mark detection and classification process. This article focuses on the design of intelligent bite marking analysis and classification using deep convolutional neural network based Xception model. The major goal of the proposed model is to determine the appropriate class labels for the bite marked images. The proposed model initially intends to pre-process the bite marked images in different ways such as hair removal, median filtering based noise removal, and adaptive histogram based contrast enhancement. Besides, Chan Vese Segmentation approach is applied for segmenting the bite marked images. The data augmentation process is performed for increasing the count of images. In addition, Xception model is employed for the extraction of features. Finally, two machine learning (ML) classifications such as support vector machine (SVM) and logistic regression (LR) models are employed for image classification. For demonstrating the enhanced performance of the presented models, a set of simulations were carried out on their own dataset and the results ensured the betterment of the proposed model over the other existing models.

Deep Learning Regression-Based Retinal Layer Segmentation Process for Early Diagnosis of Retinal Anamolies and Secure Data Transmission through ThingSpeak

Diabetic retinopathy (DR) is a progressive type of problem that affects diabetic people. In general, this condition is asymptomatic in its early stages. When the condition progresses, it can cause hazy and unclear vision of objects. As a result, it is necessary to develop a framework for early diagnosis in order to prevent visual morbidity. The suggested method entails acquiring fundus and OCT images of the retina. To acquire the lesions, techniques such as preprocessing, sophisticated Chan–Vese segmentation, and object clustering are used. Furthermore, regression-based neural network (RNN) categorization is used to achieve expected results that help foretell retinal diseases. The methodology is implemented using the MATLAB technical computing language, together with the necessary toolboxes and blocksets. The proposed system requires two steps. In the first stage, the detection of diabetic retinopathy via the proposed deep learning technique is carried out. The data collected from the MATLAB are transmitted to the approved PC via the IoT module known as ThingSpeak in the second stage. To validate the robustness of the proposed approach, comparisons with regard to plots of confusion matrices, mean square error (MSE) plots, and receiver operating characteristic (ROC) plots are performed.

An Augmented Lagrangian Model for Signal Segmentation

In this paper, we provide a new insight to the two-phase signal segmentation problem. We propose an augmented Lagrangian variational model based on Chan–Vese’s original one. Using both energy methods and PDE methods, we show, in the one-dimensional case, that the set of minimizers to the proposed functional contains only binary functions and it coincides with the set of minimizers to Chan–Vese’s one. This fact allows us to obtain two important features of the minimizers as a by-product of our analysis. First of all, for a piecewise constant initial signal, the jump set of any minimizer is a subset of the jump set of the given signal. Second, all of the jump points of the minimizer belong to the same level set of the signal, in a multivalued sense. This last property permits to design a trivial algorithm for computing the minimizers.

On optimization of active contour methods in colonoscopy image segmentation

As colonoscopy is the standard screening approach for colorectal polyps, and the first step of the correct classification and the efficient automatic diagnostics is the accurate detection and segmentation of the existing polyps, it is worth researching systematically, how colonoscopy databases are responding to two of the most influential variational segmentation methods, the geodesic and Chan–Vese active contour methods. Due to the quality variation of the colonoscopy databases, pre-processing steps are made. Then, 14 various filtered images are evaluated as different inputs for the active contour methods using the Sørensen–Dice Similarity Coefficient as a performance measurement metric. The effects of the initial mask shape and its size together with the number of iterations, contraction bias and smoothness factor were studied. The number of necessary iterations depended on the image resolution. In general, the Chan-Vese method showed more efficiency to match the actual contour of the polyp than the geodesic one with an initial mask possibly located within the polyp area. Preprocessing such as reflection removal, background subtraction and mean or median filtering improve the Sørensen–Dice coefficient can improve by 0.3.

A Machine-Learning-Based Medical Imaging Fast Recognition of Injury Mechanism for Athletes of Winter Sports.

The Beijing 2022 Winter Olympics will begin soon, which is mainly focused on winter sports. Athletes from different countries will arrive in Beijing one after another for training and competition. The health protection of athletes of winter sports is very important in training and competition. The occurrence of sports injury is characterized by multiple factors, uncertainty, and accidents. This paper mainly pays attention to the head injury with the highest severity. Athletes' high safety awareness is a part of reducing injury, but safety awareness cannot effectively reduce the occurrence of injury in competition, and timely treatment of injured athletes is particularly important. After athletes are injured, a telemedicine image acquisition system can be built, so that medical experts can identify athletes' injuries in time and provide the basis for further diagnosis and treatment. In order to improve the accuracy of medical image processing, a C-support vector machine (SVM) medical image segmentation method combining the Chan-Vese (CV) model and SVM is proposed in this paper. After segmentation, the edge and detail features of the image are more prominent, which meet the requirements of high precision for medical image segmentation. Meanwhile, a high-precision registration algorithm of brain functional time-series images based on machine learning (ML) is proposed, and the automatic optimization of high-precision registration of brain function time-series images is performed by ML algorithm. The experimental results show that the proposed algorithm has higher segmentation accuracy above 80% and less registration time below 40 ms, which can provide a reference for doctors to quickly identify the injury and shorten the time.

Distance regularization energy terms in level set image segment model: A survey

The level set is a classical image segmentation model. In order to achieve its stable evolution, the level set function should be a signed distance function (SDF). However, due to the common appearance of irregularities, it must periodically be initialized in order to remain a SDF near the zero level set. Distance regularization terms have been used to maintain the stable evolution of the level set function. We provide a survey of the various distance regularization potential functions. Firstly, we summarize many kinds of distance regularization potential functions studied in the literature. We then divide them into five classes according to the type of potential function. Secondly, we analyze the properties of every class of potential functions and their diffusion rate functions. Finally, to demonstrate the effectiveness of the distance regularization potential functions, we apply them with a region based level set energy functional for image segmentation. Experimental analyses are conducted to compare the segmentation performance of various distance regularization potential functions when combined with the classical Chan Vese model.

Evaluation of Diagnostic Value of Mediastinum for Differentiation of Drug Sensitive, Multi and Extensively Drug Resistant Tuberculosis Using Chest X-Rays

Background and ObjectiveThe rise of Drug Resistant Tuberculosis (DR TB), particularly Multi DR (MDR), and Extensively DR (XDR) has reduced the rate of control of the disease. Computer aided diagnosis using Chest X-rays (CXRs) can help in mass screening and timely diagnosis of DR TB, which is essential to administer proper treatment regimens. In CXRs, lungs and mediastinum are two significant regions which contain the information about the likelihood of DR TB. The objective of this work is to analyze the shape characteristics of lungs and mediastinum to improve the diagnostics accuracy for differentiation of Drug Sensitive (DS), MDR and XDR TB using computer aided diagnostics system. MethodsThe CXR images of DS and DR TB patients are obtained from a public database. The lung fields are segmented from the CXRs using Reaction Diffusion Level Set Evolution. Mediastinum is segmented from the delineated lung masks using Chan Vese model. The shape features from each lung and mediastinum masks are extracted and analysed. The discriminative power of individual and combination of both lung and mediastinum features are evaluated using machine learning techniques for classification of DS vs MDR, MDR vs XDR and DS vs XDR TB images. The performances of classifiers are compared using standard metrics. ResultsThe proposed segmentation methods are able to delineate lungs and mediastinum from the CXR images. The extracted lung and mediastinum features are found to be statistically significant (p < 0.05) for differentiation of DS and DR TB conditions. Using the combination of both lung and mediastinum features, Multi-Layer Perceptron classifier achieves maximum F-measure of 82.4%, 81.0% and 87.0% for differentiation of DS vs MDR, MDR vs XDR and DS vs XDR, respectively. ConclusionAnalysis of mediastinum along with the lungs in chest X-rays could improve the diagnostic performance for differentiation of drug sensitive and resistant TB conditions. The proposed methodology is able to differentiate DS, MDR and XDR TB, and found to be clinically relevant. Hence, this work is useful for computer-based early detection of DS and DR TB conditions.

Dual-Fusion Active Contour Model with Semantic Information for Saliency Target Extraction of Underwater Images

Underwater vision research is the foundation of marine-related disciplines. The target contour extraction is significant for target tracking and visual information mining. Aiming to resolve the problem that conventional active contour models cannot effectively extract the contours of salient targets in underwater images, we propose a dual-fusion active contour model with semantic information. First, the saliency images are introduced as semantic information and salient target contours are extracted by fusing Chan–Vese and local binary fitting models. Then, the original underwater images are used to supplement the missing contour information by using the local image fitting. Compared with state-of-the-art contour extraction methods, our dual-fusion active contour model can effectively filter out background information and accurately extract salient target contours. Moreover, the proposed model achieves the best results in the quantitative comparison of MAE (mean absolute error), ER (error rate), and DR (detection rate) indicators and provides reliable prior knowledge for target tracking and visual information mining.

Segmentation of cotton leaf images using a modified chan vese method

Segmentation of cotton leaf images using a modified chan vese method

Color Image Segmentation by Utilizing Coarse-to-Fine Strategy

Image segmentation is an important processing technology, which is the basis of image recognition and has been widely used in many fields. In this paper, we propose a method, termed coarse-to-fine strategy-based image segmentation (CSIS), for color image segmentation. The basic idea is to segment an image by three phases: (1) the original image is first segmented into several distinct regions by using the mean shift method; (2) the segmented regions are converted to a weighted region adjacency graph (RAG), and a new graph cut method, called multi-cut algorithm, is proposed to partition the RAG into multiple regions; (3) a one-step Chan–Vese algorithm is applied to smooth the boundaries of the segmented objectives. In each of the last two phases, a method is applied to refine the result obtained in the previous phase. By carefully balancing the efforts used in each phase, CSIS could segment color images both efficiently and effectively. These advantages are demonstrated by applying the proposed method to a variety of test instances, and the statistical results also show that it is comparable with some state-of-the-art methods.

DISEASE DETECTION FROM CASSAVA LEAF IMAGES WITH DEEP LEARNING METHODS IN WEB ENVIRONMENT

In this article, it is aimed to classify healthy and four different plant diseases from Cassava plant leaf images. For this purpose, the “Cassava-Leaf-Disease-Classification” data set, which is an up-to-date and difficult data set published in 2020, was used. The used data set includes a total of 21,397 images consisting of healthy and four different diseases. In the study, in the MATLAB environment, the images were first subjected to the Chan-Vese (CV) Segmentation method and the area of interest was determined. Features were extracted with the ResNet 50 and MobileNetV2 deep learning architectures from the detected areas. Extracted features are classified by Support Vector Machine and K-Nearest Neighbor algorithms. The images are divided into two as training and testing according to the K-fold 5 value. The average highest success rates in training and test data were achieved by using the ResNet50 architecture and SVM classifier together, as a result of segmentation. As a result of training and testing processes, 85.4% and 84.4% success rates were obtained, respectively. At the end of the test process of the study, a trained network was obtained according to ResNet50, where the highest success rates were obtained, and MobileNetV2, another deep learning architecture used in the study. It has been compiled with MATLAB Builder NE in order to run these two networks in the web environment. The methods obtained as a result of the compilation are integrated into the ASP.NET MVC5 programming language. Finally, it has been made available to manufacturers with a web-based embedded interface. Thus, a deep learning-based decision support system has been developed that can be easily used by all manufacturers in the web environment.

Fast Global Minimization of the Chan–Vese Model for Image Segmentation Problem

The segmentation of weak boundary is still a difficult problem, especially sensitive to noise, which leads to the failure of segmentation. Based on the previous works, by adding the boundary indicator function with L 2,1 norm, a new convergent variational model is proposed. A novel strategy for the weak boundary image is presented. The existence of the minimizer for our model is given, by using the alternating direction method of multipliers (ADMMs) to solve the model. The experiments show that our new method is robust in segmentation of objects in a range of images with noise, low contrast, and direction.

Single Shot MultiBox Detector for Urban Plantation Single Tree Detection and Location With High-Resolution Remote Sensing Imagery

Using high-resolution remote sensing images to automatically identify individual trees is of great significance to forestry ecological environment monitoring. Urban plantation has realistic demands for single tree management such as catkin pollution, maintenance of famous trees, landscape construction, and park management. At present, there are problems of missed detection and error detection in dense plantations and complex background plantations. This paper proposes a single tree detection method based on single shot multibox detector (SSD). Optimal SSD is obtained by adjusting feature layers, optimizing the aspect ratio of a preset box, reducing parameters and so on. The optimal SSD is applied to single tree detection and location in campuses, orchards, and economic plantations. The average accuracy based on SSD is 96.0, 92.9, and 97.6% in campus green trees, lychee plantations, and palm plantations, respectively. It is 11.3 and 37.5% higher than the latest template matching method and chan-vese (CV) model method, and is 43.1 and 54.2% higher than the traditional watershed method and local maximum method. Experimental results show that SSD has a strong potential and application advantage. This research has reference significance for the application of an object detection framework based on deep learning in agriculture and forestry.

Research on 3D crack segmentation of CT images of oil rock core.

In this paper, we propose a framework for CT image segmentation of oil rock core. According to the characteristics of CT image of oil rock core, the existing level set segmentation algorithm is improved. Firstly, an algorithm of Chan-Vese (C-V) model is carried out to segment rock core from image background. Secondly the gray level of image background region is replaced by the average gray level of rock core, so that image background does not affect the binary segmentation. Next, median filtering processing is carried out. Finally, an algorithm of local binary fitting (LBF) model is executed to obtain the crack region. The proposed algorithm has been applied to oil rock core CT images with promising results.

Unsupervised Object Segmentation Based on Bi-Partitioning Image Model Integrated with Classification

The development of convolutional neural networks for deep learning has significantly contributed to image classification and segmentation areas. For high performance in supervised image segmentation, we need many ground-truth data. However, high costs are required to make these data, so unsupervised manners are actively being studied. The Mumford–Shah and Chan–Vese models are well-known unsupervised image segmentation models. However, the Mumford–Shah model and the Chan–Vese model cannot separate the foreground and background of the image because they are based on pixel intensities. In this paper, we propose a weakly supervised model for image segmentation based on the segmentation models (Mumford–Shah model and Chan–Vese model) and classification. The segmentation model (i.e., Mumford–Shah model or Chan–Vese model) is to find a base image mask for classification, and the classification network uses the mask from the segmentation models. With the classifcation network, the output mask of the segmentation model changes in the direction of increasing the performance of the classification network. In addition, the mask can distinguish the foreground and background of images naturally. Our experiment shows that our segmentation model, integrated with a classifier, can segment the input image to the foreground and the background only with the image’s class label, which is the image-level label.

Deep Learning on MRI Images for Diagnosis of Lung Cancer Spinal Bone Metastasis.

This paper aimed to explore the adoption of deep learning algorithms in lung cancer spinal bone metastasis diagnosis. Comprehensive analysis was carried out with the aid of AdaBoost algorithm and Chan-Vese (CV) algorithm. 87 patients with lung cancer spinal bone metastasis were taken as research subjects, and comprehensive evaluation was made in terms of preliminary classification of images, segmentation results, Dice index, and Jaccard coefficient. After the case of misjudgment on whether there was hot spot was excluded, the initial classification accuracy of the AdaBoost algorithm can reach 96.55%. True positive rate (TPR) was 2.3%, and false negative rate (FNR) was 1.15%. 45 MRI images with hot spots were utilized as test set to detect the segmentation accuracy of CV, maximum between-cluster variance method (OTSU), and region growing algorithm. The results showed that the Dice index and Jaccard coefficient of the CV algorithm were 0.8591 and 0.8002, respectively, which were considerably superior to OTSU (0.6125 and 0.5541) and region growing algorithm (0.7293 and 0.6598). In summary, the AdaBoost algorithm was adopted for image preliminary classification, and CV algorithm for image segmentation was ideal for the diagnosis of lung cancer spinal bone metastasis and it was worthy of clinical promotion.

Mathematical Model for Single and Multiple Object Extraction

In the image processing, noise is referred to as the visual distortion. This undesirable by-product may be captured in an image due to unpreventable assorted reasons. The interference of natural phenomena and technical problem, such as small sensor size, long exposure time, low ISO, shadow noise etc., can pollute image. The presence of noise images affects image processing outputs that include segmentation. Segmentation for noisy images is the major concern. To tackle this issue, we propose a modernistic model that is able neutralize the negative effects of outlier using the characteristic of kernel function by different approaches such as linear approach and quadratic approach for global segmentation. Moreover the weight function is used for local segmentation of noisy images. Comparing with classical models, the proposed technique shows robust performance. In comparison with the wellknown models such as Chan-Vese (CV) model , Yongfei Wu and Chuanjiang He (Wu-He) model and Chunming Li (Li) model we conclude that performance of our new model is much better.

A method to improve the computational efficiency of the Chan-Vese model for the segmentation of ultrasound images

PurposeAdvanced image segmentation techniques like the Chan-Vese (CV) models transform the segmentation problem into a minimization problem which is then solved using the gradient descent (GD) optimization algorithm. This study explores whether the computational efficiency of CV can be improved when GD is replaced by a different optimization method. MethodsTwo GD variants from the literature (Nesterov accelerated, Barzilai-Borwein) and a newly developed hybrid variant of GD were used to improve the computational efficiency of CV by making GD insensitive to local minima. One more variant of GD from the literature (projected GD) was used to address the issue of maintaining the constraint on boundary evolution in CV which also increases computational cost. A novel modified projected GD (Barzilai-Borwein projected GD) was also used to overcome both problems at the same time. The effect of optimization method selection on processing time and the quality of the output was assessed for 25 musculoskeletal ultrasound images (five anatomical areas). ResultsThe Barzilai-Borwein projected GD method was able to significantly reduce computational time (average(±std.dev.) reduction 95.82 % (±3.60 %)) with the least structural distortion in the delineated output relative to the conventional GD (average(±std.dev.) structural similarity index: 0.91(±0.06)). ConclusionThe use of an appropriate optimization method can substantially improve the computational efficiency of CV models. This can open the way for real-time delimitation of anatomical structures to aid the interpretation of clinical ultrasound. Further research on the effect of the optimization method on the accuracy of segmentation is needed.

A Systematic Approach for MRI Brain Tumor Localization and Segmentation Using Deep Learning and Active Contouring

One of the main requirements of tumor extraction is the annotation and segmentation of tumor boundaries correctly. For this purpose, we present a threefold deep learning architecture. First, classifiers are implemented with a deep convolutional neural network (CNN) and second a region-based convolutional neural network (R-CNN) is performed on the classified images to localize the tumor regions of interest. As the third and final stage, the concentrated tumor boundary is contoured for the segmentation process by using the Chan–Vese segmentation algorithm. As the typical edge detection algorithms based on gradients of pixel intensity tend to fail in the medical image segmentation process, an active contour algorithm defined with the level set function is proposed. Specifically, the Chan–Vese algorithm was applied to detect the tumor boundaries for the segmentation process. To evaluate the performance of the overall system, Dice Score, Rand Index (RI), Variation of Information (VOI), Global Consistency Error (GCE), Boundary Displacement Error (BDE), Mean Absolute Error (MAE), and Peak Signal to Noise Ratio (PSNR) were calculated by comparing the segmented boundary area which is the final output of the proposed, against the demarcations of the subject specialists which is the gold standard. Overall performance of the proposed architecture for both glioma and meningioma segmentation is with an average Dice Score of 0.92 (also, with RI of 0.9936, VOI of 0.0301, GCE of 0.004, BDE of 2.099, PSNR of 77.076, and MAE of 52.946), pointing to the high reliability of the proposed architecture.

A new effective hybrid segmentation method based on C–V and LGDF

Image segmentation is a significant research topic in image processing and computer vision. Active contour methods (ACMs) are widely used in image segmentation. In this paper, a new hybrid ACM segmentation model based on Chan–Vese (C–V) and Local Gaussian Distribution Fitting (LGDF) methods is proposed for the images with intensity inhomogeneity. In this model, new gradient descent flow equations are proposed and applied for the energy minimization of C–V and LGDF methods. Firstly, the proposed C–V method is applied to the image to effectively and quickly find the homogeneous regions of the image. Then, the proposed LGDF method is performed in these regions to detect inhomogeneous areas of the image. Thus, more effective and successful segmentation is obtained for inhomogeneous images. Experimental results show that the satisfactory segmentation results have been obtained by the proposed method for MRI and real images. Also, the proposed method is compared with the local binary fitting, LGDF, adaptive local-fitting-based, global and local weighted signed pressure ACMs, and convolutional neural network-based methods.