A partition-based active contour model incorporating local information for image segmentation.

Uncertain active contour model based on rough and fuzzy sets for auroral oval segmentation

Local image information is crucial for accurate segmentation of images with intensity inhomogeneity. However, image information in local region is not embedded in popular region-based active contour models, such as the piecewise constant models. In this paper, we propose a region-based active contour model that is able to utilize image information in local regions. The major contribution of this paper is the introduction of a local binary fitting energy with a kernel function, which enables the extraction of accurate local image information. Therefore, our model can be used to segment images with intensity inhomogeneity, which overcomes the limitation of piecewise constant models. Comparisons with other major region-based models, such as the piece-wise smooth model, show the advantages of our method in terms of computational efficiency and accuracy. In addition, the proposed method has promising application to image denoising.

Active contours driven by local likelihood image fitting energy for image segmentation

Many medical and real images are suffered from intensity inhomogeneity and weak edges. For higher image segmentation quality, lots of level set-based methods have been proposed. Some of them however cannot take advantage of image gradient information. And severe intensity inhomogeneity and weak edges are not disposed properly. To address these problems, a new level set method integrated with local direction gradient information is presented in this paper. Firstly, according to the two assumptions on image intensity inhomogeneity adopted by many existing methods, a new pixel classification model based on image gradient is introduced. Secondly, we employ variational level set method combined with image spatial information, which improves the anti-noise capability of the proposed method. Finally, considering the gray gradients in homogeneous regions are close to constants, an improved diffusion process is incorporated into the level set function to make the evolving curves stay around true image edges. To verify our method, different testing images including synthetic images, magnetic resonance imaging (MRI) and real-world images are introduced. The image segmentation results demonstrate that our method can deal with the relatively severe intensity inhomogeneity and obtain the comparatively ideal segmentation results efficiently.

Segmentation for images with intensity inhomogeneity is very difficult. In this paper, a fuzzy clustering-based method to segment intensity inhomogeneity images is presented. Firstly, a new expression of the fuzzy C-means(FCM) object function is derived through altering the prototype of every clustering to a point-wise function. Then, a weight function defined on the local window is introduced into the objective function. The local weight makes the prototype for every pixel depends only on the information of its local region, which is more reasonable for the considered problem. The proposed method has been applied to artificial and real-world images, e.g. X-ray vessel images and MRI brain images. The comparison segmentation results have shown the proposed model is very applicable for image segmentation with intensity inhomogeneity.

Active contour models driven by local binary fitting energy can segment images with inhomogeneous intensity, while being prone to falling into a local minima. However, the segmentation result largely depends on the location of the initial contour. We propose an active contour model with global and local image information. The local information of the model is obtained by bilateral filters, which can also enhance the edge information while smoothing the image. The local fitting centers are calculated before the contour evolution, which can alleviate the iterative process and achieve fast image segmentation. The global information of the model is obtained by simplifying the C-V model, which can assist contour evolution, thereby increasing accuracy. Experimental results show that our algorithm is insensitive to the initial contour position, and has higher precision and speed.

This study presents an efficient variational region-based active contour model for segmenting images without priori knowledge about the object or background. In order to handle intensity inhomogeneities and noise, we propose to integrate into the region-based local intensity model a global density distance inspired by the Bhattacharyya flow. The local term based on local information of segmented image allows the model to deal with bias field artifact, which arises in data acquisition processes. The global term, which is based on the density distance between the probability distribution functions of image intensity inside and outside the active contour, provides information for accurate segmentation, keeps the curve from spilling, and addresses noise in the image. Intensive 2D and 3D experiments on many imaging modalities of medical fields such as computed tomography, magnetic resonance imaging, and ultrasound images demonstrate the effectiveness of the model when dealing with images with blurred object boundary, intensity inhomogeneities, and noise.

We investigated a parametric kernel-driven active contour (PKAC) model, which implicitly transfers kernel mapping and piecewise constant to modeling the image data via kernel function. The proposed model consists of curve evolution functional with three terms: global kernel-driven and local kernel-driven terms, which evaluate the deviation of the mapped image data within each region from the piecewise constant model, and a regularization term expressed as the length of the evolution curves. In the local kernel-driven term, the proposed model can effectively segment images with intensity inhomogeneity by incorporating the local image information. By balancing the weight between the global kernel-driven term and the local kernel-driven term, the proposed model can segment the images with either intensity homogeneity or intensity inhomogeneity. To ensure the smoothness of the level set function and reduce the computational cost, the distance regularizing term is applied to penalize the deviation of the level set function and eliminate the requirement of re-initialization. Compared with the local image fitting model and local binary fitting model, experimental results show the advantages of the proposed method in terms of computational efficiency and accuracy.

Biological Image Segmentation Using Region-Scalable Fitting Energy with B-Spline Level Set Implementation and Watershed

Image segmentation actually could be regarded as a computer vision pre-processing step of cloud computing and big data analysis. Active contour model is a new research direction for images segmentation in synthetic aperture radar(SAR) images. However, due to the SAR images have speckle noise and low signal-to-clutter ratio, active contour models cannot produce satisfied segmentation results. To solve these issues, active contours driven by visual saliency fitting energy is proposed in this paper. First, a saliency map is calculated by improved local contrast measure. Then, a visual saliency fitting energy is proposed based on saliency map and local binary fitting energy. The saliency map has a higher signal-to-clutter ratio and the local binary fitting energy is an active contour model that can effectively segment images with intensity inhomogeneity. In this way, the proposed method has a better segmentation result. The experiments on ship chips in SAR images demonstrate that the proposed method is robust to speckle noise and the segmentation result has good connectivity and integrity for entire ship targets.

Since the phenomena of intensity inhomogeneity in MR images are prominent and adversely affect quantitative image analysis .In this paper ,we propose a novel magnetic resonance (MR) image segmentation approach based on the kernel graph cuts technique .Because of automatic multiregion segmentation and global energy minimization ,the kernel graph cuts method can be applied to many kinds of images segmentation ,such as MR images and so on . To reduce or eliminate intensity inhomogeneity in MR images ,we add the intensity inhomogeneity correction step which is based on fuzzy c-means (FCM) algorithm before the segment procedure .Firstly , the real MR image data obtained after bias corrected by FCM algorithm .Secondly ,we segment the real MR image data by kernel graph cuts method .Experiments show that the kernel graph cuts method with intensity inhomogeneity correction have a better segment result in accuracy and over-segmentation .

Local image information is crucial for accurate segmentation of image with intensity inhomogeneity. However, the local information is not embedded in Chan-Vese model. In this paper, we propose a novel level set model which takes the local boundary information into account. The proposed model can overcome the difficulty that CV model suffered, i.e., the unsuccessful segmentation of object with intensity inhomogeneity. Finally, we validate the efficiency of our model on some synthetic and real images.

The precision of grinding and positioning by industry robot is influenced by segmentation accuracy of region of interest (ROI). The inherent defects of casting billet including perforation defects, crack defects, surface defects, and the polluted environment of post-processing may cause poor imaging quality which may have adverse effects on image segmentation. Considering the need for localization and identification in casting post-processing production line, this paper proposed a novel active contour algorithm which is suitable for the segmentation of complex images. First of all, the penalty term based on the local gradient information of image was introduced into the energy function, which will modify the deviation between evolution model of level set and Signed Distance Function (SDF) to reduce repeated initialization and improve convergence accuracy. Then, Gaussian pyramid energy term is incorporated into the energy function of geodesic active contour model to further accelerate its convergence speed. Finally, iteratively calculate the energy function of the proposed level set model to realize the evolution of contour curve, which can realize fast robust segmentation of complex image by solving the zero level set of the energy function. The comparative experiments between conventional method and the method proposed in this paper prove that the proposed method has higher processing speed than traditional C-V model on condition that the segmentation precision is guaranteed.

This study proposes an innovative approach for the recycling of dry wastewater sludge in the production of fired clay bricks, aiming to create environmentally friendly and cost-effective building materials. Consequently, the proposed research focuses on optimizing the thermal and mechanical properties of ceramic bricks, while incorporating sewage sludge as a pore-forming agent in brick manufacturing process. For the assessment of porosity in ceramic brick mixtures with varying volumetric proportions of ash, clay and wastewater sludge, a novel method for microscopy image analysis is proposed. This advanced image segmentation method uses active contours driven by local image information to accurately estimate brick porosity from light microscopy images, coping with light reflections, intensity inhomogeneity, and the defocus effect present on the sample surface. The thermal properties of the ceramic blocks were assessed using non-contact infrared lock-in thermography and thermogravimetric analysis (TGA). The obtained results indicate that incorporating sewage sludge increases porosity, thereby improving the material's insulating properties, with the cost of reduced mechanical strength. Nevertheless, the compressive strength for a mixture containing 15% of sewage sludge meets the criteria for the production of first-class bricks, according to ASTM C62 standards for brick manufacturing. The present study demonstrates that incorporating sewage sludge into fired clay ceramic bricks production is a feasible and sustainable approach, offering significant advantages for waste management and promoting energy-efficient building design.

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.