The research on DMSP nighttime cloud image segmentation

In this paper, we introduce multi-symplectic Lagrangian variational integrators for solving Chan-Vese active contour models in image segmentation. Energy functionals are discretized firstly, and numerical schemes are derived from discrete Euler-Lagrange equations based on discrete variational principle. Lagrangian variational integrators preserve native differential structure-multi-symplecticity, that makes the numerical methods have a satisfied behavior. Experiments are performed on the benchmark images from literature. We further evaluated the methods in a segmentation database containing 1023 images. It shows that the proposed numerical schemes attain relatively faster convergence rates and better segmentation accuracy. Comparisons with the standard explicit Euler method of the original Chan-Vese model and other fast numerical optimization methods show that the proposed methods have better stability, higher accuracy, and are more robust when dealing with a large number of pictures. This study provides an example for further research to improve the performance of other existing image segmentation methods based on active contour models.

Image segmentation is one of the crucial tasks in medical image processing and computer vision. The goal for image segmentation is to separate the pixels in the image into its constituent parts. Variational model in image segmentation involves formulating image segmentation as an optimization problem. The models seeking a partition of the image into meaningful regions by minimizing or maximizing an energy functional. These models often use geometric information to represent object boundaries. The models involve techniques such as active contours and level set methods. In this paper, the generalized mean model for image segmentation is investigated and modified. The model is a 2D region-based model which utilizes the fuzzy level set method. The model is compared with the active contour without edges model also known as the Chan-Vese for three types of images: without noise, with noise and with sinusoidal intensity inhomogeneity. Based on the numerical results, the generalized mean model obtained higher accuracy, Dice similarity measure, Jaccard and Kappa Index, comparedto the Chan-Vese model based on the tested images.The model is useful in medical imaging for disease detection, diagnosis, and treatment planning.

The problem of segmentation of a given image using the active contour technique is considered. An abstract calculus to find appropriate speed functions for active contour models in image segmentation or related problems based on variational principles is presented. The speed method from shape sensitivity analysis is used to derive speed functions which correspond to gradient or Newton-type directions for the underlying optimization problem. The Newton-type speed function is found by solving an elliptic problem on the current active contour in every time step. Numerical experiments comparing the classical gradient method with Newton's method are presented.

Image segmentation and image denoising are two important and fundamental topics in the field of image processing. Geometric active contour model based on level set method can deal with the problem of image segmentation, but it does not consider the problem of image denoising. In this paper, a new diffusion equation model for noisy image segmentation is proposed by incorporating some classical diffusion equation denoising models into the segmental process. An assumption about the connection between the image intensity and level set function is given firstly. Some classical denoising models are employed to describe the evolution of level set function secondly. The final nonlinear diffusion equation model for noisy image segmentation is built thirdly. Then image segmentation and image denoising are combined in a united framework. The segmental results can be presented by level set function. Experimental results show that the new model has the advantage of noise resistance and is superior to traditional segmentation model.

HomeRadiology: Imaging CancerVol. 2, No. 2 Previous Research HighlightsFree AccessA New Approach for Automated Image Segmentation of Organs at Risk in Cervical CancerMeredith Jones, Lacey R. McNallyMeredith Jones, Lacey R. McNallyMeredith JonesLacey R. McNallyPublished Online:Mar 27 2020https://doi.org/10.1148/rycan.2020204010MoreSectionsPDF ToolsImage ViewerAdd to favoritesCiteTrack CitationsPermissionsReprints ShareShare onFacebookTwitterLinked InEmail Take-Away Points■ Major Focus: Development of an automated multiclass image segmentation model to identify organs at risk (OARs) on CT scans in patients with locally advanced cervical cancer, prior to radiation therapy.■ Key Results: The convolutional neural network model outperformed model-based, intensity-based, and atlas-based segmentation algorithms, with comparable results to other deep learning segmentation models while also decreasing the time needed for image segmentation by several thousand-fold.■ Impact: Reduction of intra- and interuser variability imposed by manual OAR delineation can lead to more accurate and efficient radiation therapy planning.The recent article by Liu et al developed an automated multiclass image segmentation model to identify organs at risk (OARs) on CT scans. To plan effective and safe radiation therapy in the context of cervical cancer, a CT scan is often acquired prior to the first treatment. Currently, a radiation oncologist will segment the CT images by hand, which is a time-consuming process, subject to intra- and interobserver variability, to identify OARs. To overcome these potential limitations and biases, automated image segmentation techniques can be utilized to improve accuracy and speed of annotating each image. A major problem in creation of deep learning image segmentation models for medical images is the lack of available datasets for model training and validation. Liu et al developed a two-dimensional image segmentation model based off the established convolutional neural network architecture, U-Net. U-Net was utilized because it is feasible to train from scratch with a small training set.This study looked at the CT scans of 105 patients with locally advanced cervical cancer. OAR segmentation for cervical cancer is a particularly challenging problem due to variability in organ size and frequent unclear boundaries. Even an experienced radiologist will have trouble. Seven organs were identified as OARs for cervical cancer radiation therapy: bladder, bone marrow, femoral head left, femoral head right, rectum, small intestine, and spinal cord.The model uses context aggregation blocks where U-Net has convolution layers. These context aggregation blocks use dilation convolution layers with different dilated rates and normal convolution layers with different kernel sizes to yield a low false-positive rate and accurate feature extraction in a large receptive field. A squeeze-extract block was used to ensure proper consideration of organs that are small. A class-weighted cross-entropy loss function was also used to overcome the imbalance in the datasets created by the differences in size and shape of each OAR.A ground truth was available based on the radiation oncologist’s annotations, allowing a Dice similarity coefficient (DSC) and 95th Hausdorff distance (HD) to be calculated and used as an evaluation metric. The DSC and the HD metrics indicated a comparable performance to other deep learning image segmentation models in most of the organs analyzed; inferior results were seen in the rectum and superior results in the intestines. All results were analyzed by an oncologist who determined that greater than 90% of results produced by this model were highly acceptable to be used during radiation therapy planning. To make the model more robust and generalizable, it is necessary to acquire a larger dataset with scans from different sources. Translation of this model to three dimensional may ultimately improve clinical translation.Highlighted ArticleLiu Z, Liu X, Xiao B, et al. Segmentation of organs-at-risk in cervical cancer CT images with a convolutional neural network. Physica Medica 2020;69:184-191. doi: https://doi.org/10.1016/j.ejmp.2019.12.008Highlighted ArticleLiu Z, Liu X, Xiao B, et al. Segmentation of organs-at-risk in cervical cancer CT images with a convolutional neural network. Physica Medica 2020;69:184-191. doi: https://doi.org/10.1016/j.ejmp.2019.12.008 Crossref, Medline, Google ScholarArticle HistoryPublished online: Mar 27 2020 FiguresReferencesRelatedDetailsCited ByJournal of Biomimetics, Biomaterials and Biomedical Engineering, Vol. 49Mathematical Problems in Engineering, Vol. 2020Recommended Articles nnU-Net: Further Automating Biomedical Image AutosegmentationRadiology: Imaging Cancer2021Volume: 3Issue: 1Deep Learning: A Primer for RadiologistsRadioGraphics2017Volume: 37Issue: 7pp. 2113-2131Generative Adversarial Networks: A Primer for RadiologistsRadioGraphics2021Volume: 41Issue: 3pp. 840-857Current Applications and Future Impact of Machine Learning in RadiologyRadiology2018Volume: 288Issue: 2pp. 318-328Deep Generative Adversarial Networks: Applications in Musculoskeletal ImagingRadiology: Artificial Intelligence2021Volume: 3Issue: 3See More RSNA Education Exhibits A Multidisciplinary Approach for Program Development with Artificial Intelligence in Pancreatic Cancer: How We Fit InDigital Posters2019Abnormal Changes After Radiation Therapy: What Radiologists Should KnowDigital Posters2018Artificial Intelligence in Diagnostic Imaging: Current Applications and Future PerspectiveDigital Posters2019 RSNA Case Collection Acute Radiation EnteropathyRSNA Case Collection2021Small Bowel LymphomaRSNA Case Collection2021Hirschsprung DiseaseRSNA Case Collection2021 Vol. 2, No. 2 Metrics Downloaded 310 times Altmetric Score

We propose in this work to improve the tasks of image segmentation and modeling through an unsupervised flexible learning approach. Our focus here is to develop an alternative mixture model based on a bounded generalized Gaussian distribution, which is less sensitive to over-segmentation and offers more flexibility in data modeling than the Gaussian distribution which is certainly not the best approximation for image segmentation. A maximum likelihood- (ML) based algorithm is applied for estimating the resulted model parameters. We investigate here the integration of both a spatial information (a prior information between neighboring pixels) and a minimum description length (MDL) principle into the model learning step in order to deal with the major problems of finding the optimal number of classes and also selecting the best model that describes accurately the dataset. Therefore, the proposed model has the advantage to maintain the balance between model complexity and goodness of fit. Obtained results on a large database of medical MR images confirm the effectiveness of the proposed approach and demonstrate its superior performance compared to some conventional methods.

PET-guided radiation therapy treatment planning, clinical diagnosis, assessment of tumor growth, and therapy response rely on the accurate delineation of the tumor volume and quantification of tracer uptake. Most PET image segmentation techniques proposed thus far are suboptimal in the presence of heterogeneity of tracer uptake within the lesion. This work presents an active contour model approach based on the method of Chan and Vese ["Active contours without edges," IEEE Trans. Image Process. 10, 266-277 (2001)] designed to take into account the high level of statistical uncertainty (noise) and to handle the heterogeneity of tumor uptake typically present in PET images. In the proposed method, the fitting terms in the Chan-Vese formulation are modified by introducing new input images, including the smoothed version of the original image using anisotropic diffusion filtering (ADF) and the contourlet transform of the image. The advantage of utilizing ADF for image smoothing is that it avoids blurring the object's edges and preserves the average activity within a region, which is important for accurate PET quantification. Moreover, incorporating the contourlet transform of the image into the fitting terms makes the energy functional more effective in directing the evolving curve toward the object boundaries due to the enhancement of the tumor-to-background ratio (TBR). The proper choice of the energy functional parameters has been formulated by making a clear consensus based on tumor heterogeneity and TBR levels. This cautious parameter selection leads to proper handling of heterogeneous lesions. The algorithm was evaluated using simulated phantom and clinical studies, where the ground truth and histology, respectively, were available for accurate quantitative analysis of the segmentation results. The proposed technique was also compared to a number of previously reported image segmentation techniques. The results were quantitatively analyzed using three evaluation metrics, including the spatial overlap index (SOI), the mean relative error (MRE), and the mean classification error (MCE). Although the performance of the proposed method was analogous to other methods for some datasets, overall the proposed algorithm outperforms all other techniques. In the largest clinical group comprising nine datasets, the proposed approach improved the SOI from 0.41±0.14 obtained using the best-performing algorithm to 0.54±0.12 and reduced the MRE from 54.23±103.29 to 0.19±16.63 and the MCE from 112.86±69.07 to 60.58±18.43. The proposed segmentation technique is superior to other representative segmentation techniques in terms of highest overlap between the segmented volume and the ground truth∕histology and minimum relative and classification errors. Therefore, the proposed active contour model can result in more accurate tumor volume delineation from PET images.

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

This study proposes a novel local region-based active contour model (ACM) for image segmentation based on fractional calculus with consideration of the Riemann–Liouville operators. The proposed method aims to achieve accurate boundary segmentation in the presence of severe intensity inhomogeneity. The strength of fractional calculus is exploited with Gaussian Kernel namely, Fractional Gaussian Kernel (FGK) that provides an effective method of edge detection and has good noise immunity. An adaptive window mechanism with various sizes and orientations is employ to maintain and enhance image details especially at the object’s boundary and angle. The powerful combination of adaptive window and Fractional Gaussian Kernel (AFGK) provide an efficient way to utilize image information in local regions. Specifically, the fractional differential Heaviside function (FDH) extracts the image gradient and its various intensities for an accurate boundary segmentation outcome. Experiments on both synthetics and medical images demonstrate that the proposed local region-based ACM including fractional calculus realizes accurate boundary segmentation on images even under the most challenging situations, such as severe intensity inhomogeneity interface.

In this paper, a novel model for intensity inhomogeneous image segmentation is proposed. The proposed model uses the local information of the image to be segmented; concurrently, it incorporates the geodesic active contour (GAC) model into Chan-Vese (C-V) model in energy function. Thus, the proposed model is effective when dealing with intensity inhomogeneous images. Practical experiments prove that the proposed model can obtain exact segmented results, especially with the intensity inhomogeneous images even with hole, noise and complex background.

Convolutional neural networks (CNNs), as a typical deep learning technique, have been widely used in image segmentation, but they often require a large amount of annotated data. However, the number of available pixel-wise labeled medical images is extremely small, and this prevents the application of CNNs in many medical image segmentation tasks. We proposed a self-paced learning with diversity SPLD) framework to boost the performances of medical image segmentation models with a limited amount of annotated data. Self-paced learning (SPL) is a learning regime that selects training samples in order from the easiest to the most difficult for model training. In addition, we took the diversity of the data into consideration. The proposed self-paced learning with diversity by query-by-committee (SPLD-QBC) algorithm dynamically and diversely selects the appropriate training data to boost the performance of an image segmentation model. SPLD-QBC incorporates the query-by-committee (QBC) technique for data selection and affinity propagation for optimizing the data diversity. By dynamically selecting the optimal sequence of training samples from different probability distributions, the segmentation models achieved improved performances. To verify the effectiveness of the proposed SPLD-QBC framework, we conducted experiments on three medical image segmentation tasks with five different datasets. The experimental results indicated that the proposed SPLD-QBC algorithm significantly improved upon the segmentation performances of the baseline models and resulted in a higher Dice score, surface distance and mean intersection over union (mIoU). The proposed SPLD significantly boosts the segmentation performances of models and is easily embedded into CNN-based image segmentation models.

The Voronoi diagram-based dual-front scheme is known as a powerful and efficient technique for addressing the image segmentation and domain partitioning problems. In the basic formulation of existing dual-front approaches, the evolving contour can be considered as the interfaces of adjacent Voronoi regions. Among these dual-front models, a crucial ingredient is regarded as the geodesic metrics by which the geodesic distances and the corresponding Voronoi diagram can be estimated. In this paper, we introduce a new dual-front model based on asymmetric quadratic metrics. These metrics considered are built by the integration of the image features and a vector field derived from the evolving contour. The use of the asymmetry enhancement can reduce the risk for the segmentation contours being stuck at false positions, especially when the initial curves are far away from the target boundaries or the images have complicated intensity distributions. Moreover, the proposed dual-front model can be applied for image segmentation in conjunction with various region-based homogeneity terms. The numerical experiments on both synthetic and real images show that the proposed dual-front model indeed achieves encouraging results.

In this paper, a fuzzy weighted active contour model for synthetic aperture radar image segmentation is proposed. A feature-based approach comprising weighted active contours and level set segmentation is used to improve segmentation results. A fuzzy inference engine is used to assign weights to pixels of the level set function based on local entropy and local variance. Experiment results show that the proposed scheme improves the segmentation efficiency and accuracy as compared with the existing region-scalable fitting scheme.

This study was to analyze the impacts of the image segmentation model and computed tomography angiography (CTA) on the clinical diagnosis of aortic constriction under the background of artificial intelligence. In this study, 126 patients with congenital aortic constriction (CAC) diagnosed by surgery were selected as the research objects and routine digital subtraction angiography (DSA) and CTA were performed. Then, the traditional active contour model (AC model) was optimized based on the local area information to construct a new image segmentation model for intelligent segmentation and reconstruction of the CTA images of patients. The results revealed that compared with the AC model and the image segmentation model based on region growth (RG model) obtained from angiography segmentation, the algorithm constructed in this study showed a smaller segmentation range for angiography images and more accurate segmentation results. The quantitative data results suggested that the evolution times and running time of the constructed model were less than those of the AC and RG models P < 0.05 . Based on the gold standard of DSA examination results, there were 122 correctly diagnosed cases, 3 missed diagnosed cases, and 1 misdiagnosed by CTA, so the diagnosis coincidence rate was 96.83%. Compared with DSA, the average inner diameter and average pressure difference of patients with precatheter, paracatheter, and postcatheter type were not greatly different in CTA P > 0.05 . The CTA examination suggested there were 154 cases with intracardiac structural abnormalities, with a detection rate of 86.52%; there were 32 cases of cardiac-vascular connection abnormalities, with a detection rate of 100%; and there were 79 extracardiac vascular abnormalities, with the detection rate of 95.18%. It indicated that the optimized image segmentation model based on local area information proposed in this paper has excellent segmentation performance for CT angiography images and has good segmentation effect and efficiency. The CTA based on the artificial intelligence image segmentation model showed a better diagnostic effect on abnormal heart-vascular connection and abnormal extracardiac blood vessels and can be used as an effective examination method for clinical diagnosis of CAC.

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.