Improved Algorithm Based on Level Set Image Segmentation Methods

This paper is concerned with the segmentation of structures from 3D medical datasets where an important challenge is the need to overcome problems associated with intensity in homogeneities, noise and proximity to other tissue of similar intensity levels resulting in weak delineating boundaries. Level sets are part of an important class of methods that utilize partial differential equations (PDEs) and have been extensively applied in image segmentation. During the evolution of the curve, an isotropic diffusion is adaptively applied to the image to remove noise while preserving boundary information. The speed of curve evolution and the segmentation result has been significantly improved compared with traditional level set methods and region-based implementation of level sets. The method is tested on 3D CTA datasets containing abdominal and thoracic aneurysm (AAA and TAA). The results demonstrate the capability of the methodology to segment an AAA and TAA from these datasets.

To solve the problem that many image segmentation methods cannot be applied to forward looking sonar image accurately, an improved level set segmentation method was proposed in this paper. Firstly, the level set evolution without re-initialization was introduced. Secondly the different characteristics of forward looking sonar image from the optical image were analyzed, and we got the factors affecting segmentation. Then, to overcome these negative effects, this paper did preprocessing by morphological top-hat and bottom-hat transformation, and carried on level set method without re-initialization to construct an improved level set sonar image segmentation system. Finally, our method was compared with the traditional level set method in computer experiments. Simulation results show that it is more adapted to forward looking sonar image segmentation.

Level set method and Gaussian Mixture model (GMM) are two very valuable tools for natural image segmentation. The former aims to acquire good geometrical continuity of segmentation boundaries, while the latter focuses on analyzing statistical properties of image feature data. Some studies on the integration between them have been reported due to their complementarity in the last 10 years. However, these studies generally supposed that the image-featured data density distribution of every segmented domain is independent with each other and can be separately approximated by Gaussian model or GMM, which conflicts with the fundamental idea of GMM clustering-based image segmentation. To remedy this problem, we give a new insight at image segmentation objective under the combined framework between Bayesian theory and GMM density approximation. Thereby, a novel level set image segmentation method integrated with GMM (GMMLS) is proposed. Then, the theoretical analysis on GMMLS is given, in which some valuable results are demonstrated. At last, several types of natural image segmentation experiments are reported and the corresponding results indicate that GMMLS can obtain better or at least equivalent performance compared with existing relevant methods in almost all cases.

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.

Liver segmentation in MR Image is the foundational work for further research in our lab. Traditional Level Set methods and active contours were often used to segment the liver, but the results were not always promising due to noise and the low gradient response on the liver boundary. In this paper we propose a novel level set based variational approach that incorporates shape prior knowledge into the improved Chan-Vese’s model [1] which can overcome the leakage and over-segmentation problems. Some statistical methods are used to get the prior shape, and the training process allows the prior shape not exactly at the location of desired object. Experiments are taken on abdomen MRI series and the results reveal that our improved level set based shape prior method can segment liver shape precisely and a refined liver perfusion curve without respiration affection can be achieved.

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.

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.

We describe a novel variational level set based method for delineating the sub thalamic nucleus (STN) region of human brains from magnetic resonance (MR) images. Based on the understanding of specific imaging characteristic of STN, we apply a narrow band limitation in the region-based energy function for localizing the STN from initial contour information provided by doctors. The validity of the algorithm was tested on practical Parkinson's Patients' T1-weighted magnetic resonance images. Comparing to traditional edge-based and global region-based level set methods, our method achieves more reliable segmentation results. Also, our method is insensitive to the initial contour placement, making it applicable in practical surgical navigations.

An improved edge-based level set method combining local regional fitting information for noisy image segmentation

Image segmentation is an important analysis tool in the field of computer vision. In this paper, on the basis of the traditional level set method, a novel segmentation model using generalized divergences is proposed. The main advantage of generalized divergences is their smooth connection performance among various kinds of well-known and frequently used fundamental divergences with one formula. Therefore, the discrepancy between two probability distributions of segmented image parts can be measured by generalized divergences. We also found a solution to determine the optimal divergence automatically for different images. Experimental results on a variety of synthetic and natural images are presented, which demonstrate the potential of the proposed method. Compared with the previous active contour models formulated to solve the same nonparametric statistical segmentation problem, our method performs better both qualitatively and quantitatively.

Image segmentation is the problem of partitioning an image into different subsets, where each subset may have a different characterization in terms of color, intensity, texture, and/or other features. Segmentation is a fundamental component of image processing, and plays a significant role in computer vision, object recognition, and object tracking. Active Contour Models (ACMs) constitute a powerful energy-based minimization framework for image segmentation, which relies on the concept of contour evolution. Starting from an initial guess, the contour is evolved with the aim of approximating better and better the actual object boundary. Handling complex images in an efficient, effective, and robust way is a real challenge, especially in the presence of intensity inhomogeneity, overlap between the foreground/background intensity distributions, objects characterized by many different intensities, and/or additive noise. In this thesis, to deal with these challenges, we propose a number of image segmentation models relying on variational level set methods and specific kinds of neural networks, to handle complex images in both supervised and unsupervised ways. Experimental results demonstrate the high accuracy of the segmentation results, obtained by the proposed models on various benchmark synthetic and real images compared with state-of-the-art active contour models.

Carotid artery segmentation using level set method with double adaptive threshold (DATLS) on TOF-MRA images

With level set formulation, new contours can system that utilizes the evolution of zero-level contours for segmenting post-sawing wafer is proposed in this study. The system also utilizes a regional formulation, which improves the level set segmentation in images with intensity inhomogeneity. With a proper configuration, the initial contour can be given in arbitrary positions in the background. Hence, a small fixed initial contour is applied to evolve the zero-level set to automatically segment the wafer. Without necessity of applying any filtering in advance, the inspection can be performed directly on the segmented results. The experimental results demonstrate the effectiveness of the proposed approach.

Level set methods are the numerical techniques for tracking interfaces and shapes. They have been successfully used in image segmentation. A new variational level set evolving algorithm without re-initialization is presented in this paper. It consists of an internal energy term that penalizes deviations of the level set function from a signed distance function, and an external energy term that drives the motion of the zero level set toward the desired image feature. This algorithm can be easily implemented using a simple finite difference scheme. Meanwhile, not only can the initial contour can be shown anywhere in the image, but the interior contours can also be automatically detected.

Regional level set method is a popular approach for image segmentation that uses inside and outside information of contour to extract object boundary. Unfortunately, in many cases, such method is not adequate to model complex textured objects. In this paper, we propose a natural texture image segmentation method which incorporates the pixel-level feature into region-level feature. The multiscale local structure operation is proposed as pixel-level feature to describe the texture structure of image. So the problems of multi-scale and rotation invariance of inhomogeneous texture are addressed by introducing multi-scale local structure operation into level set energy functional. Then, the global intensity information is extracted as the region-level feature and integrated with multi-scale local structure operation. Further, we propose a so-called vector level set method to obtain the segmentation results. Here, we extend the traditional regional level set model into the vector formulation so that the multi-scale local structure operation can be suitably combined with the global intensity information to achieve the more superior image segmentation performance than that of the traditional segmentation methods for texture images. Experiments on some synthesis texture images and real natural scene images demonstrate the excellent performance of the proposed method which successfully combines local structure information and global intensity information to extract the object boundary.