Nonsubsampled Contourlet Transform Research Articles

Multimodality medical image fusion based on non-sub-sampled contourlet transform

The fusion of medical images is an essential and effective technique for disease analysis. The current study proposed a Non Sub sampled Contourlet Transform (NSCT) image fusion technique in which Neuro Fuzzy with Binary Cuckoo Search (NFBCS) and Salp Swarm Optimisation (SSO) methods were utilised. The researchers successfully fused the images retrieved from Magnetic Resonance Imaging (MRI) and Computed Tomography (CT) and created a single merged image which becomes a new integrated diagnostic method. At the beginning, two unique sets of images such as MRI and CT were considered for the fusion procedure. These pairs of images are used in NSCT to generate the image and divide it into high frequency module and low-frequency module. Mixing policies are used here to generate and combine high- and low-frequencies. Compared to existing techniques, the results of the proposed technical tests exhibited better processing efficiency and delivered excellent results on subjective and objective evaluation criteria. This is particularly advantageous for accurate clinical analysis of the disease. The proposed work was carried out in MATLAB program. The results were tested on the medical images of brain and spine. The outcomes were compared in terms of MSE, RMSE and entropy.

Image Haze Removal Algorithm Based on Nonsubsampled Contourlet Transform

In order to avoid the noise diffusion and amplification caused by traditional dehazing algorithms, a single image haze removal algorithm based on nonsubsampled contourlet transform (HRNSCT) is proposed. The HRNSCT removes haze only from the low-frequency components and suppresses noise in the high-frequency components of hazy images, preventing noise amplification caused by traditional dehazing algorithms. First, the nonsubsampled contourlet transform (NSCT) is used to decompose each channel of a hazy and noisy color image into low-frequency sub-band and high-frequency direction sub-bands. Second, according to the low-frequency sub-bands of the three channels, the color attenuation prior and dark channel prior are combined to estimate the transmission map, and use the transmission map to dehaze the low frequency sub-bands. Then, to achieve the noise suppression and details enhancement of the dehazed image, the high-frequency direction sub-bands of the three channels are shrunk, and those shrunk sub-bands are enhanced according to the transmission map. Finally, the nonsubsampled contourlet inverse transform is performed on the dehazed low-frequency sub-bands and enhanced high-frequency sub-bands to reconstruct the dehazed and noise-suppressed image. The experimental results show that the HRNSCT provides excellent haze removal and noise suppression performance and prevents noise amplification during dehazing, making it well suited for removing haze from noisy images.

Poisson-Gaussian Noise Reduction for X-Ray Images Based on Local Linear Minimum Mean Square Error Shrinkage in Nonsubsampled Contourlet Transform Domain

Noise reduction is important for X-ray images because it can reduce radiation exposure to patients. X-ray image noise has a Poisson-Gaussian distribution, and recently, noise analysis and removal in multiscale transformations have been widely implemented. The nonsubsampled contourlet transform (NSCT) is a multiscale transformation suitable for medical images that separates the scale and direction. This study proposes a Poisson-Gaussian noise-removal method using NSCT shrinkage that is based on the characteristics of Poisson-Gaussian noise in NSCT domain. It has the structure of a block-matching 3D filtering algorithm in the form of basic estimation and noise removal process; however, the main processes are modified to consider Poisson-Gaussian noise characteristics. In the basic estimation process, an NSCT shrinkage method that is suitable for Poisson-Gaussian noise characteristics is developed by optimizing the local linear minimum mean square error estimator in the NSCT domain. In the denoising step, the noise term of the Wiener filter is determined using the result of the NSCT shrinkage, and finally, the denoised image is obtained. The proposed method is applied to simulated and real X-ray images and is compared with other state-of-the-art Poisson-Gaussian noise removal methods; it exhibits excellent results in both quantitative and qualitative aspects.

A novel multi-modal fundus image fusion method for guiding the laser surgery of central serous chorioretinopathy.

The angiography and color fundus images are of great assistance for the localization of central serous chorioretinopathy (CSCR) lesions. However, it brings much inconvenience to ophthalmologists because of these two modalities working independently in guiding laser surgery. Hence, a novel fundus image fusion method in non-subsampled contourlet transform (NSCT) domain, aiming to integrate the multi-modal CSCR information, is proposed. Specifically, the source images are initially decomposed into high-frequency and low-frequency components based on NSCT. Then, an improved deep learning-based method is employed for the fusion of low-frequency components, which helps to alleviate the tedious process of manually designing fusion rules and enhance the smoothness of the fused images. The fusion of high-frequency components based on pulse-coupled neural network (PCNN) is closely followed to facilitate the integration of detailed information. Finally, the fused images can be obtained by applying an inverse transform on the above fusion components. Qualitative and quantitative experiments demonstrate the proposed scheme is superior to the baseline methods of multi-scale transform (MST) in most cases, which not only implies its potential in multi-modal fundus image fusion, but also expands the research direction of MST-based fusion methods.

Computational Effective Multimodal Medical Image Fusion in NSCT Domain

Multimodal Medical Image Fusion provides comprehensive data in recent medical image diagnosis applications. This paper presents a novel Multimodal Medical Image fusion framework based on the Angular Consistency (AC) and Sub Band Adaptive Filtering (SAF). In the proposed model, the Non-subsampled Contourlet Transform (NSSCT) decomposes the source image pair into high frequency (HF) and low frequency (LF) sub bands. The LF bands are integrated with Angular consistency rule that can fuse the finer details which are very much helpful in medical image diagnosis. Further the HF bands are processed through sub band adaptive filtering such that the redundant information is filtered out and only significant information is preserved. At last the fused sub bands are processed for Inverse Non-subsampled Contourlet Transform to produce a fused image. Experimental evaluation is accomplished through a real time image data set and the performance is measured through Edge Based Similarity index, Local Quality Index and Computational time. The obtained results show that the proposed system achieves a satisfactory results compared to the conventional methods.

Infrared and Visible Image Fusion Method Based on ResNet in a Nonsubsampled Contourlet Transform Domain

Although the traditional image fusion method can obtain rich image results, obvious artificial noise and artifacts are often present in the resulting image. Fusion algorithms based on neural networks can avoid the shortcomings of traditional methods, but they are more complex and less flexible. In this study, we proposed a fusion method using the deep residual neural network ResNet152, which can not only effectively suppress artificial noise but also preserve the edge details of the image and improve the efficiency of the neural network. The proposed method is characterized by a multiscale transformation of an infrared image and visible light image in the optimized nonsubsampled contourlet transformation domain, and the deep residual neural network ResNet152 is used to extract the deep features of the low-pass component to guide the fusion of the low-pass component. The bandpass component is fused by taking the modulus maximum. This method can fully retain the global features and structural information of the source image in the result image. Compared to existing fusion methods using public test image sets, the experimental results show that on a subjective level, the fusion method creates sharper depth edges and fewer noise artifacts than traditional fusion methods. From an objective perspective, the average value for the results of the evaluation function is greater than that of other fusion methods.

Blind Digital Watermarking Framework Based on DTCWT and NSCT for Telemedicine Application

In this work, a blind watermarking framework for medical images based on Dual-Tree Complex Wavelet Transform (DTCWT) and Non-subsampled Contourlet Transform (NSCT) is proposed. The core idea of this technique is to embed the watermark in the appropriate NSCT sub-band obtained by decomposing the cover image low coefficients purchased from the DTCWT standing on a quantization embedding function, the extraction phase is done without the requirement of the original cover image, what makes it a fully blind process. As clarity and integrity of the retrieved watermark are mandatory, a series of tests were exerted to affirm the robustness of the proposed scheme. The effectiveness of the watermarking is validated by using the peak signal-to-noise ratio (PSNR) and the structural similarity index measure (SSIM) through experiments. Simulation results demonstrate the invisibility of the proposed method and its strong robustness against various attacks, including additive noise, image filtering, JPEG compression, amplitude scaling, rotation attack, and combinational attack. Furthermore, the method in hands outperformance within the quantitative comparisons with other techniques in the literature in terms of rapid execution time, and quality extraction of hidden information, and appropriateness to be integrated for secure exchange in the healthcare sector.

Brain medical image fusion scheme based on shuffled frog‐leaping algorithm and adaptive pulse‐coupled neural network

Aiming at the problems of low contrast and blurred edge textures in medical image fusion, a new fusion scheme in non-subsampled contourlet transform (NSCT) domain is proposed to improve the quality of fused brain images which is based on pulse-coupled neural network (PCNN) and shuffled frog-leaping algorithm (SFLA). First, the source images are decomposed into low-frequency (LF) and high-frequency (HF) subbands using NSCT; if one of the source images is multicolour, then hue, saturation and brightness (HSI) transform is needed first. Second, different PCNN fusion rules are designed for LF and HF subbands according to their features, respectively. Parameters including decay time constants and amplification factors are optimised by SFLA. Finally, the fused image is reconstructed by inverse NSCT; and if necessary, an inverse HSI transform is needed. Visual and quantitative analysis of experimental results show that the fused image preserves more information of the source images, and the ability of edge retention is strong. The scheme has prominent advantages in mutual information and QAB/F for multimodal brain images, including MRI-PET, MRI-SPECT, and CT-MRI, which proves that it can obtain better visual effect and have strong robustness as well as wide applications.

Combining Segmentation Network and Nonsubsampled Contourlet Transform for Automatic Marine Raft Aquaculture Area Extraction from Sentinel-1 Images

Marine raft aquaculture (MFA) plays an important role in the marine economy and ecosystem. With the characteristics of covering a large area and being sparsely distributed in sea area, MFA monitoring suffers from the low efficiency of field survey and poor data of optical satellite imagery. Synthetic aperture radar (SAR) satellite imagery is currently considered to be an effective data source, while the state-of-the-art methods require manual parameter tuning under the guidance of professional experience. To preclude the limitation, this paper proposes a segmentation network combined with nonsubsampled contourlet transform (NSCT) to extract MFA areas using Sentinel-1 images. The proposed method is highlighted by several improvements based on the feature analysis of MFA. First, the NSCT was applied to enhance the contour and orientation features. Second, multiscale and asymmetric convolutions were introduced to fit the multisize and strip-like features more effectively. Third, both channel and spatial attention modules were adopted in the network architecture to overcome the problems of boundary fuzziness and area incompleteness. Experiments showed that the method can effectively extract marine raft culture areas. Although further research is needed to overcome the problem of interference caused by excessive waves, this paper provides a promising approach for periodical monitoring MFA in a large area with high efficiency and acceptable accuracy.

Infrared and visible image fusion using multi‐scale NSCT and rolling‐guidance filter

Image fusion is essential to produce a complementary and comprehensive image, with the source images derived from different sensors, captured from different illumination conditions. In this study, a novel multi-scale image fusion based on the combination of non-subsampled contourlet transform (NSCT) and rolling-guidance filter (RGF) is used to enhance the edges and texture details better than the conventional methods. Initially, infrared (IR) and visible (VIS) source images are multi-scale decomposed to low-frequency and high-frequency sub-band coefficients by NSCT for the best representation of edges and curves. Further, the low-frequency coefficients are decomposed into the base and detail layers by a combination of RGF and GF (Gaussian filter) to retain the features in multiple scales and to reduce halos near the edges. Base layers are fused by saliencybased fusion rule and detail layers are fused by Max absolute rule. High-frequency coefficients are fused by consistency verification based fusion rule to preserve visual details and to suppress noise from source images. Finally, the image is reconstructed by inverse NSCT with good visual perception. Experimental results are evaluated by different evaluation metrics and the results suggest that the proposed method results with better improved source information, clarity and contrast.

Hardware design of real-valued NSCT transform for biomedical video compression

Abstract Number based Non-Subsampled Contourlet Transform (NSCT) diminishes the difficulty of the transformation operation kernel in biomedical video compression by dispensing the requirement for floating number mathematics. Yet, the dynamic scope of INSCT is huge and in this manner, equipment cost is more. In this manuscript, an innovative transformation operation kernel for biomedical video compression is projected which utilizes a novel set of complex and real- esteemed -NSCT coefficients. The projected NSCT decreases the equipment cost, area utilized and propagation time by diminishing the complication with intermediary information duration. Proposed transformation can be implemented by digit parallel multiplications and serial shift operations. Thus the itinerant fault can be decreased by standardize this numeral information and shift quantity for Biomedical video compression. Intermediary information duration and area, power consumption of the proposed method is fewer.

IHS-based pan-sharpening technique for visual quality improvement using KPCA and enhanced SML in the NSCT domain

ABSTRACT Due to the different characteristics of satellite imagery, pan-sharpening has become an important field of remote sensing science. It is a reliable method to fuse the high-resolution panchromatic (Pan) image with the low-resolution multispectral (MS) images to generate a composite high-resolution MS (pan-sharpened) image. In this paper, we propose a robust pan-sharpening technique which combines the nonsubsampled contourlet transform (NSCT) with kernel principal component analysis (KPCA). An enhancement method is executed on the source MS image to retain the maximum spatial information present in the MS bands based on the intensity hue saturation (IHS) colour space transform. The proposed pan-sharpening technique is performed as follows: first, the source Pan and MS images are divided into high and low-pass coefficients by NSCT. Second, KPCA is used to propose an effective fusion rule for choosing appropriate low-pass NSCT coefficients for fusion. The high-pass coefficients are fused by proposing an enhanced sum modified Laplacian method (SML). Finally, the final pan-sharpened image is obtained by performing an inverse NSCT and inverse IHS transform on the fused low and high-pass coefficients. Four different groups of satellite image datasets are utilized in the experiments, which show that the proposed technique can both preserve the spatial details of the source images well and avoid spectral distortion. In addition, different image fusion quality metrics are adopted to evaluate the spectral and spatial qualities of the pan-sharpened image. Compared to the state-of-the-art pan-sharpening methods, the proposed technique achieved better performance in balancing spectral and spatial information and improved pan-sharpening results in terms of both visual quality and objective measurements.

Intensity image quality assessment based on multiscale gradient magnitude similarity deviation

Laser active imaging is widely used in many fields. The intensity image quality of laser active imaging is affected by various degradations, such as speckle effect and noise. Most existing objective image quality assessment (IQA) methods that consider only a single distorted image are not suitable for an intensity image. A multiscale full-reference intensity IQA method is presented. The proposed method is based on an improved gradient magnitude similarity deviation (GMSD) in the nonsubsampled contourlet transform (NSCT) domain. The reference and distorted images are decomposed by NSCT to emulate the multichannel structure of the human visual system. Then, the GMSD of each sub-band is computed to capture the intensity image quality. At last, the contrast sensitivity function implementation is employed in the sub-bands of the NSCT domain. All sub-bands’ GMSD is evaluated and pooled together to yield the objective quality index of a distorted intensity image. Experimental results show that the proposed method can effectively and accurately evaluate the quality of intensity images, and it is highly consistent with subjective perception.

Multi‐modal image fusion based on saliency guided in NSCT domain

Image fusion aims at aggregating the redundant and complementary information in multiple original images, the most challenging aspect is to design robust features and discriminant model, which enhances saliency information in the fused image. To address this issue, the authors develop a novel image fusion algorithm for preserving the invariant knowledge of the multimodal image. Specifically, they formulate a novel unified architecture based on non-subsampled contourlet transform (NSCT). Their method introduces Quadtree decomposition and Bezier interpolation to extract crucial infrared features. Furthermore, they propose a saliency advertising phase congruency-based rule and local Laplacian energy-based rule for low- and high-pass sub-bands fusion, respectively. In this approach, the fusion image could not only combine the local and global features of the source image to avoid smoothing the edge of the target, but also retain the minor scales details and resists the interference noise of the multi-modal image. Both objective assessments and subjective visions of experimental results indicate that the proposed algorithm performs competitively in both objective evaluation criteria and visual quality.

NSCT-based robust image watermarking in DC components of APDCBT using singular value decomposition

Digital watermarking is an emerging solution for securing multimedia products from illegal or unauthorized manipulation. Robustness and visual imperceptibility are two characteristics of watermarking, which can be achieved by utilizing various transform techniques. Our proposed robust watermarking technique used a hybrid transform composed of three transform techniques: nonsubsampled contourlet transform (NSCT), all phase discrete cosine biorthogonal transform (APDCBT), and singular value decomposition (SVD). NSCT has outstanding shift-invariant, and directional properties; its low-frequency band of an image contains a large amount of visual information. Besides, APDCBT can attenuate the high-frequency components, and its direct current (DC) components have more visual details of an image. For gathering these DC components, we applied APDCBT on the low-frequency band of NSCT of a cover image after 8 × 8 block portioning. Then the binary watermark is inserted into perceptually significant DC components using SVD. The feasibility of the proposed technique is justified using computer simulation as well as this scheme is passed through imperceptibility and robustness test. Finally, a comparative analysis demonstrates that our proposed method achieves satisfactory improvements over the existing two watermarking techniques.

A novel multisource pig-body multifeature fusion method based on Gabor features

The multi-source image fusion has been a hot topic during recent years because of its higher segmentation accuracy rate. However, the traditional multi-source image fusion methods could not obtain better contrast and more details of the fused image. To better detect the pig-body feature, a novel infrared and visible image fusion method for pig-body segmentation and temperature detection is proposed in non-subsampled contourlet transform (NSCT) domain, named as NSCT-GF. Firstly, the visible and infrared images were decomposed into a series of multi-scale and multi-directional sub-bands using NSCT. Then, to better represent the fine-scale of texture information, the Gabor energy map was extracted by Gabor filter with even-symmetry, and the low-frequency coefficients were fused by the maximum of Ordinal encoding. Then, to preserve the more coarse-scale and edge detail information, Gabor filter with odd-symmetry was employed to fuse high-frequency NSCT sub-bands and the fused coefficients were reconstructed into a final fusion image by inverse NSCT. Next, the pig-body shape was obtained by Ostu automatic threshold segmentation and optimized by morphological processing. Finally, the pig-body temperature was extracted based on shape segmentation. Experimental results showed that the proposed segmentation method was capable of achieving 1.84–3.89% higher average segmentation accuracy rate than the prevailing conventional methods and also improved efficiency in terms of time consumption. It lays a foundation for accurately measuring the temperature of pig-body.

Infrared and visible image fusion method of dual NSCT and PCNN.

To solve the problem that the details of fusion images are not retained well and the information of feature targets is incomplete, we proposed a new fusion method of infrared (IR) and visible (VI) image—IR and VI image fusion method of dual non-subsampled contourlet transform (NSCT) and pulse-coupled neural network (PCNN). The method makes full use of the flexible multi-resolution and multi-directional of NSCT, and the global coupling and pulse synchronization excitation characteristics of PCNN, effectively combining the features of IR image with the texture details of VI image. Experimental results show that the algorithm can combine IR and VI image features well. At the same time, the obtained fusion image can better display the texture information of image. The fusion performance in contrast, detail information and other aspects is better than the classical fusion algorithm, which has better visual effect and evaluation index.

Two-stage sparse representation of NSCT features with application to SAR target classification

To improve synthetic aperture radar (SAR) target classification performance, both the feature extraction and classification algorithms are considered. The nonsubsampled contourlet transform (NSCT) is employed to decompose SAR images to generate multi-layer components, which provide more discriminative descriptions for the targets. During the classification, the two-stage sparse representations are developed. Each NSCT component is classified using the sparse representation-based classification (SRC). Afterwards, the joint sparse representation (JSR) is adopted to represent the selected NSCT components from the first stage, which are assumed to be highly discriminative. Based on the two decisions, the target label of the test sample is determined. In the experiment, the moving and stationary target acquisition and recognition (MSTAR) dataset is used to set up scenarios for performance evaluation including the standard operating condition (SOC) and several extended operating conditions (EOCs). The results show the superior performance of the proposed methodover some current methods.

A novel fusion method based on dynamic threshold neural P systems and nonsubsampled contourlet transform for multi-modality medical images

Dynamic threshold neural P systems (DTNP systems) are a distributed parallel computing model with an interesting mechanism involving the cooperative spiking of neurons in a local region. In this paper, this mechanism is combined with the nonsubsampled contourlet transform (NSCT) to develop a novel fusion method for multi-modality medical images. The complementary information of multi-modality images is extracted using an improved novel sum-modified Laplacian (INSML) feature, which is used in the fusion rules for the low-frequency NSCT coefficients. Moreover, the high-frequency NSCT coefficients are extracted using the WLE-INSML features, which are used to construct the fusion rules for these coefficients. The proposed fusion method is evaluated on an open dataset consisting of twelve pairs of multi-modality medical images. In addition, it is compared with nine previously reported fusion methods and four deep learning based fusion methods. The qualitative and quantitative experimental results demonstrate the advantage of the proposed fusion method in terms of the visual quality and fusion performance.

Multi-modality medical image fusion technique using multi-objective differential evolution based deep neural networks.

The advancements in automated diagnostic tools allow researchers to obtain more and more information from medical images. Recently, to obtain more informative medical images, multi-modality images have been used. These images have significantly more information as compared to traditional medical images. However, the construction of multi-modality images is not an easy task. The proposed approach, initially, decomposes the image into sub-bands using a non-subsampled contourlet transform (NSCT) domain. Thereafter, an extreme version of the Inception (Xception) is used for feature extraction of the source images. The multi-objective differential evolution is used to select the optimal features. Thereafter, the coefficient of determination and the energy loss based fusion functions are used to obtain the fused coefficients. Finally, the fused image is computed by applying the inverse NSCT. Extensive experimental results show that the proposed approach outperforms the competitive multi-modality image fusion approaches.