Chan-Vese Model Research Articles

Segmentation of Satellite Imagery of Amedi Site Using Chan–Vese Model with Saliency Estimation

The images that have been taken from space satellites are described by satellite imagery. The presence of the earth's surface is detected by remote sensing. Normally the source of the satellite image is barely seen, because many points in the sky are obscured with cloud shadows. Therefore, one of the most important and ubiquitous tasks in image analysis is segmentation. Segmentation is the method of dividing a image into a collection of specific regions that vary in some essential qualitative or quantitative manner. In this paper we will focus on a method for segmenting images that was developed Three different methods to detect the location of the satellite images have been studied, implemented, and tested; these are based on Chan-Vese and saliency map segmentation, and multi-resolution segmentation to obtain a proper object segmentation. In this study, the combination of the proposed segmentation automatic detection and image enhancement technique has been performed to reduce the noise of the original image. In addition, the Bilateral filter, and histogram equalization are used in these proposed techniques. Experimental results demonstrate that the suggested method can precisely extract the objective of Amedi site from the satellite images with difficult backgrounds and overlapping regions.

Ridgeline extraction only from a single full-polarimetric SAR image

ABSTRACT The mountain hazard triggered by earthquake usually starts near the ridgeline. Knowing the distribution of the ridgeline is conducive to making sound judgements on the possible hazards after an earthquake. However, in the complex scenes of PolSAR images containing buildings, the ridgeline recognition process will produce incorrect judgements due to the existence of buildings. Aiming at the ridgeline problem in PolSAR images, a novel method of ridgeline recognition based on the image segmentation and the polarimetric characteristics of PolSAR images was proposed. This method used the principle of the Chan-Vese model and the characteristics of the double-bounce scattering component to realize the recognition of ridgelines in PolSAR images. The experimental results were compared and verified with ground truth and traditional methods. The results showed that the proposed method had higher accuracy than traditional methods and could provide a new idea for the identification of ridgelines in PolSAR images.

SEENS: Nuclei segmentation in Pap smear images with selective edge enhancement

Accurate nuclei segmentation, as an indispensable basis and core link for multi-cell cervical image analysis, plays an important role in automatic pre-cancer detection. However, poor image quality due to the uneven staining, complex backgrounds and overlapped cell clusters poses a great challenge in nuclei segmentation. In this paper, we propose a new Selective-Edge-Enhancement-based Nuclei Segmentation method (SEENS). In the proposed method, selective search is integrated with mathematical operators to segment whole slide cervical images into small regions of interest (ROI) while automatically avoiding repeated segmentation as well as eliminating non-nuclei regions. In addition, an edge enhancement method based on the canny operator and mathematical morphology is presented to extract edge information as a weight to enhance the nucleus edge selectively. As a result, the enhanced ROI is then segmented by the Chan–Vese model with a higher accuracy. We evaluate our method with 18 whole slide images for a total of 395 cell nuclei. Experimental results demonstrate that SEENS achieves higher accuracy in cervical nuclei segmentation. Notably our method performs particularly better in low-contrast scenarios than baselines.

Accelerating Chan–Vese model with cross-modality guided contrast enhancement for liver segmentation

Accurate and fast liver segmentation remains a challenging and important task for clinicians. Segmentation algorithms are slow and inaccurate due to noise and low quality images in computed tomography (CT) abdominal scans. Chan–Vese is an active contour based powerful and flexible method for image segmentation due to superior noise robustness. However, it is quite slow due to time-consuming partial differential equations, especially for large medical datasets. This can pose a problem for a real-time implementation of liver segmentation and hence, an efficient parallel implementation is highly desirable. Another important aspect is the contrast of CT liver images. Liver slices are sometimes very low in contrast which reduces the overall quality of liver segmentation. Hence, we implement cross-modality guided liver contrast enhancement as a pre-processing step to liver segmentation. GPU implementation of Chan–Vese improves average speedup by 99.811 (± 7.65) times and 14.647 (± 1.155) times with and without enhancement respectively in comparison with the CPU. Average dice, sensitivity and accuracy of liver segmentation are 0.656, 0.816 and 0.822 respectively on the original liver images and 0.877, 0.964 and 0.956 respectively on the enhanced liver images improving the overall quality of liver segmentation.

An integrated two-stage approach for image segmentation via active contours

A novel integrated two-stage approach is proposed for image segmentation, where the edge, global and local region information of images are in turn incorporated to define the intensity fitting energy. In the first stage, the Chan-Vese model flexibly assimilates the edge indicator function in the beginning, and then the Laplace operator is introduced to regularize the level set function when minimizing the energy functional. As an edge-based and global region-based active contour, it can be inclined to rapidly produce a coarse segmentation result. In the second stage, we further segment the image by absorbing the local region fitting energy, where its initialization is acquired by the final active contour of the first stage. In addition, we present a generalized level set regularization term, which efficiently eliminates the periodically re-initialization procedure of traditional level set methods and maintains the corresponding signed distance property. Compared with the first stage, the local object details are accurately segmented in the second stage, which can acquire an accurate segmentation result. Qualitative and quantitative experimental results demonstrate the accuracy, robustness and efficiency of our approach with applications to some synthetical and real-world images.

Extending contour level set model for multi-class image segmentation with Application to Breast Thermography Images

Extraction of target objects with the desired intensity value in multi-region images is a challenging task. This paper presents a new modification to the region-based level set method called Extending Contour Level Set (ECLS) model. In classical level set methods, the intensity of each pixel is compared with both average intensities inside and outside the contour; therefore, image domain is segmented into two regions with the most difference between average intensities. In multi-region images, there is no reason the selected region belongs to the desired one. In our approach, with the assumption that the initial contour is located inside the target object, outside-contour pixels are allocated to the inside-contour region if they have an intensity value close to the inside-contour average intensity. The ECLS model is compared with well-known level set methods such as the Chan-Vese model, the local binary fitting energy model, the geodesic level set model, the local statistical active contour model, and the local and global fitting image model. Simulation results indicate the remarkable advantages of the proposed method in terms of accuracy and computational complexity. To show the ability of the ECLS model in practice, a two-step approach is proposed to segment breast thermography images. In the first step, the ECLS model is exploited to produce a semi-final region. Then, in the second step, a new Controlled Chan-Vese (CCV) model is presented leading to accurate results. The CCV model controls the distance between the average intensities inside and outside the contour based on the image statistics. Comparison with other segmentation methods shows the superiority of the proposed approach. In addition, statistical measures are calculated to demonstrate the significant capabilities of the proposed method.

Convexity Shape Prior for Level Set-Based Image Segmentation Method

We propose a geometric convexity shape prior preservation method for variational level set based image segmentation methods. Our method is built upon the fact that the level set of a convex signed distanced function must be convex. This property enables us to transfer a complicated geometrical convexity prior into a simple inequality constraint on the function. An active set based Gauss-Seidel iteration is used to handle this constrained minimization problem to get an efficient algorithm. We apply our method to region and edge based level set segmentation models including Chan-Vese (CV) model with guarantee that the segmented region will be convex. Experimental results show the effectiveness and quality of the proposed model and algorithm.

A Shape Prior-Based Active Contour Model for Automatic Images Segmentation

Due to the variable shapes of objects, high noise intensity and complex environments, the field of image segmentation still has great challenges. To address these issues, we present a new image segmentation strategy based on active contour model and shape priori information, which can accurately and efficiently segment various images. The data fitting term, inspired by Chan-Vese (C-V) model, is used to guide the curve evolving to desired target boundary. Meanwhile, the contour is utilized to reconstruct a prior shape so that can help to deal with images in the presence of complex target. After that, the length regularity term of energy functional is incorporated to ensure the stable calculation of the evolution curve. The quantitative and qualitative experiments on various real and medical images indicate that our method is more efficient and accurate than the existing unified models.

Land Cover Change Detection from High-Resolution Remote Sensing Imagery Using Multitemporal Deep Feature Collaborative Learning and a Semi-supervised Chan–Vese Model

This paper presents a novel approach for automatically detecting land cover changes from multitemporal high-resolution remote sensing images in the deep feature space. This is accomplished by using multitemporal deep feature collaborative learning and a semi-supervised Chan–Vese (SCV) model. The multitemporal deep feature collaborative learning model is developed to obtain the multitemporal deep feature representations in the same high-level feature space and to improve the separability between changed and unchanged patterns. The deep difference feature map at the object-level is then extracted through a feature similarity measure. Based on the deep difference feature map, the SCV model is proposed to detect changes in which labeled patterns automatically derived from uncertainty analysis are integrated into the energy functional to efficiently drive the contour towards accurate boundaries of changed objects. The experimental results obtained on the four data sets acquired by different high-resolution sensors corroborate the effectiveness of the proposed approach.

Applying logarithmic density function to develop a segmentation model for intensity and noisy inhomogeneity images: towards informed decisions in computational and applied mathematics

This study examined the how a logarithmic intensity function could be used to develop a segmentation model for intensity and noisy homogeneity images. Whereas inhomogeneous images demand the use of local image data, it remains notable that it remains defective for noisy images. The eventuality is that active contour motions tend to be misguided by the local information. In the proposed model, the logarithmic function was able to capture minute details contained in selected images and also counter or ignore the perceived noise. As such, the model proved effective and robust, worth applying to such images. To verify the effectiveness of the model, its results were compared to the experimental outcomes previously reported after employing local Chan-Vese Model. Indeed, the proposed model exhibited superior performance in relation to the treatment of intensity and noisy inhomogeneity images.

Automated Brain Tumor Segmentation and Identification using MR Images

Automatic identification of tumor in human brain is a challenging task due to its varying in size, shape and location. This paper proposes a multi-modality technique for the segmentation of brain tumor its classification to differentiate easily between cancerous and non-cancerous tumor from MR images of the human brain. To achieve this, different segmentation and classification techniques have been applied. The important stages involved in the proposed technique are pre-processing, segmentation and classification stages. The pre-processing step is carried out using wavelet transform, segmentation stage is done by applying modified Chan-Vese model and finally the extracted tumor can be classified as benign or malignant using Support Vector Machine (SVM) classifier. The experimental results on MR images prove that, the proposed method is efficient and robust to noise. Moreover, the comparisons with existing techniques also show that, the proposed method takes less computational time and classify the tumors very accurately.

Image Segmentation Based on Generic Shape Algorithm

Level set based techniques are widely used for the aggregation of images. Prior advantage on the form of the segmentation contours. The level set version of the effective size models can be obtained by offering the previous type with the chan-vese model. The higher level model can be taken into consideration for earlier form capacity pretty efficaciously and is used for multiphase segmentation. The GSA (periodic shape Algorithm) will also be more classified into the Regional and Contour GSA for a good integrated application, the vicinity of a patriarchal graph-reduce scheme of optimization is developed, for its worldwide implementation of use. of the local GSA to get hold of desirable international segmentation outcomes, and the nearby one using the Contour GSA to refine limitations of world outcome.

Active contouring and 3D model deformable registration of radiotherapy planning and cone-beam computed tomography images

Background: To ensure high accuracy during radiation therapy (RT), the image-guided RT (IGRT) technique uses on-board cone-beam computed tomography (CBCT) scanning as an image guidance procedure for target localization before and during treatment. Adaptive RT aiming to modify RT target volumes according to kinetic changes in tumor shape during RT course is based on registration of CBCT and planning CT images. However, the re-contouring and re-planning procedures are extensively time and cost consuming. We developed a novel automatic contouring and image registration method to replace the manual re-contouring with accurate image registration. Methods: For the image sets with format of Digital Imaging and Communications in Medicine (DICOM) standard, we wrote a program in MATLAB language (Version R2016a) to read and convert CBCT images into cross-sectional (tomographic) images similar to those obtained via planning CT. For image enhancement, the active contouring by using Chan-Vese model with level set formulation was applied. To overcome the variations in spatial location of these two sets of CT images, the iterative closest point (ICP) algorithm was used for 3D model registration. The deformable image registration (DIR) with Double force Demons algorithm was performed for auto-transformation of contours from planning CT to CBCT images. Results: The customized program accurately converted the format of CBCT to planning CT. Image enhancement was achieved by our modified active contour model which solved the energy minimization problem. In 3D model registration, the variations in spatial location of the CBCT and planning CT images were corrected. After selection of most similar images, the planning CT images were registered to corresponding CBCT images. The registered images were clearer than CBCT images with removal of other confounding structures outside body contours. Conclusions: The planning CT and CBCT images could be precisely registered by using a novel established technique consisting of active contouring with 3D model and DIR. This technique would enable the on-line radiation treatment planning for adaptive radiotherapy.

Entropy-based global and local weight adaptive image segmentation models

This paper proposes a parameter adaptive hybrid model for image segmentation. The hybrid model combines the global and local information in an image, and provides an automated solution for adjusting the selection of the two weight parameters. Firstly, it combines an improved local model with the global Chan-Vese (CV) model , while the image's local entropy is used to establish the index for measuring the image's gray-level information. Parameter adjustment is then performed by the real-time acquisition of the ratio of the different functional energy in a self-adapting model responsive to gray-scale distribution in the image segmentation process. Compared with the traditional linear adjustment model, which is based on trial-and-error, this paper presents a more quantitative and intelligent method for achieving the dynamic nonlinear adjustment of global and local terms. Experiments show that the proposed model achieves fast and accurate segmentation for different types of noisy and non-uniform grayscale images and noise images. Moreover, the method demonstrates high stability and is insensitive to the position of the initial contour.

A CNN-Based Defect Inspection Method for Catenary Split Pins in High-Speed Railway

Split pins (SPs) play an important role in fixing joint components on catenary support devices (CSDs) of high-speed railway. The occurrence of loose and missing defects of SPs could make the structure of CSDs unstable. In this paper, we present a three-stage automatic defect inspection system for SPs mainly based on an improved deep convolutional neural network (CNN), which is called PVANET++. First, SPs are localized by PVANET++ and the Hough transform & Chan–Vese model, and then, three proposed criteria are applied to detect defects of SPs. In PVANET++, a new anchor mechanism is applied to produce suitable candidate boxes for objects, and multiple hidden layer features are combined to construct discriminative hyperfeatures. The performance of PVANET++ and several recent state-of-the-art deep CNNs is compared in a data set that is collected from a 60-km rail line. The results show that our model is superior to others in accuracy, and has a considerable speed.

Computer aided tool for automatic detection and delineation of nucleus from oral histopathology images for OSCC screening

Characterization of nucleus from oral tissue histology images is important for oral squamous cell carcinoma (OSCC) diagnosis. Therefore, the accurate delineation of nucleus is the foremost task to initiate the quantification of its various properties such as morphology, texture, intensity, etc. In this paper, we propose a two-stage computer-aided tool for automatic detection (the first stage) and delineation of the detected nucleus (second stage) from oral histological images to aid clinician in OSCC diagnosis. In the first stage, 81×81 patches were extracted from our image database followed by the wavelet-based downsampling process to generate 21×21 patches, which were used to train the deep convolution neural network (CNN) architecture. This approach was used to automatically detect nucleus from OSCC images and achieved 88.87% recall and 82.03% precision. After detection, 81×81 image patches were extracted from those detected nucleus regions and further processed through the second stage of proposed pipeline to segment the exact boundary of nucleus using active contour (Chan-Vese model) after applying edge enhancing nonsubsampled contourlet transform. The proposed segmentation methodology performed well with 94.22% Dice coefficient, 89.38% Jaccard index, 97.56% precision, and 91.58% recall. We view this work as the first attempt on oral tissue histology image computation for joint nucleus detection and segmentation to diagnose OSCC to the best of our knowledge.

Automatic segmentation and measurement methods of living stomata of plants based on the CV model

BackgroundThe stomata of plants mainly regulate gas exchange and water dispersion between the interior and external environments of plants and play a major role in the plants’ health. The existing methods of stomata segmentation and measurement are mostly for specialized plants. The purpose of this research is to develop a generic method for the fully automated segmentation and measurement of the living stomata of different plants. The proposed method utilizes level set theory and image processing technology and can outperform the existing stomata segmentation and measurement methods based on threshold and skeleton in terms of its versatility.ResultsThe single stomata images of different plants were the input of the method and a level set based on the Chan-Vese model was used for stomatal segmentation. This allowed the morphological features of the stomata to be measured. Contrary to existing methods, the proposed segmentation method does not need any prior information about the stomata and is independent of the plant types. The segmentation results of 692 living stomata of black poplars show that the average measurement accuracies of the major and minor axes, area, eccentricity and opening degree are 95.68%, 95.53%, 93.04%, 99.46% and 94.32%, respectively. A segmentation test on dayflower (Commelina benghalensis) stomata data available in the literature was completed. The results show that the proposed method can effectively segment the stomata images (181 stomata) of dayflowers using bright-field microscopy. The fitted slope of the manually and automatically measured aperture is 0.993, and the R2 value is 0.9828, which slightly outperforms the segmentation results that are given in the literature.ConclusionsThe proposed automated segmentation and measurement method for living stomata is superior to the existing methods based on the threshold and skeletonization in terms of versatility. The method does not need any prior information about the stomata. It is an unconstrained segmentation method, which can accurately segment and measure the stomata for different types of plants (woody or herbs). The method can automatically discriminate whether the pore region is independent or not and perform pore region extraction. In addition, the segmentation accuracy of the method is positively correlated with the stomata’s opening degree.

Weld Pool Image Segmentation of Hump Formation Based on Fuzzy C-Means and Chan-Vese Model

The geometric parameters of the weld pool can reflect welding quality, and thus, it can be important to detect the weld pool contour accurately and reliably. A passive vision system was designed to obtain the weld pool image of hump formation by an infrared transmitting filter, and a method of weld pool image segmentation strategy based on a Chan-Vese (CV) model with fuzzy C-means (FCM) is proposed. The FCM-CV algorithm uses an FCM model to set the initialization contour and then extracts the contour of the hump by the active contour CV model. FCM-CV algorithm eliminates the problem that the CV model is sensitive to the initial contour, and thus, the FCM-CV algorithm can extract the contours of the weld pool under different processing conditions. The contour of the hump is segmented, and the weld pool length feature is extracted. The results show that the truncation of the weld pool length is the main image feature reflecting the formation of the hump.

Anatomical structure segmentation from early fetal ultrasound sequences using global pollination CAT swarm optimizer-based Chan-Vese model.

The structure of an early fetal heart provides essential information for the diagnosis of fetus defects. Accurate segmentation of anatomical structure is a major challenging task because of the small size, low signal-to-noise ratio, and rapid movement of the ultrasound images. In recent years, active contour methods have found applications to ultrasound image segmentation. The familiar region-based Chan-Vese (RCV) model is a strong and flexible technique that is able to segment many types of images compared to other active contours. However, the solution trapping in local minima is the main drawback determined on the RCV model with the exposure of improper initial contours. Also, the RCV model showed poor results with this situation. More probably, the images having large intensity differences between global and local structures usually suffered from this problem. To solve this issue, we develop an improved version of the RCV model which is expected to achieve satisfactory segmentation performance, irrespective of the initial selection of the contour. We have formulated a new and hybrid meta-heuristic optimization algorithm namely global pollination-based CAT swarm (GPCATS) optimizer to solve the fitting energy minimization problem. In the GPCATS method, the global pollination step of the flower pollination algorithm (FPA) is used for improving the distance averaging of the CATS algorithm. The performance of the proposed method was analyzed on different fetal heart ultrasound videos acquired from 12 subjects. Each frame of each video was manually annotated in order to provide labels for training and validating the model. Experimental results of the proposed model proved that the precision of locating boundaries is improved greatly and requires only a reduced number of iterations (75% less) for convergence compared to the traditional RCV model. This proposed method also proved that our model not only enhances the accuracy of locating boundaries but also works stronger robustness than some other active contour methods. Graphical Abstract Anatomical structure segmentation from early fetal ultrasound sequences using GPCATS based Chan-Vese Model.

Segmentation of scanning tunneling microscopy images using variational methods and empirical wavelets

In the fields of nanoscience and nanotechnology, it is important to be able to functionalize surfaces chemically for a wide variety of applications. Scanning tunneling microscopes (STMs) are important instruments in this area used to measure the surface structure and chemistry with better than molecular resolution. Self-assembly is frequently used to create monolayers that redefine the surface chemistry in just a single-molecule-thick layer. Indeed, STM images reveal rich information about the structure of self-assembled monolayers since they convey chemical and physical properties of the studied material. In order to assist in and to enhance the analysis of STM and other images, we propose and demonstrate an image-processing framework that produces two image segmentations: one is based on intensities (apparent heights in STM images) and the other is based on textural patterns. The proposed framework begins with a cartoon+texture decomposition, which separates an image into its cartoon and texture components. Afterward, the cartoon image is segmented by a modified multiphase version of the local Chan-Vese model, while the texture image is segmented by a combination of 2D empirical wavelet transform and a clustering algorithm. Overall, our proposed framework contains several new features, specifically in presenting a new application of cartoon+texture decomposition and of the empirical wavelet transforms and in developing a specialized framework to segment STM images and other data. To demonstrate the potential of our approach, we apply it to actual STM images of cyanide monolayers on Au\{111\} and present their corresponding segmentation results.