Background:Diabetic macular edema is a prevalent retinal condition and a leading cause of visual impairment among diabetic patients’Early detection of affected areas is beneficial for effective diagnosis and treatmen...Background:Diabetic macular edema is a prevalent retinal condition and a leading cause of visual impairment among diabetic patients’Early detection of affected areas is beneficial for effective diagnosis and treatment.Traditionally,diagnosis relies on optical coherence tomography imaging technology interpreted by ophthalmologists.However,this manual image interpretation is often slow and subjective.Therefore,developing automated segmentation for macular edema images is essential to enhance to improve the diagnosis efficiency and accuracy.Methods:In order to improve clinical diagnostic efficiency and accuracy,we proposed a SegNet network structure integrated with a convolutional block attention module(CBAM).This network introduces a multi-scale input module,the CBAM attention mechanism,and jump connection.The multi-scale input module enhances the network’s perceptual capabilities,while the lightweight CBAM effectively fuses relevant features across channels and spatial dimensions,allowing for better learning of varying information levels.Results:Experimental results demonstrate that the proposed network achieves an IoU of 80.127%and an accuracy of 99.162%.Compared to the traditional segmentation network,this model has fewer parameters,faster training and testing speed,and superior performance on semantic segmentation tasks,indicating its highly practical applicability.Conclusion:The C-SegNet proposed in this study enables accurate segmentation of Diabetic macular edema lesion images,which facilitates quicker diagnosis for healthcare professionals.展开更多
Coherence analysis is a powerful tool in seismic interpretation for imaging geological discontinuities such as faults and fractures. However, subtle faults or fractures of one stratum are difficult to be distinguished...Coherence analysis is a powerful tool in seismic interpretation for imaging geological discontinuities such as faults and fractures. However, subtle faults or fractures of one stratum are difficult to be distinguished on coherence sections （time slices or profiles） due to interferences from adjacent strata, especially these with strong reflectivity. In this paper, we propose a coherence enhancement method which applies local histogram specification （LHS） techniques to enhance subtle faults or fractures in the coherence cubes. Unlike the traditional histogram specification （HS） algorithm, our method processes 3D coherence data without discretization. This method partitions a coherence cube into many sub-blocks and self-adaptively specifies the target distribution in each block based on the whole distribution of the coherence cube. Furthermore, the neighboring blocks are partially overlapped to reduce the edge effect. Applications to real datasets show that the new method enhances the details of subtle faults and fractures noticeably.展开更多
The coherence cube technology has become an important technology for the seismic attribute interpretation, which extracts the discontinuities of the events through analyzing the similarities of adjacent seismic channe...The coherence cube technology has become an important technology for the seismic attribute interpretation, which extracts the discontinuities of the events through analyzing the similarities of adjacent seismic channels to identify the fault form. The coherence cube technology which uses constant time window lengths can not balance the shallow layers and the deep layers, because the frequency band of seismic data varies with time. When analyzing the shallow layers, the time window will crossover a lot of events, which will lead to weak focusing ability and failure to delineate the details. While the time window will not be long enough for analyzing deep layers, which will lead to low accuracy because the coherences near the zero points of the events are heavily influenced by noise. For solving the problem, we should make a research on the coherence cube technology with self-adaptive time window. This paper determines the sample points＆#39; time window lengths in real time by computing the instantaneous frequency bands with Wavelet Transformation, which gives a coherence computing method with the self-adaptive time window lengths. The result shows that the coherence cube technology with self-adaptive time window based on Wavelet Transformation improves the accuracy of fault identification, and supresses the noise effectively. The method combines the advantages of long time window method and short time window method.展开更多
Three-dimensional coherent cube is an extremely effective new technique for interpreting seismic data. It has obvious advantages in many aspects compared with the conventional 3D data volume, such as recognizing fault...Three-dimensional coherent cube is an extremely effective new technique for interpreting seismic data. It has obvious advantages in many aspects compared with the conventional 3D data volume, such as recognizing faults and fractures, interpreting ancient channels, and edge detection of oil-gas reservoir. Coherent cube is to condense and extract information around a certain point in 3D data volume, and then highlight the original characteristics of the geologic body at this point. Therefore, in terms of its essence, coherent cube is a special seismic attribute cube and those points having rather small coherent value are related to the discontinuity of geologic body. In practical production, people often interpret horizontal slices or layer slices of coherent cube, and this provides advantageous foundations for resolving special problems in oil-gas exploration.……展开更多
When using a miniature single sensor boundary layer probe, the time sequences of the stream-wise velocity in the turbulent boundary layer （TBL） are measured by using a hot wire anemometer. Beneath the fully develope...When using a miniature single sensor boundary layer probe, the time sequences of the stream-wise velocity in the turbulent boundary layer （TBL） are measured by using a hot wire anemometer. Beneath the fully developed TBL, the wall pressure fluctuations are attained by a microphone mechanism with high spatial resolution. Analysis on the statistic and spectrum properties of velocity and wall pressure reveals the relationship between the wall pressure fluctuation and the energy-containing structure in the buffer layer of the TBL. Wavelet transform shows the multi-scale natures of coherent structures contained in both signals of velocity and pressure. The most intermittent wall pressure scale is associated with the coherent structure in the buffer layer. Meanwhile the most energetic scale of velocity fluctuation at y＋ = 14 provides a specific frequency f9 ≈ 147 Hz for wall actuating control with Ret = 996.展开更多
Retinal diseases are a serious threat to human visual health and their early diagnosis is crucial.Currently,most of the retinal disease diagnostic algorithms are based on a single imaging modality of fundus color phot...Retinal diseases are a serious threat to human visual health and their early diagnosis is crucial.Currently,most of the retinal disease diagnostic algorithms are based on a single imaging modality of fundus color photography(FCP)or optical coherence tomography(OCT).These methods can only reflect retinal diseases to a certain extent,ignoring the speci ficity of modalities between different imaging modalities.In this research,a newmulti-scale feature fusion network(MSFF-Net)model for multi-modal retinal image diagnosis is proposed.The MSFF-Net model employs a dualbranch architecture design,enabling efficient learning and extraction of multi-modal feature information related to retinal diseases from CFP and OCT images.MSFF-Net improves disease diagnosis by combining multi-scale features of CFP and OCT images.When evaluated on challenging datasets,the model achieved an accuracy of 95.00%and an F1-score of 95.24%for retinal disease diagnosis.Even under low-quality dataset conditions,it maintained robust performance,with diagnostic accuracy and F1-scores of 71.50%and 71.73%,respectively.In addition,the MSFFNet model outperformed eight state-of-the-art single and multi-modal models in the comparison experiments.The proposed MSFF-Net model provides ophthalmologists with a more accurate and efficient diagnostic pathway that helps them detect and treat retinal diseases earlier.展开更多
基金supported by the Guangdong Pharmaceutical University 2024 Higher Education Research Projects(GKP202403,GMP202402)the Guangdong Pharmaceutical University College Students’Innovation and Entrepreneurship Training Programs(Grant No.202504302033,202504302034,202504302036,and 202504302244).
文摘Background:Diabetic macular edema is a prevalent retinal condition and a leading cause of visual impairment among diabetic patients’Early detection of affected areas is beneficial for effective diagnosis and treatment.Traditionally,diagnosis relies on optical coherence tomography imaging technology interpreted by ophthalmologists.However,this manual image interpretation is often slow and subjective.Therefore,developing automated segmentation for macular edema images is essential to enhance to improve the diagnosis efficiency and accuracy.Methods:In order to improve clinical diagnostic efficiency and accuracy,we proposed a SegNet network structure integrated with a convolutional block attention module(CBAM).This network introduces a multi-scale input module,the CBAM attention mechanism,and jump connection.The multi-scale input module enhances the network’s perceptual capabilities,while the lightweight CBAM effectively fuses relevant features across channels and spatial dimensions,allowing for better learning of varying information levels.Results:Experimental results demonstrate that the proposed network achieves an IoU of 80.127%and an accuracy of 99.162%.Compared to the traditional segmentation network,this model has fewer parameters,faster training and testing speed,and superior performance on semantic segmentation tasks,indicating its highly practical applicability.Conclusion:The C-SegNet proposed in this study enables accurate segmentation of Diabetic macular edema lesion images,which facilitates quicker diagnosis for healthcare professionals.
基金sponsored by Important National Science and Technology Specific Projects of China (Grant No.2008ZX05023-005-011 and No. 2008ZX05040-003)the National 973 Program of China (Grant No. 2006CB202208)
文摘Coherence analysis is a powerful tool in seismic interpretation for imaging geological discontinuities such as faults and fractures. However, subtle faults or fractures of one stratum are difficult to be distinguished on coherence sections （time slices or profiles） due to interferences from adjacent strata, especially these with strong reflectivity. In this paper, we propose a coherence enhancement method which applies local histogram specification （LHS） techniques to enhance subtle faults or fractures in the coherence cubes. Unlike the traditional histogram specification （HS） algorithm, our method processes 3D coherence data without discretization. This method partitions a coherence cube into many sub-blocks and self-adaptively specifies the target distribution in each block based on the whole distribution of the coherence cube. Furthermore, the neighboring blocks are partially overlapped to reduce the edge effect. Applications to real datasets show that the new method enhances the details of subtle faults and fractures noticeably.
文摘The coherence cube technology has become an important technology for the seismic attribute interpretation, which extracts the discontinuities of the events through analyzing the similarities of adjacent seismic channels to identify the fault form. The coherence cube technology which uses constant time window lengths can not balance the shallow layers and the deep layers, because the frequency band of seismic data varies with time. When analyzing the shallow layers, the time window will crossover a lot of events, which will lead to weak focusing ability and failure to delineate the details. While the time window will not be long enough for analyzing deep layers, which will lead to low accuracy because the coherences near the zero points of the events are heavily influenced by noise. For solving the problem, we should make a research on the coherence cube technology with self-adaptive time window. This paper determines the sample points＆#39; time window lengths in real time by computing the instantaneous frequency bands with Wavelet Transformation, which gives a coherence computing method with the self-adaptive time window lengths. The result shows that the coherence cube technology with self-adaptive time window based on Wavelet Transformation improves the accuracy of fault identification, and supresses the noise effectively. The method combines the advantages of long time window method and short time window method.
文摘Three-dimensional coherent cube is an extremely effective new technique for interpreting seismic data. It has obvious advantages in many aspects compared with the conventional 3D data volume, such as recognizing faults and fractures, interpreting ancient channels, and edge detection of oil-gas reservoir. Coherent cube is to condense and extract information around a certain point in 3D data volume, and then highlight the original characteristics of the geologic body at this point. Therefore, in terms of its essence, coherent cube is a special seismic attribute cube and those points having rather small coherent value are related to the discontinuity of geologic body. In practical production, people often interpret horizontal slices or layer slices of coherent cube, and this provides advantageous foundations for resolving special problems in oil-gas exploration.……
基金Project supported by the National Basic Research Program of China(Grant Nos.2012CB720101 and 2012CB720103)the National Natural Science Foundation of China(Grant Nos.11272233,11332006,and 11411130150)
文摘When using a miniature single sensor boundary layer probe, the time sequences of the stream-wise velocity in the turbulent boundary layer （TBL） are measured by using a hot wire anemometer. Beneath the fully developed TBL, the wall pressure fluctuations are attained by a microphone mechanism with high spatial resolution. Analysis on the statistic and spectrum properties of velocity and wall pressure reveals the relationship between the wall pressure fluctuation and the energy-containing structure in the buffer layer of the TBL. Wavelet transform shows the multi-scale natures of coherent structures contained in both signals of velocity and pressure. The most intermittent wall pressure scale is associated with the coherent structure in the buffer layer. Meanwhile the most energetic scale of velocity fluctuation at y＋ = 14 provides a specific frequency f9 ≈ 147 Hz for wall actuating control with Ret = 996.
基金supported by the National Natural Science Foundation of China(Nos.82472104 and U24B2053)the Natural Science Basic Research Program of Shaanxi(No.2025JC-JCQN-023)+2 种基金the Key Core Technology Research and Development of Shaanxi(No.2024QY2-GJHX-03)the Innovation Capability Support Program of Shaanxi(Program No.2023-CX-TD-54)the Xidian University Specially Funded Project for Interdisciplinary Exploration(No.TZJHF202510).
文摘Retinal diseases are a serious threat to human visual health and their early diagnosis is crucial.Currently,most of the retinal disease diagnostic algorithms are based on a single imaging modality of fundus color photography(FCP)or optical coherence tomography(OCT).These methods can only reflect retinal diseases to a certain extent,ignoring the speci ficity of modalities between different imaging modalities.In this research,a newmulti-scale feature fusion network(MSFF-Net)model for multi-modal retinal image diagnosis is proposed.The MSFF-Net model employs a dualbranch architecture design,enabling efficient learning and extraction of multi-modal feature information related to retinal diseases from CFP and OCT images.MSFF-Net improves disease diagnosis by combining multi-scale features of CFP and OCT images.When evaluated on challenging datasets,the model achieved an accuracy of 95.00%and an F1-score of 95.24%for retinal disease diagnosis.Even under low-quality dataset conditions,it maintained robust performance,with diagnostic accuracy and F1-scores of 71.50%and 71.73%,respectively.In addition,the MSFFNet model outperformed eight state-of-the-art single and multi-modal models in the comparison experiments.The proposed MSFF-Net model provides ophthalmologists with a more accurate and efficient diagnostic pathway that helps them detect and treat retinal diseases earlier.
