AIM:To find the effective contrast enhancement method on retinal images for effective segmentation of retinal features.METHODS:A novel image preprocessing method that used neighbourhood-based improved contrast limited...AIM:To find the effective contrast enhancement method on retinal images for effective segmentation of retinal features.METHODS:A novel image preprocessing method that used neighbourhood-based improved contrast limited adaptive histogram equalization(NICLAHE)to improve retinal image contrast was suggested to aid in the accurate identification of retinal disorders and improve the visibility of fine retinal structures.Additionally,a minimal-order filter was applied to effectively denoise the images without compromising important retinal structures.The novel NICLAHE algorithm was inspired by the classical CLAHE algorithm,but enhanced it by selecting the clip limits and tile sized in a dynamical manner relative to the pixel values in an image as opposed to using fixed values.It was evaluated on the Drive and high-resolution fundus(HRF)datasets on conventional quality measures.RESULTS:The new proposed preprocessing technique was applied to two retinal image databases,Drive and HRF,with four quality metrics being,root mean square error(RMSE),peak signal to noise ratio(PSNR),root mean square contrast(RMSC),and overall contrast.The technique performed superiorly on both the data sets as compared to the traditional enhancement methods.In order to assess the compatibility of the method with automated diagnosis,a deep learning framework named ResNet was applied in the segmentation of retinal blood vessels.Sensitivity,specificity,precision and accuracy were used to analyse the performance.NICLAHE–enhanced images outperformed the traditional techniques on both the datasets with improved accuracy.CONCLUSION:NICLAHE provides better results than traditional methods with less error and improved contrastrelated values.These enhanced images are subsequently measured by sensitivity,specificity,precision,and accuracy,which yield a better result in both datasets.展开更多
The enhancement technique for color medical images is conductive to improve the resolution and accuracy of the original image. A new enhancement method combining the Young-Helmholtz(Y-H) transformation with the adapti...The enhancement technique for color medical images is conductive to improve the resolution and accuracy of the original image. A new enhancement method combining the Young-Helmholtz(Y-H) transformation with the adaptive equalization of intensity numbers matrix histogram is proposed in this paper. The adaptive histogram equalization method is applied to strengthen the details,enhance the contrast, and suppress the noise of the original image effectively. The enhanced image can be displayed in the red-greenblue(RGB) color space through inverse Y-H transformation with the same hue and saturation. The experiment results demonstrate that the method has the enhancement effect with low computational complexity, which provides the foundation for the medical diagnosis and further processing of medical images.展开更多
Alzheimer’s Disease(AD)is a progressive neurodegenerative disorder that significantly affects cognitive function,making early and accurate diagnosis essential.Traditional Deep Learning(DL)-based approaches often stru...Alzheimer’s Disease(AD)is a progressive neurodegenerative disorder that significantly affects cognitive function,making early and accurate diagnosis essential.Traditional Deep Learning(DL)-based approaches often struggle with low-contrast MRI images,class imbalance,and suboptimal feature extraction.This paper develops a Hybrid DL system that unites MobileNetV2 with adaptive classification methods to boost Alzheimer’s diagnosis by processing MRI scans.Image enhancement is done using Contrast-Limited Adaptive Histogram Equalization(CLAHE)and Enhanced Super-Resolution Generative Adversarial Networks(ESRGAN).A classification robustness enhancement system integrates class weighting techniques and a Matthews Correlation Coefficient(MCC)-based evaluation method into the design.The trained and validated model gives a 98.88%accuracy rate and 0.9614 MCC score.We also performed a 10-fold cross-validation experiment with an average accuracy of 96.52%(±1.51),a loss of 0.1671,and an MCC score of 0.9429 across folds.The proposed framework outperforms the state-of-the-art models with a 98%weighted F1-score while decreasing misdiagnosis results for every AD stage.The model demonstrates apparent separation abilities between AD progression stages according to the results of the confusion matrix analysis.These results validate the effectiveness of hybrid DL models with adaptive preprocessing for early and reliable Alzheimer’s diagnosis,contributing to improved computer-aided diagnosis(CAD)systems in clinical practice.展开更多
Background:Pneumonia remains a critical global health challenge,manifesting as a severe respiratory infection caused by viruses,bacteria,and fungi.Early detection is paramount for effective treatment,potentially reduc...Background:Pneumonia remains a critical global health challenge,manifesting as a severe respiratory infection caused by viruses,bacteria,and fungi.Early detection is paramount for effective treatment,potentially reducing mortality rates and optimizing healthcare resource allocation.Despite the importance of chest X-ray diagnosis,image analysis presents significant challenges,particularly in regions with limited medical expertise.This study addresses these challenges by proposing a computer-aided diagnosis system leveraging targeted image preprocessing and optimized deep learning techniques.Methods:We systematically evaluated contrast limited adaptive histogram equalization with varying clip limits for preprocessing chest X-ray images,demonstrating its effectiveness in enhancing feature visibility for diagnostic accuracy.Employing a comprehensive dataset of 5,863 X-ray images(1,583 pneumonia-negative,4,280 pneumonia-positive)collected from multiple healthcare facilities,we conducted a comparative analysis of transfer learning with pre-trained models including ResNet50v2,VGG-19,and MobileNetV2.Statistical validation was performed through 5-fold cross-validation.Results:Our results show that the contrast limited adaptive histogram equalization-enhanced approach with ResNet50v2 achieves 93.40%accuracy,outperforming VGG-19(84.90%)and MobileNetV2(89.70%).Statistical validation confirms the significance of these improvements(P<0.01).The development and optimization resulted in a lightweight mobile application(74 KB)providing rapid diagnostic support(1-2 s response time).Conclusion:The proposed approach demonstrates practical applicability in resource-constrained settings,balancing diagnostic accuracy with deployment efficiency,and offers a viable solution for computer-aided pneumonia diagnosis in areas with limited medical expertise.展开更多
To improve image quality under low illumination conditions,a novel low-light image enhancement method is proposed in this paper based on multi-illumination estimation and multi-scale fusion(MIMS).Firstly,the illuminat...To improve image quality under low illumination conditions,a novel low-light image enhancement method is proposed in this paper based on multi-illumination estimation and multi-scale fusion(MIMS).Firstly,the illumination is processed by contrast-limited adaptive histogram equalization(CLAHE),adaptive complementary gamma function(ACG),and adaptive detail preserving S-curve(ADPS),respectively,to obtain three components.Then,the fusion-relevant features,exposure,and color contrast are selected as the weight maps.Subsequently,these components and weight maps are fused through multi-scale to generate enhanced illumination.Finally,the enhanced images are obtained by multiplying the enhanced illumination and reflectance.Compared with existing approaches,this proposed method achieves an average increase of 0.81%and 2.89%in the structural similarity index measurement(SSIM)and peak signal-to-noise ratio(PSNR),and a decrease of 6.17%and 32.61%in the natural image quality evaluator(NIQE)and gradient magnitude similarity deviation(GMSD),respectively.展开更多
Alzheimer’s Disease(AD)is a progressive neurological disease.Early diagnosis of this illness using conventional methods is very challenging.Deep Learning(DL)is one of the finest solutions for improving diagnostic pro...Alzheimer’s Disease(AD)is a progressive neurological disease.Early diagnosis of this illness using conventional methods is very challenging.Deep Learning(DL)is one of the finest solutions for improving diagnostic procedures’performance and forecast accuracy.The disease’s widespread distribution and elevated mortality rate demonstrate its significance in the older-onset and younger-onset age groups.In light of research investigations,it is vital to consider age as one of the key criteria when choosing the subjects.The younger subjects are more susceptible to the perishable side than the older onset.The proposed investigation concentrated on the younger onset.The research used deep learning models and neuroimages to diagnose and categorize the disease at its early stages automatically.The proposed work is executed in three steps.The 3D input images must first undergo image pre-processing using Weiner filtering and Contrast Limited Adaptive Histogram Equalization(CLAHE)methods.The Transfer Learning(TL)models extract features,which are subsequently compressed using cascaded Auto Encoders(AE).The final phase entails using a Deep Neural Network(DNN)to classify the phases of AD.The model was trained and tested to classify the five stages of AD.The ensemble ResNet-18 and sparse autoencoder with DNN model achieved an accuracy of 98.54%.The method is compared to state-of-the-art approaches to validate its efficacy and performance.展开更多
Purpose-The purpose of this study is to develop a hybrid algorithm for segmenting tumor from ultrasound images of the liver.Design/methodology/approach-After collecting the ultrasound images,contrast-limited adaptive ...Purpose-The purpose of this study is to develop a hybrid algorithm for segmenting tumor from ultrasound images of the liver.Design/methodology/approach-After collecting the ultrasound images,contrast-limited adaptive histogram equalization approach(CLAHE)is applied as preprocessing,in order to enhance the visual quality of the images that helps in better segmentation.Then,adaptively regularized kernel-based fuzzy C means(ARKFCM)is used to segment tumor from the enhanced image along with local ternary pattern combined with selective level set approaches.Findings-The proposed segmentation algorithm precisely segments the tumor portions from the enhanced images with lower computation cost.The proposed segmentation algorithm is compared with the existing algorithms and ground truth values in terms of Jaccard coefficient,dice coefficient,precision,Matthews correlation coefficient,f-score and accuracy.The experimental analysis shows that the proposed algorithm achieved 99.18% of accuracy and 92.17% of f-score value,which is better than the existing algorithms.Practical implications-From the experimental analysis,the proposed ARKFCM with enhanced level set algorithm obtained better performance in ultrasound liver tumor segmentation related to graph-based algorithm.However,the proposed algorithm showed 3.11% improvement in dice coefficient compared to graph-based algorithm.Originality/value-The image preprocessing is carried out using CLAHE algorithm.The preprocessed image is segmented by employing selective level set model and Local Ternary Pattern in ARKFCM algorithm.In this research,the proposed algorithm has advantages such as independence of clustering parameters,robustness in preserving the image details and optimal in finding the threshold value that effectively reduces the computational cost.展开更多
Accurately identifying the location and type of internal defects in gas-insulated switchgear(GIS)is a challenge.To address this challenge,this study proposes a novel method for the nondestructive detection of GIS inte...Accurately identifying the location and type of internal defects in gas-insulated switchgear(GIS)is a challenge.To address this challenge,this study proposes a novel method for the nondestructive detection of GIS internal defects.This method is based on x-ray digital radiography(X-DR)technology and an improved real-time models for object detection(RTMdet)algorithm,namely GIS-specific localised internal defect-RTMdet.Firstly,the X-DR images of GIS are preprocessed by dynamic limit adaptive histogram equalisation algorithm to improve the images contrast.Then,a convolution shuffle upsample module for upsampling is proposed,which enlarges the defect feature map by multi-convolution and pixel shuffling,reduces the information loss,and enhances the interaction between the feature information.Finally,both the multi-channel attention net and the global attention mechanism are integrated into the neck network for enhancing local feature extraction and global information association.Experiments demonstrate that the pro-posed method achieves a mean average precision@0.5:0.95 of 94.9%,showcasing excellent overall performance and generalisation ability,and is more suitable for accurate nondestructive detection of internal defects of GIS in complex scenarios.展开更多
In this paper,the images of tunnel surface are obtained by tunnel lining rapid inspection system,and tunnel crack forest dataset(TCFD)is established.The disaster characteristics of tunnel cracks are analyzed and summa...In this paper,the images of tunnel surface are obtained by tunnel lining rapid inspection system,and tunnel crack forest dataset(TCFD)is established.The disaster characteristics of tunnel cracks are analyzed and summarized.Solutions of tunnel crack segmentation(TCS)method are developed for the detection and recognition of cracks on tunnel lining.According to the image features of the tunnel lining and the optical principal of detection equipment,effective image pre-processing steps are carried out before crack extraction.The tunnel image of TCFD is divided into appropriate number of blocks to magnify the local features of tunnel cracks.Local threshold segmentation method is used to traverse the blocks successively,and the first target block with crack is obtained.The seed in the target block were obtained by adaptive localization method and mapped to the whole image.Region growing is performed through crack seed until complete tunnel crack is extracted.The results show that the precision,recall rate and F-measure of tunnel cracks under the TCS method can reach 92.58%,93.07%and 92.82%without strong interference.According to the binary images processed by TCS method,the projection images of different types of tunnel cracks and their respective laws are obtained.Furthermore,the TCS method is implemented and deployed as a GUI software application.展开更多
文摘AIM:To find the effective contrast enhancement method on retinal images for effective segmentation of retinal features.METHODS:A novel image preprocessing method that used neighbourhood-based improved contrast limited adaptive histogram equalization(NICLAHE)to improve retinal image contrast was suggested to aid in the accurate identification of retinal disorders and improve the visibility of fine retinal structures.Additionally,a minimal-order filter was applied to effectively denoise the images without compromising important retinal structures.The novel NICLAHE algorithm was inspired by the classical CLAHE algorithm,but enhanced it by selecting the clip limits and tile sized in a dynamical manner relative to the pixel values in an image as opposed to using fixed values.It was evaluated on the Drive and high-resolution fundus(HRF)datasets on conventional quality measures.RESULTS:The new proposed preprocessing technique was applied to two retinal image databases,Drive and HRF,with four quality metrics being,root mean square error(RMSE),peak signal to noise ratio(PSNR),root mean square contrast(RMSC),and overall contrast.The technique performed superiorly on both the data sets as compared to the traditional enhancement methods.In order to assess the compatibility of the method with automated diagnosis,a deep learning framework named ResNet was applied in the segmentation of retinal blood vessels.Sensitivity,specificity,precision and accuracy were used to analyse the performance.NICLAHE–enhanced images outperformed the traditional techniques on both the datasets with improved accuracy.CONCLUSION:NICLAHE provides better results than traditional methods with less error and improved contrastrelated values.These enhanced images are subsequently measured by sensitivity,specificity,precision,and accuracy,which yield a better result in both datasets.
基金supported by National Natural Science Foundation of China(Nos.61273024 and 61305031)the Infrastructure Construction Foundation of Science and Technology in Jiangsu Province(No.BM2011089)+1 种基金Natural Science Fund of Jiangsu Province and 333 fund(Nos.12KJB510023 and KB2012227)the Priority Academic Program Development of Jiangsu Higher Education Institutions(PAPD)
文摘The enhancement technique for color medical images is conductive to improve the resolution and accuracy of the original image. A new enhancement method combining the Young-Helmholtz(Y-H) transformation with the adaptive equalization of intensity numbers matrix histogram is proposed in this paper. The adaptive histogram equalization method is applied to strengthen the details,enhance the contrast, and suppress the noise of the original image effectively. The enhanced image can be displayed in the red-greenblue(RGB) color space through inverse Y-H transformation with the same hue and saturation. The experiment results demonstrate that the method has the enhancement effect with low computational complexity, which provides the foundation for the medical diagnosis and further processing of medical images.
基金funded by the Deanship of Graduate Studies and Scientific Research at Jouf University under grant No.(DGSSR-2025-02-01295).
文摘Alzheimer’s Disease(AD)is a progressive neurodegenerative disorder that significantly affects cognitive function,making early and accurate diagnosis essential.Traditional Deep Learning(DL)-based approaches often struggle with low-contrast MRI images,class imbalance,and suboptimal feature extraction.This paper develops a Hybrid DL system that unites MobileNetV2 with adaptive classification methods to boost Alzheimer’s diagnosis by processing MRI scans.Image enhancement is done using Contrast-Limited Adaptive Histogram Equalization(CLAHE)and Enhanced Super-Resolution Generative Adversarial Networks(ESRGAN).A classification robustness enhancement system integrates class weighting techniques and a Matthews Correlation Coefficient(MCC)-based evaluation method into the design.The trained and validated model gives a 98.88%accuracy rate and 0.9614 MCC score.We also performed a 10-fold cross-validation experiment with an average accuracy of 96.52%(±1.51),a loss of 0.1671,and an MCC score of 0.9429 across folds.The proposed framework outperforms the state-of-the-art models with a 98%weighted F1-score while decreasing misdiagnosis results for every AD stage.The model demonstrates apparent separation abilities between AD progression stages according to the results of the confusion matrix analysis.These results validate the effectiveness of hybrid DL models with adaptive preprocessing for early and reliable Alzheimer’s diagnosis,contributing to improved computer-aided diagnosis(CAD)systems in clinical practice.
文摘Background:Pneumonia remains a critical global health challenge,manifesting as a severe respiratory infection caused by viruses,bacteria,and fungi.Early detection is paramount for effective treatment,potentially reducing mortality rates and optimizing healthcare resource allocation.Despite the importance of chest X-ray diagnosis,image analysis presents significant challenges,particularly in regions with limited medical expertise.This study addresses these challenges by proposing a computer-aided diagnosis system leveraging targeted image preprocessing and optimized deep learning techniques.Methods:We systematically evaluated contrast limited adaptive histogram equalization with varying clip limits for preprocessing chest X-ray images,demonstrating its effectiveness in enhancing feature visibility for diagnostic accuracy.Employing a comprehensive dataset of 5,863 X-ray images(1,583 pneumonia-negative,4,280 pneumonia-positive)collected from multiple healthcare facilities,we conducted a comparative analysis of transfer learning with pre-trained models including ResNet50v2,VGG-19,and MobileNetV2.Statistical validation was performed through 5-fold cross-validation.Results:Our results show that the contrast limited adaptive histogram equalization-enhanced approach with ResNet50v2 achieves 93.40%accuracy,outperforming VGG-19(84.90%)and MobileNetV2(89.70%).Statistical validation confirms the significance of these improvements(P<0.01).The development and optimization resulted in a lightweight mobile application(74 KB)providing rapid diagnostic support(1-2 s response time).Conclusion:The proposed approach demonstrates practical applicability in resource-constrained settings,balancing diagnostic accuracy with deployment efficiency,and offers a viable solution for computer-aided pneumonia diagnosis in areas with limited medical expertise.
基金supported by the National Key R&D Program of China(No.2022YFB3205101)NSAF(No.U2230116)。
文摘To improve image quality under low illumination conditions,a novel low-light image enhancement method is proposed in this paper based on multi-illumination estimation and multi-scale fusion(MIMS).Firstly,the illumination is processed by contrast-limited adaptive histogram equalization(CLAHE),adaptive complementary gamma function(ACG),and adaptive detail preserving S-curve(ADPS),respectively,to obtain three components.Then,the fusion-relevant features,exposure,and color contrast are selected as the weight maps.Subsequently,these components and weight maps are fused through multi-scale to generate enhanced illumination.Finally,the enhanced images are obtained by multiplying the enhanced illumination and reflectance.Compared with existing approaches,this proposed method achieves an average increase of 0.81%and 2.89%in the structural similarity index measurement(SSIM)and peak signal-to-noise ratio(PSNR),and a decrease of 6.17%and 32.61%in the natural image quality evaluator(NIQE)and gradient magnitude similarity deviation(GMSD),respectively.
文摘Alzheimer’s Disease(AD)is a progressive neurological disease.Early diagnosis of this illness using conventional methods is very challenging.Deep Learning(DL)is one of the finest solutions for improving diagnostic procedures’performance and forecast accuracy.The disease’s widespread distribution and elevated mortality rate demonstrate its significance in the older-onset and younger-onset age groups.In light of research investigations,it is vital to consider age as one of the key criteria when choosing the subjects.The younger subjects are more susceptible to the perishable side than the older onset.The proposed investigation concentrated on the younger onset.The research used deep learning models and neuroimages to diagnose and categorize the disease at its early stages automatically.The proposed work is executed in three steps.The 3D input images must first undergo image pre-processing using Weiner filtering and Contrast Limited Adaptive Histogram Equalization(CLAHE)methods.The Transfer Learning(TL)models extract features,which are subsequently compressed using cascaded Auto Encoders(AE).The final phase entails using a Deep Neural Network(DNN)to classify the phases of AD.The model was trained and tested to classify the five stages of AD.The ensemble ResNet-18 and sparse autoencoder with DNN model achieved an accuracy of 98.54%.The method is compared to state-of-the-art approaches to validate its efficacy and performance.
文摘Purpose-The purpose of this study is to develop a hybrid algorithm for segmenting tumor from ultrasound images of the liver.Design/methodology/approach-After collecting the ultrasound images,contrast-limited adaptive histogram equalization approach(CLAHE)is applied as preprocessing,in order to enhance the visual quality of the images that helps in better segmentation.Then,adaptively regularized kernel-based fuzzy C means(ARKFCM)is used to segment tumor from the enhanced image along with local ternary pattern combined with selective level set approaches.Findings-The proposed segmentation algorithm precisely segments the tumor portions from the enhanced images with lower computation cost.The proposed segmentation algorithm is compared with the existing algorithms and ground truth values in terms of Jaccard coefficient,dice coefficient,precision,Matthews correlation coefficient,f-score and accuracy.The experimental analysis shows that the proposed algorithm achieved 99.18% of accuracy and 92.17% of f-score value,which is better than the existing algorithms.Practical implications-From the experimental analysis,the proposed ARKFCM with enhanced level set algorithm obtained better performance in ultrasound liver tumor segmentation related to graph-based algorithm.However,the proposed algorithm showed 3.11% improvement in dice coefficient compared to graph-based algorithm.Originality/value-The image preprocessing is carried out using CLAHE algorithm.The preprocessed image is segmented by employing selective level set model and Local Ternary Pattern in ARKFCM algorithm.In this research,the proposed algorithm has advantages such as independence of clustering parameters,robustness in preserving the image details and optimal in finding the threshold value that effectively reduces the computational cost.
基金National Engineering Research Center of UHV Technology and New Electrical Equipment Basis of China Southern Power Grid Research Institute Co.,Ltd,Grant/Award Number:NERCUTNEEB-2022-KF-08。
文摘Accurately identifying the location and type of internal defects in gas-insulated switchgear(GIS)is a challenge.To address this challenge,this study proposes a novel method for the nondestructive detection of GIS internal defects.This method is based on x-ray digital radiography(X-DR)technology and an improved real-time models for object detection(RTMdet)algorithm,namely GIS-specific localised internal defect-RTMdet.Firstly,the X-DR images of GIS are preprocessed by dynamic limit adaptive histogram equalisation algorithm to improve the images contrast.Then,a convolution shuffle upsample module for upsampling is proposed,which enlarges the defect feature map by multi-convolution and pixel shuffling,reduces the information loss,and enhances the interaction between the feature information.Finally,both the multi-channel attention net and the global attention mechanism are integrated into the neck network for enhancing local feature extraction and global information association.Experiments demonstrate that the pro-posed method achieves a mean average precision@0.5:0.95 of 94.9%,showcasing excellent overall performance and generalisation ability,and is more suitable for accurate nondestructive detection of internal defects of GIS in complex scenarios.
基金This research is supported by the Fundamental Research Funds for the Central Universities,China(300102120301)Natural Science Basic Research Plan in Shaanxi Province of China(2021JQ-216)Scientific Innovation Practice Project of Postgraduates of Chang'an University(300103714017).
文摘In this paper,the images of tunnel surface are obtained by tunnel lining rapid inspection system,and tunnel crack forest dataset(TCFD)is established.The disaster characteristics of tunnel cracks are analyzed and summarized.Solutions of tunnel crack segmentation(TCS)method are developed for the detection and recognition of cracks on tunnel lining.According to the image features of the tunnel lining and the optical principal of detection equipment,effective image pre-processing steps are carried out before crack extraction.The tunnel image of TCFD is divided into appropriate number of blocks to magnify the local features of tunnel cracks.Local threshold segmentation method is used to traverse the blocks successively,and the first target block with crack is obtained.The seed in the target block were obtained by adaptive localization method and mapped to the whole image.Region growing is performed through crack seed until complete tunnel crack is extracted.The results show that the precision,recall rate and F-measure of tunnel cracks under the TCS method can reach 92.58%,93.07%and 92.82%without strong interference.According to the binary images processed by TCS method,the projection images of different types of tunnel cracks and their respective laws are obtained.Furthermore,the TCS method is implemented and deployed as a GUI software application.
