Identification of the ice channel is the basic technology for developing intelligent ships in ice-covered waters,which is important to ensure the safety and economy of navigation.In the Arctic,merchant ships with low ...Identification of the ice channel is the basic technology for developing intelligent ships in ice-covered waters,which is important to ensure the safety and economy of navigation.In the Arctic,merchant ships with low ice class often navigate in channels opened up by icebreakers.Navigation in the ice channel often depends on good maneuverability skills and abundant experience from the captain to a large extent.The ship may get stuck if steered into ice fields off the channel.Under this circumstance,it is very important to study how to identify the boundary lines of ice channels with a reliable method.In this paper,a two-staged ice channel identification method is developed based on image segmentation and corner point regression.The first stage employs the image segmentation method to extract channel regions.In the second stage,an intelligent corner regression network is proposed to extract the channel boundary lines from the channel region.A non-intelligent angle-based filtering and clustering method is proposed and compared with corner point regression network.The training and evaluation of the segmentation method and corner regression network are carried out on the synthetic and real ice channel dataset.The evaluation results show that the accuracy of the method using the corner point regression network in the second stage is achieved as high as 73.33%on the synthetic ice channel dataset and 70.66%on the real ice channel dataset,and the processing speed can reach up to 14.58frames per second.展开更多
To enhance the diversity and distribution uniformity of initial population,as well as to avoid local extrema in the Chimp Optimization Algorithm(CHOA),this paper improves the CHOA based on chaos initialization and Cau...To enhance the diversity and distribution uniformity of initial population,as well as to avoid local extrema in the Chimp Optimization Algorithm(CHOA),this paper improves the CHOA based on chaos initialization and Cauchy mutation.First,Sin chaos is introduced to improve the random population initialization scheme of the CHOA,which not only guarantees the diversity of the population,but also enhances the distribution uniformity of the initial population.Next,Cauchy mutation is added to optimize the global search ability of the CHOA in the process of position(threshold)updating to avoid the CHOA falling into local optima.Finally,an improved CHOA was formed through the combination of chaos initialization and Cauchy mutation(CICMCHOA),then taking fuzzy Kapur as the objective function,this paper applied CICMCHOA to natural and medical image segmentation,and compared it with four algorithms,including the improved Satin Bowerbird optimizer(ISBO),Cuckoo Search(ICS),etc.The experimental results deriving from visual and specific indicators demonstrate that CICMCHOA delivers superior segmentation effects in image segmentation.展开更多
With the development of underwater sonar detection technology,simultaneous localization and mapping(SLAM)approach has attracted much attention in underwater navigation field in recent years.But the weak detection abil...With the development of underwater sonar detection technology,simultaneous localization and mapping(SLAM)approach has attracted much attention in underwater navigation field in recent years.But the weak detection ability of a single vehicle limits the SLAM performance in wide areas.Thereby,cooperative SLAM using multiple vehicles has become an important research direction.The key factor of cooperative SLAM is timely and efficient sonar image transmission among underwater vehicles.However,the limited bandwidth of underwater acoustic channels contradicts a large amount of sonar image data.It is essential to compress the images before transmission.Recently,deep neural networks have great value in image compression by virtue of the powerful learning ability of neural networks,but the existing sonar image compression methods based on neural network usually focus on the pixel-level information without the semantic-level information.In this paper,we propose a novel underwater acoustic transmission scheme called UAT-SSIC that includes semantic segmentation-based sonar image compression(SSIC)framework and the joint source-channel codec,to improve the accuracy of the semantic information of the reconstructed sonar image at the receiver.The SSIC framework consists of Auto-Encoder structure-based sonar image compression network,which is measured by a semantic segmentation network's residual.Considering that sonar images have the characteristics of blurred target edges,the semantic segmentation network used a special dilated convolution neural network(DiCNN)to enhance segmentation accuracy by expanding the range of receptive fields.The joint source-channel codec with unequal error protection is proposed that adjusts the power level of the transmitted data,which deal with sonar image transmission error caused by the serious underwater acoustic channel.Experiment results demonstrate that our method preserves more semantic information,with advantages over existing methods at the same compression ratio.It also improves the error tolerance and packet loss resistance of transmission.展开更多
Semantic segmentation of driving scene images is crucial for autonomous driving.While deep learning technology has significantly improved daytime image semantic segmentation,nighttime images pose challenges due to fac...Semantic segmentation of driving scene images is crucial for autonomous driving.While deep learning technology has significantly improved daytime image semantic segmentation,nighttime images pose challenges due to factors like poor lighting and overexposure,making it difficult to recognize small objects.To address this,we propose an Image Adaptive Enhancement(IAEN)module comprising a parameter predictor(Edip),multiple image processing filters(Mdif),and a Detail Processing Module(DPM).Edip combines image processing filters to predict parameters like exposure and hue,optimizing image quality.We adopt a novel image encoder to enhance parameter prediction accuracy by enabling Edip to handle features at different scales.DPM strengthens overlooked image details,extending the IAEN module’s functionality.After the segmentation network,we integrate a Depth Guided Filter(DGF)to refine segmentation outputs.The entire network is trained end-to-end,with segmentation results guiding parameter prediction optimization,promoting self-learning and network improvement.This lightweight and efficient network architecture is particularly suitable for addressing challenges in nighttime image segmentation.Extensive experiments validate significant performance improvements of our approach on the ACDC-night and Nightcity datasets.展开更多
Magnetic resonance(MR)imaging is a widely employed medical imaging technique that produces detailed anatomical images of the human body.The segmentation of MR im-ages plays a crucial role in medical image analysis,as ...Magnetic resonance(MR)imaging is a widely employed medical imaging technique that produces detailed anatomical images of the human body.The segmentation of MR im-ages plays a crucial role in medical image analysis,as it enables accurate diagnosis,treatment planning,and monitoring of various diseases and conditions.Due to the lack of sufficient medical images,it is challenging to achieve an accurate segmentation,especially with the application of deep learning networks.The aim of this work is to study transfer learning from T1-weighted(T1-w)to T2-weighted(T2-w)MR sequences to enhance bone segmentation with minimal required computation resources.With the use of an excitation-based convolutional neural networks,four transfer learning mechanisms are proposed:transfer learning without fine tuning,open fine tuning,conservative fine tuning,and hybrid transfer learning.Moreover,a multi-parametric segmentation model is proposed using T2-w MR as an intensity-based augmentation technique.The novelty of this work emerges in the hybrid transfer learning approach that overcomes the overfitting issue and preserves the features of both modalities with minimal computation time and resources.The segmentation results are evaluated using 14 clinical 3D brain MR and CT images.The results reveal that hybrid transfer learning is superior for bone segmentation in terms of performance and computation time with DSCs of 0.5393±0.0007.Although T2-w-based augmentation has no significant impact on the performance of T1-w MR segmentation,it helps in improving T2-w MR segmentation and developing a multi-sequences segmentation model.展开更多
Deep learning has been extensively applied to medical image segmentation,resulting in significant advancements in the field of deep neural networks for medical image segmentation since the notable success of U Net in ...Deep learning has been extensively applied to medical image segmentation,resulting in significant advancements in the field of deep neural networks for medical image segmentation since the notable success of U Net in 2015.However,the application of deep learning models to ocular medical image segmentation poses unique challenges,especially compared to other body parts,due to the complexity,small size,and blurriness of such images,coupled with the scarcity of data.This article aims to provide a comprehensive review of medical image segmentation from two perspectives:the development of deep network structures and the application of segmentation in ocular imaging.Initially,the article introduces an overview of medical imaging,data processing,and performance evaluation metrics.Subsequently,it analyzes recent developments in U-Net-based network structures.Finally,for the segmentation of ocular medical images,the application of deep learning is reviewed and categorized by the type of ocular tissue.展开更多
Automatic segmentation of medical images provides a reliable scientific basis for disease diagnosis and analysis.Notably,most existing methods that combine the strengths of convolutional neural networks(CNNs)and Trans...Automatic segmentation of medical images provides a reliable scientific basis for disease diagnosis and analysis.Notably,most existing methods that combine the strengths of convolutional neural networks(CNNs)and Transformers have made significant progress.However,there are some limitations in the current integration of CNN and Transformer technology in two key aspects.Firstly,most methods either overlook or fail to fully incorporate the complementary nature between local and global features.Secondly,the significance of integrating the multiscale encoder features from the dual-branch network to enhance the decoding features is often disregarded in methods that combine CNN and Transformer.To address this issue,we present a groundbreaking dual-branch cross-attention fusion network(DCFNet),which efficiently combines the power of Swin Transformer and CNN to generate complementary global and local features.We then designed the Feature Cross-Fusion(FCF)module to efficiently fuse local and global features.In the FCF,the utilization of the Channel-wise Cross-fusion Transformer(CCT)serves the purpose of aggregatingmulti-scale features,and the Feature FusionModule(FFM)is employed to effectively aggregate dual-branch prominent feature regions from the spatial perspective.Furthermore,within the decoding phase of the dual-branch network,our proposed Channel Attention Block(CAB)aims to emphasize the significance of the channel features between the up-sampled features and the features generated by the FCFmodule to enhance the details of the decoding.Experimental results demonstrate that DCFNet exhibits enhanced accuracy in segmentation performance.Compared to other state-of-the-art(SOTA)methods,our segmentation framework exhibits a superior level of competitiveness.DCFNet’s accurate segmentation of medical images can greatly assist medical professionals in making crucial diagnoses of lesion areas in advance.展开更多
In this paper,we consider the Chan–Vese(C-V)model for image segmentation and obtain its numerical solution accurately and efficiently.For this purpose,we present a local radial basis function method based on a Gaussi...In this paper,we consider the Chan–Vese(C-V)model for image segmentation and obtain its numerical solution accurately and efficiently.For this purpose,we present a local radial basis function method based on a Gaussian kernel(GA-LRBF)for spatial discretization.Compared to the standard radial basis functionmethod,this approach consumes less CPU time and maintains good stability because it uses only a small subset of points in the whole computational domain.Additionally,since the Gaussian function has the property of dimensional separation,the GA-LRBF method is suitable for dealing with isotropic images.Finally,a numerical scheme that couples GA-LRBF with the fourth-order Runge–Kutta method is applied to the C-V model,and a comparison of some numerical results demonstrates that this scheme achieves much more reliable image segmentation.展开更多
●AIM:To investigate a pioneering framework for the segmentation of meibomian glands(MGs),using limited annotations to reduce the workload on ophthalmologists and enhance the efficiency of clinical diagnosis.●METHODS...●AIM:To investigate a pioneering framework for the segmentation of meibomian glands(MGs),using limited annotations to reduce the workload on ophthalmologists and enhance the efficiency of clinical diagnosis.●METHODS:Totally 203 infrared meibomian images from 138 patients with dry eye disease,accompanied by corresponding annotations,were gathered for the study.A rectified scribble-supervised gland segmentation(RSSGS)model,incorporating temporal ensemble prediction,uncertainty estimation,and a transformation equivariance constraint,was introduced to address constraints imposed by limited supervision information inherent in scribble annotations.The viability and efficacy of the proposed model were assessed based on accuracy,intersection over union(IoU),and dice coefficient.●RESULTS:Using manual labels as the gold standard,RSSGS demonstrated outcomes with an accuracy of 93.54%,a dice coefficient of 78.02%,and an IoU of 64.18%.Notably,these performance metrics exceed the current weakly supervised state-of-the-art methods by 0.76%,2.06%,and 2.69%,respectively.Furthermore,despite achieving a substantial 80%reduction in annotation costs,it only lags behind fully annotated methods by 0.72%,1.51%,and 2.04%.●CONCLUSION:An innovative automatic segmentation model is developed for MGs in infrared eyelid images,using scribble annotation for training.This model maintains an exceptionally high level of segmentation accuracy while substantially reducing training costs.It holds substantial utility for calculating clinical parameters,thereby greatly enhancing the diagnostic efficiency of ophthalmologists in evaluating meibomian gland dysfunction.展开更多
Deep convolutional neural network (CNN) greatly promotes the automatic segmentation of medical images. However, due to the inherent properties of convolution operations, CNN usually cannot establish long-distance inte...Deep convolutional neural network (CNN) greatly promotes the automatic segmentation of medical images. However, due to the inherent properties of convolution operations, CNN usually cannot establish long-distance interdependence, which limits the segmentation performance. Transformer has been successfully applied to various computer vision, using self-attention mechanism to simulate long-distance interaction, so as to capture global information. However, self-attention lacks spatial location and high-performance computing. In order to solve the above problems, we develop a new medical transformer, which has a multi-scale context fusion function and can be used for medical image segmentation. The proposed model combines convolution operation and attention mechanism to form a u-shaped framework, which can capture both local and global information. First, the traditional converter module is improved to an advanced converter module, which uses post-layer normalization to obtain mild activation values, and uses scaled cosine attention with a moving window to obtain accurate spatial information. Secondly, we also introduce a deep supervision strategy to guide the model to fuse multi-scale feature information. It further enables the proposed model to effectively propagate feature information across layers, Thanks to this, it can achieve better segmentation performance while being more robust and efficient. The proposed model is evaluated on multiple medical image segmentation datasets. Experimental results demonstrate that the proposed model achieves better performance on a challenging dataset (ETIS) compared to existing methods that rely only on convolutional neural networks, transformers, or a combination of both. The mDice and mIou indicators increased by 2.74% and 3.3% respectively.展开更多
BACKGROUND Small intestinal vascular malformations(angiodysplasias)are common causes of small intestinal bleeding.While capsule endoscopy has become the primary diagnostic method for angiodysplasia,manual reading of t...BACKGROUND Small intestinal vascular malformations(angiodysplasias)are common causes of small intestinal bleeding.While capsule endoscopy has become the primary diagnostic method for angiodysplasia,manual reading of the entire gastrointestinal tract is time-consuming and requires a heavy workload,which affects the accuracy of diagnosis.AIM To evaluate whether artificial intelligence can assist the diagnosis and increase the detection rate of angiodysplasias in the small intestine,achieve automatic disease detection,and shorten the capsule endoscopy(CE)reading time.METHODS A convolutional neural network semantic segmentation model with a feature fusion method,which automatically recognizes the category of vascular dysplasia under CE and draws the lesion contour,thus improving the efficiency and accuracy of identifying small intestinal vascular malformation lesions,was proposed.Resnet-50 was used as the skeleton network to design the fusion mechanism,fuse the shallow and depth features,and classify the images at the pixel level to achieve the segmentation and recognition of vascular dysplasia.The training set and test set were constructed and compared with PSPNet,Deeplab3+,and UperNet.RESULTS The test set constructed in the study achieved satisfactory results,where pixel accuracy was 99%,mean intersection over union was 0.69,negative predictive value was 98.74%,and positive predictive value was 94.27%.The model parameter was 46.38 M,the float calculation was 467.2 G,and the time length to segment and recognize a picture was 0.6 s.CONCLUSION Constructing a segmentation network based on deep learning to segment and recognize angiodysplasias lesions is an effective and feasible method for diagnosing angiodysplasias lesions.展开更多
Image segmentation is crucial for various research areas. Manycomputer vision applications depend on segmenting images to understandthe scene, such as autonomous driving, surveillance systems, robotics, andmedical ima...Image segmentation is crucial for various research areas. Manycomputer vision applications depend on segmenting images to understandthe scene, such as autonomous driving, surveillance systems, robotics, andmedical imaging. With the recent advances in deep learning (DL) and itsconfounding results in image segmentation, more attention has been drawnto its use in medical image segmentation. This article introduces a surveyof the state-of-the-art deep convolution neural network (CNN) models andmechanisms utilized in image segmentation. First, segmentation models arecategorized based on their model architecture and primary working principle.Then, CNN categories are described, and various models are discussed withineach category. Compared with other existing surveys, several applicationswith multiple architectural adaptations are discussed within each category.A comparative summary is included to give the reader insights into utilizedarchitectures in different applications and datasets. This study focuses onmedical image segmentation applications, where the most widely used architecturesare illustrated, and other promising models are suggested that haveproven their success in different domains. Finally, the present work discussescurrent limitations and solutions along with future trends in the field.展开更多
As a mainstream research direction in the field of image segmentation,medical image segmentation plays a key role in the quantification of lesions,three-dimensional reconstruction,region of interest extraction and so ...As a mainstream research direction in the field of image segmentation,medical image segmentation plays a key role in the quantification of lesions,three-dimensional reconstruction,region of interest extraction and so on.Compared with natural images,medical images have a variety of modes.Besides,the emphasis of information which is conveyed by images of different modes is quite different.Because it is time-consuming and inefficient to manually segment medical images only by professional and experienced doctors.Therefore,large quantities of automated medical image segmentation methods have been developed.However,until now,researchers have not developed a universal method for all types of medical image segmentation.This paper reviews the literature on segmentation techniques that have produced major breakthroughs in recent years.Among the large quantities of medical image segmentation methods,this paper mainly discusses two categories of medical image segmentation methods.One is the improved strategies based on traditional clustering method.The other is the research progress of the improved image segmentation network structure model based on U-Net.The power of technology proves that the performance of the deep learning-based method is significantly better than that of the traditional method.This paper discussed both advantages and disadvantages of different algorithms and detailed how these methods can be used for the segmentation of lesions or other organs and tissues,as well as possible technical trends for future work.展开更多
In intelligent perception and diagnosis of medical equipment,the visual and morphological changes in retinal vessels are closely related to the severity of cardiovascular diseases(e.g.,diabetes and hypertension).Intel...In intelligent perception and diagnosis of medical equipment,the visual and morphological changes in retinal vessels are closely related to the severity of cardiovascular diseases(e.g.,diabetes and hypertension).Intelligent auxiliary diagnosis of these diseases depends on the accuracy of the retinal vascular segmentation results.To address this challenge,we design a Dual-Branch-UNet framework,which comprises a Dual-Branch encoder structure for feature extraction based on the traditional U-Net model for medical image segmentation.To be more explicit,we utilize a novel parallel encoder made up of various convolutional modules to enhance the encoder portion of the original U-Net.Then,image features are combined at each layer to produce richer semantic data and the model’s capacity is adjusted to various input images.Meanwhile,in the lower sampling section,we give up pooling and conduct the lower sampling by convolution operation to control step size for information fusion.We also employ an attentionmodule in the decoder stage to filter the image noises so as to lessen the response of irrelevant features.Experiments are verified and compared on the DRIVE and ARIA datasets for retinal vessels segmentation.The proposed Dual-Branch-UNet has proved to be superior to other five typical state-of-the-art methods.展开更多
This paper presents a novel computerized technique for the segmentation of nuclei in hematoxylin and eosin(H&E)stained histopathology images.The purpose of this study is to overcome the challenges faced in automat...This paper presents a novel computerized technique for the segmentation of nuclei in hematoxylin and eosin(H&E)stained histopathology images.The purpose of this study is to overcome the challenges faced in automated nuclei segmentation due to the diversity of nuclei structures that arise from differences in tissue types and staining protocols,as well as the segmentation of variable-sized and overlapping nuclei.To this extent,the approach proposed in this study uses an ensemble of the UNet architecture with various Convolutional Neural Networks(CNN)architectures as encoder backbones,along with stain normalization and test time augmentation,to improve segmentation accuracy.Additionally,this paper employs a Structure-Preserving Color Normalization(SPCN)technique as a preprocessing step for stain normalization.The proposed model was trained and tested on both single-organ and multi-organ datasets,yielding an F1 score of 84.11%,mean Intersection over Union(IoU)of 81.67%,dice score of 84.11%,accuracy of 92.58%and precision of 83.78%on the multi-organ dataset,and an F1 score of 87.04%,mean IoU of 86.66%,dice score of 87.04%,accuracy of 96.69%and precision of 87.57%on the single-organ dataset.These findings demonstrate that the proposed model ensemble coupled with the right pre-processing and post-processing techniques enhances nuclei segmentation capabilities.展开更多
The semantic segmentation methods based on CNN have made great progress,but there are still some shortcomings in the application of remote sensing images segmentation,such as the small receptive field can not effectiv...The semantic segmentation methods based on CNN have made great progress,but there are still some shortcomings in the application of remote sensing images segmentation,such as the small receptive field can not effectively capture global context.In order to solve this problem,this paper proposes a hybrid model based on ResNet50 and swin transformer to directly capture long-range dependence,which fuses features through Cross Feature Modulation Module(CFMM).Experimental results on two publicly available datasets,Vaihingen and Potsdam,are mIoU of 70.27%and 76.63%,respectively.Thus,CFM-UNet can maintain a high segmentation performance compared with other competitive networks.展开更多
Medical image segmentation plays a crucial role in clinical diagnosis and therapy systems,yet still faces many challenges.Building on convolutional neural networks(CNNs),medical image segmentation has achieved tremend...Medical image segmentation plays a crucial role in clinical diagnosis and therapy systems,yet still faces many challenges.Building on convolutional neural networks(CNNs),medical image segmentation has achieved tremendous progress.However,owing to the locality of convolution operations,CNNs have the inherent limitation in learning global context.To address the limitation in building global context relationship from CNNs,we propose LGNet,a semantic segmentation network aiming to learn local and global features for fast and accurate medical image segmentation in this paper.Specifically,we employ a two-branch architecture consisting of convolution layers in one branch to learn local features and transformer layers in the other branch to learn global features.LGNet has two key insights:(1)We bridge two-branch to learn local and global features in an interactive way;(2)we present a novel multi-feature fusion model(MSFFM)to leverage the global contexture information from transformer and the local representational features from convolutions.Our method achieves state-of-the-art trade-off in terms of accuracy and efficiency on several medical image segmentation benchmarks including Synapse,ACDC and MOST.Specifically,LGNet achieves the state-of-the-art performance with Dice's indexes of 80.15%on Synapse,of 91.70%on ACDC,and of 95.56%on MOST.Meanwhile,the inference speed attains at 172 frames per second with 224-224 input resolution.The extensive experiments demonstrate the effectiveness of the proposed LGNet for fast and accurate for medical image segmentation.展开更多
There are two types of methods for image segmentation.One is traditional image processing methods,which are sensitive to details and boundaries,yet fail to recognize semantic information.The other is deep learning met...There are two types of methods for image segmentation.One is traditional image processing methods,which are sensitive to details and boundaries,yet fail to recognize semantic information.The other is deep learning methods,which can locate and identify different objects,but boundary identifications are not accurate enough.Both of them cannot generate entire segmentation information.In order to obtain accurate edge detection and semantic information,an Adaptive Boundary and Semantic Composite Segmentation method(ABSCS)is proposed.This method can precisely semantic segment individual objects in large-size aerial images with limited GPU performances.It includes adaptively dividing and modifying the aerial images with the proposed principles and methods,using the deep learning method to semantic segment and preprocess the small divided pieces,using three traditional methods to segment and preprocess original-size aerial images,adaptively selecting traditional results tomodify the boundaries of individual objects in deep learning results,and combining the results of different objects.Individual object semantic segmentation experiments are conducted by using the AeroScapes dataset,and their results are analyzed qualitatively and quantitatively.The experimental results demonstrate that the proposed method can achieve more promising object boundaries than the original deep learning method.This work also demonstrates the advantages of the proposed method in applications of point cloud semantic segmentation and image inpainting.展开更多
Cold seeps are widely developed on the seabed of continental margins and can form gas plumes due to the upward migration of methane-rich fluids.The detection and automatic segmentation of gas plumes are of great signi...Cold seeps are widely developed on the seabed of continental margins and can form gas plumes due to the upward migration of methane-rich fluids.The detection and automatic segmentation of gas plumes are of great significance in locating and studying the cold seep system that is usually accompanied by hydrate layers in the subsurface.A multibeam echo-sounder system(MBES)can record the complete backscatter intensity of the water column,and it is one of the most effective means for detecting cold seeps.However,the gas plumes recorded in multibeam water column images(WCI)are usually blurred due to the interference of the complicated water environment and the sidelobes of the MBES,making it difficult to obtain the effective segmentation.Therefore,based on the existing UNet semantic segmentation network,this paper proposes an AP-UNet network combining the convolutional block attention module and the pyramid pooling module for the automatic segmentation and extraction of gas plumes.Comparative experiments are conducted among three traditional segmentation methods and two deep learning methods.The results show that the AP-UNet segmentation model can effectively suppress complicated water column noise interference.The segmentation precision,the Dice coefficient,and the recall rate of this model are 92.09%,92.00%,and 92.49%,respectively,which are 1.17%,2.10%,and 2.07%higher than the results of the UNet.展开更多
In recent years,the soft subspace clustering algorithm has shown good results for high-dimensional data,which can assign different weights to each cluster class and use weights to measure the contribution of each dime...In recent years,the soft subspace clustering algorithm has shown good results for high-dimensional data,which can assign different weights to each cluster class and use weights to measure the contribution of each dimension in various features.The enhanced soft subspace clustering algorithm combines interclass separation and intraclass tightness information,which has strong results for image segmentation,but the clustering algorithm is vulnerable to noisy data and dependence on the initialized clustering center.However,the clustering algorithmis susceptible to the influence of noisydata and reliance on initializedclustering centers andfalls into a local optimum;the clustering effect is poor for brain MR images with unclear boundaries and noise effects.To address these problems,a soft subspace clustering algorithm for brain MR images based on genetic algorithm optimization is proposed,which combines the generalized noise technique,relaxes the equational weight constraint in the objective function as the boundary constraint,and uses a genetic algorithm as a method to optimize the initialized clustering center.The genetic algorithm finds the best clustering center and reduces the algorithm’s dependence on the initial clustering center.The experiment verifies the robustness of the algorithm,as well as the noise immunity in various ways and shows good results on the common dataset and the brain MR images provided by the Changshu First People’s Hospital with specific high accuracy for clinical medicine.展开更多
基金financially supported by the National Key Research and Development Program(Grant No.2022YFE0107000)the General Projects of the National Natural Science Foundation of China(Grant No.52171259)the High-Tech Ship Research Project of the Ministry of Industry and Information Technology(Grant No.[2021]342)。
文摘Identification of the ice channel is the basic technology for developing intelligent ships in ice-covered waters,which is important to ensure the safety and economy of navigation.In the Arctic,merchant ships with low ice class often navigate in channels opened up by icebreakers.Navigation in the ice channel often depends on good maneuverability skills and abundant experience from the captain to a large extent.The ship may get stuck if steered into ice fields off the channel.Under this circumstance,it is very important to study how to identify the boundary lines of ice channels with a reliable method.In this paper,a two-staged ice channel identification method is developed based on image segmentation and corner point regression.The first stage employs the image segmentation method to extract channel regions.In the second stage,an intelligent corner regression network is proposed to extract the channel boundary lines from the channel region.A non-intelligent angle-based filtering and clustering method is proposed and compared with corner point regression network.The training and evaluation of the segmentation method and corner regression network are carried out on the synthetic and real ice channel dataset.The evaluation results show that the accuracy of the method using the corner point regression network in the second stage is achieved as high as 73.33%on the synthetic ice channel dataset and 70.66%on the real ice channel dataset,and the processing speed can reach up to 14.58frames per second.
基金This work is supported by Natural Science Foundation of Anhui under Grant 1908085MF207,KJ2020A1215,KJ2021A1251 and 2023AH052856the Excellent Youth Talent Support Foundation of Anhui underGrant gxyqZD2021142the Quality Engineering Project of Anhui under Grant 2021jyxm1117,2021kcszsfkc307,2022xsxx158 and 2022jcbs043.
文摘To enhance the diversity and distribution uniformity of initial population,as well as to avoid local extrema in the Chimp Optimization Algorithm(CHOA),this paper improves the CHOA based on chaos initialization and Cauchy mutation.First,Sin chaos is introduced to improve the random population initialization scheme of the CHOA,which not only guarantees the diversity of the population,but also enhances the distribution uniformity of the initial population.Next,Cauchy mutation is added to optimize the global search ability of the CHOA in the process of position(threshold)updating to avoid the CHOA falling into local optima.Finally,an improved CHOA was formed through the combination of chaos initialization and Cauchy mutation(CICMCHOA),then taking fuzzy Kapur as the objective function,this paper applied CICMCHOA to natural and medical image segmentation,and compared it with four algorithms,including the improved Satin Bowerbird optimizer(ISBO),Cuckoo Search(ICS),etc.The experimental results deriving from visual and specific indicators demonstrate that CICMCHOA delivers superior segmentation effects in image segmentation.
基金supported in part by the Tianjin Technology Innovation Guidance Special Fund Project under Grant No.21YDTPJC00850in part by the National Natural Science Foundation of China under Grant No.41906161in part by the Natural Science Foundation of Tianjin under Grant No.21JCQNJC00650。
文摘With the development of underwater sonar detection technology,simultaneous localization and mapping(SLAM)approach has attracted much attention in underwater navigation field in recent years.But the weak detection ability of a single vehicle limits the SLAM performance in wide areas.Thereby,cooperative SLAM using multiple vehicles has become an important research direction.The key factor of cooperative SLAM is timely and efficient sonar image transmission among underwater vehicles.However,the limited bandwidth of underwater acoustic channels contradicts a large amount of sonar image data.It is essential to compress the images before transmission.Recently,deep neural networks have great value in image compression by virtue of the powerful learning ability of neural networks,but the existing sonar image compression methods based on neural network usually focus on the pixel-level information without the semantic-level information.In this paper,we propose a novel underwater acoustic transmission scheme called UAT-SSIC that includes semantic segmentation-based sonar image compression(SSIC)framework and the joint source-channel codec,to improve the accuracy of the semantic information of the reconstructed sonar image at the receiver.The SSIC framework consists of Auto-Encoder structure-based sonar image compression network,which is measured by a semantic segmentation network's residual.Considering that sonar images have the characteristics of blurred target edges,the semantic segmentation network used a special dilated convolution neural network(DiCNN)to enhance segmentation accuracy by expanding the range of receptive fields.The joint source-channel codec with unequal error protection is proposed that adjusts the power level of the transmitted data,which deal with sonar image transmission error caused by the serious underwater acoustic channel.Experiment results demonstrate that our method preserves more semantic information,with advantages over existing methods at the same compression ratio.It also improves the error tolerance and packet loss resistance of transmission.
基金This work is supported in part by The National Natural Science Foundation of China(Grant Number 61971078),which provided domain expertise and computational power that greatly assisted the activityThis work was financially supported by Chongqing Municipal Education Commission Grants for-Major Science and Technology Project(Grant Number gzlcx20243175).
文摘Semantic segmentation of driving scene images is crucial for autonomous driving.While deep learning technology has significantly improved daytime image semantic segmentation,nighttime images pose challenges due to factors like poor lighting and overexposure,making it difficult to recognize small objects.To address this,we propose an Image Adaptive Enhancement(IAEN)module comprising a parameter predictor(Edip),multiple image processing filters(Mdif),and a Detail Processing Module(DPM).Edip combines image processing filters to predict parameters like exposure and hue,optimizing image quality.We adopt a novel image encoder to enhance parameter prediction accuracy by enabling Edip to handle features at different scales.DPM strengthens overlooked image details,extending the IAEN module’s functionality.After the segmentation network,we integrate a Depth Guided Filter(DGF)to refine segmentation outputs.The entire network is trained end-to-end,with segmentation results guiding parameter prediction optimization,promoting self-learning and network improvement.This lightweight and efficient network architecture is particularly suitable for addressing challenges in nighttime image segmentation.Extensive experiments validate significant performance improvements of our approach on the ACDC-night and Nightcity datasets.
基金Swiss National Science Foundation,Grant/Award Number:SNSF 320030_176052Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung,Grant/Award Number:320030_176052。
文摘Magnetic resonance(MR)imaging is a widely employed medical imaging technique that produces detailed anatomical images of the human body.The segmentation of MR im-ages plays a crucial role in medical image analysis,as it enables accurate diagnosis,treatment planning,and monitoring of various diseases and conditions.Due to the lack of sufficient medical images,it is challenging to achieve an accurate segmentation,especially with the application of deep learning networks.The aim of this work is to study transfer learning from T1-weighted(T1-w)to T2-weighted(T2-w)MR sequences to enhance bone segmentation with minimal required computation resources.With the use of an excitation-based convolutional neural networks,four transfer learning mechanisms are proposed:transfer learning without fine tuning,open fine tuning,conservative fine tuning,and hybrid transfer learning.Moreover,a multi-parametric segmentation model is proposed using T2-w MR as an intensity-based augmentation technique.The novelty of this work emerges in the hybrid transfer learning approach that overcomes the overfitting issue and preserves the features of both modalities with minimal computation time and resources.The segmentation results are evaluated using 14 clinical 3D brain MR and CT images.The results reveal that hybrid transfer learning is superior for bone segmentation in terms of performance and computation time with DSCs of 0.5393±0.0007.Although T2-w-based augmentation has no significant impact on the performance of T1-w MR segmentation,it helps in improving T2-w MR segmentation and developing a multi-sequences segmentation model.
文摘Deep learning has been extensively applied to medical image segmentation,resulting in significant advancements in the field of deep neural networks for medical image segmentation since the notable success of U Net in 2015.However,the application of deep learning models to ocular medical image segmentation poses unique challenges,especially compared to other body parts,due to the complexity,small size,and blurriness of such images,coupled with the scarcity of data.This article aims to provide a comprehensive review of medical image segmentation from two perspectives:the development of deep network structures and the application of segmentation in ocular imaging.Initially,the article introduces an overview of medical imaging,data processing,and performance evaluation metrics.Subsequently,it analyzes recent developments in U-Net-based network structures.Finally,for the segmentation of ocular medical images,the application of deep learning is reviewed and categorized by the type of ocular tissue.
基金supported by the National Key R&D Program of China(2018AAA0102100)the National Natural Science Foundation of China(No.62376287)+3 种基金the International Science and Technology Innovation Joint Base of Machine Vision and Medical Image Processing in Hunan Province(2021CB1013)the Key Research and Development Program of Hunan Province(2022SK2054)the Natural Science Foundation of Hunan Province(No.2022JJ30762,2023JJ70016)the 111 Project under Grant(No.B18059).
文摘Automatic segmentation of medical images provides a reliable scientific basis for disease diagnosis and analysis.Notably,most existing methods that combine the strengths of convolutional neural networks(CNNs)and Transformers have made significant progress.However,there are some limitations in the current integration of CNN and Transformer technology in two key aspects.Firstly,most methods either overlook or fail to fully incorporate the complementary nature between local and global features.Secondly,the significance of integrating the multiscale encoder features from the dual-branch network to enhance the decoding features is often disregarded in methods that combine CNN and Transformer.To address this issue,we present a groundbreaking dual-branch cross-attention fusion network(DCFNet),which efficiently combines the power of Swin Transformer and CNN to generate complementary global and local features.We then designed the Feature Cross-Fusion(FCF)module to efficiently fuse local and global features.In the FCF,the utilization of the Channel-wise Cross-fusion Transformer(CCT)serves the purpose of aggregatingmulti-scale features,and the Feature FusionModule(FFM)is employed to effectively aggregate dual-branch prominent feature regions from the spatial perspective.Furthermore,within the decoding phase of the dual-branch network,our proposed Channel Attention Block(CAB)aims to emphasize the significance of the channel features between the up-sampled features and the features generated by the FCFmodule to enhance the details of the decoding.Experimental results demonstrate that DCFNet exhibits enhanced accuracy in segmentation performance.Compared to other state-of-the-art(SOTA)methods,our segmentation framework exhibits a superior level of competitiveness.DCFNet’s accurate segmentation of medical images can greatly assist medical professionals in making crucial diagnoses of lesion areas in advance.
基金sponsored by Guangdong Basic and Applied Basic Research Foundation under Grant No.2021A1515110680Guangzhou Basic and Applied Basic Research under Grant No.202102020340.
文摘In this paper,we consider the Chan–Vese(C-V)model for image segmentation and obtain its numerical solution accurately and efficiently.For this purpose,we present a local radial basis function method based on a Gaussian kernel(GA-LRBF)for spatial discretization.Compared to the standard radial basis functionmethod,this approach consumes less CPU time and maintains good stability because it uses only a small subset of points in the whole computational domain.Additionally,since the Gaussian function has the property of dimensional separation,the GA-LRBF method is suitable for dealing with isotropic images.Finally,a numerical scheme that couples GA-LRBF with the fourth-order Runge–Kutta method is applied to the C-V model,and a comparison of some numerical results demonstrates that this scheme achieves much more reliable image segmentation.
基金Supported by Natural Science Foundation of Fujian Province(No.2020J011084)Fujian Province Technology and Economy Integration Service Platform(No.2023XRH001)Fuzhou-Xiamen-Quanzhou National Independent Innovation Demonstration Zone Collaborative Innovation Platform(No.2022FX5)。
文摘●AIM:To investigate a pioneering framework for the segmentation of meibomian glands(MGs),using limited annotations to reduce the workload on ophthalmologists and enhance the efficiency of clinical diagnosis.●METHODS:Totally 203 infrared meibomian images from 138 patients with dry eye disease,accompanied by corresponding annotations,were gathered for the study.A rectified scribble-supervised gland segmentation(RSSGS)model,incorporating temporal ensemble prediction,uncertainty estimation,and a transformation equivariance constraint,was introduced to address constraints imposed by limited supervision information inherent in scribble annotations.The viability and efficacy of the proposed model were assessed based on accuracy,intersection over union(IoU),and dice coefficient.●RESULTS:Using manual labels as the gold standard,RSSGS demonstrated outcomes with an accuracy of 93.54%,a dice coefficient of 78.02%,and an IoU of 64.18%.Notably,these performance metrics exceed the current weakly supervised state-of-the-art methods by 0.76%,2.06%,and 2.69%,respectively.Furthermore,despite achieving a substantial 80%reduction in annotation costs,it only lags behind fully annotated methods by 0.72%,1.51%,and 2.04%.●CONCLUSION:An innovative automatic segmentation model is developed for MGs in infrared eyelid images,using scribble annotation for training.This model maintains an exceptionally high level of segmentation accuracy while substantially reducing training costs.It holds substantial utility for calculating clinical parameters,thereby greatly enhancing the diagnostic efficiency of ophthalmologists in evaluating meibomian gland dysfunction.
文摘Deep convolutional neural network (CNN) greatly promotes the automatic segmentation of medical images. However, due to the inherent properties of convolution operations, CNN usually cannot establish long-distance interdependence, which limits the segmentation performance. Transformer has been successfully applied to various computer vision, using self-attention mechanism to simulate long-distance interaction, so as to capture global information. However, self-attention lacks spatial location and high-performance computing. In order to solve the above problems, we develop a new medical transformer, which has a multi-scale context fusion function and can be used for medical image segmentation. The proposed model combines convolution operation and attention mechanism to form a u-shaped framework, which can capture both local and global information. First, the traditional converter module is improved to an advanced converter module, which uses post-layer normalization to obtain mild activation values, and uses scaled cosine attention with a moving window to obtain accurate spatial information. Secondly, we also introduce a deep supervision strategy to guide the model to fuse multi-scale feature information. It further enables the proposed model to effectively propagate feature information across layers, Thanks to this, it can achieve better segmentation performance while being more robust and efficient. The proposed model is evaluated on multiple medical image segmentation datasets. Experimental results demonstrate that the proposed model achieves better performance on a challenging dataset (ETIS) compared to existing methods that rely only on convolutional neural networks, transformers, or a combination of both. The mDice and mIou indicators increased by 2.74% and 3.3% respectively.
基金Chongqing Technological Innovation and Application Development Project,Key Technologies and Applications of Cross Media Analysis and Reasoning,No.cstc2019jscx-zdztzxX0037.
文摘BACKGROUND Small intestinal vascular malformations(angiodysplasias)are common causes of small intestinal bleeding.While capsule endoscopy has become the primary diagnostic method for angiodysplasia,manual reading of the entire gastrointestinal tract is time-consuming and requires a heavy workload,which affects the accuracy of diagnosis.AIM To evaluate whether artificial intelligence can assist the diagnosis and increase the detection rate of angiodysplasias in the small intestine,achieve automatic disease detection,and shorten the capsule endoscopy(CE)reading time.METHODS A convolutional neural network semantic segmentation model with a feature fusion method,which automatically recognizes the category of vascular dysplasia under CE and draws the lesion contour,thus improving the efficiency and accuracy of identifying small intestinal vascular malformation lesions,was proposed.Resnet-50 was used as the skeleton network to design the fusion mechanism,fuse the shallow and depth features,and classify the images at the pixel level to achieve the segmentation and recognition of vascular dysplasia.The training set and test set were constructed and compared with PSPNet,Deeplab3+,and UperNet.RESULTS The test set constructed in the study achieved satisfactory results,where pixel accuracy was 99%,mean intersection over union was 0.69,negative predictive value was 98.74%,and positive predictive value was 94.27%.The model parameter was 46.38 M,the float calculation was 467.2 G,and the time length to segment and recognize a picture was 0.6 s.CONCLUSION Constructing a segmentation network based on deep learning to segment and recognize angiodysplasias lesions is an effective and feasible method for diagnosing angiodysplasias lesions.
基金supported by the Information Technology Industry Development Agency (ITIDA),Egypt (Project No.CFP181).
文摘Image segmentation is crucial for various research areas. Manycomputer vision applications depend on segmenting images to understandthe scene, such as autonomous driving, surveillance systems, robotics, andmedical imaging. With the recent advances in deep learning (DL) and itsconfounding results in image segmentation, more attention has been drawnto its use in medical image segmentation. This article introduces a surveyof the state-of-the-art deep convolution neural network (CNN) models andmechanisms utilized in image segmentation. First, segmentation models arecategorized based on their model architecture and primary working principle.Then, CNN categories are described, and various models are discussed withineach category. Compared with other existing surveys, several applicationswith multiple architectural adaptations are discussed within each category.A comparative summary is included to give the reader insights into utilizedarchitectures in different applications and datasets. This study focuses onmedical image segmentation applications, where the most widely used architecturesare illustrated, and other promising models are suggested that haveproven their success in different domains. Finally, the present work discussescurrent limitations and solutions along with future trends in the field.
基金supported partly by the Open Project of State Key Laboratory of Millimeter Wave under Grant K202218partly by Innovation and Entrepreneurship Training Program of College Students under Grants 202210700006Y and 202210700005Z.
文摘As a mainstream research direction in the field of image segmentation,medical image segmentation plays a key role in the quantification of lesions,three-dimensional reconstruction,region of interest extraction and so on.Compared with natural images,medical images have a variety of modes.Besides,the emphasis of information which is conveyed by images of different modes is quite different.Because it is time-consuming and inefficient to manually segment medical images only by professional and experienced doctors.Therefore,large quantities of automated medical image segmentation methods have been developed.However,until now,researchers have not developed a universal method for all types of medical image segmentation.This paper reviews the literature on segmentation techniques that have produced major breakthroughs in recent years.Among the large quantities of medical image segmentation methods,this paper mainly discusses two categories of medical image segmentation methods.One is the improved strategies based on traditional clustering method.The other is the research progress of the improved image segmentation network structure model based on U-Net.The power of technology proves that the performance of the deep learning-based method is significantly better than that of the traditional method.This paper discussed both advantages and disadvantages of different algorithms and detailed how these methods can be used for the segmentation of lesions or other organs and tissues,as well as possible technical trends for future work.
基金supported by National Natural Science Foundation of China(NSFC)(61976123,62072213)Taishan Young Scholars Program of Shandong Provinceand Key Development Program for Basic Research of Shandong Province(ZR2020ZD44).
文摘In intelligent perception and diagnosis of medical equipment,the visual and morphological changes in retinal vessels are closely related to the severity of cardiovascular diseases(e.g.,diabetes and hypertension).Intelligent auxiliary diagnosis of these diseases depends on the accuracy of the retinal vascular segmentation results.To address this challenge,we design a Dual-Branch-UNet framework,which comprises a Dual-Branch encoder structure for feature extraction based on the traditional U-Net model for medical image segmentation.To be more explicit,we utilize a novel parallel encoder made up of various convolutional modules to enhance the encoder portion of the original U-Net.Then,image features are combined at each layer to produce richer semantic data and the model’s capacity is adjusted to various input images.Meanwhile,in the lower sampling section,we give up pooling and conduct the lower sampling by convolution operation to control step size for information fusion.We also employ an attentionmodule in the decoder stage to filter the image noises so as to lessen the response of irrelevant features.Experiments are verified and compared on the DRIVE and ARIA datasets for retinal vessels segmentation.The proposed Dual-Branch-UNet has proved to be superior to other five typical state-of-the-art methods.
文摘This paper presents a novel computerized technique for the segmentation of nuclei in hematoxylin and eosin(H&E)stained histopathology images.The purpose of this study is to overcome the challenges faced in automated nuclei segmentation due to the diversity of nuclei structures that arise from differences in tissue types and staining protocols,as well as the segmentation of variable-sized and overlapping nuclei.To this extent,the approach proposed in this study uses an ensemble of the UNet architecture with various Convolutional Neural Networks(CNN)architectures as encoder backbones,along with stain normalization and test time augmentation,to improve segmentation accuracy.Additionally,this paper employs a Structure-Preserving Color Normalization(SPCN)technique as a preprocessing step for stain normalization.The proposed model was trained and tested on both single-organ and multi-organ datasets,yielding an F1 score of 84.11%,mean Intersection over Union(IoU)of 81.67%,dice score of 84.11%,accuracy of 92.58%and precision of 83.78%on the multi-organ dataset,and an F1 score of 87.04%,mean IoU of 86.66%,dice score of 87.04%,accuracy of 96.69%and precision of 87.57%on the single-organ dataset.These findings demonstrate that the proposed model ensemble coupled with the right pre-processing and post-processing techniques enhances nuclei segmentation capabilities.
基金Young Innovative Talents Project of Guangdong Ordinary Universities(No.2022KQNCX225)School-level Teaching and Research Project of Guangzhou City Polytechnic(No.2022xky046)。
文摘The semantic segmentation methods based on CNN have made great progress,but there are still some shortcomings in the application of remote sensing images segmentation,such as the small receptive field can not effectively capture global context.In order to solve this problem,this paper proposes a hybrid model based on ResNet50 and swin transformer to directly capture long-range dependence,which fuses features through Cross Feature Modulation Module(CFMM).Experimental results on two publicly available datasets,Vaihingen and Potsdam,are mIoU of 70.27%and 76.63%,respectively.Thus,CFM-UNet can maintain a high segmentation performance compared with other competitive networks.
基金supported by the Open-Fund of WNLO (Grant No.2018WNLOKF027)the Hubei Key Laboratory of Intelligent Robot in Wuhan Institute of Technology (Grant No.HBIRL 202003).
文摘Medical image segmentation plays a crucial role in clinical diagnosis and therapy systems,yet still faces many challenges.Building on convolutional neural networks(CNNs),medical image segmentation has achieved tremendous progress.However,owing to the locality of convolution operations,CNNs have the inherent limitation in learning global context.To address the limitation in building global context relationship from CNNs,we propose LGNet,a semantic segmentation network aiming to learn local and global features for fast and accurate medical image segmentation in this paper.Specifically,we employ a two-branch architecture consisting of convolution layers in one branch to learn local features and transformer layers in the other branch to learn global features.LGNet has two key insights:(1)We bridge two-branch to learn local and global features in an interactive way;(2)we present a novel multi-feature fusion model(MSFFM)to leverage the global contexture information from transformer and the local representational features from convolutions.Our method achieves state-of-the-art trade-off in terms of accuracy and efficiency on several medical image segmentation benchmarks including Synapse,ACDC and MOST.Specifically,LGNet achieves the state-of-the-art performance with Dice's indexes of 80.15%on Synapse,of 91.70%on ACDC,and of 95.56%on MOST.Meanwhile,the inference speed attains at 172 frames per second with 224-224 input resolution.The extensive experiments demonstrate the effectiveness of the proposed LGNet for fast and accurate for medical image segmentation.
基金funded in part by the Equipment Pre-Research Foundation of China,Grant No.61400010203in part by the Independent Project of the State Key Laboratory of Virtual Reality Technology and Systems.
文摘There are two types of methods for image segmentation.One is traditional image processing methods,which are sensitive to details and boundaries,yet fail to recognize semantic information.The other is deep learning methods,which can locate and identify different objects,but boundary identifications are not accurate enough.Both of them cannot generate entire segmentation information.In order to obtain accurate edge detection and semantic information,an Adaptive Boundary and Semantic Composite Segmentation method(ABSCS)is proposed.This method can precisely semantic segment individual objects in large-size aerial images with limited GPU performances.It includes adaptively dividing and modifying the aerial images with the proposed principles and methods,using the deep learning method to semantic segment and preprocess the small divided pieces,using three traditional methods to segment and preprocess original-size aerial images,adaptively selecting traditional results tomodify the boundaries of individual objects in deep learning results,and combining the results of different objects.Individual object semantic segmentation experiments are conducted by using the AeroScapes dataset,and their results are analyzed qualitatively and quantitatively.The experimental results demonstrate that the proposed method can achieve more promising object boundaries than the original deep learning method.This work also demonstrates the advantages of the proposed method in applications of point cloud semantic segmentation and image inpainting.
基金Supported by the National Natural Science Foundation of China (Nos.41930535,41906165)the High-level Foreign Expert Introduction Program (No.G2021025006L)the SDUST Research Fund (No.2019TDJH103)。
文摘Cold seeps are widely developed on the seabed of continental margins and can form gas plumes due to the upward migration of methane-rich fluids.The detection and automatic segmentation of gas plumes are of great significance in locating and studying the cold seep system that is usually accompanied by hydrate layers in the subsurface.A multibeam echo-sounder system(MBES)can record the complete backscatter intensity of the water column,and it is one of the most effective means for detecting cold seeps.However,the gas plumes recorded in multibeam water column images(WCI)are usually blurred due to the interference of the complicated water environment and the sidelobes of the MBES,making it difficult to obtain the effective segmentation.Therefore,based on the existing UNet semantic segmentation network,this paper proposes an AP-UNet network combining the convolutional block attention module and the pyramid pooling module for the automatic segmentation and extraction of gas plumes.Comparative experiments are conducted among three traditional segmentation methods and two deep learning methods.The results show that the AP-UNet segmentation model can effectively suppress complicated water column noise interference.The segmentation precision,the Dice coefficient,and the recall rate of this model are 92.09%,92.00%,and 92.49%,respectively,which are 1.17%,2.10%,and 2.07%higher than the results of the UNet.
基金This work was supported in part by the National Natural Science Foundation of China under Grant 62171203in part by the Suzhou Key Supporting Subjects[Health Informatics(No.SZFCXK202147)]+2 种基金in part by the Changshu Science and Technology Program[No.CS202015,CS202246]in part by the Changshu City Health and Health Committee Science and Technology Program[No.csws201913]in part by the“333 High Level Personnel Training Project of Jiangsu Province”.
文摘In recent years,the soft subspace clustering algorithm has shown good results for high-dimensional data,which can assign different weights to each cluster class and use weights to measure the contribution of each dimension in various features.The enhanced soft subspace clustering algorithm combines interclass separation and intraclass tightness information,which has strong results for image segmentation,but the clustering algorithm is vulnerable to noisy data and dependence on the initialized clustering center.However,the clustering algorithmis susceptible to the influence of noisydata and reliance on initializedclustering centers andfalls into a local optimum;the clustering effect is poor for brain MR images with unclear boundaries and noise effects.To address these problems,a soft subspace clustering algorithm for brain MR images based on genetic algorithm optimization is proposed,which combines the generalized noise technique,relaxes the equational weight constraint in the objective function as the boundary constraint,and uses a genetic algorithm as a method to optimize the initialized clustering center.The genetic algorithm finds the best clustering center and reduces the algorithm’s dependence on the initial clustering center.The experiment verifies the robustness of the algorithm,as well as the noise immunity in various ways and shows good results on the common dataset and the brain MR images provided by the Changshu First People’s Hospital with specific high accuracy for clinical medicine.