Nonsubsampled Shearlet Transform Domain Research Articles

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.

Multimodality Medical Image Fusion Using Clustered Dictionary Learning in Non-Subsampled Shearlet Transform.

Imaging data fusion is becoming a bottleneck in clinical applications and translational research in medical imaging. This study aims to incorporate a novel multimodality medical image fusion technique into the shearlet domain. The proposed method uses the non-subsampled shearlet transform (NSST) to extract both low- and high-frequency image components. A novel approach is proposed for fusing low-frequency components using a modified sum-modified Laplacian (MSML)-based clustered dictionary learning technique. In the NSST domain, directed contrast can be used to fuse high-frequency coefficients. Using the inverse NSST method, a multimodal medical image is obtained. Compared to state-of-the-art fusion techniques, the proposed method provides superior edge preservation. According to performance metrics, the proposed method is shown to be approximately 10% better than existing methods in terms of standard deviation, mutual information, etc. Additionally, the proposed method produces excellent visual results regarding edge preservation, texture preservation, and more information.

Parameter adaptive unit-linking pulse coupled neural network based MRI–PET/SPECT image fusion

Medical image fusion has many applications to healthcare that is accomplished by extracting and then combining the complementary information from multiple medical images into a single image. The pulse coupled neural network (PCNN) is greatly applied to image fusion due to its efficient coupling of surrounding neurons, but suffers from network complexity and manual parameter settings. This paper proposes a novel parameter adaptive unit-linking PCNN (PAULPCNN) model, whose parameters are automatically obtained from the external stimulus and exhibits a simpler structure than the original PCNN. The multi-scale decomposition-based methods, particularly based on the non-subsampled shearlet transform (NSST), are widely employed by researchers to perform medical image fusion due to their efficient separation of spatial details at different scales. Motivated by the advantages of the PCNN and multi-scale decomposition-based methods, a hybrid medical image fusion method based on the PAULPCNN model is introduced in the NSST domain that merges the salient complementary details from a gray-scale and the corresponding pseudo-color medical image captured at different modalities to produce a more informative image that is more useful to medical experts in computer-aided diagnosis of diseases. The proposed method first employs NSST to transform the source images into a low-pass and several high-pass sub-bands, respectively. The high-pass sub-bands are combined using the firing times of the proposed PAULPCNN model, whereas a new distance-weighted regional energy-based rule is applied to construct the fused low-pass sub-band, which while estimating regional energy, assigns weight to different neighboring pixels depending on their distance from the central pixel. Finally, inverse NSST is applied on the fused sub-bands to construct the fused image. The effectiveness of the proposed technique is shown using the fusion results of eleven state-of-the-art methods on thirty gray-scale and pseudo-color brain medical image pairs comprising mild Alzheimer’s disease, Huntington’s disease, motor neuron disease, sagittal plane, coronal plane, transaxial plane, and glioma image pairs, where eight objective metrics are considered for the quantitative assessment. Experimental results demonstrate that the proposed method is competitive with the state-of-the-art methods, which even outperforms some of these methods by providing fused images with better visual quality and greater objective performance, which are more suitable for specialists to diagnose brain diseases.

The Effective 3D MRI Reconstruction Method Driven by the Fusion Strategy in NSST Domain

The 3D reconstruction of medical images plays an important role in modern clinical diagnosis. Although the analytic-based, the iterative-based and the deep learning-based methods have been popularly used, there are still many problems to deal with. The analysis-based methods are not accurate enough, the iteration-based methods are computationally intensive, and the deep learning based methods are heavily dependent on the training of the data. To solve the default that only the single scan sequence is included in the traditional methods, a reconstruction method driven by the non-subsampled shearlet transform (NSST) and the algebraic reconstruction technique (ART) is proposed. Firstly, the multiple magnetic resonance imaging (MRI) sequences are decomposed into high-frequency and low-frequency components by NSST. Secondly, the low-frequency parts are fused with the weighted average fusion scheme and the high-frequency parts are fused with the weighted coefficient scheme that guided by the regional average gradient and energy. Finally, the 3D reconstruction is performed by using the ART algorithm. Compared with the traditional reconstruction methods, the proposed method is able to capture more information from the multiple MRI sequences, which makes the reconstruction results much clearer and more accurate. By comparing with the single-sequence reconstruction model without fusion, the experiments fully prove the accuracy and effectiveness.

Seismic Coherent Noise Removal of Source Array in the NSST Domain

The technique of the source array based on the vibroseis can provide the strong energy of a seismic wave field, which better meets the need for seismic exploration. The seismic coherent noise reduces the signal-to-noise ratio (SNR) of the source array seismic data and affects the seismic data processing. The traditional coherent noise removal methods often cause some damage to the effective signal while suppressing coherent noise or cannot suppress the interference wave effectively at all. Based on the multi-scale and multi-direction properties of the non-subsampled Shearlet transform (NSST) and its simple mathematical structure, the seismic coherent noise removal method of source array in NSST domain is proposed. The method is applied to both the synthetic seismic data and the filed seismic data. After processing with this method, the coherent noise of the seismic data is greatly removed and the effective signal information is greatly protected. The analysis of the results demonstrates the effectiveness and practicability of the proposed method on coherent noise attenuation.

MS-Pansharpening Algorithm Based on Dual Constraint Guided Filtering

The difference and complementarity of spatial and spectral information between multispectral (MS) image and panchromatic (PAN) image have laid the foundation for the fusion of the two types of images. In recent years, MS and PAN image fusion (also known as MS-Pansharpening) has gained attention as an important research area in remote sensing (RS) image processing. This paper proposes an MS-Pansharpening algorithm based on dual constraint Guided Filtering in the nonsubsampled shearlet transform (NSST) domain. The innovation is threefold. First, the dual constraint guided image filtering (DCGIF) model, based on spatial region average gradient correlation and vector correlation formed by neighborhood elements is proposed. Further, the PAN image detail information extraction scheme, based on the model, is provided, which extracts more complete and accurate detail information, thus avoiding, to some extent, the spectral distortion caused by the injection of non-adaptive information. Second, the weighted information injection model, based on the preservation of the correlation between the band spectra, is proposed. The model determines the information injection weight of each band pixel based on the spectral proportion between bands of the original MS image, which ensures the spectral correlation between bands of the fused MS image. Finally, a new MS-Pansharpening algorithm in NSST domain is proposed. The MS and PAN high frequency sub-bands of NSST are used to extract more effective spatial details. Then the proposed DCGIF model is used to extract the effective spatial detail injection information through the weighted joint method based on the regional energy matrix. Finally, the weighted information injection model is used to inject it into each band of MS to complete information fusion. Experimental results show that the proposed approach has better fusion effect than some conventional MS-Pansharpening algorithms, which can effectively improve the spatial resolution of the fused MS image and maintain the spectral characteristics of MS.

Multivariate time series forecasting method based on nonlinear spiking neural P systems and non-subsampled shearlet transform

Multivariate time series forecasting remains a challenging task because of its nonlinear, non-stationary, high-dimensional, and spatial–temporal characteristics, along with the dependence between variables. To address this limitation, we propose a novel method for multivariate time series forecasting based on nonlinear spiking neural P (NSNP) systems and non-subsampled shearlet transform (NSST). A multivariate time series is first converted into the NSST domain, and then NSNP systems are automatically constructed, trained, and predicted in the NSST domain. Because NSNP systems are used as nonlinear prediction models and work in the NSST domain, the proposed prediction method is essentially a multiscale transform (MST)–based prediction method. Therefore, the proposed prediction method can process nonlinear and non-stationary time series, and the dependence between variables can be characterized by the multiresolution features of the NSST transform. Five real-life multivariate time series were used to compare the proposed prediction method with five state-of-the-art and 28 baseline prediction methods. The comparison results demonstrate the effectiveness of the proposed method for multivariate time-series forecasting.

Guided filter random walk and improved spiking cortical model based image fusion method in NSST domain

Image fusion has become a hot issue in the field of information processing. In this paper, a novel image fusion method based on guided filter random walk and improved spiking cortical model (ISCM) in non-subsampled shearlet transform (NSST) is presented. The core process is composed of three steps. Firstly, the source images to be fused are decomposed into the low-frequency parts and high-frequency parts via NSST. Then, two models including guided filter and random walk are combined to complete the fusion of low-frequency sub-images. As for the fusion of the high-frequency parts, the traditional spiking cortical model is improved to be ISCM to capture and fuse the details information of the source images. Finally, the fused image can be obtained by inverse NSST. In order to verify the effectiveness of the proposed method, lots of datasets with different categories are selected as the source images to conduct the simulation experiments. Experimental results demonstrate that the proposed method own obvious superiorities over the representative ones in terms of both subjective visual performance and objective evaluation data.

Multi-modal medical image fusion framework using co-occurrence filter and local extrema in NSST domain

The fusion of vital imaging information has turned into a primary issue for biomedical applications. In this paper, a new multi-modality medical image fusion method is proposed in the shearlet domain. In the proposed algorithm, input images are decomposed using Non-subsampled shearlet transform (NSST) to get low and high frequencies components. A novel procedure to decompose and combine the base layers and detail layers using the local extrema (LE) approach is used and fused using a Co-occurrence filter (CoF) in low-frequency components. In high-frequency components, an edge-preserving image fusion strategy is performed using sum modified Laplacian (SML) to combine the high-frequency coefficients. The experimental outcomes and comparative analysis are performed over the Multi-modal medical image dataset using proposed and existing methods. It is exhibited through test outcomes and assessments that the proposed technique beats the cutting-edge fusion strategies concerning edge preservation in subjective and objective assessment criteria.

A Fusion Method for Atomic Force Acoustic Microscopy Cell Imaging Based on Local Variance in Non-Subsampled Shearlet Transform Domain

Atomic force acoustic microscopy (AFAM) is a measurement method that uses the probe and acoustic wave to image the surface and internal structures of different materials. For cellular material, the morphology and phase images of AFAM reflect the outer surface and internal structures of the cell, respectively. This paper proposes an AFAM cell image fusion method in the Non-Subsampled Shearlet Transform (NSST) domain, based on local variance. First, NSST is used to decompose the source images into low-frequency and high-frequency sub-bands. Then, the low-frequency sub-band is fused by the weight of local variance, while a contrast limited adaptive histogram equalization is used to improve the source image contrast to better express the details in the fused image. The high-frequency sub-bands are fused using the maximum rule. Since the AFAM image background contains a lot of noise, and improved segmentation algorithm based on the Otsu algorithm is proposed to segment the cell region, and the image quality metrics based on the segmented region will make the evaluation more accurate. Experiments with different groups of AFAM cell images demonstrated that the proposed method can clearly show the internal structures and the contours of the cells, compared with traditional methods.

Multimodal image fusion based on global-regional-local rule in NSST domain

An accurate representation of the source image is vital in image fusion processing. However, among most of the traditional multi-scale decomposition based image fusion methods, they fail to accurately interpret the source images, only capturing local information without considering the regional and global information. To overcome this drawback, a novel image fusion method based on the global-regional-local rule is proposed in the nonsubsampled shearlet transform (NSST) domain. First, the source paired images are decomposed into the low-pass subbands and high-pass subbands with the NSST transformation. Second, for comprehensively representing the statistical correlation of source image, three-level statistical models, that is, global contextual hidden Markov model (G-CHMM), regional contextual hidden Markov model (R-CHMM), and local contextual hidden Markov model (L-CHMM) are established for the high-pass subbands. In the R-CHMM, a novel feature vector is extracted for getting the region map of source image by the fuzzy cluster method (FCM). Third, to get accurate active measures of the source image, a global-regional-local fusion rule based on the statistical characteristics extracted from global-regional-local CHMM is designed, which is used to fuse the high-pass subbands. The low-pass subbands are fused with the choose-max rule based on the local gradient measure. Finally, the fused image is obtained by the inverse NSST. Experimental results on serials of infrared and visible images, medical images and multi-focus images demonstrate the advantage of the proposed method in terms of detail preserving.

Locally optimum watermark decoder in NSST domain using RSS-based Cauchy distribution

It is important to consider the tradeoff between imperceptibility and robustness requirements in developing an image-watermarking technique, some statistical model-based image watermarking schemes have been designed in the past decade. The effectiveness of a statistical watermark decoder depends highly on the modeling of the transform-domain coefficients and the use of hypothesis testing. In this study, a multiplicative image watermarking scheme is proposed in the nonsubsampled Shearlet transform (NSST) domain, where NSST coefficients are modeled by ranked set sample (RSS) based Cauchy statistical distribution and locally most powerful (LMP) test criterion is applied. Digital image watermarking technique consists of two parts, namely, embedding and extracting. In the embedding process, to achieve relatively good imperceptibility and robustness, watermark data is inserted into the significant NSST directional subband that has the highest energy value, by modifying nonlinearly the significant NSST coefficients. In the extracting phase, a scheme is proposed for designing a blind NSST domain watermark decoder incorporating the RSS based Cauchy statistical distribution and LMP test criterion. Here, the RSS method is used to estimate the location parameter and shape parameter of Cauchy statistical distribution instead of traditional maximum-likelihood (ML) method, which can provide the Cauchy model with higher precision parameters. Experimental results on a set of standard test images show significant improvements in imperceptibility and robustness using the proposed method compared with the best known state-of-the-art approaches.

Medical Image Fusion Method Based on Coupled Neural P Systems in Nonsubsampled Shearlet Transform Domain

Coupled neural P (CNP) systems are a recently developed Turing-universal, distributed and parallel computing model, combining the spiking and coupled mechanisms of neurons. This paper focuses on how to apply CNP systems to handle the fusion of multi-modality medical images and proposes a novel image fusion method. Based on two CNP systems with local topology, an image fusion framework in nonsubsampled shearlet transform (NSST) domain is designed, where the two CNP systems are used to control the fusion of low-frequency NSST coefficients. The proposed fusion method is evaluated on 20 pairs of multi-modality medical images and compared with seven previous fusion methods and two deep-learning-based fusion methods. Quantitative and qualitative experimental results demonstrate the advantage of the proposed fusion method in terms of visual quality and fusion performance.

Brain Image Enhancement Approach Based on Singular Value Decomposition in Nonsubsampled Shearlet Transform Domain

In this work, a novel image enhancement algorithm using NSST and SVD is proposed to improve the definition of the acquired brain images. The input brain image is computed by CLAHE, then the processed brain image and input brain image are decomposed into low- and high-frequency components by NSST, the singular value matrix of the low-frequency component is estimated. The final enhancement image is obtained by inverse NSST. Results of this experiment demonstrate that the proposed technique has good performance in terms of brain image enhancement when compared to other methods.

A novel approach for multi-focus image fusion based on SF-PAPCNN and ISML in NSST domain

In order to further improve the contrast and sharpness of fused image, a novel multi-focus image fusion algorithm based on spatial frequency-motivated parameter-adaptive pulse coupled neural network (SF-PAPCNN) and improved sum-modified-laplacian (ISML) in nonsubsampled shearlet transform (NSST) domain is proposed in this paper. In its procedural steps, at first, the source images are decomposed into low-frequency and high-frequency components by NSST. The low-frequency components are fused by SF-PAPCNN model, the PAPCNN is designed to estimate the PCNN parameters adaptively according to the input information, and the high-frequency components are fused by ISML model. Finally, the inverse NSST is employed to the fused coefficients to reconstruct the fused image. The superiority of the proposed fusion technique is confirmed by many analytical experimentations on the gray and color multi-focus image data sets. Compared with the state-of-the-art image fusion methods, the proposed fusion algorithm has superior performance in terms of visual inspection and objective evaluation.

Medical image fusion using the PCNN based on IQPSO in NSST domain

In this study, an improved quantum-behaved particle swarm optimisation based pulse-coupled neural network (IQPSO-PCNN) is proposed in the non-subsampled shearlet transform (NSST) domain for medical image fusion. First, NSST tool is used to decompose the source image into low-frequency and high-frequency subbands. Then, for low-frequency subbands, the fusion rules of two different functions are presented, which simultaneously addresses two key issues of energy preservation and detail extraction. For high-frequency subbands, unlike conventional PCNN-based methods, parameters are manually set based on experience, and the decomposed high-frequency subbands share a set of parameters. The IQPSO-PCNN model can obtain the optimal parameters for each high-frequency subband adaptively according to its own information. Finally, the fused low-frequency subband and high-frequency subbands are inversely transformed by NSST to acquire the final fused image. The proposed algorithm uses >90 pairs of images with four different modalities. In addition, fusion experiments are performed on different sequences of the three modes. The experimental results demonstrate that the proposed method is superior to existing state-of-art methods in subjective visual performance and objective evaluation.

Brain Medical Image Fusion Based on Dual-Branch CNNs in NSST Domain.

Computed tomography (CT) images show structural features, while magnetic resonance imaging (MRI) images represent brain tissue anatomy but do not contain any functional information. How to effectively combine the images of the two modes has become a research challenge. In this paper, a new framework for medical image fusion is proposed which combines convolutional neural networks (CNNs) and non-subsampled shearlet transform (NSST) to simultaneously cover the advantages of them both. This method effectively retains the functional information of the CT image and reduces the loss of brain structure information and spatial distortion of the MRI image. In our fusion framework, the initial weights integrate the pixel activity information from two source images that is generated by a dual-branch convolutional network and is decomposed by NSST. Firstly, the NSST is performed on the source images and the initial weights to obtain their low-frequency and high-frequency coefficients. Then, the first component of the low-frequency coefficients is fused by a novel fusion strategy, which simultaneously copes with two key issues in the fusion processing which are named energy conservation and detail extraction. The second component of the low-frequency coefficients is fused by the strategy that is designed according to the spatial frequency of the weight map. Moreover, the high-frequency coefficients are fused by the high-frequency components of the initial weight. Finally, the final image is reconstructed by the inverse NSST. The effectiveness of the proposed method is verified using pairs of multimodality images, and the sufficient experiments indicate that our method performs well especially for medical image fusion.

Remote sensing image enhancement based on the combination of adaptive nonlinear gain and the PLIP model in the NSST domain

To enhance image detail and contrast effectively, we present a novel enhancement method for remotely sensed images. This method is based on the combination of adaptive nonlinear gain and the parameterized logarithmic image processing model (PLIP) in the nonsubsampled shearlet transform (NSST) domain. The algorithm works in several stages by deconstructing the image into low- and high-frequency components, applying different functions to each set of frequency components, and then applying further enhancement functions to the reconstructed image. The experimental results show that the proposed method performs well in terms of definition gain, the contrast improvement index (CII) and the measure of enhancement by entropy (EMEE) when compared to several state-of-the-art image enhancement algorithms, including the nonsubsampled contourlet transform (NSCT) with fuzzy field enhancement, the NSCT with unsharp masking, the feature-linking model, linking synaptic computation for image enhancement and improved fuzzy contrast in the NSST domain.

Low-Light Image Enhancement Based on Nonsubsampled Shearlet Transform

To improve the observability of low-light images, a low-light image enhancement algorithm based on nonsubsampled shearlet transform (NSST) is presented (LIEST). The proposed algorithm can synchronously achieve contrast improvement, noise suppression, and the enhancement of specific directional details. An enhancement framework of low-light noisy images is first derived, and then, according to the framework, a low-light noisy image is decomposed into low-pass subband coefficients and bandpass direction subband coefficients by NSST. Then, in the NSST domain, an illumination map is estimated based on a bright channel of the low-pass subband coefficients, and noise is simultaneously suppressed by shrinking the bandpass direction subband coefficients. Finally, based on the estimated illumination map, the low-pass subband coefficients, and the shrunken bandpass direction subband coefficients, inverse NSST is implemented to achieve low-light image enhancement. Experiments demonstrate that the LIEST exhibits superior performance in improving contrast, suppressing noise, and highlighting specific details as compared to seven similar algorithms.

Medical Image Fusion via PCNN Based on Edge Preservation and Improved Sparse Representation in NSST Domain

Medical image fusion integrates image features of different modalities to provide comprehensive information for clinical diagnosis, treatment planning, and image-guided surgery. The information of the fused image is richer and clearer, which makes up for the defect of the single-mode medical image and retains the characteristic information of the source image. This paper proposes a novel multi-modality medical image fusion method based on gray medical images and color medical images. In the proposed method, the nonsubsampled shearlet transform (NSST) method is first used to decompose the source image into a low frequency sub-band and several high frequency sub-bands. The improved sparse representation is utilized to fuse the low frequency sub-band, which can remove the detail features through the sobel operator and the guided filter to improve the ability to preserve energy effectively. Meanwhile, the high frequency sub-bands are fused by a pulse coupled neural network (PCNN) based on edge preservation. This method fully considers the imaging characteristics of different medical modalities, which can process edge information well and process image details better. Finally, the fused low frequency sub-band and high frequency sub-bands are inversely transformed to obtain the final fused image. Seven different categories of medical images and seven fusion methods are used to verify the effectiveness of the proposed method. In addition, the medical images of three different modalities are merged to testify the influence of the fusion sequence. The experimental results show that the proposed method is superior to existing state-of-art methods in subjective visual performance and objective evaluation.