Stability Of Segmentation Research Articles

High-Definition Garden Plant Images Threshold Segmentation Mechanism Based on PSO and DRL

The accuracy of the threshold determines the quality of high-definition garden plant image segmentation. How to accurately and quickly search for the best combination of multiple thresholds is currently a research difficulty. In this regard, this article proposes an improved adaptive particle swarm optimization algorithm with extremal disturbance (IAPSO), which can to some extent prevent the PSO from falling into local optima by implementing extreme perturbation strategies. Then, by combining IAPSO and Deep Reinforcement Learning (DRL), the IAPSO-RL based on policy gradient off policy is proposed. It enhances information exchange between DRL and PSO. The IAPSO-RL can improve the sample efficiency of PSO. Experiments have shown that it can improve the performance and stability of threshold segmentation for high-definition garden plant images.

3D Point Cloud Instance Segmentation Considering Global Shape Contour Constraints

Aiming to solve the problem that spatially distributed similar instances cannot be distinguished in 3D point cloud instance segmentation, a 3D point cloud instance segmentation network, considering the global shape contour, was proposed. This research used the global-to-local design idea and added the global shape constraint to solve this problem. A Transformer module (Global Shape Attention, GSA) that can capture the shape contour information of the instance in the scene was designed. This module encoded the shape contour information into the Transformer structure as a Key-Value and extracted the instance fused with the global shape contour features, for instance, segmentation. At the same time, the network directly predicted the instance mask in an end-to-end mode, avoiding heavy post-processing algorithms. Many experiments have been conducted on the S3DIS, ScanNet, and STPL3D datasets, and our experimental results showed that our proposed network can efficiently capture the shape contour information of scene instances and can help to alleviate the problem of the difficulty distinguishing between spatially distributed similar instances in a scene, improving the efficiency and stability of instance segmentation.

To be and not to be: wide-field Ca2+ imaging reveals neocortical functional segmentation combines stability and flexibility.

The stability and flexibility of the functional parcellation of the cerebral cortex is fundamental to how familiar and novel information is both represented and stored. We leveraged new advances in Ca2+ sensors and microscopy to understand the dynamics of functional segmentation in the dorsal cerebral cortex. We performed wide-field Ca2+ imaging in head-fixed mice and used spatial independent component analysis (ICA) to identify independent spatial sources of Ca2+ fluorescence. The imaging data were evaluated over multiple timescales and discrete behaviors including resting, walking, and grooming. When evaluated over the entire dataset, a set of template independent components (ICs) were identified that were common across behaviors. Template ICs were present across a range of timescales, from days to 30seconds, although with lower occurrence probability at shorter timescales, highlighting the stability of the functional segmentation. Importantly, unique ICs emerged at the shorter duration timescales that could act to transiently refine the cortical network. When data were evaluated by behavior, both common and behavior-specific ICs emerged. Each behavior is composed of unique combinations of common and behavior-specific ICs. These observations suggest that cerebral cortical functional segmentation exhibits considerable spatial stability over time and behaviors while retaining the flexibility for task-dependent reorganization.

Generalizable Pancreas Segmentation Modeling in CT Imaging via Meta-Learning and Latent-Space Feature Flow Generation.

Accurate pancreas segmentation is highly crucial for diagnosing and treating pancreatic diseases. Although CNN has demonstrated promising outcomes, the performance on unseen data can be significantly compromised by the wide appearance-style variations induced by different imaging factors. Thus, we propose a generalizable pancreas segmentation model based on a meta-learning strategy and latent-space feature flow generation method. Our approach enhances the generalizability by systematically reducing the interference from the cluttered background and appearance-style discrepancies through a coarse-to-fine workflow. Specifically, the integrity-preserving coarse segmentation module is designed to adaptively balance the pancreas coverage and segmentation accuracy with the meta-learning strategy for filtering out background clutter. It also enhances the generalization of the coarse model to reasonably-accurate ROIs thereby promoting the stability of fine segmentation. Subsequently, the appearance-style feature flow generation method is developed to generate a series of progressively-varying style-related intermediate representations between two latent spaces. This feature flow effectively models the distribution variations caused by appearance-style discrepancies, and thus enhances the adaptability of the fine model. Our method achieves superior performance on three pancreas datasets and outperforms state-of-the-art generalization methods. Besides, it can be easily integrated into other workflows, leading to a potential paradigm for enhancing generalization performance.

An Efficient Tongue Segmentation Model Based on U-Net Framework

In the intelligently processing of the tongue image, one of the most important tasks is to accurately segment the tongue body from a whole tongue image, and the good quality of tongue body edge processing is of great significance for the relevant tongue feature extraction. To improve the performance of the segmentation model for tongue images, we propose an efficient tongue segmentation model based on U-Net. Three important studies are launched, including optimizing the model’s main network, innovating a new network to specially handle tongue edge cutting and proposing a weighted binary cross-entropy loss function. The purpose of optimizing the tongue image main segmentation network is to make the model recognize the foreground and background features for the tongue image as well as possible. A novel tongue edge segmentation network is used to focus on handling the tongue edge because the edge of the tongue contains a number of important information. Furthermore, the advantageous loss function proposed is to be adopted to enhance the pixel supervision corresponding to tongue images. Moreover, thanks to a lack of tongue image resources on Traditional Chinese Medicine (TCM), some special measures are adopted to augment training samples. Various comparing experiments on two datasets were conducted to verify the performance of the segmentation model. The experimental results indicate that the loss rate of our model converges faster than the others. It is proved that our model has better stability and robustness of segmentation for tongue image from poor environment. The experimental results also indicate that our model outperforms the state-of-the-art ones in aspects of the two most important tongue image segmentation indexes: IoU and Dice. Moreover, experimental results on augmentation samples demonstrate our model have better performances.

Three-Dimension Measurement of Mechanical Parts Based on Structure from Motion(SfM) Algorithm

Background: As an important branch of computer vision, visual measurement is a fast developing cutting-edge technology, which has been widely used in the manufacturing field. In recent years, the visual measurement of feature size of probes through small IC probes has aroused wide concern. Objective: This study aims to take small shaft parts as the research object in order to provide a full set of novel and reliable technical means for the three-dimension measurement of mechanical parts. Methods: Firstly, the trinocular vision measurement system based on the curved cantilever mechanism was designed and constructed. Secondly, the measurement system was used to collect the part images from different angles, and the images derived from the four categories of segmentation algorithms such as threshold-based, region-based segmentation algorithm were compared and analyzed. Lazy Snapping image segmentation algorithm was used to extract the foreground parts of each image. After comparing and analyzing SfM-based algorithm and Visual Hull-based algorithm, the SfM-based algorithm was adopted to reconstruct the 3D morphology of the parts. The measurement of the relevant dimensions was performed. Results: The results shows that Lazy Snapping's human-computer interaction brush function improves the accuracy and stability of image segmentation of different algorithms, such as threshold value method, regional method, Grab Cut, and Dense Cut. The SfM-based 3D reconstruction algorithm is of high robustness and fast speed. Conclusion: This study provides an effective method for measuring small mechanical parts, which will shorten the measurement cycle, improve the measurement speed, and reduce the measurement cost.

Domain Adaptation for Medical Image Segmentation: A Meta-Learning Method.

Convolutional neural networks (CNNs) have demonstrated great achievement in increasing the accuracy and stability of medical image segmentation. However, existing CNNs are limited by the problem of dependency on the availability of training data owing to high manual annotation costs and privacy issues. To counter this limitation, domain adaptation (DA) and few-shot learning have been extensively studied. Inspired by these two categories of approaches, we propose an optimization-based meta-learning method for segmentation tasks. Even though existing meta-learning methods use prior knowledge to choose parameters that generalize well from few examples, these methods limit the diversity of the task distribution that they can learn from in medical image segmentation. In this paper, we propose a meta-learning algorithm to augment the existing algorithms with the capability to learn from diverse segmentation tasks across the entire task distribution. Specifically, our algorithm aims to learn from the diversity of image features which characterize a specific tissue type while showing diverse signal intensities. To demonstrate the effectiveness of the proposed algorithm, we conducted experiments using a diverse set of segmentation tasks from the Medical Segmentation Decathlon and two meta-learning benchmarks: model-agnostic meta-learning (MAML) and Reptile. U-Net and Dice similarity coefficient (DSC) were selected as the baseline model and the main performance metric, respectively. The experimental results show that our algorithm maximally surpasses MAML and Reptile by 2% and 2.4% respectively, in terms of the DSC. By showing a consistent improvement in subjective measures, we can also infer that our algorithm can produce a better generalization of a target task that has few examples.

Local binary pattern encoding schemes for computed tomography image segmentation: An experimental and comparative study

AbstractLocal binary patterns (LBPs) are used for effective texture representation in various applications. This study explores the clustering consistency and stability of image segmentation when distance‐based clustering methods are used with an LBP. Because data are described by features and attributes, distances among data are also dominated by the definition of features. Moreover, four popular LBP encoding schemes for segmenting computed tomography (CT) images by using fuzzy C‐means are discussed and compared. The experimental results demonstrate several notable phenomena: When the pixels are encoded in different LBP encoding schemes, the distance between these pixels varies considerably. The experimental results indicate that each LBP encoding scheme emphasizes a specific image texture that dominates how pixels are described in the feature space and thus affects the clustering results. Based on the evaluation of CT image segmentation by using the clustering inconsistency index, linear and corner‐like LBPs are particularly suitable for clustering longitudinal sections and cross sections of CT images, respectively.

The State of Segment Reporting by U.S. Public Entities: 1976–2017

SYNOPSISThis paper offers an in-depth data-driven overview of the history and status as of 2017 of segment reporting by public entities trading in U.S. capital markets. Our analysis focuses on the perceived issues identified in the Financial Accounting Standards Board (FASB) 2016 Invitation to Comment on the FASB's Agenda—the extent of disaggregation into reportable segments, the stability of segmentation over time, the line items disclosed, and the reconciliation of segment to consolidated totals. We document the trends in and status of segment reporting as of 2017 as another round of efforts to improve segment reporting proceeds. The paper concludes with a discussion of several unanswered questions suggested by the data.Data Availability: Most of the data are available from public sources. The FAF user survey and FAF preparer survey are subject to a nondisclosure agreement with the Financial Accounting Foundation.JEL Classifications: K20; K22; M40; M48.

BRAIN TUMOR SEGMENTATION BASED ON SUPERPIXELS AND HYBRID CLUSTERING WITH FAST GUIDED FILTER

Brain tumor segmentation from magnetic resonance (MR) image is vital for both the diagnosis and treatment of brain cancers. To alleviate noise sensitivity and improve stability of segmentation, an effective hybrid clustering algorithm combined with fast guided filter is proposed for brain tumor segmentation in this paper. Preprocessing is performed using adaptive Wiener filtering combined with a fast guided filter. Then simple linear iterative clustering (SLIC) is utilized for pre-segmentation to effectively remove scatter. During the clustering, K-means[Formula: see text] and Gaussian kernel-based fuzzy C-means (K[Formula: see text]GKFCM) clustering algorithm are combined to segment, and the fast-guided filter is introduced into the clustering. The proposed algorithm not only improves the robustness of the algorithm to noise, but also improves the stability of the segmentation. In addition, the proposed algorithm is compared with other current segmentation algorithms. The results show that the proposed algorithm performs better in terms of accuracy, sensitivity, specificity and recall.

The State of Segment Reporting by US Public Entities

This paper offers an in-depth data driven overview of the history and status as of 2017 of segment reporting by public entities trading in U.S. capital markets. Our analysis focuses on the perceived issues identified in the Financial Accounting Standards Board (FASB) 2016 Invitation to Comment on FASB’s Agenda – the extent of disaggregation into reportable segments, the stability of segmentation over time, the line-items disclosed, and the reconciliation of segment to consolidated totals. We document the trends in and status of segment reporting as of 2017 as another round of efforts to improve segment reporting proceeds. The paper concludes with a discussion of several unanswered questions suggested by the data.

RESEARCH ON IMPROVED REGION GROWING POINT CLOUD ALGORITHM

Abstract. The effective segmentation of point clouds is a prerequisite for surface reconstruction, blind spot repair, and so on. Among them, regional growth is widely used due to its simple and easy to implement algorithm. However, the traditional regional growth segmentation algorithm often causes problems such as over-segmentation or voiding of the segmentation result due to the instability of the local features of the point cloud or the unreasonable selection of the initial seed nodes. In view of the above shortcomings, this paper proposes an improved region growing point cloud algorithm. Firstly, by calculating the Gaussian curvature and the average curvature of the point cloud data and sorting them, and setting the minimum curvature point as the seed node, the total number of clusters is reduced, and the quality of the classification result is improved. Secondly, the growth of the point cloud region growth criterion is determined by combining the normal angles. Finally, according to the shape characteristics of the point cloud and the preliminary segmentation results, each threshold is adjusted and determined, and the segmentation result is optimized.The experimental results show that compared with the traditional regional growth segmentation algorithm, this method can not only reduce the total number of segmentation regions, but also segment the point cloud data quickly and effectively, and solve the segmentation result caused by the traditional region growth point cloud segmentation method. Problems such as stability improve the accuracy and stability of point cloud segmentation.

Multi-View Fusion-Based 3D Object Detection for Robot Indoor Scene Perception.

To autonomously move and operate objects in cluttered indoor environments, a service robot requires the ability of 3D scene perception. Though 3D object detection can provide an object-level environmental description to fill this gap, a robot always encounters incomplete object observation, recurring detections of the same object, error in detection, or intersection between objects when conducting detection continuously in a cluttered room. To solve these problems, we propose a two-stage 3D object detection algorithm which is to fuse multiple views of 3D object point clouds in the first stage and to eliminate unreasonable and intersection detections in the second stage. For each view, the robot performs a 2D object semantic segmentation and obtains 3D object point clouds. Then, an unsupervised segmentation method called Locally Convex Connected Patches (LCCP) is utilized to segment the object accurately from the background. Subsequently, the Manhattan Frame estimation is implemented to calculate the main orientation of the object and subsequently, the 3D object bounding box can be obtained. To deal with the detected objects in multiple views, we construct an object database and propose an object fusion criterion to maintain it automatically. Thus, the same object observed in multi-view is fused together and a more accurate bounding box can be calculated. Finally, we propose an object filtering approach based on prior knowledge to remove incorrect and intersecting objects in the object dataset. Experiments are carried out on both SceneNN dataset and a real indoor environment to verify the stability and accuracy of 3D semantic segmentation and bounding box detection of the object with multi-view fusion.

Brain Tumor Segmentation Based on Hybrid Clustering and Morphological Operations.

Inference of tumor and edema areas from brain magnetic resonance imaging (MRI) data remains challenging owing to the complex structure of brain tumors, blurred boundaries, and external factors such as noise. To alleviate noise sensitivity and improve the stability of segmentation, an effective hybrid clustering algorithm combined with morphological operations is proposed for segmenting brain tumors in this paper. The main contributions of the paper are as follows: firstly, adaptive Wiener filtering is utilized for denoising, and morphological operations are used for removing nonbrain tissue, effectively reducing the method's sensitivity to noise. Secondly, K-means++ clustering is combined with the Gaussian kernel-based fuzzy C-means algorithm to segment images. This clustering not only improves the algorithm's stability, but also reduces the sensitivity of clustering parameters. Finally, the extracted tumor images are postprocessed using morphological operations and median filtering to obtain accurate representations of brain tumors. In addition, the proposed algorithm was compared with other current segmentation algorithms. The results show that the proposed algorithm performs better in terms of accuracy, sensitivity, specificity, and recall.

Image segmentation via <inline-formula><tex-math id="M1">\begin{document}$ L_1 $\end{document}</tex-math></inline-formula> Monge-Kantorovich problem

This paper provides a fast approach to apply the Earth Mover's Distance (EMD) (a.k.a optimal transport, Wasserstein distance) for supervised and unsupervised image segmentation. The model globally incorporates the transportation costs (original Monge-Kantorovich type) among histograms of multiple dimensional features, e.g. gray intensity and texture in image's foreground and background. The computational complexity is often high for the EMD between two histograms on Euclidean spaces with dimensions larger than one. We overcome this computational difficulty by rewriting the model into a \begin{document}$ L_1 $\end{document} type minimization with the linear dimension of feature space. We then apply a fast algorithm based on the primal-dual method. Compare to several state-of-the-art EMD models, the experimental results based on image data sets demonstrate that the proposed method has superior performance in terms of the accuracy and the stability of the image segmentation.

Metal stamping character recognition algorithm based on multi-directional illumination image fusion enhancement technology

Metal stamping character (MSC) automatic identification technology plays an important role in industrial automation. To improve the accuracy and stability of segmentation and recognition for MSCs, an algorithm based on multi-directional illumination image fusion technology is proposed. First, four grayscale images are taken with four bar-shape directional light sources from different directions. Next, based on the difference in surface grayscale characteristics for the different illumination directions of the surface’s stamped depression regions and flat regions, the image background is extracted and eliminated. Second, the images are fused using the difference processing on the images in the two groups of relative illuminant directions. Third, mean filter, binarization, and morphological closing operations are performed on the fused image to locate and segment the character string in the image, and the characters are normalized by correcting the skew of the segmented character string. Finally, histogram of oriented gradient features and a backpropagation neural network algorithm are employed to identify the normalized characters. Experimental results show that the algorithm can effectively eliminate the interference of factors such as oil stains, rust, oxide, shot-blasting pits, and different background colors and enhance the contrast between MSCs and background. The resulting character recognition rate can reach 99.6%.

A Cognitive Vision Method for Insect Pest Image Segmentation

Segmentation of pests from crop leaves is the prerequisite step for pest intelligent diagnosis. To improve the accuracy and stability of segmentation, a cognitive segmentation approach to pest images is present in this paper. The method works in the follow way. First, a pest image is divided into blocks via an image block processing method. Second, an adaptive learning algorithm is used to accurately select the initial cluster centers. Third, preliminary segmentation results are achieved using K-means clustering. Finally, three digital morphological features of an ellipse are adopted to remove leaf veins. Segmentation experiments were performed on crop images of whiteflies. Compared with the conventional methods, the proposed cognitive segmentation method was accurate and robust for segmentation of whitefly images, and will provide a foundation for further identifying these pests.

Segmentation of Crop Disease Images with an Improved K-means Clustering Algorithm

Abstract. Disease spot segmentation from crop leaf images is a key prerequisite for disease early warning and diagnosis. To improve the accuracy and stability of disease spot segmentation, an adaptive segmentation method for crop disease images based on K-means clustering is proposed. The approach is based on three stages. First, the excess green feature and the a* component of the CIE (L*a*b*) color space were combined to adaptively learn the initial cluster centers. Second, iterative color clustering of two clusters was conducted using the squared Euclidian distance as the similarity distance. Finally, the distance of a* components between two clusters as the clustering criterion function was used to correct the clustering results. To verify the effectiveness of the proposed method, segmentation experiments were performed on images of three kinds of cucumber diseases and one kind of soybean disease. The results of the experiments were compared with the results obtained using a fixed threshold method, the Otsu method, the traditional K-means clustering method, and the Renyi entropy method, which showed that our adaptive segmentation method was accurate and robust for segmentation of crop disease images. Keywords: Adaptive, CIE L*a*b*, Disease spot, Image segmentation, K-means clustering.

Improving care for older adults: a model to segment the senior population.

Risk stratification and tailored interventions are key population-level care management strategies among older adults, whose needs range from screening and prevention to end-of-life care. To validate the Senior Segmentation Algorithm, a tool using administrative and clinical data from the electronic health record to identify each member aged 65 years and older as belonging to 1 of 4 Care Groups with similar needs: those without chronic conditions, with one or more chronic conditions, with advanced illness or end-organ failure, or with extreme frailty or nearing the end of life. Multiple validation methods. Concordance with physician judgment, stability of segmentation over time, convergence with mortality, hospitalization, and readmission rates, and costs of care. Concordance of the algorithm with physician-assessed segmentation of 1615 Medicare recipients was 85%. After 1 year, approximately 85% of 86,140 surviving seniors remained in the same care group; 3.9% moved to a lower need group; and 11% moved to a higher need group. Six-month and 12-month mortality rates varied substantially across care groups. The algorithm performed similarly to the likelihood of hospitalization score in predicting hospitalization and readmissions. The Senior Segmentation Algorithm accurately identifies older adults in care groups with similar needs, trajectories, and utilization patterns. It is being implemented in all Kaiser Permanente Regions, with the goal of determining key elements of care for members in each group. In addition, future efforts will aim to slow progression to higher need care groups and to identify necessary improvements in delivery system design.

Stability of market segmentation with cluster analysis – A methodological approach

Market segmentation is a very popular marketing tool. In the food sector, the characteristics of different consumer attitudes and consumption habits are often used as the basis for segmentation. However, the success of a target-oriented marketing approach to selected groups of consumers depends on the results of the methodology applied. So far, relatively little attention has been paid to the reliability of the analysis used for attitude-based market segmentation, to the validity or internal stability of results or to the dynamic stability over time with regard to number, size and properties of the segments.In our study, we used data from a panel of more than 10,000 German households. The participants were segmented using a statement battery and the application of cluster analysis. In order to ensure an internally stable cluster solution, our focus was on the analytical and technical process of decision making when clustering a large dataset. A combination of various statistical measures was applied in order to enable objective decision making in the determination of the optimal number of clusters. The dynamic stability of the resulting segments was determined by confirmatory cluster analyses using data from the same individuals in three subsequent years.The results of the analyses show that neither the internal nor the dynamic stability of market segments should be taken for granted. Therefore, marketers face the challenge of designing segment-specific marketing strategies in a way that allows changes in consumer preferences to be integrated.