The main cause of skin cancer is the ultraviolet radiation of the sun.It spreads quickly to other body parts.Thus,early diagnosis is required to decrease the mortality rate due to skin cancer.In this study,an automati...The main cause of skin cancer is the ultraviolet radiation of the sun.It spreads quickly to other body parts.Thus,early diagnosis is required to decrease the mortality rate due to skin cancer.In this study,an automatic system for Skin Lesion Classification(SLC)using Non-Subsampled Shearlet Transform(NSST)based energy features and Support Vector Machine(SVM)classifier is proposed.Atfirst,the NSST is used for the decomposition of input skin lesion images with different directions like 2,4,8 and 16.From the NSST’s sub-bands,energy fea-tures are extracted and stored in the feature database for training.SVM classifier is used for the classification of skin lesion images.The dermoscopic skin images are obtained from PH^(2) database which comprises of 200 dermoscopic color images with melanocytic lesions.The performances of the SLC system are evaluated using the confusion matrix and Receiver Operating Characteristic(ROC)curves.The SLC system achieves 96%classification accuracy using NSST’s energy fea-tures obtained from 3^(rd) level with 8-directions.展开更多
In this paper,an Automated Brain Image Analysis(ABIA)system that classifies the Magnetic Resonance Imaging(MRI)of human brain is presented.The classification of MRI images into normal or low grade or high grade plays ...In this paper,an Automated Brain Image Analysis(ABIA)system that classifies the Magnetic Resonance Imaging(MRI)of human brain is presented.The classification of MRI images into normal or low grade or high grade plays a vital role for the early diagnosis.The Non-Subsampled Shearlet Transform(NSST)that captures more visual information than conventional wavelet transforms is employed for feature extraction.As the feature space of NSST is very high,a statistical t-test is applied to select the dominant directional sub-bands at each level of NSST decomposition based on sub-band energies.A combination of features that includes Gray Level Co-occurrence Matrix(GLCM)based features,Histograms of Positive Shearlet Coefficients(HPSC),and Histograms of Negative Shearlet Coefficients(HNSC)are estimated.The combined feature set is utilized in the classification phase where a hybrid approach is designed with three classifiers;k-Nearest Neighbor(kNN),Naive Bayes(NB)and Support Vector Machine(SVM)classifiers.The output of individual trained classifiers for a testing input is hybridized to take a final decision.The quantitative results of ABIA system on Repository of Molecular Brain Neoplasia Data(REMBRANDT)database show the overall improved performance in comparison with a single classifier model with accuracy of 99% for normal/abnormal classification and 98% for low and high risk classification.展开更多
Super-resolution techniques are employed to enhance image resolution by reconstructing high-resolution images from one or more low-resolution inputs.Super-resolution is of paramount importance in the context of remote...Super-resolution techniques are employed to enhance image resolution by reconstructing high-resolution images from one or more low-resolution inputs.Super-resolution is of paramount importance in the context of remote sensing,satellite,aerial,security and surveillance imaging.Super-resolution remote sensing imagery is essential for surveillance and security purposes,enabling authorities to monitor remote or sensitive areas with greater clarity.This study introduces a single-image super-resolution approach for remote sensing images,utilizing deep shearlet residual learning in the shearlet transform domain,and incorporating the Enhanced Deep Super-Resolution network(EDSR).Unlike conventional approaches that estimate residuals between high and low-resolution images,the proposed approach calculates the shearlet coefficients for the desired high-resolution image using the provided low-resolution image instead of estimating a residual image between the high-and low-resolution image.The shearlet transform is chosen for its excellent sparse approximation capabilities.Initially,remote sensing images are transformed into the shearlet domain,which divides the input image into low and high frequencies.The shearlet coefficients are fed into the EDSR network.The high-resolution image is subsequently reconstructed using the inverse shearlet transform.The incorporation of the EDSR network enhances training stability,leading to improved generated images.The experimental results from the Deep Shearlet Residual Learning approach demonstrate its superior performance in remote sensing image recovery,effectively restoring both global topology and local edge detail information,thereby enhancing image quality.Compared to other networks,our proposed approach outperforms the state-of-the-art in terms of image quality,achieving an average peak signal-to-noise ratio of 35 and a structural similarity index measure of approximately 0.9.展开更多
With the development of digitalization in healthcare,more and more information is delivered and stored in digital form,facilitating people’s lives significantly.In the meanwhile,privacy leakage and security issues co...With the development of digitalization in healthcare,more and more information is delivered and stored in digital form,facilitating people’s lives significantly.In the meanwhile,privacy leakage and security issues come along with it.Zero watermarking can solve this problem well.To protect the security of medical information and improve the algorithm’s robustness,this paper proposes a robust watermarking algorithm for medical images based on Non-Subsampled Shearlet Transform(NSST)and Schur decomposition.Firstly,the low-frequency subband image of the original medical image is obtained by NSST and chunked.Secondly,the Schur decomposition of low-frequency blocks to get stable values,extracting the maximum absolute value of the diagonal elements of the upper triangle matrix after the Schur decom-position of each low-frequency block and constructing the transition matrix from it.Then,the mean of the matrix is compared to each element’s value,creating a feature matrix by combining perceptual hashing,and selecting 32 bits as the feature sequence.Finally,the feature vector is exclusive OR(XOR)operated with the encrypted watermark information to get the zero watermark and complete registration with a third-party copyright certification center.Experimental data show that the Normalized Correlation(NC)values of watermarks extracted in random carrier medical images are above 0.5,with higher robustness than traditional algorithms,especially against geometric attacks and achieve watermark information invisibility without altering the carrier medical image.展开更多
Medical image fusion has been developed as an efficient assistive technology in various clinical applications such as medical diagnosis and treatment planning.Aiming at the problem of insufficient protection of image ...Medical image fusion has been developed as an efficient assistive technology in various clinical applications such as medical diagnosis and treatment planning.Aiming at the problem of insufficient protection of image contour and detail information by traditional image fusion methods,a new multimodal medical image fusion method is proposed.This method first uses non-subsampled shearlet transform to decompose the source image to obtain high and low frequency subband coefficients,then uses the latent low rank representation algorithm to fuse the low frequency subband coefficients,and applies the improved PAPCNN algorithm to fuse the high frequency subband coefficients.Finally,based on the automatic setting of parameters,the optimization method configuration of the time decay factorαe is carried out.The experimental results show that the proposed method solves the problems of difficult parameter setting and insufficient detail protection ability in traditional PCNN algorithm fusion images,and at the same time,it has achieved great improvement in visual quality and objective evaluation indicators.展开更多
文摘The main cause of skin cancer is the ultraviolet radiation of the sun.It spreads quickly to other body parts.Thus,early diagnosis is required to decrease the mortality rate due to skin cancer.In this study,an automatic system for Skin Lesion Classification(SLC)using Non-Subsampled Shearlet Transform(NSST)based energy features and Support Vector Machine(SVM)classifier is proposed.Atfirst,the NSST is used for the decomposition of input skin lesion images with different directions like 2,4,8 and 16.From the NSST’s sub-bands,energy fea-tures are extracted and stored in the feature database for training.SVM classifier is used for the classification of skin lesion images.The dermoscopic skin images are obtained from PH^(2) database which comprises of 200 dermoscopic color images with melanocytic lesions.The performances of the SLC system are evaluated using the confusion matrix and Receiver Operating Characteristic(ROC)curves.The SLC system achieves 96%classification accuracy using NSST’s energy fea-tures obtained from 3^(rd) level with 8-directions.
文摘In this paper,an Automated Brain Image Analysis(ABIA)system that classifies the Magnetic Resonance Imaging(MRI)of human brain is presented.The classification of MRI images into normal or low grade or high grade plays a vital role for the early diagnosis.The Non-Subsampled Shearlet Transform(NSST)that captures more visual information than conventional wavelet transforms is employed for feature extraction.As the feature space of NSST is very high,a statistical t-test is applied to select the dominant directional sub-bands at each level of NSST decomposition based on sub-band energies.A combination of features that includes Gray Level Co-occurrence Matrix(GLCM)based features,Histograms of Positive Shearlet Coefficients(HPSC),and Histograms of Negative Shearlet Coefficients(HNSC)are estimated.The combined feature set is utilized in the classification phase where a hybrid approach is designed with three classifiers;k-Nearest Neighbor(kNN),Naive Bayes(NB)and Support Vector Machine(SVM)classifiers.The output of individual trained classifiers for a testing input is hybridized to take a final decision.The quantitative results of ABIA system on Repository of Molecular Brain Neoplasia Data(REMBRANDT)database show the overall improved performance in comparison with a single classifier model with accuracy of 99% for normal/abnormal classification and 98% for low and high risk classification.
文摘Super-resolution techniques are employed to enhance image resolution by reconstructing high-resolution images from one or more low-resolution inputs.Super-resolution is of paramount importance in the context of remote sensing,satellite,aerial,security and surveillance imaging.Super-resolution remote sensing imagery is essential for surveillance and security purposes,enabling authorities to monitor remote or sensitive areas with greater clarity.This study introduces a single-image super-resolution approach for remote sensing images,utilizing deep shearlet residual learning in the shearlet transform domain,and incorporating the Enhanced Deep Super-Resolution network(EDSR).Unlike conventional approaches that estimate residuals between high and low-resolution images,the proposed approach calculates the shearlet coefficients for the desired high-resolution image using the provided low-resolution image instead of estimating a residual image between the high-and low-resolution image.The shearlet transform is chosen for its excellent sparse approximation capabilities.Initially,remote sensing images are transformed into the shearlet domain,which divides the input image into low and high frequencies.The shearlet coefficients are fed into the EDSR network.The high-resolution image is subsequently reconstructed using the inverse shearlet transform.The incorporation of the EDSR network enhances training stability,leading to improved generated images.The experimental results from the Deep Shearlet Residual Learning approach demonstrate its superior performance in remote sensing image recovery,effectively restoring both global topology and local edge detail information,thereby enhancing image quality.Compared to other networks,our proposed approach outperforms the state-of-the-art in terms of image quality,achieving an average peak signal-to-noise ratio of 35 and a structural similarity index measure of approximately 0.9.
基金supported in part by the Natural Science Foundation of China under Grants 62063004the Key Research Project of Hainan Province under Grant ZDYF2021SHFZ093+1 种基金the Hainan Provincial Natural Science Foundation of China under Grants 2019RC018 and 619QN246the postdoctoral research from Zhejiang Province under Grant ZJ2021028.
文摘With the development of digitalization in healthcare,more and more information is delivered and stored in digital form,facilitating people’s lives significantly.In the meanwhile,privacy leakage and security issues come along with it.Zero watermarking can solve this problem well.To protect the security of medical information and improve the algorithm’s robustness,this paper proposes a robust watermarking algorithm for medical images based on Non-Subsampled Shearlet Transform(NSST)and Schur decomposition.Firstly,the low-frequency subband image of the original medical image is obtained by NSST and chunked.Secondly,the Schur decomposition of low-frequency blocks to get stable values,extracting the maximum absolute value of the diagonal elements of the upper triangle matrix after the Schur decom-position of each low-frequency block and constructing the transition matrix from it.Then,the mean of the matrix is compared to each element’s value,creating a feature matrix by combining perceptual hashing,and selecting 32 bits as the feature sequence.Finally,the feature vector is exclusive OR(XOR)operated with the encrypted watermark information to get the zero watermark and complete registration with a third-party copyright certification center.Experimental data show that the Normalized Correlation(NC)values of watermarks extracted in random carrier medical images are above 0.5,with higher robustness than traditional algorithms,especially against geometric attacks and achieve watermark information invisibility without altering the carrier medical image.
文摘Medical image fusion has been developed as an efficient assistive technology in various clinical applications such as medical diagnosis and treatment planning.Aiming at the problem of insufficient protection of image contour and detail information by traditional image fusion methods,a new multimodal medical image fusion method is proposed.This method first uses non-subsampled shearlet transform to decompose the source image to obtain high and low frequency subband coefficients,then uses the latent low rank representation algorithm to fuse the low frequency subband coefficients,and applies the improved PAPCNN algorithm to fuse the high frequency subband coefficients.Finally,based on the automatic setting of parameters,the optimization method configuration of the time decay factorαe is carried out.The experimental results show that the proposed method solves the problems of difficult parameter setting and insufficient detail protection ability in traditional PCNN algorithm fusion images,and at the same time,it has achieved great improvement in visual quality and objective evaluation indicators.
