Nonsubsampled Contourlet Transform Research Articles

Detection and segmentation of melanoma skin cancer in dermoscopy images using modified Alexnet convolutional neural network‐morphological methodology

SummaryMelanoma is most vicious and dangerous type of skin cancer that its early detection can save patients' lives. Computer‐aided methods can be used for this early detection with acceptable performance. In this article, the melanoma skin cancer dermoscopy images are detected and cancer regions are segmented using the proposed modified AlexNet convolutional neural network‐morphological segmentation (CNN‐MS) method. This proposed work consists of enhancement stage, feature matrix construction stage, CNN classification stage, and segmentation stage. In enhancement stage, the skin pixels are contrast enhanced by histogram equalization technique and then non‐sub sampled contourlet transform (NSCT) is applied on the enhanced skin image to obtain the decomposed coefficients. Further, local ternary pattern (LTP) features are computed from the enhanced image and the feature matrix (FM) is constructed by integrating the NSCT coefficients and the LTP features. This constructed FM is fed to the CNN model for the classification of Melanoma skin cancer images from the normal skin images. Finally, morphological functions are applied on the classified melanoma skin image to segment the cancer regions. The performance of this developed model for skin cancer detection is analyzed on international skin imaging collaboration (ISIC) dataset images and achieves 98.11% of Se, 98.5% of Sp, 99.11% of Acc, and 0.96 of MCC. Also, the performance of this developed model for skin cancer detection is analyzed on SIIM‐ISIC dataset images and achieves 98.04% Se, 98.46% Sp, 99.12% Acc, and 0.97 MCC.

Multiresolution-Based Singular Value Decomposition Approach for Breast Cancer Image Classification.

Breast cancer is the most prevalent form of cancer that can strike at any age; the higher the age, the greater the risk. The presence of malignant tissue has become more frequent in women. Although medical therapy has improved breast cancer diagnostic and treatment methods, still the death rate remains high due to failure of diagnosing breast cancer in its early stages. A classification approach for mammography images based on nonsubsampled contourlet transform (NSCT) is proposed in order to investigate it. The proposed method uses multiresolution NSCT decomposition to the region of interest (ROI) of mammography images and then uses Z-moments for extracting features from the NSCT-decomposed images. The matrix is formed by the components that are extracted from the region of interest and are then subjected to singular value decomposition (SVD) in order to remove the essential features that can generalize globally. The method employs a support vector machine (SVM) classification algorithm to categorize mammography pictures into normal, benign, and malignant and to identify and classify the breast lesions. The accuracy of the proposed model is 96.76 percent, and the training time is greatly decreased, as evident from the experiments performed. The paper also focuses on conducting the feature extraction experiments using morphological spectroscopy. The experiment combines 16 different algorithms with 4 classification methods for achieving exceptional accuracy and time efficiency outcomes as compared to other existing state-of-the-art approaches.

Denoising Seismic Data via a Threshold Shrink Method in the Non-Subsampled Contourlet Transform Domain

In seismic exploration, effective seismic signals can be seriously distorted by and interfered with noise, and the performance of traditional seismic denoising approaches can hardly meet the requirements of high-precision seismic exploration. To remarkably enhance signal-to-noise ratios (SNR) and adapt to high-precision seismic exploration, this work exploits the non-subsampled contourlet transform (NSCT) and threshold shrink method to design a new approach for suppressing seismic random noise. NSCT is an excellent multiscale, multidirectional, and shift-invariant image decomposition scheme, which can not only calculate exact contourlet transform coefficients through multiresolution analysis but also give an almost optimized approximation. It has better high-frequency response and stronger ability to describe curves and surfaces. Specifically, we propose to utilize the superior performance NSCT to decomposing the noisy seismic data into various frequency sub-bands and orientation response sub-bands, obtaining fine enough transform high frequencies to effectively achieve the separation of signals and noises. Besides, we use the adaptive Bayesian threshold shrink method instead of traditional handcraft threshold scheme for denoising the high-frequency sub-bands of NSCT coefficients, which pays more attention to the internal characteristics of the signals/data itself and improve the robustness of method, which can work better for preserving richer structure details of effective signals. The proposed method can achieve seismic random noise attenuation while retaining effective signals to the maximum degree. Experimental results reveal that the proposed method is superior to wavelet-based and curvelet-based threshold denoising methods, which increases synthetic seismic data with lower SNR from −8.2293 dB to 8.6838 dB, and 11.8084 dB and 9.1072 dB higher than two classic sparse transform based methods, respectively. Furthermore, we also apply the proposed method to process field data, which achieves satisfactory results.

SDH: Secure Data Hiding in Fused Medical Image for Smart Healthcare

Fusing the single modality medical images to obtain a distinct multimodality image is needed to ensure a better clinical experience. However, the distribution of these fused images brings the issues of ownership and authentication and has attracted many researchers. Presently, large volumes of medical data are stored on cloud platforms. However, outsourcing medical data to this popular platform may introduce security issues. Following this, we introduce a secure data hiding in fused medical image for smart healthcare, since it is also suitable for applications in the cloud. To achieve this, we first create a fused medical image as a cover by nonsubsampled contourlet transform (NSCT). The method, which is based on NSCT, QR, and Schur decomposition, allows concealing the image and electronic patient records (EPR) mark into the fused image. Importantly, EPR watermark includes a hash value of cover is created first and then embedded into the cover via magic cube-based procedure. Finally, the marked image is encrypted using deoxyribonucleic acid (DNA), chaotic maps, and a hash function-based encryption scheme. The introduced watermarking scheme has been evaluated using 25 pairs of medical images and several embedding/extracting parameters. Apart from being satisfactorily imperceptible, the proposed work is also robust and secure against well-known signal processing attacks and promising results are obtained when compared with similar techniques. It indicates a considerable improvement in robustness of 66.7% and 99.7% over existing discrete wavelet transform (DWT)–singular value decomposition (SVD)-based watermarking schemes.

Development of 3-stage hybrid computer aided design (3-HCAD) system for multi-modal medical images to identify brain tumor

The latest developments in medical imaging and computer-aided solutions for image processing problems attract attention of various researchers to impart their research in the medical imaging field. Designing and developing efficient algorithms to present the medical information effectively have become critical areas of research in this field. A 3-Stage Hybrid Computer Aided Design system is introduced to identify Brain tumor in earlier stages by extracting meaningful information from multimodal medical images. The preferred multi-modality images is Magnetic Resonance Imaging (MRI) and Computed Tomography (CT). The CAD system proposed in this paper can eliminate the dependency on human operators as it is an efficient software-based system. The first stage in this model consists of image pre-processing with Wavelet and Curvelet transforms. The second stage of the CAD system is an image segmentation process, which involves a combination of Wavelet Transform and Watershed Technique. The third stage involves image fusion, where the individually segmented CT and MRI images are fused together to obtain an integrated complementary information from two different images. This is followed by decomposing CT and MRI images using the Dual Tree Complex Wavelet Transform (DTCWT) and Nonsubsampled Contourlet Transform (NSCT).

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.

High Performance Image Super-Resolution Using Convolutional Neural Networks and Nonsubsampled Contourlet Transform

High Performance Image Super-Resolution Using Convolutional Neural Networks and Nonsubsampled Contourlet Transform

An efficient image steganography approach based on QR factorization and singular value decomposition in non‐subsampled contourlet transform domain

AbstractAs the amount of information communicated through the virtual world grows dramatically day by day, data security becomes a significant concern while communicating over the internet. Steganography can play an integral part in safeguarding the data from unauthorized users through a hiding mechanism. This article describes a three‐step steganographic algorithm. This algorithm applied QR factorization and singular value decomposition (SVD) in the non‐subsampled contourlet transform (NSCT) domain. First, this algorithm scrambles the secret image using Arnold transforms, and then NSCT decomposes the carrier and the scrambled hidden image into coefficient sub‐bands. Second, the QR factorization and SVD are used on the specific coefficients of carrier and scrambled secret images, respectively. Finally, the modified secret image inserts into the carrier image for communication. The reverse mechanism is applied to discover the hidden message at the receiving end. The methodology gives superior imperceptibility as well as robustness. Furthermore, the anticipated strategy is compared with current methods in terms of effectiveness.

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.

Blind watermarking algorithm combiningNSCT,DWT,SVD, andHVS

AbstractIn order to solve the contradiction between watermark transparency and robustness, this paper combines Non‐subsampled Contourlet Transformation (NSCT), Discrete Wavelet Transformation (DWT), Singular Value Decomposition (SVD), and Human Visual System (HVS) to improve a watermarking algorithm. Firstly, this algorithm performs a first‐level NSCT decomposition on the carrier image to obtain its low‐frequency part, and then performs a second‐level DWT on the low‐frequency part, extracts its second‐level low‐frequency sub bands and block these sub bands to sub‐blocks according to their size. Secondly, SVD is performed for each sub‐block and the best embedding position of the watermark according to the sensitivity of HVS for the average brightness and the modified cosine similarity measurement method is calculated. Finally, the watermark scrambled and encrypted by Arnold is embedded into the selected singular value. This algorithm can realize the blind extraction of watermark. Experimental results show that the algorithm can not only satisfy the transparency but the robustness of the watermark at the same time as well.

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.

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.

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.

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>

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.