GVF Snake Research Articles

HoloBrain: 3D low-cost mobile augmented reality rendering of brain tumour using the GVF snake model segmentation

ABSTRACT Augmented reality (AR) is a dynamic field intersecting various disciplines, with applications spanning medicine and diagnostics. Our focus on advancing medical imaging has led to groundbreaking progress in brain tumor diagnosis, fostering optimism in treatment. In this pursuit, we introduce HoloBrain, an accessible AR application designed to transform medical education. Utilizing smartphones, HoloBrain enables automatic segmentation, AR visualization, and 3D interaction without the need for head-mounted devices. Emphasizing practicality over hyper-realism, our innovation allows medical students to seamlessly integrate a virtual brain tumor model into their surroundings. This immersive approach facilitates exploration of the 3D representation of an afflicted brain, enhancing educational experiences. Tests in a controlled environment using a phantom head yielded promising results, with our solution demonstrating average errors of 2 mm to 4.5 mm on smartphone displays. These outcomes highlight the precision and effectiveness of our contribution, underlining its potential in transforming medical education and training. HoloBrain represents a step forward in making AR accessible and impactful for aspiring medical professionals, offering a valuable tool for enhancing their understanding of complex medical scenarios.

Image segmentation using active contours with image structure adaptive gradient vector flow external force

IntroductionGradient vector flow (GVF) has been proven as an effective external force for active contours. However, its smoothness constraint does not take the image structure into account, such that the GVF diffusion is isotropic and cannot preserve weak edges well.MethodsIn this article, an image structure adaptive gradient vector flow (ISAGVF) external force is proposed for active contours. In the proposed ISAGVF model, the smoothness constraint is first reformulated in matrix form, and then the image structure tensor is incorporated. As the structure tensor characterizes the image structure well, the proposed ISAGVF model can be adaptive to image structure, and the ISAGVF snake performs well on weak edge preservation and deep concavity convergence while possessing some other desirable properties of the GVF snake, such as enlarged capture range and insensitivity to initialization.ResultsExperiments on synthetic and real images manifest these properties of the ISAGVF snake.

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.

GVFOM: a novel external force for active contour based image segmentation

The GVF snake is effective on converging into concave areas and being insensitive to initialization. However, it fails to converge to areas with deep and narrow concavities or preserve weak edges. In this paper, a novel external force called gradient vector flow over manifold (GVFOM) is proposed for active contours. The proposed model takes the GVF vector as a 2D manifold embedded into a 4D Euclidean space, leading to the generalization of the Laplacian operator in Euclidean Space to that over manifold, and the two components of the external force are couples with each other to improve the properties of the GVF snake. The GVFOM snake has been tested with different kinds of images, and experimental results and comparison manifest that the GVFOM snake has better performance than the GVF and other state-of-the-art snakes on object separation, deep and narrow concavity convergence, weak edge protection, noise robustness while keeping some desirable properties of the GVF model such as initialization insensitivity and large capture range.

Novel Image Segmentation Algorithm Based on Automatic GVF Snake Model

To address issues that GVF Snake model needs to initialize the contour manually so that image segmentation cannot be automatically handled, the effect of segmentation is also related to the initial contour, and its efficiency and accuracy of the model are not ideal, a novel algorithm for image segmentation based on automatic GVF Snake model is proposed in this paper. We introduce the concepts of flat redundant point and fitting outsourcing polygon. In our algorithm the discrete feature edge points set of target image is first obtained by SUSAN corner detection algorithm. Secondly, finding the fitting outsourcing polygon of discrete edge points set, Finally, the GVF Snake curve is evolved by using the fitting outsourcing polygon as the initial contour curve to complete the image segmentation. It is not only proved that the fitting outsourcing polygon as the initial contour curve can converge to the real target image edge by the algorithm in theory, but the experimental results also show that the proposed algorithm can effectively improve the segmentation accuracy, greatly reduce the number of iterations of the model, and improve the efficiency of the algorithm.

Visual Three-Dimensional Reconstruction of Aortic Dissection Based on Medical CT Images

With the rapid development of CT technology, especially the higher resolution of CT machine and a sharp increase in the amount of slices, to extract and three-dimensionally display aortic dissection from the huge medical image data became a challenging task. In this paper, active shape model combined with spatial continuity was adopted to realize automatic reconstruction of aortic dissection. First, we marked aortic feature points from big data sample library and registered training samples to build a statistical model. Meanwhile, gray vectors were sampled by utilizing square matrix, which set the landmarks as the center. Posture parameters of the initial shape were automatically adjusted by the method of spatial continuity between CT sequences. The contrast experiment proved that the proposed algorithm could realize accurate aorta segmentation without selecting the interested region, and it had higher accuracy than GVF snake algorithm (93.29% versus 87.54% on aortic arch, 94.30% versus 89.25% on descending aorta). Aortic dissection membrane was extracted via Hessian matrix and Bayesian theory. Finally, the three-dimensional visualization of the aortic dissection was completed by volume rendering based on the ray casting method to assist the doctors in clinical diagnosis, which contributed to improving the success rate of the operations.

Vector Image Model to Object Boundary Detection in Noisy Images

A New model is designed for boundary detection and applied it to object segmentation problem in medical images. Our edge following technique incorporates a vector image model and the edge map information. The proposed technique was applied to detect the object boundaries in several types of noisy images where the ill-defined edges were encountered. The proposed techniques performances on object segmentation and computation time were evaluated by comparing with the popular methods, i.e., the ACM, GVF snake models. Several synthetic noisy images were created and tested. The method is successfully tested in different types of medical images including aortas in cardiovascular MR images, and heart in CT images.

Snake-assisted quality-guided phase unwrapping for discontinuous phase fields.

Quality-guided phase unwrapping (QGPU) has been proven as an effective phase unwrapping method. As the QGPU assumes that the true phase map is continuous, it suffers from the discontinuity problem. To solve this problem, the windowed Fourier transform (WFT) based methods have been adopted to obtain effective quality measures. Nevertheless, in some special cases, using the quality maps from the WFT-based methods could not lead to a correct result. In this paper, we propose a method that incorporates the GVF snake model to assist the unwrapping process. This new method achieves better results than using the WFT-based methods directly in some discontinuous examples.

Accurate segmentation of partially overlapping cervical cells based on dynamic sparse contour searching and GVF snake model.

Overlapping cells segmentation is one of the challenging topics in medical image processing. In this paper, we propose to approximately represent the cell contour as a set of sparse contour points, which can be further partitioned into two parts: the strong contour points and the weak contour points. We consider the cell contour extraction as a contour points locating problem and propose an effective and robust framework for segmentation of partially overlapping cells in cervical smear images. First, the cell nucleus and the background are extracted by a morphological filtering-based K-means clustering algorithm. Second, a gradient decomposition-based edge enhancement method is developed for enhancing the true edges belonging to the center cell. Then, a dynamic sparse contour searching algorithm is proposed to gradually locate the weak contour points in the cell overlapping regions based on the strong contour points. This algorithm involves the least squares estimation and a dynamic searching principle, and is thus effective to cope with the cell overlapping problem. Using the located contour points, the Gradient Vector Flow Snake model is finally employed to extract the accurate cell contour. Experiments have been performed on two cervical smear image datasets containing both single cells and partially overlapping cells. The high accuracy of the cell contour extraction result validates the effectiveness of the proposed method.

Boundary Extraction Using Region-Based GVF Snake Model

Traditional GVF snake model has enlarged capture range and improved the convergence capability for boundary concavities, but it cannot efficiently solve the convergence problem for an image with deep boundary concavities and high noise. In this paper, by integrating the region force derived from the region information of interested object in an image into the force balance equation, a novel snake model was proposed to guide the evolvement of contour curve. Compared with traditional GVF snake model, our model further effectively improves capability of reducing noise sensitivity and converging into deeply boundary concavities. Experimental results with synthetic images and real images demonstrated the feasibility and robustness of our model.

Damaged Red Dates Detection Based PCNN and GVF Snake Model

According to the feature of moldy red dates, Pulse Coupled Neural Networks and Snake model are presented to detect damaged region of red dates so that the bad ones can be picked. Firstly, Pulse Coupled Neural Networks is used to segment red date images. Segmented images are binary images, in which wrinkled and decayed regions separate from other well regions. Then edge is detected using Pulse Coupled Neural Networks and this edge will be defined as initial contour of GVF Snake model. Finally, GVF Snake model is used to detect the decayed regions. Experiments show that this proposed method can extract decayed regions of red dates efficiently.

The recognition and modelling of a backbone and its deformity

In this article the authors present a method for the backbone recognition and modelling. The process of recognition combines some classical techniques (Hough transformation, GVF snakes) with some new (authors present a method for initial curvature detection, which they call the Falling Ball method). The result enables us to identify high-quality features of the spine and to detect the major deformities of backbone: the intercrestal line, centre sacral vertical line, C7 plumbline; as well as angles: proximal thoracic curve, main thoracic curve, thoracolumbar/lumbar. These features are used for measure in adolescent idiopathic scoliosis, especially in the case of treatment. Input data are just radiographic images, meet in everyday practice.

Analysis for Diagnosing Myocardial Ischemia by Detecting the Boundary of Left Ventricle in Echocardiography Sequences using GVF snake

Left ventricular performance, one of hallmarks of coronary artery disease, can be detected by echocardiography. Coronary artery disease (CAD) also known as myocardial ischemic heart disease (MIHD), is most common type of heart disease and cause of heart attacks or heart failure. Heart failure (HF) can result from any structural or functional cardiac disorder that impairs ability of ventricle to fill with or eject blood. Echocardiography represents the gold standard in assessment of left ventricle LV systolic and diastolic dysfunction. Left ventricular dimensions, volumes and wall thicknesses are measured from systolic and diastolic movements that are widely used in clinical practice and research. To obtain accurate linear measurements from echocardiography accurate segmentation of LV is essential. This paper proposes a method to segment left ventricle to detect systolic and diastole movements which is obtained by extracting frames from video of echocardiogram which is further processed to detect systolic and diastole movements so that movement of heart can be clear to cardiologist to visualize left ventricle. The obtained results are efficient and can be utilized for purpose of detecting abnormal heart wall motion so that many heart problems can be detected in echocardiography instead of using advanced diagnosis like angiogram which may have risk.

A Novel Method in Extracranial Removal of Brain MR Images

The removal of extracranial elements from brain M R images provides doctors a reliable method to analyze and diagnose dynamic data in E-Health. In traditional segmentation, experts are required to identify every element of the whole image, subjectivity and large amount of works make the mission uncertain and intolerant. GVF Snake model leads to efficiently dealing with the segmentation by giving an initial contour around the final object, but manual participation is still inevitable. This paper proposes an automatic morphology -based algorithm to generate the initial contour for active contour model to implement the removal accurately. To achieve the removal, initial contours will be produced by the original contour generator, which are utilized to approach more precise contours implemented by improved GVF Snake model. After a self-correction among the elements, segmentation missions are done. Experimental result shows that with simple steps and little time, the proposed algorithm can complete the segmentation task successfully, and is of good robustness as well as high accuracy.

Colorectal Cancer Detection using Image Processing based on IGVF Model

Colorectal cancer is the third most common cancer in both men and women. Computed Tomography (CT) Colonography is a valid technique for detecting and screening colorectal cancers. In order to accomplish an effective way to identify colon cancer at an early stage, digital images of colon polyps have been investigated. In this paper, an improved gradient vector flow (IGVF) is considered as an essential method to segment an image appropriately. This new algorithm, IGVF can improve GVF snake model's ability to capture thin boundary indentation like the boundary of cancer images. The segmented candidates are typically characterized by features describing like the polyp shape and its internal intensity distribution. Such features will serve as input for the classification system. Classification is performed by Support Vector Machine (SVM).Finally, measure the overlap between the manual and the algorithm segmentation to test the accuracy of frame work. It is expected to obtain greater accuracy and to produce good segmentation results of colonic polyps.

Performance Evaluation Study of Active Contour Models in Medical Imaging

Over the past years image segmentation has played an important role in medical imaging. Segmented images are used for a number of applications such as computer aided surgery, treatment planning, diagnosis, study of anatomical structures and many more. Deformable models provide a general method of performing non-rigid object segmentation. They offer an attractive approach to overcome the limitations of traditional low level segmentation approaches. A snake is a flexible deformable model which can be matched to an image contour by energy minimisation. Snakes are a powerful technique in segmenting formless shapes when little or no prior knowledge about the shape is available. Rather than handcrafting a prior knowledge into the model, the shape variation is extracted from a training set by applying PCA to PDM. In this paper some familiar Active Contour models such as Snakes by Kass et al, Balloon model, Greedy snakes method, Gradient Vector Flow Snakes are compared on the basis of qualitative and quantitative measures. The GVF Snakes have the ability to move into boundary concavities whereas the Greedy Snakes, Original Snakes have problems trying to locate the contour of an object which has a boundary concavity.

A New Contour Initialization of CT Image Sequences in GVF Model

In order to overcome the shortcomings that GVF model is susceptible to structures with slender topology, an improved watershed algorithm is proposed to determine initial contour of GVF Snake model. The organs in medical CT images often were irregular and had deep boundary concavities, and CT serial images were up to hundreds pieces. Firstly, in a CT image the improved watershed algorithm grows an organ from a seed block by the principle of the similarity between gray scale and texture, then using its edge as the start contour of the adjacent CT sequence, with GVF algorithm segment the organ from a sequence of images, the process is repeated until get all slices of the entire abdomen. Experiment results which are the basic of 3-D reconstruction and cancer detection show that the new contour initialization algorithm can obtain segmentation result efficiently, accurately and cost less time.

A novel optic disc detection scheme on retinal images

Robust and effective optic disc detection is a necessary processing component in automatic retinal screening systems. In this paper, optic disc localization is achieved by a novel illumination correction operation, and contour segmentation is completed by a supervised gradient vector flow snake (SGVF snake) model. Conventional GVF snake is not sufficient to segment contour due to vessel occlusion and fuzzy disc boundaries. In view of this reason, the SGVF snake is extended in each time of deformation iteration, so that the contour points can be classified and updated according to their corresponding feature information. The classification relies on the feature vector extraction and the statistical information generated from training images. This approach is evaluated by means of two publicly available databases, Digital Retinal Images for Vessel Extraction (DRIVE) database and Structured Analysis of the Retina (STARE) database, of color retinal images. The experimental results show that the overall performance is with 95% correct optic disc localization from the two databases and 91% disc boundaries are correctly segmented by the SGVF snake algorithm.

基于颜色、梯度矢量流活动轮廓及支持向量机实现白细胞的提取和分类

A leukocyte classification method was proposed by using image technologies.Firstly, based on image color information, image distance transformation and the Snake of Gradient Vector Flow (GVF Snake) ...

Elastic models: A comparative study applied to retinal images

In this work various methods of parametric elastic models are compared, namely the classical snake, the gradient vector field snake (GVF snake) and the topology-adaptive snake (t-snake), as well as the method of self-affine mapping system as an alternative to elastic models. We also give a brief overview of the methods used. The self-affine mapping system is implemented using an adapting scheme and minimum distance as optimization criterion, which is more suitable for weak edges detection. All methods are applied to glaucomatic retinal images with the purpose of segmenting the optical disk. The methods are compared in terms of segmentation accuracy and speed, as these are derived from cross-correlation coefficients between real and algorithm extracted contours and segmentation time, respectively. As a result, the method of self-affine mapping system presents adequate segmentation time and segmentation accuracy, and significant independence from initialization.