Medical image segmentation,i.e.,labeling structures of interest in medical images,is crucial for disease diagnosis and treatment in radiology.In reversible data hiding in medical images(RDHMI),segmentation consists of...Medical image segmentation,i.e.,labeling structures of interest in medical images,is crucial for disease diagnosis and treatment in radiology.In reversible data hiding in medical images(RDHMI),segmentation consists of only two regions:the focal and nonfocal regions.The focal region mainly contains information for diagnosis,while the nonfocal region serves as the monochrome background.The current traditional segmentation methods utilized in RDHMI are inaccurate for complex medical images,and manual segmentation is time-consuming,poorly reproducible,and operator-dependent.Implementing state-of-the-art deep learning(DL)models will facilitate key benefits,but the lack of domain-specific labels for existing medical datasets makes it impossible.To address this problem,this study provides labels of existing medical datasets based on a hybrid segmentation approach to facilitate the implementation of DL segmentation models in this domain.First,an initial segmentation based on a 33 kernel is performed to analyze×identified contour pixels before classifying pixels into focal and nonfocal regions.Then,several human expert raters evaluate and classify the generated labels into accurate and inaccurate labels.The inaccurate labels undergo manual segmentation by medical practitioners and are scored based on a hierarchical voting scheme before being assigned to the proposed dataset.To ensure reliability and integrity in the proposed dataset,we evaluate the accurate automated labels with manually segmented labels by medical practitioners using five assessment metrics:dice coefficient,Jaccard index,precision,recall,and accuracy.The experimental results show labels in the proposed dataset are consistent with the subjective judgment of human experts,with an average accuracy score of 94%and dice coefficient scores between 90%-99%.The study further proposes a ResNet-UNet with concatenated spatial and channel squeeze and excitation(scSE)architecture for semantic segmentation to validate and illustrate the usefulness of the proposed dataset.The results demonstrate the superior performance of the proposed architecture in accurately separating the focal and nonfocal regions compared to state-of-the-art architectures.Dataset information is released under the following URL:https://www.kaggle.com/lordamoah/datasets(accessed on 31 March 2025).展开更多
Prior versions of reversible data hiding with contrast enhancement(RDHCE)algorithms strongly focused on enhancing the contrast of grayscale images.However,RDHCE has recently witnessed a rise in contrast enhance-ment a...Prior versions of reversible data hiding with contrast enhancement(RDHCE)algorithms strongly focused on enhancing the contrast of grayscale images.However,RDHCE has recently witnessed a rise in contrast enhance-ment algorithms concentrating on color images.This paper implies a method for color images that uses the RGB(red,green,and blue)color model and is based on bi-histogram shifting and image adjustment.Bi-histogram shifting is used to embed data and image adjustment to achieve contrast enhancement by adjusting the images resulting from each channel of the color images before combining them to generate the final enhanced image.Images are first divided into three channels-R,G,and B-and the Max,Med,and Min channels are then determined from these.Before histogram shifting,some calculations are done to determine how many iterations there will be for each channel.The images are adjusted to improve visual quality in the enhanced images after data has been embedded in each channel.The experimental results show that the enhanced images produced by the proposed method are qualitatively and aesthetically superior to those produced by some earlier methods,and their quality was assessed using PSNR,SSIM,RCE,RMBE,and CIEDE2000.The embedding rate obtained by the suggested method is acceptable.展开更多
Recent contrast enhancement(CE)methods,with a few exceptions,predominantly focus on enhancing gray-scale images.This paper proposes a bi-histogram shifting contrast enhancement for color images based on the RGB(red,gr...Recent contrast enhancement(CE)methods,with a few exceptions,predominantly focus on enhancing gray-scale images.This paper proposes a bi-histogram shifting contrast enhancement for color images based on the RGB(red,green,and blue)color model.The proposed method selects the two highest bins and two lowest bins from the image histogram,performs an equalized number of bidirectional histogram shifting repetitions on each RGB channel while embedding secret data into marked images.The proposed method simultaneously performs both right histogram shifting(RHS)and left histogram shifting(LHS)in each histogram shifting repetition to embed and split the highest bins while combining the lowest bins with their neighbors to achieve histogram equalization(HE).The least maximum number of histograms shifting repetitions among the three RGB channels is used as the default number of histograms shifting repetitions performed to enhance original images.Compared to an existing contrast enhancement method for color images and evaluated with PSNR,SSIM,RCE,and RMBE quality assessment metrics,the experimental results show that the proposed method's enhanced images are visually and qualitatively superior with a more evenly distributed histogram.The proposed method achieves higher embedding capacities and embedding rates in all images,with an average increase in embedding capacity of 52.1%.展开更多
Contrast enhancement in medical images has been vitalsince the prevalence of image representationsin healthcare.In this research,the PRDHMCE(pairwise reversible data hiding for medical images with contrast enhancement...Contrast enhancement in medical images has been vitalsince the prevalence of image representationsin healthcare.In this research,the PRDHMCE(pairwise reversible data hiding for medical images with contrast enhancement)algorithm is proposed as an automatic contrast enhancement(CE)method for medical images based on region ofinterest(ROI)and non-region of interest(NROI).The PRDHMCE algorithm strategically enhances the ROI aftersegmentation using histogram stretching and data embedding.An initial histogram evaluation compares histogrambins with their neighbours to select the bin with the maximum pixel count.The selected bin is set as the point forcontrast stretching with enhancement and secret data embedding in the ROI.The remaining data is embedded inthe NROIwhile reducing image distortions.Experimentalresultsshowthe effectiveness of PRDHMCE in optimallyimproving image contrast and increasing embedding capacity comparedwith existing methods based on qualitativeand objective metricssuch as peak signal-to-noise ratio(PSNR),structuralsimilarity index(SSIM),relative contrasterror(RCE),relative mean brightness error(RMBE)and mean opinion score(MOS).Additionally,PRDHMCErecovers medical images fully without data loss.展开更多
基金supported by the National Natural Science Foundation of China(Grant Nos.62072250,61772281,61702235,U1636117,U1804263,62172435,61872203 and 61802212)the Zhongyuan Science and Technology Innovation Leading Talent Project of China(Grant No.214200510019)+3 种基金the Suqian Municipal Science and Technology Plan Project in 2020(S202015)the Plan for Scientific Talent of Henan Province(Grant No.2018JR0018)the Opening Project of Guangdong Provincial Key Laboratory of Information Security Technology(Grant No.2020B1212060078)the Priority Academic Program Development of Jiangsu Higher Education Institutions(PAPD)Fund.
文摘Medical image segmentation,i.e.,labeling structures of interest in medical images,is crucial for disease diagnosis and treatment in radiology.In reversible data hiding in medical images(RDHMI),segmentation consists of only two regions:the focal and nonfocal regions.The focal region mainly contains information for diagnosis,while the nonfocal region serves as the monochrome background.The current traditional segmentation methods utilized in RDHMI are inaccurate for complex medical images,and manual segmentation is time-consuming,poorly reproducible,and operator-dependent.Implementing state-of-the-art deep learning(DL)models will facilitate key benefits,but the lack of domain-specific labels for existing medical datasets makes it impossible.To address this problem,this study provides labels of existing medical datasets based on a hybrid segmentation approach to facilitate the implementation of DL segmentation models in this domain.First,an initial segmentation based on a 33 kernel is performed to analyze×identified contour pixels before classifying pixels into focal and nonfocal regions.Then,several human expert raters evaluate and classify the generated labels into accurate and inaccurate labels.The inaccurate labels undergo manual segmentation by medical practitioners and are scored based on a hierarchical voting scheme before being assigned to the proposed dataset.To ensure reliability and integrity in the proposed dataset,we evaluate the accurate automated labels with manually segmented labels by medical practitioners using five assessment metrics:dice coefficient,Jaccard index,precision,recall,and accuracy.The experimental results show labels in the proposed dataset are consistent with the subjective judgment of human experts,with an average accuracy score of 94%and dice coefficient scores between 90%-99%.The study further proposes a ResNet-UNet with concatenated spatial and channel squeeze and excitation(scSE)architecture for semantic segmentation to validate and illustrate the usefulness of the proposed dataset.The results demonstrate the superior performance of the proposed architecture in accurately separating the focal and nonfocal regions compared to state-of-the-art architectures.Dataset information is released under the following URL:https://www.kaggle.com/lordamoah/datasets(accessed on 31 March 2025).
基金This work was supported in part by the National Natural Science Foundation of China under Grant No.61662039in part by the Jiangxi Key Natural Science Foundation under No.20192ACBL20031+1 种基金in part by the Startup Foundation for Introducing Talent of Nanjing University of Information Science and Technology(NUIST)under Grant No.2019r070in part by the Priority Academic Program Development of Jiangsu Higher Education Institutions(PAPD)Fund.
文摘Prior versions of reversible data hiding with contrast enhancement(RDHCE)algorithms strongly focused on enhancing the contrast of grayscale images.However,RDHCE has recently witnessed a rise in contrast enhance-ment algorithms concentrating on color images.This paper implies a method for color images that uses the RGB(red,green,and blue)color model and is based on bi-histogram shifting and image adjustment.Bi-histogram shifting is used to embed data and image adjustment to achieve contrast enhancement by adjusting the images resulting from each channel of the color images before combining them to generate the final enhanced image.Images are first divided into three channels-R,G,and B-and the Max,Med,and Min channels are then determined from these.Before histogram shifting,some calculations are done to determine how many iterations there will be for each channel.The images are adjusted to improve visual quality in the enhanced images after data has been embedded in each channel.The experimental results show that the enhanced images produced by the proposed method are qualitatively and aesthetically superior to those produced by some earlier methods,and their quality was assessed using PSNR,SSIM,RCE,RMBE,and CIEDE2000.The embedding rate obtained by the suggested method is acceptable.
基金supported in part by the National Natural Science Foundation of China under Grant No.61662039in part by the Jiangxi Key Natural Science Foundation under No.20192ACBL20031+1 种基金in part by the Startup Foundation for Introducing Talent of Nanjing University of Information Science and Technology(NUIST)under Grant No.2019r070in part by the Priority Academic Program Development of Jiangsu Higher Education Institutions(PAPD)Fund.
文摘Recent contrast enhancement(CE)methods,with a few exceptions,predominantly focus on enhancing gray-scale images.This paper proposes a bi-histogram shifting contrast enhancement for color images based on the RGB(red,green,and blue)color model.The proposed method selects the two highest bins and two lowest bins from the image histogram,performs an equalized number of bidirectional histogram shifting repetitions on each RGB channel while embedding secret data into marked images.The proposed method simultaneously performs both right histogram shifting(RHS)and left histogram shifting(LHS)in each histogram shifting repetition to embed and split the highest bins while combining the lowest bins with their neighbors to achieve histogram equalization(HE).The least maximum number of histograms shifting repetitions among the three RGB channels is used as the default number of histograms shifting repetitions performed to enhance original images.Compared to an existing contrast enhancement method for color images and evaluated with PSNR,SSIM,RCE,and RMBE quality assessment metrics,the experimental results show that the proposed method's enhanced images are visually and qualitatively superior with a more evenly distributed histogram.The proposed method achieves higher embedding capacities and embedding rates in all images,with an average increase in embedding capacity of 52.1%.
基金supported in part by the National Natural Science Foundation of China under Grant No.61662039in part by the Jiangxi Key Natural Science Foundation under No.20192ACBL20031+1 种基金in part by the Startup Foundation for Introducing Talent of Nanjing University of Information Science and Technology(NUIST)under Grant No.2019r070in part by the Priority Academic Program Development of Jiangsu Higher Education Institutions(PAPD)Fund.
文摘Contrast enhancement in medical images has been vitalsince the prevalence of image representationsin healthcare.In this research,the PRDHMCE(pairwise reversible data hiding for medical images with contrast enhancement)algorithm is proposed as an automatic contrast enhancement(CE)method for medical images based on region ofinterest(ROI)and non-region of interest(NROI).The PRDHMCE algorithm strategically enhances the ROI aftersegmentation using histogram stretching and data embedding.An initial histogram evaluation compares histogrambins with their neighbours to select the bin with the maximum pixel count.The selected bin is set as the point forcontrast stretching with enhancement and secret data embedding in the ROI.The remaining data is embedded inthe NROIwhile reducing image distortions.Experimentalresultsshowthe effectiveness of PRDHMCE in optimallyimproving image contrast and increasing embedding capacity comparedwith existing methods based on qualitativeand objective metricssuch as peak signal-to-noise ratio(PSNR),structuralsimilarity index(SSIM),relative contrasterror(RCE),relative mean brightness error(RMBE)and mean opinion score(MOS).Additionally,PRDHMCErecovers medical images fully without data loss.
