Non-subsampled Shearlet Research Articles

Contrast enhancement of medical images using fuzzy set theory and nonsubsampled shearlet transform

AbstractNoises and artifacts are introduced in medical images during the process of imaging and transmission, resulting in reduced definition and lack of detail. Therefore, a contrast enhancement method, based on fuzzy set theory and nonsubsampled shearlet transform (NSST), is proposed. First, the original image is decomposed into several high‐frequency components and a low‐frequency component by NSST. Then, the threshold method is used to remove noises in the high‐frequency components. In addition, a linear stretch is used to improve the overall contrast in the low‐frequency component. Then, the reconstruct image is reconstructed by applying the inverse NSST to the processed high‐frequency and low‐frequency components. Finally, the fuzzy contrast is used to improve the detail information and global contrast in the reconstruct image. Experimental results indicate that, relative to contrast algorithms, the peak signal‐to‐noise ratio of the proposed method is improved by approximately 18%, and the root mean square error (RMSE) is optimized to approximately 48%. The proposed method also improves the image definition and texture information. Moreover, when compared with the Improved Fuzzy Contrast Combined Adaptive Threshold in NSCT for Medical Image Enhancement, the processing time (time) of this proposed method optimizes about 86%, which can obviously improve the computational efficiency of this method.

Multimodal Medical Image Sensor Fusion Model Using Sparse K-SVD Dictionary Learning in Nonsubsampled Shearlet Domain

Multimodal medical image sensor fusion (MMISF) has a significant role for better visualization of the diagnostic statistics computed by integrating the vital information taken from input source images acquired using multimodal imaging sensors. The MMISF also helps the medical professionals in precise diagnosis of several critical diseases and its treatment. Often, images taken from different imaging sensors are degraded by noise interferences during acquisition or data transmission that leads to the false perception of noise as a useful feature of the image. This paper presents a novel fusion framework for multimodal neurological images, which is able to capture small-scale details of input images with original structural details. In its procedural steps, at first, source images get decomposed by the nonsubsampled shearlet transform (NSST) into a low-frequency (lf) and several high-frequency (hf) components to separate out the two basic characteristics of source image, i.e., principal information and edge details. The lf layers get fused with a sparse representation-based model, and the hf components are merged by the guided filtering-based approach. Finally, fused images are reconstructed by employing the inverse NSST. The superiority of the proposed MMISF approach is confirmed by a large extent of analytical experimentations on the different real magnetic resonance single-photon emission computed tomography, magnetic resonance-positron emission tomography and computed tomography magnetic resonance neurological image data sets. Based on all these experimental results, it is stated that the proposed MMISF approach is superior to several other approaches as it produces better visually fused images with improved computational measures.

Multimodal Sensor Medical Image Fusion Based on Local Difference in Non-Subsampled Domain

Medical imaging sensors, such as positron emission tomography and single-photon emission computed tomography, can provide rich information, but each has its inherent drawbacks. In this scenario, multimodal sensor medical image fusion becomes an effective solution. The chief objective of medical imaging is to extract as much preponderant and complementary information as possible from the source into a single output that can play a critical role in medical diagnosis and clinical operations. In this paper, a novel fusion method is presented for multimodal sensor medical images, based on local difference (LD) in non-subsampled domain. In this method, the source medical images are first decomposed into low-frequency and high-frequency subimages, via non-subsampled schemes. Then, the coefficients of sub-bands are fused by an operator, called LD. The final fused image is reconstructed, via the inverse non-subsampled schemes, with all composite coefficients. The proposed fusion method was applied in several clinical studies, and the results show that it is a much more straightforward and effective method than some of the state-of-the-art methods, in terms of both subjective visual performance and objective evaluation results. Also, the performance of the proposed method was compared with that of two non-subsampled schemes, namely, non-subsampled contourlet transform and non-subsampled shearlet transform.

Infrared and visible image fusion based on BEMSD and improved fuzzy set

This study proposes a new fusion framework to solve the problem of weak self-adaptation in traditional multiscale transformation. A non-subsampled shearlet transform (NSST) decomposes the source image in multiscales and multidirections through a non-subsampled pyramid and shearlet transform. There is a problem in that the filter bank needs to be designed independently. The characteristics of the source images cannot be fully utilized. Bidimensional empirical mode decomposition (BEMD) can obtain a set of intrinsic mode functions (IMFs) and a residue according to the characteristics of the image itself. The high-frequency and low-frequency information decomposed by BEMD are more suitable to the source image features. Thus, using BEMD instead of the non-subsampled pyramid in an NSST allows the fusion framework to analyze the characteristics of the image with better multiscale and multidirection adaptability. For the proposed fusion framework, the corresponding fusion rules are needed. In this study, an improved fuzzy set is used as the low-frequency fusion rule. A contrast analysis combined with Euclidean distance is used as the high-frequency fusion rule. The proposed fusion rules are designed to enhance the adaptability while preserving the details of the source image as much as possible. The experimental results demonstrate that compared with the traditional method, the fusion results obtained by the proposed method are more realistic, and the infrared targets are more evident. Furthermore, the objective evaluation metrics are better.

Multi-band images synchronous fusion based on NSST and fuzzy logical inference

This study addresses the novel problem of combining multiple band (far-infrared, near-infrared, and visible) images of the same scene to produce a synthetic image containing more information. The proposed approach first decomposes the source images using the non-subsampled shearlet transform (NSST). Then, fuzzy logic inference (FLI) is employed to solve the fuzzy problem of low-frequency image fusion. Notably, fuzzy rules (FRs) are derived according to basic human judgments, and membership functions (MFs) are obtained through a small number of experiments. Furthermore, some valuable conclusions are obtained while setting the parameters. Next, the absolute values of the high-pass coefficients are utilized to measure the activity level for high-frequency image fusion. Finally, the ultimate merged image is obtained by applying the inverse NSST to all integrated sub-images. Experimental results demonstrate that the proposed method not only effectively enhances targets and preserves details by successfully merging the far-infrared, near-infrared, and visible images, but also ensures improvements in the various quantitative parameters compared to existing methods.

Image Enhancement Based on Pulse Coupled Neural Network in the Nonsubsample Shearlet Transform Domain

In this study, pulse coupled neural network (PCNN) was modified and applied to the enhancement of blur images. In the transform domain of nonsubsample shearlet transform (NSST), PCNN was used to enhance the details of images in the low- and high-frequency subbands, and then the enhanced low- and high-frequency coefficients were used for NSST inverse transformation to obtain the enhanced images. The results showed that the proposed method can produce higher-quality images and suppress noise better than traditional image enhancement strategies.

Pansharpening with a Gradient Domain GIF Based on NSST

In order to improve the fusion quality of multispectral (MS) and panchromatic (PAN) images, a pansharpening method with a gradient domain guided image filter (GIF) that is based on non-subsampled shearlet transform (NSST) is proposed. First, multi-scale decomposition of MS and PAN images is performed by NSST. Second, different fusion rules are designed for high- and low-frequency coefficients. A fusion rule that is based on morphological filter-based intensity modulation (MFIM) technology is proposed for the low-frequency coefficients, and the edge refinement is carried out based on a gradient domain GIF to obtain the fused low-frequency coefficients. For the high-frequency coefficients, a fusion rule based on an improved pulse coupled neural network (PCNN) is adopted. The gradient domain GIF optimizes the firing map of the PCNN model, and then the fusion decision map is calculated to guide the fusion of the high-frequency coefficients. Finally, the fused high- and low-frequency coefficients are reconstructed with inverse NSST to obtain the fusion image. The proposed method was tested using the WorldView-2 and QuickBird data sets; the subjective visual effects and objective evaluation demonstrate that the proposed method is superior to the state-of-the-art pansharpening methods, and it can efficiently improve the spatial quality and spectral maintenance.

Edge-preserving image denoising using a deep convolutional neural network

Abstract This paper introduces a novel denoising approach making use of a deep convolutional neural network to preserve image edges. The network is trained by using the edge map obtained from the well-known Canny algorithm and aims at determining if a noisy patch in non-subsampled shearlet domain corresponds to the location of an edge. In the first step of the proposed denoising algorithm, we use the non-subsampled shearlet transform to decompose the noisy image into a low-frequency subband and a series of high-frequency subbands. Subsequently, 3D blocks are formed by stacking 2D blocks of high-frequency subbands along a specific direction. Each 3D patch is then fed to the trained deep convolutional neural network to determine if it belongs to the edge-related class or not. Finally, the NSST (non-subsampled shearlet transform) coefficients belonging to the edge-related class remain unchanged, and those not belonging to the edge-related class are denoised by the shrinkage method using an adaptive threshold. Experimental results on various test images including benchmark grayscale images and medical ultrasound images demonstrate that the proposed method achieves better performance compared to some state-of-the-art denoising approaches.

Enhancement of hyperspectral remote sensing images based on improved fuzzy contrast in nonsubsampled shearlet transform domain

In order to deal with the pseudo-Gibbs phenomenon in the process of hyperspectral remote sensing image enhancement, a novel image enhancement method based on nonsubsampled shearlet transform (NSST) is proposed in this paper. The main motivation of this study is to adjust the coefficient of remote sensing image enhancement as a pattern recognition task. Firstly, the input image is decomposed into a low-frequency component and some high-frequency components by NSST decomposition; Secondly, the guided filter is applied to process the low-frequency component to improve the contrast, and the improved fuzzy contrast is used to suppress the noise of the high-frequency components; Thirdly, the processed coefficients of low-frequency and high-frequency are reconstructed by inverse nonsubsampled shearlet transform (INSST), and the final enhanced image is obtained. The experimental results demonstrate that the proposed approach has obvious advantages in terms of objective data and subjective vision.

Multi-Focus Image Fusion Based on Adaptive Dual-Channel Spiking Cortical Model in Non-Subsampled Shearlet Domain

To get a better fused performance in the multi-focus image fusion based on a transform domain, a new multi-focus image algorithm combined with the adaptive dual-channel spiking cortical model (SCM) in non-subsampled shearlet (NSST) domain and the difference images is proposed in this paper. First, a basic fused image is constructed in the NSST domain by registering the source image and adaptive dual channel SCM (dual-channel SCM). Next, the focus areas of the sources input images based on the difference images between the basic fused image and the sources images are detected. In the end, the final fused image generated in this paper is realized by combining the focal regions. Because of the global coupling of the dual-SCM, the synchronization characteristics of the pulse, and the multi-resolution and direction of the NSST, the proposed algorithm can preserve the information of the source’s image well and present a clear image more in line with the human visual effects. In summary, the image fusion algorithm that we have designed is superior to the most advanced algorithms.

Multimodal Neuroimaging Fusion in Nonsubsampled Shearlet Domain Using Location-Scale Distribution by Maximizing the High Frequency Subband Energy

A fusion of medical imaging data obtained from different modalities plays an important role in the current clinical practice. In this paper, we propose a novel multimodal fusion algorithm for brain imaging data based on the statistical properties of nonsubsampled shearlet transform (NSST) coefficients and a novel energy maximization fusion rule. The marginal distributions of the high-frequency NSST coefficients exhibit heavier tails than the Gaussian distribution. As a consequence, after studying its characteristics, we use a heavy-tailed probability density function, student’s $t$ location-scale distribution, to describe the highly non-Gaussian statistics of empirical NSST coefficients by learning the parameters using maximum likelihood estimation. Then, we employ this model to develop a maximum a posteriori estimator to obtain the noise-free coefficients. Then, for the first time, a novel fusion rule for obtaining the fused NSST coefficients based on maximizing the energy in the high-frequency subbands is proposed. Experiments are carried out on fusing two or more multimodal neuroimages taken from the BrainWeb, Alzheimer’s Disease Neuroimaging Initiative (ADNI), and Whole Brain Atlas databases. It is seen from the subjective and objective results that the proposed multimodal neuroimaging fusion method significantly outperforms the state-of-the-art methods including under noisy scenarios, and hence, it is more robust. It is also observed that the signal intensities in the fused images are better enhanced when a more number of source images are being fused. The proposed technique should benefit the medical professionals in diagnosing neurological disorders, such as Alzheimer, epilepsy, and multiple sclerosis.

Multi-Focus Image Fusion Based on Residual Network in Non-Subsampled Shearlet Domain

In order to obtain a panoramic image which is clearer, and has more layers and texture features, we propose an innovative multi-focus image fusion algorithm by combining with non-subsampled shearlet transform (NSST) and residual network (ResNet). First, NSST decomposes a pair of input images to produce subband coefficients of different frequencies for subsequent feature processing. Then, ResNet is applied to fuse the low frequency subband coefficients, and improved gradient sum of Laplace energy (IGSML) perform high frequency feature information processing. Finally, the inverse NSST is performed on the fused coefficients of different frequencies to obtain the final fused image. In our method, we fully consider the low frequency global features and high frequency detail information in image by using NSST. For low-frequency coefficients fusion, we can also obtain the spatial information features of low-frequency coefficient images by using ResNet, which has a deep network structure. IGSML can use different directional gradients to process high-frequency subband coefficients of different levels and directions, which is more conducive to the fusion of the coefficients. The experiment results show that the proposed method has been improved in the structural features and edge texture in the fusion images.

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.

Non-Subsampled Shearlet Transform and Log-Transform Methods for Despeckling of Medical Ultrasound Images

Non-Subsampled Shearlet Transform and Log-Transform Methods for Despeckling of Medical Ultrasound Images

Medical Image Fusion With Parameter-Adaptive Pulse Coupled Neural Network in Nonsubsampled Shearlet Transform Domain

As an effective way to integrate the information contained in multiple medical images with different modalities, medical image fusion has emerged as a powerful technique in various clinical applications such as disease diagnosis and treatment planning. In this paper, a new multimodal medical image fusion method in nonsubsampled shearlet transform (NSST) domain is proposed. In the proposed method, the NSST decomposition is first performed on the source images to obtain their multiscale and multidirection representations. The high-frequency bands are fused by a parameter-adaptive pulse-coupled neural network (PA-PCNN) model, in which all the PCNN parameters can be adaptively estimated by the input band. The low-frequency bands are merged by a novel strategy that simultaneously addresses two crucial issues in medical image fusion, namely, energy preservation and detail extraction. Finally, the fused image is reconstructed by performing inverse NSST on the fused high-frequency and low-frequency bands. The effectiveness of the proposed method is verified by four different categories of medical image fusion problems [computed tomography (CT) and magnetic resonance (MR), MR-T1 and MR-T2, MR and positron emission tomography, and MR and single-photon emission CT] with more than 80 pairs of source images in total. Experimental results demonstrate that the proposed method can obtain more competitive performance in comparison to nine representative medical image fusion methods, leading to state-of-the-art results on both visual quality and objective assessment.

Shearlet and guided filter based despeckling method for medical ultrasound images

Ultrasound diagnostic techniques are widely used in medical clinical diagnostics. However, the presence of speckle noise in the ultrasound imaging process reduces the image quality and make inconveniences for the clinical diagnosis of the physician. Therefore, to decline the influence of speckle noise has important significance in medical ultrasound image diagnosis. In order to solve the problem of speckle noise, this paper proposes a novel de-speckle method for medical ultrasound imaging, which is based on nonsubsampled shearlet transformation (NSST) and guided filter. Firstly, the nonsubsampled Laplacian pyramid filtering is used to decompose the noisy image, and thus the image is decomposed into high frequency subbands and low frequency subbands. Under the direction of non-sampling filter bank, we obtain a high-frequency subband multi-directional decomposition; Secondly, based on the threshold function and the correlation of the shearlet coefficients in the transformation domain, an improved threshold shrinkage algorithm is proposed to perform the threshold shrinkage processing on the shearlet coefficients of the high frequency subbands. Finally, the low frequency subbands in the transformation domain are processed by the guided filter, and the denoised ultrasonic image is obtained by the inverse transformation of the shearlet. In order to verify the effectiveness of the proposed method, experiments are conducted, and the results were compared with those of other existing denoising filter. Showing that the proposed method has a strong de-noising performances and maintain image details.

Non-Subsampled Shearlet Transform and Log-Transform Methods for Despeckling of Medical Ultrasound Images

Non-Subsampled Shearlet Transform and Log-Transform Methods for Despeckling of Medical Ultrasound Images

Multi-Modal Medical Image Fusion With Adaptive Weighted Combination of NSST Bands Using Chaotic Grey Wolf Optimization

Recently, medical image fusion has emerged as an impressive technique in merging the medical images of different modalities. Certainly, the fused image assists the physician in disease diagnosis for effective treatment planning. The fusion process combines multi-modal images to incur a single image with excellent quality, retaining the information of original images. This paper proposes a multi-modal medical image fusion through a weighted blending of high-frequency subbands of nonsubsampled shearlet transform (NSST) domain via chaotic grey wolf optimization algorithm. As an initial step, the NSST is applied on source images to decompose into the multi-scale and multi-directional components. The low-frequency bands are fused based on a simple max rule to sustain the energy of an individual. The texture details of input images are preserved by an adaptively weighted combination of high-frequency images using a recent chaotic grey wolf optimization algorithm to minimize the distance between the fused image and source images. The entire process emphasizes on retaining the energy of the low-frequency band and the transferring of texture features from source images to the fused image. Finally, the fused image is formed using inverse NSST of merged low and high-frequency bands. The experiments are carried out on eight different disease datasets obtained from Brain Atlas, which consists of MR-T1 and MR-T2, MR and SPECT, MR and PET, and MR and CT. The effectiveness of the proposed method is validated using more than 100 pairs of images based on the subjective and objective quality assessment. The experimental results confirm that the proposed method performs better in contrast with the current state-of-the-art image fusion techniques in terms of entropy, VIFF, and FMI. Hence, the proposed method will be helpful for disease diagnosis, medical treatment planning, and surgical procedure.

A Novel Image Fusion Framework Based on Sparse Representation and Pulse Coupled Neural Network

Image fusion techniques are applied to the synthesis of two or more images captured in the same scene to obtain a high-quality image. However, most of the existing fusion algorithms are aimed at single-mode images. To improve the fusion quality of multi-modal images, a novel multi-sensor image fusion framework based on non-subsampled shearlet transform (NSST) is proposed. First, the proposed solution uses NSST to decompose source images into high- and low-frequency components. Then, an improved pulse coupled neural network (PCNN) is proposed to process high-frequency components. Thus, the feature extraction effect of the high-frequency component is meliorated. After that, a sparse representation (SR) based measure, including compact dictionary learning and Max-L1 fusion rule, is designed to enhance the detailed features of the low-frequency component. Finally, the final image is obtained by the reconstruction of high- and low-frequency components via NSST inverse transformation. The proposed method is compared with several existing fusion methods. The experiment results show that the proposed algorithm outperforms other algorithms in both subjective and objective evaluation.

Multi-Focus Image Fusion Based on a Non-Fixed-Base Dictionary and Multi-Measure Optimization

Multi-scale geometric analysis is a popular tool that is widely used in the field of multi-focus image fusion. It plays a large role in extracting the features from input images. In this paper, a novel multi-focus image fusion method based on a non-fixed-base dictionary and multi-measure optimization is presented in the non-subsampled shearlet transform (NSST) domain. The proposed approach contains the following four steps. The input images are first decomposed by NSST into low- and high-frequency coefficients. Second, a sparse representation (SR)-based framework is proposed and applied to merge the low-frequency coefficients of the input images. In this framework, a non-fixed-base dictionary iteratively trained by the previous dictionary is constructed, which represents the complex details of the source images. Third, a type-2 fuzzy logic scheme is introduced for the high-frequency coefficient fusion, which can effectively select the high-quality coefficients from the source images so that it can achieve better performance. Finally, an inverse NSST operation is conducted in the merged low- and high-pass subbands and thus obtains the initial fused image. A multi-measure optimization method is then employed to optimize the initial fused image, and thus, the final fused result is achieved. In this method, three different measures, namely, pixel difference, visual saliency, and similarity, are designed to select the focus regions more completely. The experimental results demonstrate that our proposed approach yields a better effect than other methods in both the visual quality and the objective assessment.