Edge-based Level Set Method Research Articles

A survey of level set method for image segmentation with intensity inhomogeneity

Image segmentation is a fundamental task in computer vision and image processing. Due to the presence of high noise, low resolution and intensity inhomogeneity, it is still a difficult problem in the practical applications. Level set methods have been widely used in image processing and computer vision. During the past decades, many models based on level set methods have been proposed to solve image segmentation with intensity inhomogeneity. It is necessary to conduct a comprehensive review and comparison of these models. Specifically, level set methods can be categorized into two groups, including edge-based level set methods (EBLSMs) and region-based level set methods (RBLSMs). This paper reviews some of the recent advances in EBLSMs and RBLSMs for segmenting image with intensity inhomogeneity. Their advantages and disadvantages are discussed in an objective point of view, and their performance is compared on image segmentation with intensity inhomogeneity. Finally, this paper further explores and discusses some open questions in segmenting images with intensity inhomogeneity.

A weighted edge-based level set method based on multi-local statistical information for noisy image segmentation

Image segmentation plays a fundamental role in image processing. Active contour models have been widely used since they handle topological change easily and provide smooth contours. However, noise presents challenges for edge-based level set methods since it leads contours easily passing through objects or falling into local minima. In this paper, we propose a weighted edge-based level set method based on multi-local statistical information to better segment noisy images. Through analysing the deficiencies of constant length and regional coefficients and traditional edge stop function in noisy image segmentation, weighted length and regional coefficients and modified edge stop function are proposed to overcome their shortcomings, respectively. The weighted edge-based level set method is used to segment synthetic and real images that have added different types and levels of noise. The experiments indicate that our method provides higher segmentation accuracies and more accurate segmentation results, which demonstrate its effectiveness and robustness.

Reconstructing granular particles from X-ray computed tomography using the TWS machine learning tool and the level set method

X-ray computed tomography (CT) has emerged as the most prevalent technique to obtain three-dimensional morphological information of granular geomaterials. A key challenge in using the X-ray CT technique is to faithfully reconstruct particle morphology based on the discretized pixel information of CT images. In this work, a novel framework based on the machine learning technique and the level set method is proposed to segment CT images and reconstruct particles of granular geomaterials. Within this framework, a feature-based machine learning technique termed Trainable Weka Segmentation is utilized for CT image segmentation, i.e., to classify material phases and to segregate particles in contact. This is a fundamentally different approach in that it predicts segmentation results based on a trained classifier model that implicitly includes image features and regression functions. Subsequently, an edge-based level set method is applied to approach an accurate characterization of the particle shape. The proposed framework is applied to reconstruct three-dimensional realistic particle shapes of the Mojave Mars Simulant. Quantitative accuracy analysis shows that the proposed framework exhibits superior performance over the conventional watershed-based method in terms of both the pixel-based classification accuracy and the particle-based segmentation accuracy. Using the reconstructed realistic particles, the particle-size distribution is obtained and validated against experiment sieve analysis. Quantitative morphology analysis is also performed, showing promising potentials of the proposed framework in characterizing granular geomaterials.

Level Set Based Hippocampus Segmentation in MR Images with Improved Initialization Using Region Growing.

The hippocampus has been known as one of the most important structures referred to as Alzheimer's disease and other neurological disorders. However, segmentation of the hippocampus from MR images is still a challenging task due to its small size, complex shape, low contrast, and discontinuous boundaries. For the accurate and efficient detection of the hippocampus, a new image segmentation method based on adaptive region growing and level set algorithm is proposed. Firstly, adaptive region growing and morphological operations are performed in the target regions and its output is used for the initial contour of level set evolution method. Then, an improved edge-based level set method utilizing global Gaussian distributions with different means and variances is developed to implement the accurate segmentation. Finally, gradient descent method is adopted to get the minimization of the energy equation. As proved by experiment results, the proposed method can ideally extract the contours of the hippocampus that are very close to manual segmentation drawn by specialists.

Boosting Proximal Dental Caries Detection via Combination of Variational Methods and Convolutional Neural Network

Proximal dental caries are diagnosed using dental X-ray images. Unfortunately, the diagnosis of proximal dental caries is often stifled due to the poor quality of dental X-ray images. Therefore, we propose an automatic detection system to detect proximal dental caries in periapical images for the first time. The system comprises four modules: horizontal alignment of pictured teeth, probability map generation, crown extraction, and refinement. We first align the pictured teeth horizontally as a pre-process to minimize performance degradation due to rotation. Next, a fully convolutional network are used to produce a caries probability map while crown regions are extracted based on optimization schemes and an edge-based level set method. In the refinement module, the caries probability map is refined by the distance probability modeled by crown regions since caries are located near tooth surfaces. Also we adopt non-maximum suppression to improve the detection performance. Experiments on various periapical images reveal that the proposed system using a convolutional neural network (CNN) and crown extraction is superior to the system using a naive CNN.

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

Level set methods (LSMs) have been widely used in image segmentation because of their good properties which provide more smooth and accurate segmentation results. The edge-based LSMs use the gradient information of images through edge stop functions (ESFs) to guide the contour curve approaching to object edges. The traditional edge-based LSMs cannot obtain satisfactory segmentation results for noisy images because their regional coefficients are constant and their ESFs are easily influenced by noises. To solve the problems, this paper analyzes the different properties between noise points and object edge points and uses the local regional properties of images points to distinguish noises and object edge points. Based on the local regional properties, we introduce a variable regional coefficient and an improved ESF to overcome shortcomings of the constant regional coefficient and the traditional ESFs. Then we propose an improved edge-based level set method combining local regional fitting information by applying the proposed variable regional coefficient and the improved ESF to the energy function of level set function. The experimental results show that our method obtains accurate segmentation results for noisy images with insensitive to noises and without missing object edges and prove that the method is efficient and robust.

An Edge-and Region-Based Level Set Method with Shape Priors for Image Segmentation

Traditional level set methods could not deal with the segmentation of overlapped objects and the ones having the similar gray value with background,since these methods just take image data into consideration.Aiming for this problem,we propose a new edge-and region-based level set method for image segmentation.Our method firstly fuses the regional information into the edge-based level set method,and then incorporates shape priors to design a new level set method with shape priors.The comparison results show that the proposed method can segment the overlapped objects better and also achieve better performance on the objects with the similar gray value as background since it integrates multiple kinds of information together.

A Relay Level Set Method for Automatic Image Segmentation

This paper presents a new image segmentation method that applies an edge-based level set method in a relay fashion. The proposed method segments an image in a series of nested subregions that are automatically created by shrinking the stabilized curves in their previous subregions. The final result is obtained by combining all boundaries detected in these subregions. The proposed method has the following three advantages: 1) It can be automatically executed without human-computer interactions; 2) it applies the edge-based level set method with relay fashion to detect all boundaries; and 3) it automatically obtains a full segmentation without specifying the number of relays in advance. The comparison experiments illustrate that the proposed method performs better than the representative level set methods, and it can obtain similar or better results compared with other popular segmentation algorithms.