Geodesic Active Contour Model Research Articles

Fast edge integration based active contours for color images

In this paper, a novel geometric active contour model for color image is presented. It combines squared local contrast based alignment term about directional information of edge location as part of driving force, together with the improved geodesic active contour model containing Bays error based two-region segmentation information. And all these measures are integrated as a unified framework. Finally, an unconditionally stable and simple numerical scheme, including the narrow band and fast marching methods, is introduced for efficiently implementing this novel model. Experimental results on color image segmentation show that it can extract the contour of color images precisely and its performance is shown much better than the geodesic-aided C– V (GACV) method when directional information about edge location is supplied.

An extension of min/max flow framework

In this paper, the min/max flow scheme for image restoration is revised. The novelty consists of the following three parts. The first is to analyze the reason of the speckle generation and then to modify the original scheme. The second is to point out that the continued application of this scheme cannot result in an adaptive stopping of the curvature flow. This is followed by modifications of the original scheme through the introduction of the Gradient Vector Flow (GVF) field and the zero-crossing detector, so as to control the smoothing effect. Our experimental results with image restoration show that the proposed schemes can reach a steady state solution while preserving the essential structures of objects. The third is to extend the min/max flow scheme to deal with the boundary leaking problem, which is indeed an intrinsic shortcoming of the familiar geodesic active contour model. The min/max flow framework provides us with an effective way to approximate the optimal solution. From an implementation point of view, this extended scheme makes the speed function simpler and more flexible. The experimental results of segmentation and region tracking show that the boundary leaking problem can be effectively suppressed.

A New Curve Control Function for the Detection of the Brain Ventricle Area

This paper proposed a region-based curve control function to detect the brain ventricle area by utilizing a geodesic active contour model. This is based on the average brightness of the brain ventricle area which is brighter in MRI images. Compared numerically by using various types of measurements, the proposed method can detect the brain ventricle area better than the existing methods.

Directional geodesic active contour for image segmentation

By incorporating the image gradient directional information into the geodesic active contour model, we propose a novel active contour model called directional geodesic active contour, which has the advantage of selectively detecting the image edges with different gradient directions. The experiment results show the high performance of the proposed active contour in image segmentation, especially when multiple edges with different gradient directions are present near the object boundary to confuse the active contour.

Delineating lakes and enclosed islands in satellite imagery by geodesic active contour model

The objective of the present paper is to develop a new method for delineating lakes and enclosed islands from shuttle radar topography mission (SRTM) digital elevation model (DEM). The Thousand‐Island Lake in China is chosen as the study site. DEM may have missing values or be inaccurate over water bodies. Thus, it is not trivial to delineate the shorelines of lake directly from DEM. We achieve this objective by overlaying the boundary derived from the Landsat image of the same area. Unlike traditional water body delineation techniques, e.g. the band ratio method, which make use of physical quantities, we only use the colour information from Landsat ETM+ band 7, 4 and 2. The main reason is that the colour information is the only resource available for most publicly available satellite data such as the maps from Google Earth. Thus, it is necessary to develop a method depending on only colour information. In the Landsat image, a discrimination function to determine whether a pixel belongs to the lake area is obtained by studying sample pixels chosen from the lake area. The delineation of shorelines is an evolutionary process. The evolution equation is derived according to the active contour model and the discrimination function. The initial contour is inside the lake and expands according to the evolution equation. The evolving curve converges to the boundaries of the lake efficiently with a satisfactory result. Finally, the shorelines are overlaid on the DEM according to latitude and longitude. Our geodesic active contour method is a general one, and could be used to delineate objects of interest such as oil slicks and burn scars in satellite images.

Lung Segmentation by New Curve Stopping Function Using Geodesic Active Contour Model

To detect lung area, this paper proposes curve stopping function that is based on CT coefficient of area of lung parenchyma instead of existing edge indication function. The proposed method was compared numerically using various measures and this method can detect better lung parenchyma region than existing methods. In addition, detecting procedure of the area of lung parenchyma was visually verified in lung images.

Curvature-Driven PDE Methods for Matrix-Valued Images

Matrix-valued data sets arise in a number of applications including diffusion tensor magnetic resonance imaging (DT-MRI) and physical measurements of anisotropic behaviour. Consequently, there arises the need to filter and segment such tensor fields. In order to detect edge-like structures in tensor fields, we first generalise Di Zenzo's concept of a structure tensor for vector-valued images to tensor-valued data. This structure tensor allows us to extend scalar-valued mean curvature motion and self-snakes to the tensor setting. We present both two-dimensional and three-dimensional formulations, and we prove that these filters maintain positive semidefiniteness if the initial matrix data are positive semidefinite. We give an interpretation of tensorial mean curvature motion as a process for which the corresponding curve evolution of each generalised level line is the gradient descent of its total length. Moreover, we propose a geodesic active contour model for segmenting tensor fields and interpret it as a minimiser of a suitable energy functional with a metric induced by the tensor image. Since tensorial active contours incorporate information from all channels, they give a contour representation that is highly robust under noise. Experiments on three-dimensional DT-MRI data and an indefinite tensor field from fluid dynamics show that the proposed methods inherit the essential properties of their scalar-valued counterparts.

Moving object segmentation and detection for monocular robot based on active contour model

A novel geodesic active contour model based on optical flow information is proposed to segment and detect the moving object for monocular robots. More specifically, an active contour is formulated using the level set method, which eliminates the need of re-initialisation. The developed scheme alleviates the effect of optical flow noise, increasing the robustness of the detection of moving objects. Experimental results show that this algorithm can successfully track a moving target, e.g. a human being.

X-ray bone fracture subtraction using geodesic active contour and mathematical morphology operations

This paper presents a new approach on deformable models that combines geodesic active contour model with mathematical morphology operations and minimum path-finding algorithms. Morphology operations are applied to define the constraints of the geodesic active contour model. The size and shape of the structuring element for morphology operations describe a local window that collect and filter the local image gradient information. This local information is fed to the global constraints of the energy function of the geodesic active contour. The focus of this research is to investigate the optimal constraints that allow the model to be able to perform the segmentation task of the bone fracture subtraction of X-ray image of human arms over a noisy cast background. The model developed in this project speeded up the method of geodesic active contour, improved the result of the edge finding by minimizing the energy function based on a local window and avoided the curve attracted to the small gaps on the weak edges to improve the accuracy of segmentation.

Geodesic Active Regions and Level Set Methods for Supervised Texture Segmentation

This paper presents a novel variational framework to deal with frame partition problems in Computer Vision. This framework exploits boundary and region-based segmentation modules under a curve-based optimization objective function. The task of supervised texture segmentation is considered to demonstrate the potentials of the proposed framework. The textured feature space is generated by filtering the given textured images using isotropic and anisotropic filters, and analyzing their responses as multi-component conditional probability density functions. The texture segmentation is obtained by unifying region and boundary-based information as an improved Geodesic Active Contour Model. The defined objective function is minimized using a gradient-descent method where a level set approach is used to implement the obtained PDE. According to this PDE, the curve propagation towards the final solution is guided by boundary and region-based segmentation forces, and is constrained by a regularity force. The level set implementation is performed using a fast front propagation algorithm where topological changes are naturally handled. The performance of our method is demonstrated on a variety of synthetic and real textured frames.

Fast geodesic active contours

We use an unconditionally stable numerical scheme to implement a fast version of the geodesic active contour model. The proposed scheme is useful for object segmentation in images, like tracking moving objects in a sequence of images. The method is based on the Weickert-Romeney-Viergever (additive operator splitting) AOS scheme. It is applied at small regions, motivated by the Adalsteinsson-Sethian level set narrow band approach, and uses Sethian's (1996) fast marching method for re-initialization. Experimental results demonstrate the power of the new method for tracking in color movies.

Some Remarks on the Equivalence between 2D and 3D Classical Snakes and Geodesic Active Contours

Recently, Caselles et al. have shown the equivalence between a classical snake problem of Kass et al. and a geodesic active contour model. The PDE derived from the geodesic problem gives an evolution equation for active contours which is very powerfull for image segmentation since changes of topology are allowed using the level set implementation. However in Caselles‘ paper the equivalence with classical snake is only shown for 2D images and 1D curves, by using concepts of Hamiltonian theory which have no meanings for active surfaces. This paper propose to examine the notion of equivalence and to revisite Caselles et al. arguments. Then a notion equivalence is introduced and shown for classical snakes and geodesic active contours in the 2D (active contour) and 3D (active surface) case.