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PURPOSE: Accurate and rapid needle localization on 3D magnetic resonance imaging (MRI) is critical for MRI-guided percutaneous interventions. The current workflow requires manual needle localization on 3D MRI, which is time-consuming and cumbersome. Automatic methods using 2D deep learning networks for needle segmentation require manual image plane localization, while 3D networks are challenged by the need for sufficient training datasets. This work aimed to develop an automatic deep learning-based pipeline for accurate and rapid 3D needle localization on in vivo intra-procedural 3D MRI using a limited training dataset. METHODS: The proposed automatic pipeline adopted Shifted Window (Swin) Transformers and employed a coarse-to-fine segmentation strategy: (1) initial 3D needle feature segmentation with 3D Swin UNEt TRansfomer (UNETR); (2) generation of a 2D reformatted image containing the needle feature; (3) fine 2D needle feature segmentation with 2D Swin Transformer and calculation of 3D needle tip position and axis orientation. Pre-training and data augmentation were performed to improve network training. The pipeline was evaluated via cross-validation with 49 in vivo intra-procedural 3D MR images from preclinical pig experiments. The needle tip and axis localization errors were compared with human intra-reader variation using the Wilcoxon signed rank test, with p

The processing and analysis of computed tomography (CT) imaging is important for both basic scientific development and clinical applications. In AutoCT, we provide a comprehensive pipeline that integrates an end-to-end automatic preprocessing, registration, segmentation, and quantitative analysis of 3D CT scans. The engineered pipeline enables atlas-based CT segmentation and quantification leveraging diffeomorphic transformations through efficient forward and inverse mappings. The extracted localized features from the deformation field allow for downstream statistical learning that may facilitate medical diagnostics. On a lightweight and portable software platform, AutoCT provides a new toolkit for the CT imaging community to underpin the deployment of artificial intelligence-driven applications.

Estimates of the land area occupied by wind energy differ by orders of magnitude due to data scarcity and inconsistent methodology. We developed a method that combines machine learning-based imagery analysis and geographic information systems and examined the land area of 318 wind farms (15,871 turbines) in the U.S. portion of the Western Interconnection. We found that prior land use and human modification in the project area are critical for land-use efficiency and land transformation of wind projects. Projects developed in areas with little human modification have a land-use efficiency of 63.8 ± 8.9 W/m2 (mean ±95% confidence interval) and a land transformation of 0.24 ± 0.07 m2/MWh, while values for projects in areas with high human modification are 447 ± 49.4 W/m2 and 0.05 ± 0.01 m2/MWh, respectively. We show that land resources for wind can be quantified consistently with our replicable method, a method that obviates >99% of the workload using machine learning. To quantify the peripheral impact of a turbine, buffered geometry can be used as a proxy for measuring land resources and metrics when a large enough impact radius is assumed (e.g., >4 times the rotor diameter). Our analysis provides a necessary first step toward regionalized impact assessment and improved comparisons of energy alternatives.

In medical imaging, the classification and segmentation of lesions have always been significant topics in clinical research. Different categories of lesions require different treatment strategies, and accurate segmentation helps to assist in improving the effect of the clinical treatment. The Segment anything model (SAM) is an image segmentation model trained on a large-scale dataset with strong prompt segmentation capability, but it cannot be directly applied to the classification and segmentation tasks of medical images due to insufficient training on medical image data. In this paper, we propose a deep learning method for the classification and segmentation of lesions, called GMM-based segment anything model (G-SAM). Prompt-tuning is utilized in the model with the LoRA strategy, and the lesion feature extraction (GFE) module based on the Gaussian mixture model (GMM), is designed to effectively improve the effect of lesion classification and segmentation on the basis of the SAM. Notably, G-SAM exhibits greater sensitivity to early stage of the lesions, aiding in tumor detection and prevention, which holds important clinical value. G-SAM overcomes the limitation that SAM is not suitable for the medical image classification and segmentation tasks due to insufficient training data with minimal cost. Moreover, it enhances classification accuracy and segmentation precision compared to traditional Gaussian model-based methods. The effectiveness of G-SAM in classifying and segmenting lesions is validated on the LIDC dataset, demonstrating advantages over state-of-the-art (SOTA) methods. The study further validates the applicability of G-SAM on large publicly available datasets across three different image modalities, achieving superior performance. [ABSTRACT FROM AUTHOR]

Medical imaging is essential in modern healthcare because it assists physicians in the diagnosis of cancer. Various tissues and features in medical imaging can be recognized using image segmentation algorithms. This feature makes it possible to pinpoint and define particular areas, which makes it easier to precisely locate and characterize anomalities or lesions for cancer diagnosis. Among cancers affecting women, breast cancer is particularly prevalent, underscoring the urgent need to improve the accuracy of image segmentation for breast cancer in order to assist medical practitioners. Multi-threshold image segmentation is widely acknowledged for its direct and effective characteristics. In this context, this paper suggests a refined whale optimization algorithm to improve the segmentation accuracy of breast cancer data. This algorithm optimizes performance by combining a quantum phase interference mechanism and an enhanced solution quality strategy. This work compares the method with classical, homogeneous, state-of-the-art algorithms and runs experiments on the IEEE CEC2017 benchmark to validate its practical optimization performance. Furthermore, a multi-threshold image segmentation algorithm-based image segmentation technique is presented in this study. The Berkeley segmentation dataset and the breast invasive ductal carcinomas segmentation dataset are segmented using the approach using a non-local means two-dimensional histogram and Renyi's entropy. Experimental results demonstrate the excellent performance of this segmentation method in image segmentation applications across both low and high threshold levels. As a result, it emerges as a valuable image segmentation technique with practical applications. [ABSTRACT FROM AUTHOR]

Image segmentation is the process of splitting a digital image into distinct segments or categories based on shared characteristics like texture, color, and intensity. Its primary aim is to simplify the image for easier analysis while preserving its important features. Each pixel in the image is assigned a label, grouped together by pixels with similar traits together. Segmentation helps to delineate boundaries and identify objects such as curves or lines within the image. The process generates a series of segmented images that cover the entire original image. This article reviews emerging applications of image segmentation in medical diagnostics, specifically employing nature-inspired optimization algorithms (NIOAs). It begins by outlining different segmentation methods and NIOAs types, then by examining relevant databases and medical imaging technologies. The study draws on a diverse range of research sources. Finally, this paper briefly discusses the challenges and future trends of medical image segmentation using NIOAs to detect different diseases. [ABSTRACT FROM AUTHOR]

Global optimization and biomedical image segmentation are crucial in diverse scientific and medical fields. The Honey Badger Algorithm (HBA) is a newly developed metaheuristic that draws inspiration from the foraging behavior of honey badgers. Similar to other metaheuristic algorithms, HBA encounters difficulties associated with exploitation, being trapped in local optima, and the pace at which it converges. This study aims to improve the performance of the original HBA by implementing the Enhanced Solution Quality (ESQ) method. This strategy helps to prevent becoming stuck in local optima and speeds up the convergence process. We conducted an assessment of the enhanced algorithm, mHBA, by utilizing a comprehensive collection of benchmark functions from IEEE CEC'2020. In this evaluation, we compared mHBA with well-established metaheuristic algorithms. mHBA demonstrates exceptional performance, as shown by both qualitative and quantitative assessments. Our study not only focuses on global optimization but also investigates the field of biomedical image segmentation, which is a crucial process in numerous applications involving digital image analysis and comprehension. We specifically focus on the problem of multi-level thresholding (MT) for medical image segmentation, which is a difficult process that becomes more challenging as the number of thresholds needed increases. In order to tackle this issue, we suggest a revised edition of the standard HBA, known as mHBA, which utilizes the ESQ approach. We utilized this methodology for the segmentation of Magnetic Resonance Images (MRI). The evaluation of mHBA utilizes existing metrics to gauge the quality and performance of its segmentation. This evaluation showcases the resilience of mHBA in comparison to many established optimization algorithms, emphasizing the effectiveness of the suggested technique. [ABSTRACT FROM AUTHOR]

Women are commonly diagnosed with breast cancer (BC), and early detection can significantly increase the cure rate. This study suggested a multi-threshold image segmentation (MTIS) technique for dividing BC histological slice images to assist in identifying lesions and boost diagnostic effectiveness. The selection of the threshold combination, a challenging combinatorial optimization problem, is the key to the MTIS approach. To enhance the MTIS method, a variant of INFO (BQINFO) is proposed to optimize the threshold combination selection procedure. BQINFO is constructed by introducing the barebones mechanism (BM) and quasi-opposition-based learning (QOBL) to INFO and addressing its slow convergence and weakness in local stagnation. To evaluate the optimization performance of BQINFO and the positive impact influence of introducing QOBL and BM to the original INFO for the acceleration of convergence speed and the solution of local stagnation, a series of comparative experiments were carried out using CEC2014 and CEC2021. The comprehensive results and comparisons obtained from the optimization indicators indicate the outstanding performance of BQINFO in overcoming the slow convergence and local stagnation problems when dealing with benchmark function problems. Besides, to further validate BQINFO's performance optimization of threshold combination selection, this paper performed an MTIS experiment with Rényi's entropy as the objective function on BSD500 images and BC histological slice images, respectively, providing qualitative and quantitative analysis with three evaluation metrics, FSIM, PSNR, and SSIM at low and high threshold levels. Ultimately, the experimental results demonstrate that BQINFO performs better and finds the optimal combination of thresholds faster than other comparison algorithms for both low and high threshold levels. [ABSTRACT FROM AUTHOR]

Evidence c-means(ECM) has certain advantages in dealing with uncertainty and imprecision, and it is widely applied to data clustering and image segmentation. However, ECM does not utilize spatial information and unable to recover noise, resulting in poor performance for noisy image segmentation. To address these problems, we propose a dynamic noise self-recovery ECM clustering algorithm with adaptive spatial constraints for image segmentation. The proposed algorithm has the following novelties. Firstly, the non-local spatial information is modified by initializing the noise probability to obtain more reliable spatial information. Secondly, the adaptive constraint factors are constructed by using the absolute difference between the original image and the modified non-local spatial information, which can reduce the sensitivity of the algorithm to noise. Finally, the self-recovery factors are constructed on the basis of the neighborhood belief degrees. And a dynamic anti-noise distance is proposed to replace the Euclidean distance. The dynamic anti-noise distance is more suitable for noise self-recover, enabling noise self-recovery during the iterative process. Extensive experiments on synthetic, natural, SAR and MR images show that the proposed algorithm has good performance for image segmentation. [ABSTRACT FROM AUTHOR]

Image muscle segmentation is useful to quantitatively assess musculoskeletal diseases by extracting biomarkers such as shape, texture and water diffusivity metrics. Although volumetric manual segmentation is time consuming and a bottleneck in practice, fully automatic approaches are still in progress to reach an acceptable accuracy. In this paper, we provide a robust semi-automated tool to segment two musculoskeletal systems, i.e. thigh and shoulder in MRI and CT modalities, respectively. The tool only needs a few manually labelled cross-sections to build a directed graph-structure of corresponding points between the successive spaced slices. The boundaries of each muscle are obtained by performing a spline interpolation based on the directed graph-structure. Each muscle label and its corresponding 3D mesh are deduced using post-processing techniques. We evaluated the tool on 26 MRI thighs and 16 CT shoulders. Three metrics along with inter-muscle overlapping were employed to evaluate the tool by comparison to an expert manual segmentation and a publicly available tools (ITK-SNAP, 3D Slicer). The results showed a mean Dice $0.988 \pm 0.003$ 0.988 ± 0.003 , and Hausdorff Distance $4.86 \pm 1.67$ 4.86 ± 1.67 mm in comparison to the manual reference for thigh muscle segmentation, and a mean Dice $0.961 \pm 0.005$ 0.961 ± 0.005 and Hausdorff Distance $2.42 \pm 0.79$ 2.42 ± 0.79 mm for shoulder muscle segmentation, outperformed the other methods. The tool is proposed as slicer module available at . [ABSTRACT FROM AUTHOR]

Hip fractures contribute significantly to mortality in older adults. New methods to identify those at risk use dual-energy X-ray absorptiometry (DXA) images and advanced image processing. However, DXA images have an overlapping femur and pelvis and may contain boundary lines, making automation challenging. Herein, a 5-layer U-net convolutional neural network (CNN) was developed to segment the femur from hip DXA images. Images were used from the Canadian Longitudinal Study on Aging (CLSA, N=104) and Canadian Multicentre Osteoporosis Study (CaMos, N=105) databases for training, with manual contour drawing defining the 'true output' of each image. An algorithm was then developed to mask each hip image, and trained to predict subsequent masks. The CNN was tested with 44 additional CLSA images and 42 CaMos images. This proposed approach had an accuracy and intersection over union (IoU) of 97% and 0.57, and 93% and 0.51, for CaMos and CLSA scans, respectively. Furthermore, a series of augmentation techniques was applied to increase the data size, with accuracies of 96% and 94%, and IoU of 0.53 and 0.50. Overall, our strategy automatically determined the contour of the proximal femur using various clinical DXA images, a key step to automate fracture risk assessment in clinical practice. [ABSTRACT FROM AUTHOR]

Retinal image segmentation process deals with problems like spurious vascularisation and thin vessel detection. In this paper, a three-step methodology has been proposed for retinal vessel segmentation. In first step, RGB to YIQ conversion is performed. In second step, Y component enhancement is performed. A novel Chaotic weighted Elephant Herding Optimization (CWEHO) has been proposed to optimize the clip limit and block size values of Contrast Limited Adaptive Histogram Equalization (CLAHE). CWEHO-based CLAHE along with morphological operations, non-local means filter, and median filter is applied to enhance retinal images. In third step, thin and thick vessel segmentation is performed. Top hat transformation, otsu thresholding algorithm, and vessel point selection are applied for thick vessel extraction. The first-order Gaussian derivative in conjunction with the match filter is used to extract thin vessels. DRIVE and HRF datasets are used to assess the effectiveness of proposed methodology. The average values of segmentation accuracy, specificity, sensitivity, and Mathew Correlation Coefficient (MCC) are observed to be 0.9650, 0.9895, 0.7007, 0.7650, respectively, for observer1 and 0.9696, 0.9912, 0.7390, 0.7901 for observer2 using DRIVE dataset. Similarly, 0.9592, 0.9839, 0.6850, and 0.7116, respectively, metrics for HRF dataset. Compared to state-of-the-art methods, the proposed segmentation methodology provides better results. [ABSTRACT FROM AUTHOR]

Setting up a complex CNN requires a powerful platform, several hours of run-time, and a lot of data for training. Here, we propose a generalised lightweight solution that exploits super-resolution and scalable vector graphics and uses a small-scale UNet as the baseline framework to segment different organs in MR and CT data. We selected the UNet since many researchers use it as the baseline, modify it in their proposal, and perform an ablation study to show the effectiveness of the proposed modification. First, we downsample the input 2D CT slices by bicubic interpolation. Using the architecture of the conventional UNet, we reduce the size of the network's input, and the number of layers and filters to construct a lightweight UNet. The network segments the low-resolution images and prepares the mask of an organ. Then, we upscale the boundary of the output mask by the Scalable Vector Graphics technique to obtain the final border. This design reduces the number of parameters and the run-time by a factor of two. We segmented several tissues to prove the stability of our method to the type of organ. The experiments proved the feasibility of setting up complex deep neural networks with conventional platforms. [ABSTRACT FROM AUTHOR]

Corneal Ulcers are defined as inflammation or even infection. They are one of the most frequent diseases that affect eye health. The proper measurement of corneal ulcer lesions enables the physician to evaluate the treatment effectiveness and decision making. This paper presents a segmentation method that aims to assist doctors in monitoring the treatment of corneal ulcer lesions. We applied a stepwise fine-tuning in U-Net Convolutional Neural Network architecture to train a model with 358 Point-flaky corneal ulcer images. The result from the model using U-Net architecture is then submitted to post-processing operations. Based on experiments performed with 91 Flaky corneal ulcer images, our approach achieved 0.823 of the Dice Coefficient on average, 88.9% of Recall, 99.4% of Specificity, and True Dice Coefficient of 0.835. The results are promising; we then have evidence that the proposed method could generalise the data from the training phase to segment the test data. [ABSTRACT FROM AUTHOR]

Pre-treatment processing technology plays a crucial role in the overall freshwater fish processing procedure, and automatic head and tail cutting stands out as a significant pre-treatment technique within the industry. The system for removing the head and tail of freshwater fish comprised a Cartesian coordinate manipulator, a fish transfer device, a control system, and an image acquisition device. In the vision system, five image segmentation methods were utilized for fish head and tail image segmentation comparison tests. These methods include U-Net (U-shaped Deep Neural Network), DeeplabV3, PSPNet (Pyramid Scene Parsing Network), FastSCNN (Fast Semantic Segmentation Network), and ICNet (Image Cascade Network), all of which were employed to evaluate their performance. Among the tested segmentation methods, the ICNet demonstrated the most excellent segmentation capability. The experimental results indicated a segmentation accuracy of 99.01%, a mean intersection over union (MIoU) of 82.50%, and an image processing time of 15.25 ms. The results showed that the fish head and tail were successfully cut off using this model for recognition with a circular knife. Consequently, the segmentation model employed in the machine vision system within this study has demonstrated successful applicability in automatically cutting the heads and tails of freshwater fish of various sizes. [ABSTRACT FROM AUTHOR]

The meso‐structure of pervious concrete significantly influences its overall performance. Accurately identifying the meso‐structure of pervious concrete is imperative for optimizing the design of pervious concrete, considering its mechanical properties and functionality. Therefore, to address the difficulty of recognizing the meso‐structures of pervious concrete, a method utilizing deep learning image semantic segmentation techniques is proposed in this study. First, based on the classical deep learning model, three models, namely, Res‐UNet, ED‐SegNet, and G‐ENet, are proposed for recognizing pervious concrete meso‐structure using deep learning image semantic segmentation techniques. These models introduce a residual module, a hybrid loss function, and a differential recognition branching structure to enhance the ability to recognize detailed information within pervious concrete meso‐structure and small targets. Second, the respective recognition performances of these methods on the meso‐structure of pervious concrete were thoroughly analyzed by experiment. The results indicate that the proposed three recognition methods for recognizing the meso‐structure of permeable concrete outperform conventional techniques not only in terms of efficiency but also in recognition accuracy and the ability to distinguish and identify aggregates, pores, and cement binders. In terms of comprehensive recognition effectiveness, the Res‐UNet model outperforms, followed by ED‐SegNet and G‐ENet. Furthermore, the computational efficiency of these three recognition methods meets the requirements of engineering applications. [ABSTRACT FROM AUTHOR]

Designing a single domain generalization (DG) framework that generalizes from one source domain to arbitrary unseen domains is practical yet challenging in medical image segmentation, mainly due to the domain shift and limited source domain information. To tackle these issues, we reason that domain-adaptive classifier learning and domain-agnostic feature extraction are key components in single DG, and further propose an adaptive infinite prototypes (InfProto) scheme to facilitate the learning of the two components. InfProto harnesses high-order statistics and infinitely samples class-conditional instance-specific prototypes to form the classifier for discriminability enhancement. We then introduce probabilistic modeling and provide a theoretic upper bound to implicitly perform the infinite prototype sampling in the optimization of InfProto. Incorporating InfProto, we design a hierarchical domain-adaptive classifier to elasticize the model for varying domains. This classifier infinitely samples prototypes from the instance and mini-batch data distributions, forming the instance-level and mini-batch-level domain-adaptive classifiers, thereby generalizing to unseen domains. To extract domain-agnostic features, we assume each instance in the source domain is a micro source domain and then devise three complementary strategies, i.e., instance-level infinite prototype exchange, instance-batch infinite prototype interaction, and consistency regularization, to constrain outputs of the hierarchical domain-adaptive classifier. These three complementary strategies minimize distribution shifts among micro source domains, enabling the model to get rid of domain-specific characterizations and, in turn, concentrating on semantically discriminative features. Extensive comparison experiments demonstrate the superiority of our approach compared with state-of-the-art counterparts, and comprehensive ablation studies verify the effect of each proposed component. Notably, our method exhibits average improvements of 15.568% and 17.429% in dice on polyp and surgical instrument segmentation benchmarks. [ABSTRACT FROM AUTHOR]

Domain Incremental Learning (DIL) focuses on handling complex domain shifts of a continuous data stream for visual tasks such as image classification and image segmentation. In real life, severe domain gaps in DIL are generated from various sources such as data style shifts, data quality degradation, environment changes, and so on. The well-known catastrophic forgetting issue in DIL becomes even more critical when simultaneously considering multiple sources of domain shifts. In this paper, we propose a unified and effective paradigm named Compositional Prompting (C-Prompt) to mitigate the critical forgetting challenge in DIL for image classification tasks. Unlike a popular type of conventional DIL approaches that need to retain abundant exemplars from the old domains, our exemplar-free C-Prompt leverages a prompt-guided Batch-wise Exponential Moving Average (BEMA) strategy to adaptively consolidate learned knowledge without retaining any exemplars. A set of prompts shared across different domains is designed to estimate the knowledge shifts for automatically balancing knowledge acquisition and forgetting. To enhance the learning ability, our proposed C-Prompt explores a domain-specific pool of learnable prompts for each domain, and all the prompt pools are further exploited in a cross-domain compositional manner to facilitate inference. Since the latest prompting-based DIL methods aim to learn one individual prompt for each domain, they always suffer from critical performance degradation caused by the incorrect prediction of domain index during inference and the limited learning capacity by using a single prompt per domain. Instead, our C-Prompt can not only readily acquire domain-specific knowledge but also exploit domain-shared knowledge. Extensive experiments on various large-scale multi-domain benchmarks have demonstrated the superiority of our proposed C-Prompt compared with state-of-the-art methods. Code is available at https://github.com/zhoujiahuan1991/IJCV2024-C-Prompt. [ABSTRACT FROM AUTHOR]

Brain structure segmentation in 3D Magnetic Resonance Images is crucial for understanding neurodegenerative disorders. Manual segmentation is error-prone, necessitating robust automated techniques. In this paper, we introduce a novel and robust approach for the simultaneous segmentation of multiple brain structures in MRI images. Our method involves the concurrent evolution of 3D surfaces toward predefined anatomical targets, employing an efficient multi-object generalized fast marching method (MOGFMM) for simultaneous object detection. Additionally, we propose an effective evolution function that integrates prior knowledge from anatomical and probabilistic atlases, as well as spatial relationships among the segmented structures. Each deformable surface corresponds to a specific structure. To validate our approach, we conducted experiments on a dataset of real brain images (IBSR) and compared the results with several state-of-the-art methods. The obtained results were promising, demonstrating the effectiveness and superiority of our developed method. [ABSTRACT FROM AUTHOR]

In this paper, multilevel thresholding with Tangent Hybrid Leader Coronavirus Herd Optimization (THLCHO) is introduced for the image segmentation of foreground and background, The input image is passed to the pre-processing that is done by the adaptive wiener filtering (AWF) technique and the Region of Interest (RoI) extraction. Multilevel thresholding approaches such as threshold and threshold levels are employed to segment the images by entropy-based Kapur, and ostu thresholding, masi entropy and Renyi's entropy, then the threshold values are optimally selected by THLCHO and considered as the output of the techniques. Secondly, the pre-processed image is subjected to the background and foreground image segmentation utilizing the entropy-based Fuzzy C-Means (EFCM) algorithm, which is assumed as an output of EFCM. Lastly, both outcomes are merged to produce the resultant output. The evaluation metrics employed for Multithresholding- THLCHO, namely, dice co-efficient, peak signal-to-noise-ratio (PSNR) and uniformity measure acquired the maximal values of 0.900, 38.274 dB and 0.918, respectively. [ABSTRACT FROM AUTHOR]

Analysis of scanning electron microscope (SEM) images is crucial for characterising aluminide diffusion coatings deposited via the slurry route on steels, yet challenging due to various factors like imaging artefacts, noise, and overlapping features such as resin, precipitates, cracks, and pores. This study focuses on determining the thicknesses of the coating layers Fe2Al5 and, if present, FeAl, pore characteristics, and chromium precipitate fractions after the heat treatment that forms the diffusion coating. A deep learning SEM image segmentation model utilising U-Net architecture is proposed. Ground truth data were generated using the trainable Weka segmentation plugin in ImageJ, manually refined for accuracy, and supplemented with synthetic data from Blender 3D software for data augmentation of a limited number of SEM label images. The deep learning model trained on a combination of synthetic and real SEM data achieved mean dice scores of 98.7% ± 0.2 for the Fe2Al5 layer, 82.6% ± 8.1 for pores, and 81.48% ± 3.6 for precipitates when evaluated on manually labelled SEM data. The deep learning procedure was applied to evaluate a series of SEM images of diffusion coatings obtained with three different slurry compositions. The evaluation revealed that using a slurry without a rheology modifier may lead to a thicker partial Fe2Al5 layer that is formed by inward diffusion. The relation between the outward and inward diffusion Fe2Al5 layers was not affected by the coating thickness. The thinner diffusion coating presents lower pores and chromium precipitate fractions independently of the slurry selected. [ABSTRACT FROM AUTHOR]

Aiming at the weak robustness of possibilistic fuzzy clustering against noise, a robust possibilistic fuzzy additive partition clustering with master–slave neighborhood information constraints is proposed for high noise image segmentation. This algorithm first constructs a master–slave neighborhood model, which consists of the master neighborhood window of the current pixel and the slave neighborhood window around the master neighborhood pixel. Then, the master–slave neighborhood information is integrated into the possibilistic fuzzy additive partition clustering model, and a novel robust possibilistic fuzzy clustering model incorporating deep local information is constructed. Next, this clustering model is further simplified by Cauchy inequality and a robust master–slave neighborhood information-driven possibilistic fuzzy clustering algorithm is derived by optimization theory. Extensive experimental results indicate that the proposed algorithm is very effective for noisy image segmentation, and its segmentation performance is significantly better than many existing state-of-the-art fuzzy clustering-related algorithms. In short, the work of this paper has profound significance for the development of robust fuzzy clustering theory. [ABSTRACT FROM AUTHOR]

Referring image segmentation aims to accurately align image pixels and text features for object segmentation based on natural language descriptions. This paper proposes NSNPRIS (convolutional nonlinear spiking neural P systems for referring image segmentation), a novel model based on convolutional nonlinear spiking neural P systems. NSNPRIS features NSNPFusion and Language Gate modules to enhance feature interaction during encoding, along with an NSNPDecoder for feature alignment and decoding. Experimental results on RefCOCO, RefCOCO + , and G-Ref datasets demonstrate that NSNPRIS performs better than mainstream methods. Our contributions include advances in the alignment of pixel and textual features and the improvement of segmentation accuracy. [ABSTRACT FROM AUTHOR]