Shearlet Transform Research Articles

Pan-sharpening Through Weighted Total Generalized Variation Driven Spatial Prior and Shearlet Transform Regularization

Pan-sharpening Through Weighted Total Generalized Variation Driven Spatial Prior and Shearlet Transform Regularization

Detection and Segmentation of Glioma Tumors Utilizing a UNet Convolutional Neural Network Approach with Non-Subsampled Shearlet Transform.

The prompt and precise identification and delineation of tumor regions within glioma brain images are critical for mitigating the risks associated with this life-threatening ailment. In this study, we employ the UNet convolutional neural network (CNN) architecture for glioma tumor detection. Our proposed methodology comprises a transformation module, a feature extraction module, and a tumor segmentation module. The spatial domain representation of brain magnetic resonance imaging images undergoes decomposition into low- and high-frequency subbands via a non-subsampled shearlet transform. Leveraging the selective and directive characteristics of this transform enhances the classification efficacy of our proposed system. Shearlet features are extracted from both low- and high-frequency subbands and subsequently classified using the UNet-CNN architecture to identify tumor regions within glioma brain images. We validate our proposed glioma tumor detection methodology using publicly available datasets, namely Brain Tumor Segmentation (BRATS) 2019 and The Cancer Genome Atlas (TCGA). The mean classification rates achieved by our system are 99.1% for the BRATS 2019 dataset and 97.8% for the TCGA dataset. Furthermore, our system demonstrates notable performance metrics on the BRATS 2019 dataset, including 98.2% sensitivity, 98.7% specificity, 98.9% accuracy, 98.7% intersection over union, and 98.5% disc similarity coefficient. Similarly, on the TCGA dataset, our system achieves 97.7% sensitivity, 98.2% specificity, 98.7% accuracy, 98.6% intersection over union, and 98.4% disc similarity coefficient. Comparative analysis against state-of-the-art methods underscores the efficacy of our proposed glioma brain tumor detection approach.

Innovative Noise Reduction Strategies in Ultrasound Images Using Shearlet Transform and Bayesian Thresholding

Innovative Noise Reduction Strategies in Ultrasound Images Using Shearlet Transform and Bayesian Thresholding

Enhancement of X-Ray Images by Gradient Domain Guided Filtering and Non Sub Sampled Shearlet Transform

Abstract: In order to address the issues of low resolution, noise amplification, missing details, and weak edge gradient retention in the X-ray image improvement process, we propose in this study an image enhancement technique that combines nonSubsampled Shearlet transform with gradient-domain guided filtering. We start by breaking down the non Subsampled Shearlet transform and histogram equalization into the original image. We receive multiple high-frequency sub-bands as well as a lowfrequency sub-band. To enhance the overall contrast of the image and bring out the contour information in the low-frequency sub-band, adaptive gamma correction with weighting distribution is applied. To reduce image noise and emphasize edge information and detail, gradient-domain guided filtering is applied to the high-frequency sub-bands. In order to generate the final enhanced image, we use the inverse non-Subsampled Shearlet transform to reconstruct all of the sub-bands that were successfully processed. The experimental findings demonstrate the effectiveness of the suggested algorithm in improving X-ray images, as well as the clear advantages its objective index provides over certain conventional algorithms.

Digital Pathology Image Reconstruction with Alternating Direction Method of Multipliers using Wavelet, Contourlet and Shearlet Transforms

Digital pathology refers to image-based environment in which acquisition, extraction and interpretation of pathology information is supported by computational techniques. It has a huge potential to facilitate the diagnostic process, however, big data size and necessity of large storage areas are challenging. Therefore, in this research, Compressed Sensing (CS) scheme is studied with digital pathology images in order to reduce the amount of data for reconstruction. CS requires the sparsity of signals for a successful recovery which means that different sparsifying bases can alter the final performance. Wavelet, Contourlet and Shearlet Transforms are investigated to sparsify the digital pathology images, it is seen that Contourlet Transform is superior. Alternating Direction Method of Multipliers (ADMM) is chosen for reconstruction since it is a robust and fast convex optimization method. Despite the fact that digital pathology images are less sparse than classical images, CS reconstruction is satisfactory, which emphasizes the potential of CS for digital pathology. This study can be pioneering in the field of CS with digital pathology so it can encourage further studies of CS based imaging with different type of microscopes or at different wavelengths.

Self-attention-based generative adversarial network optimized with color harmony algorithm for brain tumor classification

ABSTRACT This paper proposes a novel approach, BTC-SAGAN-CHA-MRI, for the classification of brain tumors using a SAGAN optimized with a Color Harmony Algorithm. Brain cancer, with its high fatality rate worldwide, especially in the case of brain tumors, necessitates more accurate and efficient classification methods. While existing deep learning approaches for brain tumor classification have been suggested, they often lack precision and require substantial computational time.The proposed method begins by gathering input brain MR images from the BRATS dataset, followed by a pre-processing step using a Mean Curvature Flow-based approach to eliminate noise. The pre-processed images then undergo the Improved Non-Sub sampled Shearlet Transform (INSST) for extracting radiomic features. These features are fed into the SAGAN, which is optimized with a Color Harmony Algorithm to categorize the brain images into different tumor types, including Gliomas, Meningioma, and Pituitary tumors. This innovative approach shows promise in enhancing the precision and efficiency of brain tumor classification, holding potential for improved diagnostic outcomes in the field of medical imaging. The accuracy acquired for the brain tumor identification from the proposed method is 99.29%. The proposed BTC-SAGAN-CHA-MRI technique achieves 18.29%, 14.09% and 7.34% higher accuracy and 67.92%,54.04%, and 59.08% less Computation Time when analyzed to the existing models, like Brain tumor diagnosis utilizing deep learning convolutional neural network with transfer learning approach (BTC-KNN-SVM-MRI); M3BTCNet: multi model brain tumor categorization under metaheuristic deep neural network features optimization (BTC-CNN-DEMFOA-MRI), and efficient method depending upon hierarchical deep learning neural network classifier for brain tumour categorization (BTC-Hie DNN-MRI) respectively.

A Novel Hybrid Multimodal Medical Image Fusion Scheme Based on Non-subsampled Shearlet Transform

A Novel Hybrid Multimodal Medical Image Fusion Scheme Based on Non-subsampled Shearlet Transform

Regularized Full-Waveform Inversion With Shearlet Transform and Total Generalized Variation

Regularized Full-Waveform Inversion With Shearlet Transform and Total Generalized Variation

Phase-Congruency Reflector Detection of Orbital Radar Data via Shearlet Transform on Shannon-Gabor Wavelet

Phase-Congruency Reflector Detection of Orbital Radar Data via Shearlet Transform on Shannon-Gabor Wavelet

Non-sample fuzzy based convolutional neural network model for noise artifact in biomedical images

The use of a light-weight deep learning Convolutional Neural Network (CNN) augmented with the power of Fuzzy Non-Sample Shearlet Transformation (FNSST) has successfully solved the problem of reducing noise and artifacts in Low-Dose Computed Tomography (LDCT) pictures. Both the Normal-Dose Computed Tomography (NDCT) and the Low-Dose Computed Tomography (LDCT) images from the dataset are subjected to the FNSST decomposition procedure during the training phase, producing high-frequency sub-images that act as input for the CNN. The CNN creates a meaningful connection between the high-frequency sub-images from LDCT and their corresponding residual sub-images during the training operation. The CNN is given the capacity to distinguish between LDCT high-frequency sub-images and expected high-frequency sub-images, which frequently have varying levels of noise or artifacts, especially in a fuzzy setting. The FNSST-CNN then successfully distinguishes LDCT high-frequency sub-images from the expected high-frequency sub-images during the testing phase, thereby reducing noise and artifacts. When compared to other approaches like KSVD, BM3D, and conventional image domain CNNs, the performance of FNSST-CNN is impressive as shown by better peak signal-to-noise ratios, stronger structural similarity, and a closer likeness to NDCT pictures.

Processing and analysis of scanning microscopy images in an experiment withimplants

The study is devoted to improving the computational methodology for assessing the parameters of the tissue regeneration process using mesh nickelidetitanium implants with shape memory in an experiment. Processing and analysis of observational data from electron microscopy and classical histological examination were performed using proprietary algorithms and their modifications, which significantly simplify the data analysis procedure and increase the accuracy of estimates (15-20%).The proposed method as a computational tool for analyzing the dynamics of the processunder study as well as for highlighting the internal geometric features of experimentalimages of objects of interest contains shearlet transformation algorithms as well as algorithms for constructing elastic maps for effective visualization of spatial data. Important aspects of the methodology are computational tools for preprocessing visual data to increase the contrast and brightness of analyzed images based on Retinextechnology which significantly affects the quality of the use of computer evaluation tools.

Infrared and Visible Image Fusion Based on Saliency Detection Dynamic Threshold Neural P Systems in Non-Subsampled Shearlet Transform Domain

Infrared and Visible Image Fusion Based on Saliency Detection Dynamic Threshold Neural P Systems in Non-Subsampled Shearlet Transform Domain

Adaptive Non-subsampled Shearlet Transform and its Application to Surface Wave Suppression

Adaptive Non-subsampled Shearlet Transform and its Application to Surface Wave Suppression

Medical image fusion method based on saliency measurement improvement and local structure similarity correction

Medical image fusion is an important tool in medical diagnosis, because the quality of the fused image is closely related to the diagnosis result. However, the approaches of highlighting details through the acquisition of high-contrast lead to information distortion in the fused image and then increase the misdiagnosis rate. In order to solve the problem, a medical image fusion method based on saliency measurement improvement and local structural similarity correction is proposed in this paper. First, multi-level decomposition latent low-rank representation (MDLatLRR) combined with nonsubsampled shearlet transformation (NSST) is presented to decompose source images to obtain high SNR base layers and multi-scale detail layers. Then, the saliency measurement improvement with the source image mask filtering is advanced for base layer fusion rule. Specially, after the base layer is refined by the source image mask for the salient area extraction, the salient area is refreshed by its mean gray to calculate the fusion coefficient. Finally, a correction method based on structural similarity index measure (SSIM) is put forward to deal with the information distortion in the initial fused image, in which the correction weight is acquired through the weighted structural similarity between the initial fused image and the source ones. Experiments show that our method has excellent subjective visual effects, and outperforms the other states-of-the-art methods in objective indexes.

A dual fusion deep convolutional network for blind universal image denoising

Blind image denoising and edge-preserving are two primary challenges to recover an image from low-level vision to high-level vision. Blind denoising requires a single denoiser can denoise images with any intensity of noise, and it has practical utility since accurate noise levels cannot be acquired from realistic images. On the other hand, edge preservation can provide more image features for subsequent processing which is also important for the denoising. In this paper, we propose a novel blind universal image denoiser to remove synthesis and realistic noise while preserving the image texture. The denoiser consists of noise network and prior network parallelly, and then a fusion block is used to give the weight between these two networks to balance computation cost and denoising performance. We also use the Non-subsampled Shearlet Transform (NSST) to enlarge the size of receptive field to obtain more detailed information. Extensive denoising experiments on synthetic images and realistic images show the effectiveness of our denoiser.

Lieb's Inequality for Continuous Modulated Shearlet Transform on LCA Groups

We have proved a version of Lieb's inequality for continuous modulated shearlet transform on locally compact Abelian (LCA) groups. An alternative proof for the inequality has also been provided using Riesz-Thorin interpolation theorem.

Estimation of interlayer textural relationships to discriminate the benignancy/malignancy of brain tumors

A computer-aided diagnosis system is a popular tool to predict the risk factors of brain tumors. However, the existing techniques are unable to provide high detection accuracy due to the inability of capturing the hidden features of brain tumors. In this view, the present work decomposes the MR modality images into different layers using non-sub-sampled shearlet transformation (NSST). Following this, the proposed detection pipeline employs adaptive pulse-coupled neural network (A-PCNN) module and a fuzzy c-means (FCM) clustering algorithm for enhancement and segmentation of suspicious regions respectively. Interlayer textural relationships of tumors are estimated in terms of the layer-wise difference of the gray-level cooccurrence matrix (GLCM), similarity indices, and entropy distributions. Thenceforth, those feature coefficients are coded with binary sequences to express the textural homogeneity/randomness of tumors. Finally, these handcrafted multi-scale feature quantifiers are fed to some standard classifiers such as the k-nearest neighbor (kNN) technique, the linear square support vector machine (LS-SVM), and the decision tree (DT) for discriminating the state of benignancy/malignancy of tumors. Experimental results ensure that the proposed detection model shows superior performance compared to existing methods.

A novel framework of multimodal medical image fusion using adaptive NSST and optimized deep learning approach

ABSTRACT Multimodal medical image fusion plays a pivotal role in the medical and imaging industry. Existing works of deep learning method suffers from blurred texture characteristics and computing efficiency. Thus, a novel deep learning model is proposed for multimodal medical image fusion. Initially, an Adaptive Non-Subsampled Shearlet Transform (ANSST) approach is developed for decomposing the images, where the filter coefficient is optimized by Hybrid Water Strider-Dingo Optimization (HWS-DOX). The fusion of the high sub-bands of source image 1 and high sub-bands of source image 2 is done by an Optimized Deep Neural Network (ODNN). Then, the lower sub-bands of source image 1 and lower sub-bands of source image 2 will be fused through a weighted averaging scheme. Finally, the final fused images are attained by applying the inverse ANSST. Thus, the performance of recommended module is compared over classical heuristics and various transform methods.

Multimodal medical image fusion in NSST domain with structural and spectral features enhancement

For the past 25 years, medical imaging has been extensively used for clinical diagnosis. The main difficulties in medicine are accurate disease recognition and improved therapy. Using a single imaging modality to diagnose disease is challenging for clinical personnel. In this paper, a novel structural and spectral feature enhancement method in NSST Domain for multimodal medical image fusion (MMIF) is proposed. Initially, the proposed method uses the Intensity, Hue, Saturation (IHS) method to generate two pairs of images. The input images are then decomposed using the Non-Subsampled Shearlet Transform (NSST) method to obtain low frequency and high frequency sub-bands. Next, a proposed Structural Information (SI) fusion strategy is employed to Low Frequency Sub-bands (LFS's). It will enhance the structural (texture, background) information. Then, Principal Component Analysis (PCA) is employed as a fusion rule to High Frequency Sub-bands (HFS's) to obtain the pixel level information. Finally, the fused final image is obtained by employing inverse NSST and IHS. The proposed algorithm was validated using different modalities containing 120 image pairs. The qualitative and quantitative results demonstrated that the algorithm proposed in this research work outperformed numerous state-of-the-art MMIF approaches.

Gray wolf optimization and image enhancement with NLM Algorithm for multimodal medical fusion imaging system

Multimodal Medical fusion imaging is a significant aspect of image-guided medical diagnosis. A new improved non-sub sampled shearlet transform (NSST) based Multimodal Medical Fusion imaging System based on Gray Wolf Optimization (GWO) and Image Enhancement with NLM Algorithmic computer guided medical procedures. This paper proposes an efficient medical fusion imaging system that is evolved from the concept of Non-Sub sampled Shearlet Transform and the Gray Wolf Optimization algorithm. The initial procedure in this system includes histogram matching among one image and the other to facilitate common dynamic range for both images. Decomposition of multimodal medical images is done by NSST to be fused into their coefficients. Optimum decomposition level is scaled by EGOA with optimal gain parameters. Furthermore, denoising with enhancement procedure is performed to enhance its visual quality. If the images are corrupted by noise, the these traditional fusion methods performance is greatly hampered. Therefore, this is essential to create a fusion approach that can maintain accurate information even while distorting the images.