Nonsubsampled Contourlet Research Articles

WatMIF: Multimodal Medical Image Fusion-Based Watermarking for Telehealth Applications.

Over recent years, the volume of big data has drastically increased for medical applications. Such data are shared by cloud providers for storage and further processing. Medical images contain sensitive information, and these images are shared with healthcare workers, patients, and, in some scenarios, researchers for diagnostic and study purposes. However, the security of these images in the transfer process is extremely important, especially after the COVID-19 pandemic. This paper proposes a secure watermarking algorithm, termed WatMIF, based on multimodal medical image fusion. The proposed algorithm consists of three major parts: the encryption of the host media, the fusion of multimodal medical images, and the embedding and extraction of the fused mark. We encrypt the host media with a key-based encryption scheme. Then, a nonsubsampled contourlet transform (NSCT)-based fusion scheme is employed to fuse the magnetic resonance imaging (MRI) and computed tomography (CT) scan images to generate the fused mark image. Furthermore, the encrypted host media conceals the fused watermark using redundant discrete wavelet transform (RDWT) and randomised singular value decomposition (RSVD). Finally, denoising convolutional neural network (DnCNN) is used to improve the robustness of the WatMIF algorithm. The simulation experiments on two standard datasets were used to evaluate the algorithm in terms of invisibility, robustness, and security. When compared with the existing algorithms, the robustness is improved by 20.14%. Overall, the implementation of proposed watermarking for hiding fused marks and efficient encryption improved the identity verification, invisibility, robustness and security criteria in our WatMIF algorithm.

Multimodal Medical Image Fusion Using Stacked Auto-encoder in NSCT Domain.

Medical image fusion is a process that aims to merge the important information from images with different modalities of the same organ of the human body to create a more informative fused image. In recent years, deep learning (DL) methods have achieved significant breakthroughs in the field of image fusionbecause of their great efficiency. The DL methods in image fusion have become an active topic due to their high feature extraction and data representation ability. In this work, stacked sparse auto-encoder (SSAE), a general category of deep neural networks, is exploited inmedical image fusion. The SSAE is an efficient technique for unsupervised feature extraction. It has high capability of complex data representation. The proposed fusion method is carried as follows. Firstly, the source images are decomposed into low- and high-frequency coefficient sub-bands with the non-subsampled contourlet transform (NSCT). The NSCT is a flexible multi-scale decomposition technique, and it issuperior to traditional decomposition techniques in several aspects. After that, the SSAE is implemented for feature extraction to obtain a sparse and deep representation from high-frequency coefficients. Then, thespatial frequencies are computed for the obtained features to be used for high-frequency coefficient fusion. After that, a maximum-based fusion rule is applied to fuse the low-frequency sub-band coefficients. The final integrated image is acquired by applying theinverse NSCT. The proposed method has been applied and assessed on various groups of medical image modalities. Experimental results prove that the proposed method could effectively merge the multimodal medical images, while preserving the detail information, perfectly.

Deformable Registration Algorithm via Non-subsampled Contourlet Transform and Saliency Map

Deformable Registration Algorithm via Non-subsampled Contourlet Transform and Saliency Map

Improvement of the medical fusion process of images by fuzzy entropy and transformation of the contourlet

<span>Many types of medical pictures have to be fused, as single-modality medical images can give limited information because of the imagery and the complicated architecture of the human organ. This study proposes to offer a platform on which to make clinical diagnoses and to increase the accuracy of the target identification and the quality of the fused pictures by combining the benefits of nonsubsampled contourlet transform (NSCT) and fuzzy entropy. A picture is first broken down into low frequency or high frequency subbands through NSCT. In line with the various features of the low and high frequency components the respective fusion rules must be implemented. It calculates the level of membership of low frequency coefficients. The fusion of coefficients is also calculated and then utilized to retain picture features. By increasing regional energy, high-frequency components are merged. Inverse transformation produces the final fused picture. Experimental results have shown that, based on subjective visual effect and objective assessment standards, the suggested technique produces a satisfactory fusion effect. This process may also achieve high average gradient, standard deviation (SD), and edge preservation and maintain the fused picture features well. Effective reference can be provided by the outcome of the suggested algorithm for patients' assessment.</span>

Identification of endometrial tuberculosis in infertility using Non-Subsampled Contourlet based convolution neural network

Background and ObjectiveFemale Genital Tuberculosis (FGTB) has a major impact on female fertility but it can be effectively treated on timely diagnosis. Early, objective and reliable diagnostics of FGTB causing infertility is a need of the hour in a populous country like India. As per our literature study, there is no computational method available for the same till date. Since, Transvaginal Ultrasound (TVUS) imaging is a non-invasive, primary and first line investigative technique; aim of this work is to develop an effective method for diagnosis of abnormal endometrial TB from TVUS images. MethodTVUS images of female patients coming for infertility treatments to medical centers in India are collected under the supervision of medical expert. These images are preprocessed and used to train the proposed model. In this paper, a multi-scale and multi-direction Non-Subsampled Contourlet Transform (NSCT) based CNN model is proposed that integrates different levels of NSCT transformed features with different layers of a pre-trained CNN model. NSCT does spatial as well as spectral analysis and extracts significant features irrespective of orientation. The integration of these features makes the model more effective in discriminating normal and abnormal TB images. Further, the integrated model has less number of trainable parameters due to intrinsic multi-scale nature of NSCT. Results and ConclusionTo test the effectiveness of the proposed NSCT-CNN model, traditional CNN with different pooling methods, Wavelet-CNN and Contourlet-CNN models were also implemented. The experimental results show that the proposed model has an improved efficacy over other related models implemented with an average testing accuracy of 88%, a sensitivity of 0.832 and F1-score of 0.869. Two tailed t-test conducted on the model performances are statistically significant at 95% confidence level for the data in hand. The model shows an improved efficiency over traditional model with 16.01% reduction in number of trainable parameters and 41.08 % reduction in training time.

Latent low-rank representation with sparse consistency constraint for infrared and visible image fusion

Existing studies on infrared and visible image fusion generally need to first decompose the fusion image and then extract features beneficial to image fusion from the decomposition results for better fusion results. However, they usually focus on decomposing single-modality images though various techniques such as latent low-rank representation (LatLRR), without considering the spatial consistency of both infrared and visible image modalities, which may fail to effectively capture inherent image features. In this paper, we propose a sparse consistency constrained latent low-rank representation (SccLatLRR) method to fuse infrared and visible images. Firstly, infrared and visible images are performed low-rank representation decomposition simultaneously as the inputs of different tasks. In the decomposition process, the L2,1 norm is used to constrain the rank to maintain sparse consistency, and the low-rank consensus representations of infrared and visible images are obtained simultaneously. Secondly, the basic information is further mined respectively using the Very Deep Convolutional Network (VGG) network and non-subsampled contourlet transforms (NSCT) method to extract more effective fusion features. Finally, different fusion strategies are used for the base part and the salient part. An effective iterative algorithm optimization model is proposed. Experimental results on the public dataset TNO suggest the effectiveness of our method compared with several state-of-the-art methods.

A high capacity multi-image steganography technique based on golden ratio and non-subsampled contourlet transform

A high capacity multi-image steganography technique based on golden ratio and non-subsampled contourlet transform

A homomorphic non-subsampled contourlet transform based ultrasound image despeckling by novel thresholding function and self-organizing map

The speckle noise leaves an adverse effect on ultrasound images due to which loss of information occurs. Hence this paper proposes a homomorphic Non-Subsampled Contourlet Transform (NSCT) based ultrasound image despeckling technique using a novel thresholding function, bilateral filter, and self-organizing map (SOM). The bilateral filter is utilized over the low-pass NSCT sub-band for speckle component removal and sharp features. To get better noise suppression and edge preservation, a novel thresholding function is proposed and performed over the high-pass NSCT sub-band. In the proposed method, Kohonen’s SOM is implemented as a post-processing step for deblurring purposes. The significance of the proposed scheme is also tested where it was found that using Kohonen’s SOM as post-processing works better than without post-processing. Experimental outcomes were also evaluated on real speckled ultrasound images and synthetic added speckled noisy images. The results are evaluated and compared using visual analysis and performance metrics using with and without reference images. For more critical analysis, intensity profile along a line and experts observation were also evaluated to find the performance analysis of the proposed methodology. From all experimental and comparative evaluations, it was found that the proposed approach gives better outcomes compared to similar and recent methods.

Multiscale Geometric Analysis Fusion-Based Unsupervised Change Detection in Remote Sensing Images via FLICM Model.

Remote sensing image change detection is widely used in land use and natural disaster detection. In order to improve the accuracy of change detection, a robust change detection method based on nonsubsampled contourlet transform (NSCT) fusion and fuzzy local information C-means clustering (FLICM) model is introduced in this paper. Firstly, the log-ratio and mean-ratio operators are used to generate the difference image (DI), respectively; then, the NSCT fusion model is utilized to fuse the two difference images, and one new DI is obtained. The fused DI can not only reflect the real change trend but also suppress the background. The FLICM is performed on the new DI to obtain the final change detection map. Four groups of homogeneous remote sensing images are selected for simulation experiments, and the experimental results demonstrate that the proposed homogeneous change detection method has a superior performance than other state-of-the-art algorithms.

A combined approach of non-subsampled contourlet transform and convolutional neural network to detect gastrointestinal polyp

A combined approach of non-subsampled contourlet transform and convolutional neural network to detect gastrointestinal polyp

Infrared and visible image fusion based on QNSCT and Guided Filter

Image fusion is the process of fusing multiple images of the same scene to obtain a more informative image for human eye perception. In this paper, a new fusion framework based on Quaternion Non-Subsampled Contourlet Transform (QNSCT) and Guided Filter detail enhancement is designed to address the problems of inconspicuous infrared targets and poor background texture in Infrared and visible image fusion. The proposed method uses the quaternion wavelet transform for the first time instead of the traditional Non-Subsampled Pyramid Filter Bank structure in the Non-Subsampled Contourlet Transform (NSCT). The flexible multi-resolution of quaternion wavelet and the multi-directionality of NSCT are fully utilized to refine the multi-scale decomposition scheme. On the other hand, the coefficient matrix obtained from the proposed QNSCT algorithm is fused using a weight refinement algorithm based on the guided filter. The fusion scheme is divided into four steps. First, the Infrared and visible images are decomposed into multi-directional and multiscale coefficient matrices using QNSCT. The experimental results show that the proposed algorithm not only extracts important visual information from the source image, but also preserves the texture information in the scene better. Meanwhile, the scheme outperforms state-of-the-art methods in both subjective and objective evaluations.

Spatially adaptive multi-scale image enhancement based on nonsubsampled contourlet transform

Low or uneven luminance results in low contrast of near-infrared and optical remote sensing images, making it challenging to analyze their contents. Traditional image enhancement methods cannot simultaneously take detail preservation, contrast enhancement, and brightness improvement into account. In order to cope with this problem, this paper proposes a spatially adaptive multi-scale image enhancement (SAMSIE) scheme, including three key procedures: First, nonsubsampled contourlet transform (NSCT) is employed to decompose a low-contrast image into multi-scale layers. Second, a spatially adaptive Gamma correction strategy based on improved histogram equalization is proposed to enhance the base layer which is used as a guide layer. Third, an adaptive enhancement operator is proposed to enhance fine details. Finally, a high-contrast optical infrared image is obtained by the inverse NSCT with usage of these enhanced layers. The effectiveness of the proposed SAMSIE method is validated by both visualization assess and the evaluation of three quantitative indexes including discrete entropy (DE), contrast gain (CG), and mean brightness improvement (MBI), with comparison of the state-of-the-arts.

MRI image enhancement based on feature clustering in the NSCT domain

<abstract> <p>The noise and low clarity in magnetic resonance imaging (MRI) images impede the doctor's diagnosis. An MRI image enhancement method is proposed in the non-subsampled contourlet transform (NSCT) domain. The coefficients of the NSCT are classified as noise component, weak edges component and strong edges component by feature clustering. We modified the transform coefficients to enhance the MRI images. The coefficients corresponding to noise are set to zero, the coefficients corresponding to strong edges are essentially unchanged, and the coefficients corresponding to weak edges are enhanced by a simplified nonlinear gain function. It is shown that the proposed MRI image enhancement method has advantages in visual quality and objective evaluation indexes compared to the state-of-the-art methods.</p> </abstract>

Simultaneous Seismic Data Interpolation and Denoising Based on Nonsubsampled Contourlet Transform Integrating With Two-Step Iterative Log Thresholding Algorithm

Seismic data interpolation and denoising play vital roles in obtaining complete and clean data in seismic data processing. Seismic data usually misses along various spatial axes and always mix with random noise. In order to obtain complete and clean seismic data, reconstruction technology can interpolate missing data and attenuate random noise. Nonsubsampled contourlet transform is an effective transform to obtain multi-scale and multi-direction sparse domain data for compression sensing interpolation and denoising. However, conventional iterative shrinkage/thresholding cannot handle ill-posed and ill-conditioned equations for solving linear inverse problem. We present a two-step iterative log thresholding method to overcome ill-posed and ill-conditioned problems and improve the convergence rate and solution accuracy, which can interpolate and denoise seismic data simultaneously in the nonsubsampled contourlet transform framework. First, we use nonsubsampled contourlet transform to convert the seismic missing data with random noise to sparse domain. Then, we apply two-step iterative log thresholding algorithm to interpolate and denoise data in sparse domain. The result of each iteration is based on the results of the previous two iterations, which can accelerate convergence rate. In addition, log thresholding can further improve convergence rate and solution accuracy. Finally, we use inverse nonsubsampled contourlet transform to obtain the interpolated and denoised seismic data. The new method can reconstruct the irregularly missing data and attenuate random noise to obtain complete and clean seismic data with high accuracy, which is crucial for seismic imaging and inversion. We demonstrate the applicability and effectiveness of this simultaneous interpolation and denoising technique with successful applications to both synthetic and field data examples.

Visible-Infrared Person Re-Identification Based on Frequency-Domain Simulated Multispectral Modality for Dual-Mode Cameras

With the prevalence of dual-mode cameras in surveillance systems, visible-infrared person re-identification (VI-ReID) has become an emerging topic. Existing studies of VI-ReID roughly fall into three categories: straightforwardly extracting features, improving loss functions, and conducting visible-infrared modality generation. The generation methods avoid the shortcoming of the former two that training models are generally vulnerable to parameter changes. However, these generation methods are usually based on spatial domain and are unavoidable to damage the original information of images. To tackle these limitations, we propose a novel frequency-domain simulated multispectral (FSMS) modality and visible-FSMS-infrared collaborative learning. FSMS modality consists of three-channel images generated by a channel-level reconstruction of visible images, primarily based on the nonsubsampled contourlet transform (NSCT) cooperating with a lightweight network. The generation exploits crucial spectral information and edge information contained in frequency domain. Then, we design a multi-modality network to conduct the tri-modality collaborative learning where FSMS modality is utilized as an intermediate, thereby preserving the original spatial structure of images. Additionally, a dynamic-weight tri-modality heterogeneous retrieval (THR) loss and a modality-shared classification (MSI) loss are devised to mine discriminative modality-invariant features. A cross-modality invariant (CMI) constraint for further exploring triplet-wise relationships and an intra-modality regularizer for relatively stable convergence are introduced. Finally, experimental results show that our algorithm significantly outperforms the latest state-of-the-arts by 5.7% and 4.4% CMC-1 accuracy on two mainstream benchmark datasets, respectively. And the reasons underlying the observed increase in performance are deeply discussed.

A Pansharpening Based on the Non-Subsampled Contourlet Transform and Convolutional Autoencoder: Application to QuickBird Imagery

This paper presents a pansharpening technique based on the non-subsampled contourlet transform (NSCT) and convolutional autoencoder (CAE). NSCT is exceptionally proficient at presenting orientation information and capturing the internal geometry of objects. First, it’s used to decompose the multispectral (MS) and panchromatic (PAN) images into high-frequency and low-frequency components using the same number of decomposition levels. Second, a CAE network is trained to generate original low-frequency PAN images from their spatially degraded versions. Low-resolution multispectral images are then fed into the trained convolutional autoencoder network to generate estimated high-resolution multispectral images. Third, another CAE network is trained to generate original high-frequency PAN images from their spatially degraded versions. The result of low-pass CAE is fed to the trained high-pass CAE to generate estimated high-resolution multispectral images. The final pan-sharpened image is accomplished by injecting the detailed map of the spectral bands into the corresponding estimated high-resolution multispectral bands. The proposed method is tested on QuickBird datasets and compared with some existing pan-sharpening techniques. Objective and subjective results demonstrate the efficiency of the proposed method.

Change Detection in Multitemporal SAR Images Based on Slow Feature Analysis Combined With Improving Image Fusion Strategy

Change detection in multitemporal synthetic aperture radar (SAR) images has been an important research content in the field of remote sensing for a long time. In this article, based on the slow feature analysis (SFA) theory and the nonsubsampled contourlet transform (NSCT) algorithm, we propose a novel unsupervised change detection method called NSCT nonlocal means (NSCT-NLM). The powerful extraction to the changed information of SFA and the superior information fusion of NSCT are jointly adopted in this method. The main framework consists of the following parts. First, SFA and the log-ratio operator are used to generate difference images (DIs) independently. Then, the NSCT is used to fuse two DIs into a new higher quality DI. The newly fused DI combines the complementary information of the two kinds of original DI. Therefore, the contrast of the changed regions and unchanged regions is greatly enhanced, as well as the changed details are preserved more completely. Furthermore, an NLM filtering algorithm is employed to suppress the strong speckles in the fused DI. We use the fuzzy C-means algorithm to generate the final binary change map. The experiments are carried out on two public datasets and three real-world SAR datasets from different scenarios. The results demonstrate that the proposed method has higher detection accuracy by comparing with the reference methods.

Unsupervised Spatial-Spectral CNN-Based Feature Learning for Hyperspectral Image Classification

The rapid development of remote sensing sensors makes the acquisition, analysis, and application of hyperspectral images (HSIs) more and more extensive. However, the limited sample sets, high-dimensional features, highly correlated bands, and mixing spectral information make the classification of HSIs a great challenge. In this article, an unsupervised multiscale and diverse feature learning (UMsDFL) method is proposed for HSI classification, which deeply considers the spatial–spectral features via convolutional neural networks (CNNs). Specifically, after employing the simple noniterative clustering (SNIC) algorithm with the heuristic calculation of superpixel size, the HSIs are segmented into superpixels for feature learning. The unsupervised network is designed with the convolutional encoder and decoder, the additional clustering branch, and the multilayer feature fusion to enhance the distinguishability of feature learning and the reusability of feature maps. Then, the spatial relationships and object attributes in large- and small-scale contexts are learned collaboratively through the unsupervised network to utilize the complementary multiscale characteristics. Moreover, the diverse features of hyperspectral information and nonsubsampled contourlet transform (NSCT) textures are learned simultaneously via the unsupervised network to alleviate the insufficiency of geometric representation. Finally, the random forest (RF) is adopted as the comprehensive classifier for land cover mapping based on the UMsDFL, and superpixel regularization is adopted to optimize the classification results. A series of experiments are performed on three real-world HSI datasets to demonstrate the effectiveness of our UMsDFL approach. The experimental results show that the proposed UMsDFL can achieve the overall accuracy of 79.23%, 96.49%, and 77.26% for Houston, Pavia, and Dioni datasets, respectively, when there are only five samples per class for training.

Enhancing MRI Brain Images Using Contourlet Transform and Adaptive Histogram Equalization

Human brain can be viewed using MRI images. These images will be useful for physicians, only if their quality is good. We propose a new method called, Contourlet Based Two Stage Adaptive Histogram Equalization (CBTSA), that uses Nonsubsampled Contourlet Transform (NSCT) for smoothing images and adaptive histogram equalization (AHE), under two occasions, called stages, for enhancement of the low contrast MRI images. The given MRI image is fragmented into equal sized sub-images and NSCT is applied to each of the sub-images. AHE is imposed on each resultant sub-image. All processed images are merged and AHE is applied again to the merged image. The clarity of the output image obtained by our method has outperformed the output image produced by traditional methods. The quality was measured and compared using criteria like, Entropy, Absolute Mean Brightness Error (AMBE) and Peak Signal to Noise Ratio (PSNR).

A method for authenticating digital records for healthcare systems

The healthcare digital records include personal and sensitive information about the patient. These records are easily being distributed via different platform for clinical diagnosis purpose. This may lead to compromise with the security of the transmitted records. A fusion-based watermarking algorithm to secure medical records using non-subsampled contourlet transforms (NSCT), fast Walsh-Hadamard transform (FWHT) and Schur-decomposition is proposed in this paper. In the first part of the paper, mark images are fused using the NSCT algorithm, which generate final mark image. In the second part, using NSCT, FWHT and Schur-decomposition hide the fused-mark into the host medical image. The final part is marked data encryption based on hyperchaotic and DNA to provide the integrity and authenticity of the record. Objective measures confirm that the strength of the suggested algorithm since it is also suitable for cloud environment. Further, it is demonstrated that the proposed algorithm has a strong ability to extract hidden mark than conventional ones.