Geometric Active Contour Research Articles

A Study of Chan-Vese Model with the Introduction of Edge Information

In image segmentation, the image edge information and the selection of edge-stopping function have a extremely important influence on the segmentation effect. As a classical geometric active contour model, Chan-Vese model completely utilizes image global information for segmentation, which has the problems of inability to handle grayscale uneven images and slow curve evolution. This paper introduces edge information into the Chan-Vese model, chooses a new edge stopping function and simplifies the driving force term to build a new image segmentation model. The new model can detect the target fast, displaying better detection effect on the targets of images with uneven grayscale and the detection effect of the target in the uneven gray image is better

A New Medical Analytical Framework for Automated Detection of MRI Brain Tumor Using Evolutionary Quantum Inspired Level Set Technique.

Segmenting brain tumors in 3D magnetic resonance imaging (3D-MRI) accurately is critical for easing the diagnostic and treatment processes. In the field of energy functional theory-based methods for image segmentation and analysis, level set methods have emerged as a potent computational approach that has greatly aided in the advancement of the geometric active contour model. An important factor in reducing segmentation error and the number of required iterations when using the level set technique is the choice of the initial contour points, both of which are important when dealing with the wide range of sizes, shapes, and structures that brain tumors may take. To define the velocity function, conventional methods simply use the image gradient, edge strength, and region intensity. This article suggests a clustering method influenced by the Quantum Inspired Dragonfly Algorithm (QDA), a metaheuristic optimizer inspired by the swarming behaviors of dragonflies, to accurately extract initial contour points. The proposed model employs a quantum-inspired computing paradigm to stabilize the trade-off between exploitation and exploration, thereby compensating for any shortcomings of the conventional DA-based clustering method, such as slow convergence or falling into a local optimum. To begin, the quantum rotation gate concept can be used to relocate a colony of agents to a location where they can better achieve the optimum value. The main technique is then given a robust local search capacity by adopting a mutation procedure to enhance the swarm's mutation and realize its variety. After a preliminary phase in which the cranium is disembodied from the brain, tumor contours (edges) are determined with the help of QDA. An initial contour for the MRI series will be derived from these extracted edges. The final step is to use a level set segmentation technique to isolate the tumor area across all volume segments. When applied to 3D-MRI images from the BraTS' 2019 dataset, the proposed technique outperformed state-of-the-art approaches to brain tumor segmentation, as shown by the obtained results.

AI Techniques of Dermoscopy Image Analysis for the Early Detection of Skin Lesions Based on Combined CNN Features.

Melanoma is one of the deadliest types of skin cancer that leads to death if not diagnosed early. Many skin lesions are similar in the early stages, which causes an inaccurate diagnosis. Accurate diagnosis of the types of skin lesions helps dermatologists save patients' lives. In this paper, we propose hybrid systems based on the advantages of fused CNN models. CNN models receive dermoscopy images of the ISIC 2019 dataset after segmenting the area of lesions and isolating them from healthy skin through the Geometric Active Contour (GAC) algorithm. Artificial neural network (ANN) and Random Forest (Rf) receive fused CNN features and classify them with high accuracy. The first methodology involved analyzing the area of skin lesions and diagnosing their type early using the hybrid models CNN-ANN and CNN-RF. CNN models (AlexNet, GoogLeNet and VGG16) receive lesions area only and produce high depth feature maps. Thus, the deep feature maps were reduced by the PCA and then classified by ANN and RF networks. The second methodology involved analyzing the area of skin lesions and diagnosing their type early using the hybrid CNN-ANN and CNN-RF models based on the features of the fused CNN models. It is worth noting that the features of the CNN models were serially integrated after reducing their high dimensions by Principal Component Analysis (PCA). Hybrid models based on fused CNN features achieved promising results for diagnosing dermatoscopic images of the ISIC 2019 data set and distinguishing skin cancer from other skin lesions. The AlexNet-GoogLeNet-VGG16-ANN hybrid model achieved an AUC of 94.41%, sensitivity of 88.90%, accuracy of 96.10%, precision of 88.69%, and specificity of 99.44%.

Classification of Gender Individual Identification Using Local Binary Pattern on Palatine Rugae Image

Major disasters caused many casualties with the condition of the damaged bodies. It causes the individual identification process to be ineffective through biometric characteristics (such as lips and fingerprints). However, the palatine rugae can carry the individual identification process. Palatine rugae have unique and individual characteristics and are more resistant to trauma because of their internal location. In this study, an individual identification system is proposed to identify gender using the image of palatine rugae. The proposed system is developed by several algorithms and methods, such as Local Binary Pattern (LBP) as the feature extraction method and K-Nearest Neighbor (KNN) as the classification method. Based on the result of the system performed test, the proposed system can identify the gender of an individual by the combination of recognized palatine rugae patterns. The system achieved an accuracy test result of 100% with a specific configuration of LBP and KNN. The research contribution in this study is to develop the individual gender identification system, which proceeds with the palatine rugae pattern image with unique biometric characteristics as an input. The system applied several methods and algorithms, such as Geometric Active Contour (GAC) as a segmentation algorithm, Local Binary Pattern (LBP) as a feature extraction method, and K Nearest Neighbor (KNN) as a classification method.

An automatic segmentation framework for computer-assisted renal scintigraphy procedure.

One of the techniques for achieving unique and reliable information in medicine is renal scintigraphy. A key step for quantitative renal scintigraphy is segmentation of the kidneys. Here, an automatic segmentation framework was proposed for computer-aided renal scintigraphy procedures. To extract kidney boundary in dynamic renal scintigraphic images, a multi-step approach was proposed. This technique is featured with key steps, namely, localization and segmentation. At first, the ROI of each kidney was estimated using Otsu's thresholding, anatomical constraint, and integral projection, which is done in an automatic process. Afterwards, the ROI obtained for the kidneys was used as the initial contours to create the final counter of kidneys using geometric active contours. At this step and for the segmentation, an improved variational level set was utilized through Mumford-Shah formulation. Using e.cam gamma camera system (SIEMENS), 30 data sets were used to assess the proposed method. By comparing the manually outlined borders, the performance of the proposed method was shown. Different measures were used to examine the performance. It was found that the proposed segmentation method managed to extract the kidney boundary in renal scintigraphic images. The proposed technique achieved a sensitivity of 95.15% and a specificity of 95.33%. In addition, the section under the curve in the ROC analysis was equal to 0.974. The proposed technique successfully segmented the renal contour in dynamic renal scintigraphy. Using all the data sets, a correct segmentation of the kidney was performed. In addition, the technique was successful with noisy and low-resolution images and challenging cases with close interfering activities such as liver and spleen activities.

Research on Target Tracking Algorithm Based on Kernel Correlation

With the development of sensor and image processing technology, computer vision plays an increasingly significant role in the chemical engineering because of its characteristics such as low cost, high resolution, and non-contact measurement. In this paper, the motion probability map can be obtained by sparse optical flow based on Harris corner point. Then the coarse contour of silicon dioxide particles which is the input of kernelized correlation filtering (KCF) algorithm can be generalized. KCF algorithm can easily complete tracking task under the influence of disturbance including light change, video shaking and so forth. A contour refining and tracking method are proposed. The geometric active contour (GAC) algorithm can use function as implicit expression of contour and can design the different energy functional to control contour evolution. By minimizing of energy functional, the refining contour is evolved. Then the target tracking is realized according to the refined contour.

Integrating anisotropic filtering, level set methods and convolutional neural networks for fully automatic segmentation of brain tumors in magnetic resonance imaging

An accurate, fully automatic detection and segmentation technique for brain tumors in magnetic resonance images (MRI) is introduced. The approach basically combines geometric active contours segmentation with a deep learning-based initialization. As a pre-processing step, an anisotropic filter is used to smooth the image; afterwards, the segmentation process takes place in two phases: the first one is based on the concept of transfer learning, where a pre-trained convolutional neural network coupled with a detector is fine-tuned using a training set of 388 T1-weighted contrast enhanced MRI images that contain a brain tumor (Meningioma); this trained network is able to automatically detect the location of the tumor by generating a bounding box with certain coordinates. The second phase takes place by using the coordinates of the bounding box to initialize the geometric active contour that iteratively evolves towards the tumor's boundaries. While most of the ingredients of this processing chain are more or less well known, the main contribution of this work is in integrating the various techniques in a novel and hopefully clever form, which could take the best of both geometric segmentation algorithms and neural networks, with a relatively light training phase. The performance of such a processing network is evaluated using a separate testing set of 97 MRI images containing the same type of brain tumor. The technique proves to be remarkably effective, with a precision of 97.92%, recall of 96.91%, F-measure of 97.41% and an average Dice similarity coefficient (DSC) for segmented images above 0.95.

Evaluation and Correlation Analysis of Mental and Psychological Factors and Premature Ejaculation in Patients with Benign Prostatic Hyperplasia in Mobile Medical System.

Chronic prostatitis is a very common and very difficult disease. Based on the mobile medical system, this paper carried out a correlation analysis on the psychological factors and the evaluation of premature ejaculation in patients with benign prostatic hyperplasia. The article first analyzes the application in the field of mobile medical and then introduces the prostate image segmentation method based on the geometric active contour model. The emergence of automatic organ tissue segmentation technology is timely; it can help clinicians save a lot of manual segmentation time and has better reversibility and objectivity, and the theory of curve evolution is analyzed. Finally, this paper introduces the experimental research object and purpose, makes a statistical analysis of the symptoms of benign prostatic hyperplasia, and compares the incidence of psychological symptoms in patients with prostatitis under different factors. The experimental results of this paper show that 90% of prostatitis patients have mild psychological problems, and 10% have moderate psychological problems. Among them, the main reason for the psychological disorder of patients is depression, for which we should strengthen the care for patients.

Image Segmentation with a Priori Conditions: Applications to Medical and Geophysical Imaging

In this paper, we propose a method for semi-supervised image segmentation based on geometric active contours. The main novelty of the proposed method is the initialization of the segmentation process, which is performed with a polynomial approximation of a user defined initialization (for instance, a set of points or a curve to be interpolated). This work is related to many potential applications: the geometric conditions can be useful to improve the quality the segmentation process in medicine and geophysics when it is required (weak contrast of the image, missing parts in the image, non-continuous contour…). We compare our method to other segmentation algorithms, and we give experimental results related to several medical and geophysical applications.

The Role of Three-Dimensional Reconstruction of Medical Images and Virtual Reality in Nursing Experimental Teaching.

With the continuous advancement of medicine and computer science, medical image processing technology is also constantly advancing, and at the same time, it puts forward the needs of its own development. The purpose of this article is to combine the three-dimensional reconstruction of medical images and virtual reality (VR) technology in nursing experiment teaching to help students understand more easily and to simplify the teachers' teaching process and make the VR application technology. It is the most popular and effective in medical teaching. This article proposes the C-V model and the geometric active contour model to help us more clearly understand the pathology in this environment, where the specific symptoms appear, and bring a more easy-to-understand model for teaching and improving teaching quality. This article also designs nursing experiment teaching. The experimental results of this paper show that, after using VR courseware for teaching, the optimal test rate of the experimental class is 15% higher than that of the control class, and the transition rate is 8%. The actual test excellent rate and success rate of the experimental class are much higher than those of the control class. Therefore, it can be concluded that the application of VR technology in nursing teaching helps teachers improve their practical ability. The excellent teaching feedback rate is 95%, which is higher than 80.5% in the control group, indicating that the patient teaching simulation is approved by the observation group. The program can effectively improve the feedback rate of excellent teaching and provide students with better teaching services.

Improving Parotid Gland Tumor Segmentation and Classification Using Geometric Active Contour Model and Deep Neural Network Framework

Manual assessment of parotid gland status from magnetic resonance imaging is a subjective, time consuming and error prone process. Automatic image analysis methods offer the possibility to get consistent, objective and rapid diagnoses of parotid gland tumor. In this kind of cancer, a large variety in their characteristics, that brings various difficulties for traditional image analysis methods. In this paper, we propose an automatic method to perform both segmentation and classification of Parotid gland region in order to give quantitative assessment and uniform indicators of PGs status that will help pathologists in their diagnostic. The experimental result illustrates the high accuracy of the results of the proposed method compared to the ground truth. Furthermore, a comparative study with existing techniques is presented in order to demonstrate the efficiency and the superiority of the proposed method.

Microstructure identification based on vessel pores feature extraction of high-value hardwood species

Because of the diversity of vessel pores in different hardwood species, they are important for wood species identification. In this paper, a Micro CT was used to collect wood images. The experiment was based on six wood types, Pterocarpus macrocarpus, Pterocarpus erinaceus, Dalbergia latifolia, Dalbergia frutescens var. tomentosa, Pterocarpus indicus, and Pterocarpus soyauxii. One-thousand cross-sectional images of 2042 px × 1640 px were collected for each species. One pixel represents 1.95 µm of the real physical dimension. The level set geometric active contour model was used to obtain the contour of the vessel pores. Combined with a variety of morphological processing methods, the binary images of the vessel pores were obtained. The features of the binary images were extracted for classification. Classifiers such as BP neural network and support vector machine were used, the number, roundness, area, perimeter, and other characteristic parameters of the vessel pores were classified, and the accuracy rate was more than 98.9%. The distribution and arrangement of the vessel pores of six kinds of hardwood were obtained through the level set geometric active contour model and image morphology. Then BP neural network and support vector machine were used for realizing the classification of hardwood species.

Shoreline Extraction in SAR Image Based on Advanced Geometric Active Contour Model

Rapid and accurate extraction of shoreline is of great significance for the use and management of sea area. Remote sensing has a strong ability to obtain data and has obvious advantages in shoreline survey. Compared with visible-light remote sensing, synthetic aperture radar (SAR) has the characteristics of all-weather and all-day working. It has been well-applied in shoreline extraction. However, due to the influence of natural conditions there is a problem of weak boundary in extracting shoreline from SAR images. In addition, the complex micro topography near the shoreline makes it difficult for traditional visual interpretation and image edge detection methods based on edge information to obtain a continuous and complete shoreline in SAR images. In order to solve these problems, this paper proposes a method to detect the land–sea boundary based on a geometric active contour model. In this method, a new symbolic pressure function is used to improve the geometric active-contour model, and the global regional smooth information is used as the convergence condition of curve evolution. Then, the influence of different initial contours on the number and time of iterations is studied. The experimental results show that this method has the advantages of fewer iteration times, good stability and high accuracy.

Hybrid medical image compression method using quincunx wavelet and geometric actif contour

The purpose of this article is to find an efficient and optimal method of compression by reducing the file size while retaining the information for a good quality processing and to produce credible pathological reports, based on the extraction of the information characteristics contained in medical images. In this article, we proposed a novel medical image compression that combines geometric active contour model and quincunx wavelet transform. In this method it is necessary to localize the region of interest, where we tried to localize all the part that contain the pathological, using the level set for an optimal reduction, then we use the quincunx wavelet coupled with the set partitioning in hierarchical trees (SPIHT) algorithm. After testing several algorithms we noticed that the proposed method gives satisfactory results. The comparison of the experimental results is based on parameters of evaluation.

Finding and tracking local communities by approximating derivatives in networks

Since various complex systems are represented by networks, detecting and tracking local communities has become a crucial task nowadays. Local community detection methods are getting much attention because they can address large networks. One famous class of local community detection is to find communities around a seed node. In this research, a novel local community detection method, inspired by geometric active contours, is proposed for finding a community surrounding an initial seed. While most of real world networks are dynamic and the majority of local community detection cannot tackle dynamic networks, the proposed model has the ability to track a local community in a dynamic network. The proposed model introduces and uses the derivative-based concepts curvature and gradient of the boundary of a connected sub-graph in networks. Then, a velocity function based on curvature and gradient is proposed to determine if the boundary of a community should evolve to include a neighbouring candidate. Approximating derivatives in discrete Euclidean space has a long history. However, compared to Euclidean space, graphs follow a non-uniform space in which the dimensionality, given by the the fluctuation in degrees of nodes, fluctuates from one node to another. This complexity complicates the approximation of derivatives which are needed for defining the curvature and gradient of a node in the boundary of a community. A new framework to approximate derivatives in graphs is proposed for such a purpose. For finding local communities, benchmarking our method against two recent methods indicates that it is capable of finding communities with equal or better conductance; and, for tracking dynamic local communities, benchmarking of the proposed method against ground-truth dataset shows a noticeable level of accuracy.

Evaluation and benchmarking of level set-based three forces via geometric active contours for segmentation of white blood cell nuclei shape

The segmentation of white blood cells and their nuclei is still difficult and challenging for many reasons, including the differences in their colour, shape, background and staining techniques, the overlapping of cells, and changing cell topologies. This paper shows how these challenges can be addressed by using level set forces via edge-based geometric active contours. In this work, three level set forces-based (curvature, normal direction, and vector field) are comprehensively studied in the context of the problem of segmenting white blood cell nuclei based on geometric flows. Cell images are first pre-processed, using contrast stretching and morphological opening and closing in order to standardise the image colour intensity, to create an initial estimate of the cell foreground and to remove the narrow links between lobes and cell bulges. Next, segmentation is conducted to prune out the white blood cell nucleus region from the cell wall and cytoplasm by combining the theory of curve evolution using curvature, normal direction, and vector field-based level set forces and edge-based geometric active contours. The overall performance of the proposed segmentation method is compared and benchmarked against existing techniques for nucleus shape detection, using the same databases. The three level set forces studied here (curvature, normal direction, and vector field) via edge-based geometric active contours achieve F-index values of 92.09%, 91.13%, and 90.76%, respectively, and the proposed segmentation method results in better performance than all other techniques for all indices, including Jaccard distance, boundary displacement error, and Rand index.

Dynamic Waterline Mapping of Inland Great Lakes Using Time-Series SAR Data From GF-3 and S-1A Satellites: A Case Study of DJK Reservoir, China

Great inland freshwater lakes play an important role in regulating inland water resources, and the usage of synthetic aperture radar (SAR) images for the accurate waterline mapping is an effective technical means to study the dynamic changes of great inland lakes. In this article, the Danjiangkou (DJK) reservoir is selected as a study case, and a novel waterline mapping method with four main parts is proposed to monitor the water area dynamically. First, a coarse segmentation method is implemented to extract the initial waterline. Second, a strategy of division in local regions is given to speed up the subsequent processes. Third, a combination of a speckle filter and an improved geometric active contour model is used for refined segmentation. Finally, a change detection method is used to study the changing lake. Furthermore, six SAR images obtained by the Gaofen-3 (GF-3) and Sentinel-1A (S-1A) satellites in the DJK reservoir, Hongze lake and Poyang lake are tested to verify the universality of the proposed water area extraction method. The results demonstrate excellent performances with an accuracy of over 97% and an average contour offset under 0.7 pixels. Besides, the time-series analysis of the DJK reservoir is applied based on the mapped waterlines of 37 SAR images collected from January to December in 2017. Comparing with the changing tendency of the water level surveyed in the DJK reservoir, the waterline mapping results and the filed survey data have great consistency, which further proves the validity of the proposed method, also presents the significant potential of the GF-3 and S-1A SAR images for managing the water resource.

Benchmarking HEp-2 specimen cells classification using linear discriminant analysis on higher order spectra features of cell shape

Human Epithelial Type-2 (HEp-2) specimen patterns in microscopic images have been widely used for diagnosing autoimmune diseases. Manual cell classification is inefficient, time consuming and subject to human error, therefore, researchers have attempted to automate the procedure of HEp-2 cell classification. Challenges include large data volumes, variation in cell densities, shape, rotation, scaling and shifting, and overlapped cells. A computer-aided diagnosis (CAD) system, based on image-based classification, can help in terms of effort, time, and reliability of classification HEp-2 specimens. HEp-2 cells are classified into seven classes: speckled, homogeneous, centromere, nuclear membrane, nucleolar, golgi, and mitotic spindle. In this work, we focus on the task of HEp-2 specimen cell classification in terms of feature extraction representations, features selection and classifier design. We propose a method of linear discriminant analysis relying on bispectral invariant features from a segmented HEp-2 specimen cell shape that allows us to generalize features to being variation in shape, rotation, scaling and shifting. HEp-2 specimen cell shape segmentation is determined using level set based forces in normal direction and geometric active contours. Both linear and kernel support vector machines as well as fuzzy C mean are employed for fast classification of specimens and achieved Mean Class Rates (MCAs) of 91.25%, 86.02% and 89.14%, respectively, over the ICPR2014 and ICPR2016 contests HEp-2 cell task-2 datasets. The results validate the competitive performance of the proposed system compared with other approaches reported on both contests.

Structured graph regularized shape prior and cross-entropy induced active contour model for myocardium segmentation in CTA images

Accurate segmentation of myocardium tissue from computed tomography angiography (CTA) images is a crucial step in development of cardiac clinical applications. Automatic methods are highly desirable to release the burden of manual delineation, however, it is challenging due to the presence of images with intensity non-uniformity and coarse, weak and even pseudo boundary. To this end, a new geometric active contour (GAC) model that integrates high-level shape prior and low-level local intensity statistics is proposed. First, the cardiac-type specific shape prior is learned by structured graph-regularized principal component analysis, so that allows to be faithful to the shape of the desired myocardium. Second, local intensity distribution with inhomogeneity is modeled by cross-entropy energy functional and segmentation-oriented image decomposition. Third, the proposed model is solved as variational level set formulation and a distance regularized energy functional is also incorporated for the stability of numerical computation. The model was evaluated on a set of cardiac CTA images with comparison to related shape prior and local region-based methods and multi-atlas joint label fusion methods, and experimental results show it achieves competitive accuracies of segmenting myocardial epicardium and endocardium parts. It also presents advantage of computational burden in comparison to some popular methods such as multi-atlas joint label fusion. Since the framework of proposed method is general and adaptive, it could be potentially extended to segment similar objects in CT images.

Feature Segmentation for Blurred Edge of Ship Image Based on Depth Learning

ABSTRACT Shu, J.; Chen, Z., and Xu, C., 2018. Feature segmentation for blurred edge of ship image based on depth learning. In: Liu, Z.L. and Mi, C. (eds.), Advances in Sustainable Port and Ocean Engineering. Journal of Coastal Research, Special Issue No. 83, pp. 781–785. Coconut Creek (Florida), I SSN 0749-0208. In traditional feature segmentation for blurred edge of ship image, geometric active contour model is often used, and it does not consider the global feature of ship image, which is easy to occur over segmentation phenomenon, resulting in longtime segmentation and inaccurate segmentation results. On the basis of deep learning, feature segmentation technique for blurred edge of ship image is proposed. Ship image is preprocessed to remove the impurities and noises in the image, and segment the blurred edge of the processed ship image. The experimental results show that the method can reduce the segmentation time of the blurred edge of ship image, and has high efficiency and accurate segmentation res...