Local Binary Fitting Research Articles

Processing thermography images for pitting corrosion quantification on small diameter ductile iron pipe

The condition of a metallic pipe is determined mainly by the extent to which it is corroded. Thus, the quantification of pitting corrosion in terms of metal loss is required for the understanding of pipe condition. There are different ways to quantify corrosion pit geometry. Direct methods measure the pit depth of pipe samples, which are sand/grit blasted to remove corrosion products, and are often adopted in laboratory. Indirect methods employ non-destructive inspection techniques to detect and quantify the corrosion without sandblasting, which is preferred for field test. In this study, the pulsed thermography is considered for fast quantification of pitting corrosion in a ductile iron pipe, which is sandblasted. The thermography testing generates a sequence of infrared images, which reflect the diffusion process of heat through pipe wall. This paper proposes a processing procedure to extract the corrosion information from the thermography images with phase congruency measurement and local binary fitting. The extracted feature is further related to the corrosion damage. Among all the features, the second principal component demonstrates a reasonably good linear relationship with metal loss of the pipe. The application potential is demonstrated by the experimental results.

A local region-based Chan–Vese model for image segmentation

In this paper, a new region-based active contour model, namely local region-based Chan-Vese (LRCV) model, is proposed for image segmentation. By considering the image local characteristics, the proposed model can effectively and efficiently segment images with intensity inhomogeneity. To reduce the dependency on manual initialization in many active contour models and for an automatic segmentation, a degraded CV model is proposed, whose segmentation result can be taken as the initial contour of the LRCV model. In addition, we regularize the level set function by using Gaussian filtering to keep it smooth in the evolution process. Experimental results on synthetic and real images show the advantages of our method in terms of both effectiveness and robustness. Compared with the well-know local binary fitting (LBF) model, our method is much more computationally efficient and much less sensitive to the initial contour.

Convex Active Contour Segmentation Model of Strip Steel Defects Image Based on Local Information

In order to solve problems existing in Chan-Vese model and local binary fitting(LBF) model,such as model sensitivity to the initial contour position and running slow in the segmentation of strip steel defect image,a novel model local information-based convex active contour(LICAC) is proposed.By converting non-convex optimization problem to a convex optimization problem via convex optimization technology,and applying the Split Bregman method for fast solution,the issues of the sensitivity to the initial contour position occurring in Chan-Vese model and LBF model are solved.With introduction of the local information,the new model is efficient in the segmentation of the strip surface defect image which is non-uniform gray.By using this model to segment single-target region strip defect image,four common defect categories,including weld,rust,holes and scratches are experimented,and experimental results show that the segmentation effect and operation time of the proposed model are better than the rest two kinds.In addition,this model can also be used to segment multi-target regions defect image,four common defect categories are experimented,including scratches,inclusion,pitting,and wrinkles,and experimental results have verified the validity of the model.

Level set segmentation of intensity inhomogeneous images based on local linear approximation of difference image

This study presents a new local image approximation active contour model in a variational level set formulation for image segmentation. The model consists of two parts: a local linear approximation term and the regularisation terms. The local linear approximation term is more accurate than the local average in the local binary fitting (LBF) model. The authors’ proposed model achieves sub-pixel segmentation result. Comparative experiments show that the proposed model achieves satisfying results, and demonstrates its efficiency.

The integration of directional information and local region information for accurate image segmentation

The original local binary fitting (LBF) model is sensitive to contour initialization and thus easily obtains an inaccurate result due to improper initialization. This paper presents a new method that not only can arrive at sub-pixel accuracy, but also allows for more flexible initialization of the contour. Two important terms play main role in our new method. One is an image gradient alignment term (IGA) which uses the directional information of the image gradient, the other is a local intensity fitting term (LIF) which makes use of local region information. The integration of the above two terms prevents our method from being sensitive to contour initialization. In addition, a global intensity fitting term (GIF) multiplied by a stopping function is included, which can speed up our algorithm while do not influence the accuracy of the segmentation result. Using the simple central difference, the gradient descend flow equation for the level set function can be easily and efficiently implemented. The results on several synthetic and real images demonstrate the effectiveness and accuracy of our method.

Fast and stable local bianry fitting approach for image segmentation

The Local Binary Fitting(LBF) model based on local region information has its certain advantages in image segmentation of weak boundary or uneven greay.But,the segmentation results are very sensitive to the initial contours,and improper initial contour can directly lead to segmentation failure.Thus,a fast and stable LBF approach was proposed.First,after adding the area item with variable weights to the traditional LBF model,better initial contour could be obtained than manual one.Second,the traditional LBF model would be used for further segmentation.The experimental results show that,under the precondition of preferable results,this method can not only get promising segmentation results with flexible initial contour selection,but also faster than the traditional LBF model.

Integrating local distribution information with level set for boundary extraction

This paper presents a general object boundary extraction model for piecewise smooth images, which incorporates local intensity distribution information into an edge-based implicit active contour. Unlike traditional edge-based active contours that use gradient to detect edges, our model derives the neighborhood distribution and edge information with two different region-based operators: a Gaussian mixture model (GMM)-based intensity distribution estimator and the Hueckel operator. We propose the local distribution fitting model for more accurate segmentation, which incorporates the operator outcomes into the recent local binary fitting (LBF) model. The GMM and the Hueckel model parameters are estimated before contour evolution, which enables the use of the proposed model without the need for initial contour selection, i.e., the level set function is initialized with a random constant instead of a distance map. Thus our model essentially alleviates the initialization sensitivity problem of most active contours. Experiments on synthetic and real images show the improved performance of our approach over the LBF model.

Active contours driven by local image fitting energy

A new region-based active contour model that embeds the image local information is proposed in this paper. By introducing the local image fitting (LIF) energy to extract the local image information, our model is able to segment images with intensity inhomogeneities. Moreover, a novel method based on Gaussian filtering for variational level set is proposed to regularize the level set function. It can not only ensure the smoothness of the level set function, but also eliminate the requirement of re-initialization, which is very computationally expensive. Experiments show that the proposed method achieves similar results to the LBF (local binary fitting) energy model but it is much more computationally efficient. In addition, our approach maintains the sub-pixel accuracy and boundary regularization properties.

An efficient local Chan–Vese model for image segmentation

In this paper, a new local Chan–Vese (LCV) model is proposed for image segmentation, which is built based on the techniques of curve evolution, local statistical function and level set method. The energy functional for the proposed model consists of three terms, i.e., global term, local term and regularization term. By incorporating the local image information into the proposed model, the images with intensity inhomogeneity can be efficiently segmented. In addition, the time-consuming re-initialization step widely adopted in traditional level set methods can be avoided by introducing a new penalizing energy. To avoid the long iteration process for level set evolution, an efficient termination criterion is presented which is based on the length change of evolving curve. Particularly, we proposed constructing an extended structure tensor (EST) by adding the intensity information into the classical structure tensor for texture image segmentation. It can be found that by combining the EST with our LCV model, the texture image can be efficiently segmented no matter whether it presents intensity inhomogeneity or not. Finally, experiments on some synthetic and real images have demonstrated the efficiency and robustness of our model. Moreover, comparisons with the well-known Chan–Vese (CV) model and recent popular local binary fitting (LBF) model also show that our LCV model can segment images with few iteration times and be less sensitive to the location of initial contour and the selection of governing parameters.

An Automated Pipeline for Dendrite Spine Detection and Tracking of 3D Optical Microscopy Neuron Images of In Vivo Mouse Models

The variations in dendritic branch morphology and spine density provide insightful information about the brain function and possible treatment to neurodegenerative disease, for example investigating structural plasticity during the course of Alzheimer's disease. Most automated image processing methods aiming at analyzing these problems are developed for in vitro data. However, in vivo neuron images provide real time information and direct observation of the dynamics of a disease process in a live animal model. This paper presents an automated approach for detecting spines and tracking spine evolution over time with in vivo image data in an animal model of Alzheimer's disease. We propose an automated pipeline starting with curvilinear structure detection to determine the medial axis of the dendritic backbone and spines connected to the backbone. We, then, propose the adaptive local binary fitting (aLBF) energy level set model to accurately locate the boundary of dendritic structures using the central line of curvilinear structure as initialization. To track the growth or loss of spines, we present a maximum likelihood based technique to find the graph homomorphism between two image graph structures at different time points. We employ dynamic programming to search for the optimum solution. The pipeline enables us to extract dynamically changing information from real time in vivo data. We validate our proposed approach by comparing with manual results generated by neurologists. In addition, we discuss the performance of 3D based segmentation and conclude that our method is more accurate in identifying weak spines. Experiments show that our approach can quickly and accurately detect and quantify spines of in vivo neuron images and is able to identify spine elimination and formation.

A Novel Automatic Extraction Method of Lung Texture Tree from HRCT Images

Computed tomography (CT) is the primary imaging modality for investigation of lung function and lung diseases. High resolution CT slice images of chest contain lots of texture information, which provides powerful datasets to research computer aid-diagnosis (CAD) system. But the extraction of lung tissue textures is a challenge task. In this paper, we introduce a novel method based on level set to extract lung tissue texture tree, which is automatic and effectual. Firstly, we propose an improved implicit active contour model driven by local binary fitting energy, and the parameters are dynamic and modulated by image gradient information. Secondly, a new technique of painting background based on intensity nonlinear mapping is brought forward to remove the influence of background during the evolution of single level set function. At last, a number of contrast experiments are performed, and the results of 3D surface reconstruction show our method is efficient and powerful for the segmentation of fine lung tree texture structures.