A Novel Level Set Model Based on Multi-scale Local Structure Operation for Texture Image Segmentation

Segmentation methods have been the subject of much research because they lead to many practical applications. Medical imaging is particularly concerned: it is indeed based on images of the inside of a patient's body (ultrasounds, x-rays, ...) on which it is desired to automatically identify the presence of abnormalities (for example the detection of micro-calcifications in a mammogram may reveal the presence of breast cancer. We can refer to for an overview of the possible applications of wavelets and segmentation problems in medical imaging); but image segmentation is also used in many other areas: shape tracking in video documents, detection of stellar objects in astronomical images, detection of fronts in satellite images for data assimilation in meteorology,....... In this paper, we study a New robust Level Set image segmentation method developed by Wavelet Transformation and Level set model. Proposed algorithm is verified for simulated images and applied for a medical image processing in the MATLAB environment.

The application of segmentation technology in CT images can not only retain the clear characteristics of CT images, but also have the advantages of high resolution, clear anatomical relationship and clear pathological development. Level set segmentation method combines region and boundary information to solve the problems on complex texture and blurred edge of CT image. Therefore, this paper proposes an algorithm for regional growth and level set interaction for the problem of boundary sensitivity and lack of regional information in level set segmentation. In order to improve the efficiency and reduce the error of segmentation, this algorithm uses Gaussian filter to remove noise. Meaning while it adopts Ostu method to transform the image into a binary image. In addition, this algorithm provides an ideal growth environment for the seed point of the region growing algorithm correspondingly. The results of region growth are accomplished as the initial contour of the level set to modify the initial boundary. The boundary indication function introduces the region information; the level set segmentation algorithm achieves the purpose of accelerating in the pixel similar region and stopping at the target boundary as a result. Comparing the overall improved algorithms with the previous both region growing method and the level set method, the experiment results shows convincingly that the interaction between the level set segmentation and the region growing reduces the number of iterations. Under the condition of weak boundary and high noise, the boundary indicator function can accurately and completely segment the organ region and save the details of the internal organ completely, which improves the segmentation accuracy to a certain extent.

In recent years, image segmentation based on level set formulations was encompassed by proficiently implementing various image processing applications, especially in case of reformative models for medical image segmentation. Existing level set methods under this criterion are not up to the mark in terms of noise, weak boundaries, and inhomogeneity. In this paper, we put forward a new formulation for selective segmentation with level set model using optimised fuzzy region clustering and implementation is carried out in two stages. In first, optimised fuzzy region clustering (OFR) is employed in which particle swarm optimisation technique is utilised at the initialisation of FCM clustering to obtain optimal cluster centres and then it is incorporated with region competition. While in second, implementing OFR clustering using level set methods. The proposed formulation is successfully implemented on some contemporary medicinal images. It is capable to identify the weak boundaries and segment the desired components of an image such as white substance, grey substance, cerebrospinal fluid, bone sarcoma, breast cancer and Alzheimer’s disease, etc. The visual results and quantitative analysis show its effectiveness, superiority and accuracy over the existing formulations.

Level set methods (LSMs) have been extensively investigated for medical image segmentation. However, there are a few inherent drawbacks in common level set formulations using image variation or region competition. For example, edge-based LSMs are susceptible to weak or broken boundaries, while region-based ones are often dominated by suboptimal solutions. By incorporating the functional of fuzzy controlling, we propose a new level set model in this paper to combine the merits of edge-based and region-based LSMs while overcoming their drawbacks. It also provides a convenient framework to integrate prior information or knowledge for medical image segmentation. Its performance has been preliminarily verified for medical images of computed tomography (CT) and magnetic resonance imaging (MRI).

The purpose of the research is to develop a universal algorithm for partial texture segmentation of any visual images. The main peculiarity of the proposed segmentation procedure is the extraction of only homogeneous fine-grained texture segments present in the images. At first, an initial seed point is found for the largest and most homogeneous segment of the image. This initial seed point of the segment is expanded using a region growing method. Other texture segments of the image are extracted analogously in turn. At the second stage, the procedure of merging the extracted segments belonging to the same texture class is performed. Then, the detected texture segments are input to a neural network with competitive layers which accomplishes more accurate delineation of the shapes of the extracted texture segments. The proposed segmentation procedure is fully unsupervised, i.e., it does not use any a priori knowledge on either the type of textures or the number of texture segments in the image. The research results in development of the segmentation algorithm realized as a computer program tested in a series of experiments that demonstrate its efficiency on grayscale natural scenes.

Level set methods have been widely used in image processing and computer vision. In conventional level set formulations, the level set function typically develops irregularities during its evolution, which may cause numerical errors and eventually destroy the stability of the evolution. Therefore, a numerical remedy, called reinitialization, is typically applied to periodically replace the degraded level set function with a signed distance function. However, the practice of reinitialization not only raises serious problems as when and how it should be performed, but also affects numerical accuracy in an undesirable way. This paper proposes a new variational level set formulation in which the regularity of the level set function is intrinsically maintained during the level set evolution. The level set evolution is derived as the gradient flow that minimizes an energy functional with a distance regularization term and an external energy that drives the motion of the zero level set toward desired locations. The distance regularization term is defined with a potential function such that the derived level set evolution has a unique forward-and-backward (FAB) diffusion effect, which is able to maintain a desired shape of the level set function, particularly a signed distance profile near the zero level set. This yields a new type of level set evolution called distance regularized level set evolution (DRLSE). The distance regularization effect eliminates the need for reinitialization and thereby avoids its induced numerical errors. In contrast to complicated implementations of conventional level set formulations, a simpler and more efficient finite difference scheme can be used to implement the DRLSE formulation. DRLSE also allows the use of more general and efficient initialization of the level set function. In its numerical implementation, relatively large time steps can be used in the finite difference scheme to reduce the number of iterations, while ensuring sufficient numerical accuracy. To demonstrate the effectiveness of the DRLSE formulation, we apply it to an edge-based active contour model for image segmentation, and provide a simple narrowband implementation to greatly reduce computational cost.

Image segmentation plays an important role in computer vision and image processing. Level set model is a classical image segmentation method. During level set evolution, almost all the level set energy minimization of image segmentation are based on the gradient descent method and the finite difference scheme. The speed of evolution is slow and easy to fall into local minima. In this paper, we propose a fast sweeping optimization algorithm to minimize global cosine fitting (GCF) model. When moving a pixel from the one side region to the another side region of evolving contour, the sweeping algorithm calculates the energy change directly and checks whether the cosine fitting energy is decreased. With this, we can avoid solving the Euler-Lagrange equation and the partial differential equation, which usually take a lot of time. Moreover, our proposal is robust to initial level set contour and it automatically handles the topological variety, algorithm automatic termination and no longer requires the reinitialization step, parameter adjustment and the distance regularization term. The experiments on real noise and synthetic images show the effectiveness of the sweeping algorithm.

The variational level set model for piecewise constant multiphase image segmentation on the plane and the related Split-Augmented-Lagrangian Projection Method (SALPM) are investigated in this paper. On the analysis of the current problems based on the variational level set method for image segmentation, we also design a rapid SALPM method for two-phase image segmentation model, getting the general model of multiple phase level set in order to facilitate the generic design and program. In addition, the concrete formula of the rapid split algorithms for multiphase image segmentation with level set model and the calculation steps are given, and simultaneously taking liver tumor CT image as examples of the multiphase segmentation. Moreover, by comparing with the traditional methods, the experiments show that our algorithm presented in this paper have higher computational efficiency and accuracy, and better the extraction of liver contour.

Level set methods have previously been successfully implemented in interface propagation for etching and deposition processes. In this article, the authors show that level set methods can be used to model solid phase epitaxial regrowth. The model incorporates orientation dependence of regrowth as found by Csepregi et al. [J. Appl. Phys. 49, 3906 (1978)]. The orientation dependent velocity data are taken from Csepregi’s paper and fitted to a polynomial function to give the growth velocity for level set methods. Simulations show the capability of our model in predicting the pinching of the corners in ⟨111⟩ direction and humplike shape in ⟨100⟩ direction. This is confirmed by the transmission electron microscope pictures from recent papers. This modeling holds special interest because of the different diffusivities of boron in amorphous and crystalline silicon (approximately five orders of magnitude difference) and because of the various defects forming at the pinching corners which could lead to higher leakage current in scaled devices. The level set model is implemented in FLOOPS.

Active contour model (ACM) approaches for image segmentation and feature extraction have emerged as very appealing and powerful tools in image processing. The basis of ACM approach is to evolve a curve, called level set, to extract the desired object (s) under some constraints. In this course, various extensions of earlier Osher's level set model have been suggested in the litareture. More recently, a new ACM model referred to selective binary and Gaussian filtering regularized level set (SBGFRIL) has been put forward as a fruitful combination of geodesic active contour model (GAC) and Chan-Vese (C-V) active contour models. This paper attempts to put forward some appealing performance indices to assess the performances of the suggested SBGFRIL compared with GAC and V-C models. The performance metrics involve the clustering based quality evaluations.

Images captured under non uniform lightening and illumination conditions consist of unwanted shadow regions. Segmentation of these images using conventional level set methods show improper results which is a major problem in many real time applications such as medical imaging, pattern recognition etc. In this paper we propose an adaptive Level set model by introducing a new adaptive force function on the level sets evolution based on reflectance & illumination present in the image. This method is proved to be successful in segmentation of images by excluding the shadow regions present in the images.

Concurrent two-scale topological design of multiple unit cells and structure using combined velocity field level set and density model

The Chan-Vese level set method has wide potential application as an image segmentation method. But the complex computing restricts its application field. In order to improve the speed of image segmentation, this paper presents a new level set initialization method on Chan-Vese level set model. After a simple iterative, we can separate out the outline of objects. Experiments show that the method is simple and efficient, with better separation effects than the old method.

A novel level set method integrating local and global statistical information is proposed in this paper. In our method, a new signed pressure force (SPF) function is constructed by two parts. One is the global average intensity of the image, which can accelerate the evolution of the curve when the contour far away from the object boundaries. The other is the intensity average of difference image between the averaging convolution image and the original image, which can guide the evolving curve to catch the boundaries of the objects. In addition, an adaptive weighting function is utilized to adjust the ratio between the global and local terms, which can eliminate the inconvenient selection of weighting parameter. By substituting the new SPF function for the edge stopping function of the geodesic active contour model, we obtain a novel adaptive hybrid segmentation model, which is capable of segmenting the images with intensity inhomogeneity. What is more, in our method, the level set function is initialized with a binary function, which reduces the computational cost for the re-initialization step. The experimental results and comparisons with several popular models on synthetic and real images indicate that our method achieves superior performance in segmenting images with noise, low contrast and intensity inhomogeneity.

Unsupervised segmentation of noisy and textured images using Markov random fields