The line- and block-like structures extraction via ingenious snake

The geodesic active contour (GAC) model has been widely used due to the high precision of edge detection and the continuity of boundaries. However, compared to the gradient vector flow (GVF) field, it usually has poor performance in the object boundaries with concave shape. In this paper, the improvements that integrate the good performance of the GAC model and the GVF field are described in detail and the GAC_GVF&B model combining the GAC model with the GVF field and a balloon force is proposed. The experimental results demonstrate that the proposed method is insensitive to the positions of initial curves and can segment complex-shape or multi-objects images correctly with higher convergence.

Active contour model has a good performance in consecutive boundary extraction for medical images. The gradient vector flow (GVF) field is one of the most popular external forces that can increase the capture range and converge to concavities, although it is sensitive to image noise and easy to leak in weak edge. Here we propose a novel sigmoid gradient vector flow (SGVF) force model for improving contour performance. This novel external force field is insensitive to noises and may prevent the weak edge leakage. To further illustrate the advantages associated with the proposed GVF field formulation, synthetic images and real images are conducted when the proposed method is applied in ultrasound image and magnetic resonance image for suppressing noise and extracting the weak edges. Experimental results demonstrate that the proposed method leads to more accurate segmentation.

Active contours driven by divergence of gradient vector flow

Snakes, or active contours, have been widely used in image processing applications. An external force for snakes called gradient vector flow (GVF) attempts to address traditional snake problems of initialization sensitivity and poor convergence to concavities, while generalized GVF (GGVF) aims to improve GVF snake convergence to long and thin indentations (LTIs). In this paper, we find and show that both GVF and GGVF snakes essentially yield the same performance in capturing LTIs of odd widths, and generally neither can converge to even-width LTIs. Based on a thorough investigation of the GVF and GGVF fields within the LTI during their iterative processes, we identify the crux of the convergence problem, and accordingly propose a novel external force termed as component-normalized GGVF (CN-GGVF) to eliminate the problem. CN-GGVF is obtained by normalizing each component of initial GGVF vectors with respect to its own magnitude. Experimental results and comparisons against GGVF snakes show that the proposed CN-GGVF snakes can capture LTIs regardless of odd or even widths with a remarkably faster convergence speed, while preserving other desirable properties of GGVF snakes with lower computational complexity in vector normalization.

The aim of this paper is to present advances in segmentation for visualization and quantitative analysis in bioimaging. Here, we combine two existing approaches for segmentation with snakes. Firstly, we use cubic B-splines to represent the snake using coarse-to-fine control point insertion; this allows to smooth adaptively the resulting contour while reducing the risk to get attracted from misdetected edges. Secondly, we put the snake in a gradient vector flow (GVF) field. This enables the snake to evolve into concavities of the shape. Further, sensitive parameters drop out in our setting and the attraction range with respect to initialization of the snake is enlarged.

Automatic bone segmentation of Computed Tomography (CT) images is an important step in imageguided surgery where segmentation errors could be critical. Previous attempts include intensity-, edge-, region-, and deformable curve-based approaches [1], but none claims fully satisfactory performance. In this study, we have tested most widely used active contour (AC) -based approaches to segment knee bones from CT imagery, namely the Gradient Vector Flow (GVF) AC, the original geometric AC, the geodesic AC, the GVF fast geometric AC, and the Chan-Vese multiphase AC without edges. Among the techniques, only the Chan-Vese multiphase AC demonstrated satisfactory performance, proving its suitability for bone segmentation from CT images.

This paper presents a new hybrid speed function needed to perform image segmentation within the level-set framework. This speed function provides a general form that incorporates the alignment term as a part of the driving force for the proper edge direction of an active contour by using the probability term derived from the region partition scheme and, for regularization, the geodesics contour term. First, we use an external force for active contours as the Gradient Vector Flow field. This is computed as the diffusion of gradient vectors of a gray level edge map derived from an image. Second, we partition the image domain by progressively fitting statistical models to the intensity of each region. Here we adopt two Gaussian distributions to model the intensity distribution of the inside and outside of the evolving curve partitioning the image domain. Third, we use the active contour model that has the computation of geodesics or minimal distance curves, which allows stable boundary detection when the model’s gradients suffer from large variations including gaps or noise. Finally, we test the accuracy and robustness of the proposed method for various medical images. Experimental results show that our method can properly segment low contrast, complex images.

Generalized gradient vector flow external forces for active contours

Active contours, or snakes, are widely applied on biomedical image segmentation. They are curves defined within an image domain that can move to object boundaries under the influence of internal forces and external forces, in which the internal forces are generally computed from curves themselves and external forces from image data. Designing external forces properly is a key point with active contour algorithms since the external forces play a leading role in the evolution of active contours. One of most popular external forces for active contour models is gradient vector flow (GVF). However, GVF is sensitive to noise and false edges, which limits its application area. To handle this problem, in this paper, we propose using GVF as reference to train a convolutional neural network to derive an external force. The derived external force is then integrated into the active contour models for curve evolution. Three clinical applications, segmentation of optic disk in fundus images, fluid in retinal optical coherence tomography images and fetal head in ultrasound images, are employed to evaluate the proposed method. The results show that the proposed method is very promising since it achieves competitive performance for different tasks compared to the state-of-the-art algorithms.

The Active Contour Model (ACM) was introduced in 1987 and allows the image segmentation of objects in digital images through edge detection, obtained from image information. This ACM consists in outlining an initial curve around or inside an object of interest. This curve is deformed, according to some forces which act on it, moving it to the edges of the object. This process is performed through successive minimizing iterations of a given energy associated to the curve. Recently, various applications have proposed changes in traditional ACMs, generating new methods, applications, benefits and performance improvements in certain applications. Due to that, these methods have also been expanded to three dimensions (3D), being called Active Contour Methods 3D (ACM 3D), as is the case of the proposed method, called Adaptive Balloon ACM 3D. This method is applied in the segmentation of 3D models and compared to the 3D Region Growing method (RG 3D). The results showed that the RG is superior to the 3D proposed method when applied in closed objects, but inferior in hollow objects, in which the proposed method is stably displayed in all applications. Therefore, one can conclude that the proposed method can be applied to the segmentation of 3D objects in a promising way.

Liver segmentation with new supervised method to create initial curve for active contour

Extracting the shape of the gallbladder from an ultrasonography (USG) image is an important step in software supporting medical diagnostics, as it allows superfluous information which is immaterial in the diagnostic process to be eliminated. In this project, several active contour models were used to segment the shape of the gallbladder, both for cases free of lesions, and for those showing specific disease units, namely: lithiasis, polyps, and anatomical changes, such as folds of the gallbladder. The approximate edge of the gallbladder is found by applying one of the active contour models like the membrane and motion equation as well as the gradient vector flow model (GVF-snake). Then, the fragment of the image located outside the identified gallbladder contour is eliminated from the image. The tests carried out showed that the average value of the Dice similarity coefficient for the three active contour models applied reached 81.8%.

An automatic and markerless tracking method of deformable structures (digestive organs) during laparoscopic cholecystectomy intervention that uses the (PSO) behavour and the preoperative a priori knowledge is presented The associated shape to the global best particles of the population determines a coarse representation of the targeted organ (the gallbladder) in monocular laparoscopic colored images The swarm behavour is directed by a new fitness function to be optimized to improve the detection and tracking performance The function is defined by a linear combination of two terms, namely, the human a priori knowledge term (H) and the particle's density term (D) Under the limits of standard (PSO) characteristics, experimental results on both synthetic and real data show the effectiveness and robustness of our method Indeed, it outperforms existing methods without need of explicit initialization (such as active contours, deformable models and Gradient Vector Flow) on accuracy and convergence rate.

This paper presents a novel image segmentation method based on multiple active contours driven by particle swarm optimization (MACPSO). The proposed method uses particle swarm optimization over a polar coordinate system to increase the energy-minimizing capability with respect to the traditional active contour model. In the first stage, to evaluate the robustness of the proposed method, a set of synthetic images containing objects with several concavities and Gaussian noise is presented. Subsequently, MACPSO is used to segment the human heart and the human left ventricle from datasets of sequential computed tomography and magnetic resonance images, respectively. Finally, to assess the performance of the medical image segmentations with respect to regions outlined by experts and by the graph cut method objectively and quantifiably, a set of distance and similarity metrics has been adopted. The experimental results demonstrate that MACPSO outperforms the traditional active contour model in terms of segmentation accuracy and stability.

Accurate left ventricle border delineation is highly desirable on inherently noisy clinical echocardiograms. Active contour (or snake) is a powerful model-based image segmentation approach. In this work, we propose to use a modified gradient vector flow (GVF) snake to segment noisy echocardiographic image cycles. The first modification is to use a speckle reducing anisotropic diffusion (SRAD) operator to reduce the inherent speckle noise within the image. The second modification is to utilize the movement of the vessels and tissues (identified by means of optical flow analysis) and incorporate it into the external energy of the GVF snake. This will provide the necessary structural information while ignoring static noise prone areas of the image cine. Finally, the incorporation of an iterative priori knowledge process into the proposed solution will retract an expanding curve or correct a caving one when an expected border is occluded by noise. Results are compared with expert-defined segmentations yielding better sensitivity, precision rate and overlap ratio than that of the standard GVF model.