Nonsubsampled Contourlet Research Articles

Improved partial differential equation-based total variation approach to non-subsampled contourlet transform for medical image denoising

This article proposes an improved partial differential equation (PDE)-based total variation (TV) model that enhances grey and coloured brain tumour images obtained by magnetic resonance imaging. A nonsubsampled contourlet transform was applied to images from standard databases that converted into lowpass and highpass (or bandpass) contourlet coefficients. An improved version of the power-law transform method was used on the lowpass contourlet coefficients, and an adaptive threshold method was applied to the highpass (or bandpass) contourlet coefficients. The inverse contourlet transform was performed on all the enhanced contourlet coefficients to generate a complete brain tumour image. Finally, the PDE-based TV model was applied to this image to produce the denoised image. The performance of the suggested method was calculated in terms of the peak signal-to-noise ratio, mean square error, and structural similarity index. This method achieved the best peak signal-to-noise ratio, mean square error, and structural similarity index of 77.9846 dB, 0.00012612, and 97.895%, respectively, compared to the conventional PDE+modified transform-based gamma correction, adaptive PDE+generalized cross-validation, parallel magnetic resonance imaging, and Berkeley wavelet transform+support vector machine methods.

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.

Detection and classification of electroencephalogram signals for epilepsy disease using machine learning methods

AbstractThe electroencephalogram (EEG) signal plays a key role in the diagnosis of epilepsy. This study describes an automated classification of EEG signal for the detection of Epilepsy disease using soft computing methods. The proposed method is comprised of three modules: (a) transformation, (b) feature computation, and (c) feature classifications. In the first module, the nonsubsampled contourlet transform is applied on the EEG signal which decomposes the signal into approximate and directional subbands. The decomposition is done using nonsubsampled pyramid filter bank and nonsubsampled directional filter bank respectively. Secondly, the statistical features are extracted from the decomposed directional subbands using wavelet packet decomposition method. Finally, these features are classified by adaptive neuro‐Fuzzy inference system classification method, which classifies the EEG signal into either focal or nonfocal signal. The proposed method is tested on a set of EEG signals for validation. The average classification rate of the proposed EEG signal classification system is 99.4%. The proposed EEG signal classification methodology achieves a sensitivity of 99.7%, a specificity of 99.7%, and an accuracy of 99.4%. The results confirmed that the proposed method has a potential in the classification of EEG signals and thereby could further improve the diagnosis of epilepsy.

An optimized SVM based possibilistic fuzzy c-means clustering algorithm for tumor segmentation

To design an efficient partial differential equation-based total variation method for denoising and possibilistic fuzzy c-means clustering algorithm for segmentation and these methods presented the more detailed information of the MRI medical images compared to traditional methods. In this article, the pipeline of the proposed method described by two modules like pre-processing and segmentation. In pre-processing, noisy image is decomposed using nonsubsampled contourlet transform and it contains highpass contourlet coefficient (i.e., noisy coefficient) is removed by the threshold method as well. After reconstruction, the primary denoised image is enhanced by an improved partial differential equation-based total variation method in terms of image details like edges, boundaries, etc. In segmentation, the enhanced primary denoised image is segmented by an improved possibilistic fuzzy c-means clustering algorithm that avoids limitations in possibilistic c-means, fuzzy c-means, and K-means clustering. Next, a support vector machine classifier is utilized to identify brain tissues into gray matter, white matter, cerebrospinal fluid, and tumor part. The parameters were optimally selected by a grey wolf optimization algorithm for the classification of brain tissues. The performance of the proposed method is computed with reference to peak signal-to-noise ratio, mean square error, structural similarity index, sensitivity, specificity, and accuracy. The experimental results claimed that the proposed method is better than the traditional methods.

Improving medical image fusion method using fuzzy entropy and nonsubsampling contourlet transform

AbstractMany types of medical images must be fused, as single‐modality medical images can only provide limited information due to the imaging principles and the complexity of human organ structures. In this paper, a multimodal medical image fusion method that combines the advantages of nonsubsampling contourlet transform (NSCT) and fuzzy entropy is proposed to provide a basis for clinical diagnosis and improve the accuracy of target recognition and the quality of fused images. An image is initially decomposed into low‐ and high‐frequency subbands through NSCT. The corresponding fusion rules are adopted in accordance with the different characteristics of the low‐ and high‐frequency components. The membership degree of low‐frequency coefficients is calculated. The fuzzy entropy is also computed and subsequently used to guide the fusion of coefficients to preserve image details. High‐frequency components are fused by maximizing the regional energy. The final fused image is obtained by inverse transformation. Experimental results show that the proposed method achieves good fusion effect based on the subjective visual effect and objective evaluation criteria. This method can also obtain high average gradient, SD, and edge preservation and effectively retain the details of the fused image. The results of the proposed algorithm can provide effective reference for doctors to assess patient condition.

An Adaptive Arbitrary Multiresolution Decomposition for Multiscale Geometric Analysis

The nonsubsampled Laplacian pyramid (NSLP) is widely used as a common multiresolution decomposition method in various nonsubsampled image transforms. However, the NSLP has a fixed spectrum partition, and thus cannot represent images accurately, and flexibly. We propose a new adaptive arbitrary multiresolution decomposition to solve these problems. First, using the affine characteristics of a pseudopolar Fourier transform (PPFT), we apply a 1-D nonuniform filter bank to the modulated PPFT to obtain a 2-D arbitrary resolution filter bank. This filter surpasses the limitation of fixed spectrum partitioning of the traditional tree structure. We then demonstrate that the proposed method satisfies the compact frame condition, and has translation invariance, and a linear phase. Furthermore, we propose an adaptive spectrum division approach at various scales based on image spectrum information based on a 2-D filter bank of arbitrary multiresolution, so our method can capture important visual information more accurately. Finally, we combine our method with a nonsubsampled directional filter bank of the nonsubsampled contourlet transform to create a new multiscale geometric analysis (MGA) method, and verify that the new method can have perfect reconstruction properties. The new MGA also performs better in image denoising, and recognition experiments than state-of-the-art MGA methods with translation invariance.

Blind watermarking scheme based on Schur decomposition and non-subsampled contourlet transform

An invisibility and blind watermarking algorithm based on Schur decomposition and non-subsampled contourlet transform is designed to protect copyright. The cover image is decomposed by the non-subsampled contourlet transform. And the low pass sub-band of the non-subsampled contourlet transform is divided into 8 × 8 non-overlapping blocks. Then each block is performed by the Schur decomposition and the watermark information is embedded by modifying the largest energy element in the Schur domain. Before embedding into the cover image, the original watermark is scrambled with logistic map and Arnold transform to ensure the security. Besides, a synchronization mechanism based on scale invariant feature transform is designed for resisting geometrical attacks. The proposed watermarking algorithm is evaluated with structural similarity index, peak signal to noise ratio and bit error rate. Experimental results demonstrate that the proposed watermarking scheme performs better in terms of invisibility, robustness and payload than other similar schemes.

High-resolution SAR images segmentation using NSCT denoising and QIGA based parameters selection of PCNN model

The high-resolution synthetic aperture radar (SAR) images usually contain inhomogeneous coherent speckle noises. For the high-resolution SAR image segmentation with such noises, the conventional methods based on pulse coupled neural networks (PCNN) have to face heavy parameters with a low efficiency. In order to solve the problems, this paper proposes a novel SAR image segmentation algorithm based on non-subsampling Contourlet transform (NSCT) denoising and quantum immune genetic algorithm (QIGA) improved PCNN models. The proposed method first denoising the SAR images for a pre-processing based on NSCT. Then, by using the QIGA to select parameters for the PCNN models, such models self-adaptively select the suitable parameters for segmentation of SAR images with different scenes. This method decreases the number of parameters in the PCNN models and improves the efficiency of PCNN models. At last, by using the optimal threshold to binary the segmented SAR images, the small objects and large scales from the original SAR images will be segmented. To validate the feasibility and effectiveness of the proposed algorithm, four different comparable experiments are applied to validate the proposed algorithm. Experimental results have shown that NSCT pre-processing has a better performance for coherent speckle noises suppression, and QIGA-PCNN model based on denoised SAR images has an obvious segmentation performance improvement on region consistency and region contrast than state-of-the-arts methods. Besides, the segmentation efficiency is also improved than conventional PCNN model, and the level of time complexity meets the state-of-the-arts methods. Our proposed NSCT+QIGA-PCNN model can be used for small object segmentation and large scale segmentation in high-resolution SAR images. The segmented results will be further used for object classification and recognition, regions of interest extraction, and moving object detection and tracking.

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.

Absolute Transformation and Clahe Based High Performance Lucid Proposal for Image Processing

Image fusion is a one of the enhancement technique which is used to take the decision the images by the various types of sensors. Image fusion is nothing but the combination of two images which is helps to improve the quality of the image. In this paper, visible image and Infrared image are combined to acquire the informative image. Before and after image fusion, a new transformation technique is introduced to improve the quality of the image. To prove the quality of the image after applying new transformation technique, the fusion is done by four different techniques is used like Discrete Cosine Transform (DCT), Discrete Wavelet Transform (DWT), Non-Subsampled Contourlet Transform (NSCT) and Dual Tree Complex Wavelet Transform (DT-CWT). The comparison of following parameter values such as Entropy, Standard deviation, Mean gradient, Average pixel intensity and spatial frequency shows that proposed method is better to improve the image quality.

RETRACTED ARTICLE: Research on underwater flexible target recognition algorithm under non-uniform light

In underwater optical detection, due to the introduction of uneven light, the accuracy of underwater target recognition is reduced, especially when identifying underwater flexible targets. In this paper, a new type of depth wish-deformable convolutional neural network structure (DD-CNN) underwater target recognition algorithm is proposed for underwater flexible target recognition under the influence of non-uniform light. Firstly, the underwater invariant extraction algorithm based on non-subsampled contour let transform is used to extract the essential features of underwater images, and the underwater image after extraction is used as the input of the new depth wish-deformable convolutional neural network for target recognition. The experimental results show that the accuracy of using the proposed algorithm to identify underwater flexible targets is improved compared with the accuracy of the original structure, and the network convergence speed is faster.

Change Detection in Multispectral Remote Sensing Images with Leader Intelligence PSO and NSCT Feature Fusion

Change detection (CD) using Remote sensing images have been a challenging problem over the years. Particularly in the unsupervised domain it is even more difficult. A novel automatic change detection technique in the unsupervised framework is proposed to address the real challenges involved in remote sensing change detection. As the accuracy of change map is highly dependent on quality of difference image (DI), a set of Normalized difference images and a complementary set of Normalized Ratio images are fused in the Nonsubsampled Contourlet Transform (NSCT) domain to generate high quality difference images. The NSCT is chosen as it is efficient in suppressing noise by utilizing its unique characteristics such as multidirectionality and shift-invariance that are suitable for change detection. The low frequency sub bands are fused by averaging to combine the complementary information in the two DIs, and, the higher frequency sub bands are merged by minimum energy rule, for preserving the edges and salient features in the image. By employing a novel Particle Swarm Optimization algorithm with Leader Intelligence (LIPSO), change maps are generated from fused sub bands in two different ways: (i) single spectral band, and (ii) combination of spectral bands. In LIPSO, the concept of leader and followers has been modified with intelligent particles performing Lévy flight randomly for better exploration, to achieve global optima. The proposed method achieved an overall accuracy of 99.64%, 98.49% and 97.66% on the three datasets considered, which is very high. The results have been compared with relevant algorithms. The quantitative metrics demonstrate the superiority of the proposed techniques over the other methods and are found to be statistically significant with McNemar’s test. Visual quality of the results also corroborate the superiority of the proposed method.

A novel multi-source image fusion method for pig-body multi-feature detection in NSCT domain

The multi-source image fusion has been a hot topic during the recent years because of its higher detection rate. To improve the accuracy of pig-body multi-feature detection, a multi-source image fusion method was adopted in this field. However, the traditional multi-source image fusion methods could not obtain better contrast and more details of the fused image. To better detect shape and temperature feature of pig-body, a novel infrared and visible image fusion method was proposed in non-subsampled contourlet transform (NSCT) domain and named NSCT-GF-IAG. Through this technique, the visible and infrared images were first decomposed into a series of multi-scale and multi-directional sub-bands using NSCT. Then, to better represent the fine-scale of texture information and coarse-scale detail information, Gabor filter with even-symmetry and improved average gradient (IAG) were employed to fuse low-frequency and high-frequency sub-bands, respectively. Next, the fused coefficients were reconstructed into a final fusion image by inverse NSCT. Finally, the shape feature of pig-body was obtained by automatic threshold segmentation and optimized by morphological processing. Moreover, the highest temperature was extracted based on shape segmentation of pig-body. Experimental results showed that the proposed fusion method for detecting multi-feature was capable of achieving 2.175–5.129% higher average segmentation rate than the prevailing conventional methods. Besides this, the proposed method also improved efficiency in terms of time consumption.

A novel multi-modal medical image fusion algorithm

In order to improve the contrast of image fusion and highlight the unique characteristics of medical images, a multi-modal medical image fusion algorithm in the framework of non-subsampled contourlet transform (NSCT) is proposed in this paper. Firstly, the computed tomography images and magnetic resonance image are decomposed into low- and high-frequency sub-bands through the NSCT of multi-scale geometric transformation; secondly, for the low-frequency sub-band, the local area standard deviation method is selected or the fusion, while for the high-frequency sub-band, an adaptive pulse coupling neural network model is constructed and the fusion rules are set by the cumulative ignition times of iterative operation in the network; finally, the fusion image is obtained through image reconstruction. Experimental results show that the fusion results of the algorithm in this paper can improve the image fusion quality significantly and it has certain advantages in both visual effects and objective evaluation indexes, which provides a more reliable basis for clinical diagnosis and treatment of diseases.

Nonsubsampled contourlet transform-based conditional random field for SAR images segmentation

In this paper, we propose a new texture-based conditional random field (CRF) for Synthetic Aperture Radar (SAR) image segmentation. In our proposed algorithm to overcome the limitations of the intensity-based features, feature extraction is performed in the contourlet transform domain. We use the nonsubsampled contourlet transform (NSCT) as an overcomplete transform which compensates the shortcomings of the traditional contourlet. Applying the generalized Gaussian distribution (GGD) for the statistical description of NSCT coefficients, we simultaneously extract proper statistics from SAR image in the conditional random field model and overcome the speckle effects in the intensity-based features. In this way, not only there is no need to consider an additional term in unary function to model the statistics of SAR image but also, we no longer need to calculate the several criteria based on the histogram of speckled gray levels. Experimental results show the superiority of NSCT compared to the other transform-based features such as wavelet and also demonstrate the improvement of the accuracy in contrast to the schemes which are based on the intensity in the CRF model.

Fractal dimension based parameter adaptive dual channel PCNN for multi-focus image fusion

Multi-focus image fusion combines the focused parts of multiple images of the same scene to generate a fully focused image. Dual Channel Pulse coupled neural network (DCPCNN) is frequently used in image fusion framework due to its characteristics like global coupling, pulse synchronization of neurons and simultaneous processing of two images. However, it suffers from the manual setting of its parameters. This paper proposes a transform domain multi-focus image fusion method based on a novel parameter adaptive DCPCNN (PA-DCPCNN) model, in which the parameters are adaptively estimated using the inputs. Moreover, the linking strengths of the PA-DCPCNN model are computed per neuron using a new fractal dimension based focus measure (FDFM). First, the multi-scale and multi-direction decomposition of source images are performed using non-subsampled contourlet transform (NSCT) to obtain the low-pass sub-band and a number of band-pass directional sub-bands. Second, the band-pass directional sub-bands of the source images are fused using the PA-DCPCNN model. Third, the low-pass sub-bands of source images are combined using multi-scale morphological gradient (MSMG) and FDFM to generate the fused low-pass sub-band. Finally, the fused image is obtained by applying inverse NSCT to the fused sub-bands. The performance of the proposed method is compared with fourteen state-of-the-art multi-focus image fusion methods using six objective quality metrics. Experimental results demonstrate that the proposed method is competitive with the state-of-the-art methods in terms of both subjective and objective assessments.

Perceptual quality assessment for multimodal medical image fusion

Recent years have witnessed that the multimodal medical image fusion (MMIF) plays critical roles in clinical diagnostics and treatment. Many MMIF algorithms have been proposed to improve the MMIF images quality. The quality of multimodal medical fused images will significantly affect the results of the clinical diagnosis. However, little work has been designed to evaluate the effectiveness of MMIF algorithms and the quality of MMIF images. To this end, this paper presents a perceptual quality assessment method for MMIF. A MMIF image database (MMIFID) is first built to employ the classical MMIF algorithms, and the subjective experiment is conducted to assess the quality of each fused image. Then, a no-reference objective method is proposed for the perceptual quality evaluation of MMIF images,which uses Pulse Coupled Neural Network (PCNN) in Non-subsampled Contourlet Transform (NSCT). A fused image is decomposed by NSCT into low frequency sub-band (LFS) and high frequency sub-band (HFS). It is used to motivate the PCNN processing, and large firing times are employed to measure LFS and HFS. Finally, two components evaluation results are combined to obtain the overall objective quality score. Experimental results based on the MMIFID indicate that our presented method outperforms the existing image fusion quality evaluation metrics, and it provides a satisfactory correlation with subjective scores, which shows effectiveness in the quality assessment of medical fused images.

Performance degradation assessment for aircraft environmental control system: A method based on visual cognition

The aircraft environmental control system (AECS) plays a vital role in the normal operation of an airplane; thus, performance degradation of the AECS may result in failure and even catastrophic consequences. Because performance degradation is inevitable, performance degradation assessment is critical to provide information for appropriate maintenance actions. This paper proposed an integrated framework for performance degradation assessment of AECS by introducing the visual cognition theory into the specific method. First, an observer is used to generate a residual error, which contains degradation information of the AECS. Second, the generated residual error is transformed into a two-dimensional image based on a permutation method. Third, inspired by the multichannel characteristic (MCC), the transformed image is decomposed into several subbands based on the nonsubsampled contourlet transform (NCST) for multiscale and multidirectional feature extraction, thus capturing the precise characteristics of degradation. Then, inspired by the manifold sensing characteristic (MSC), the manifold space is established based on diffusion maps (DM) to reveal the intrinsic evolutionary law of health degradation. Finally, the geodesic distance is employed to represent the deviation between the current state and health baseline. As a health indicator, the confidence value (CV), which is derived from the geodesic distance, is capable of representing the degradation trend of the AECS. The whole process of degradation can be tracked in real time based on the proposed method. An AECS simulation model was established in MATLAB/Simulink, and two typical faults were introduced. The results based on simulation data demonstrate that the proposed assessment method can effectively reflect the degradation process of the AECS and the constructed health indicator is reasonable.

Pan-sharpening via multi-scale and multiple deep neural networks

Interpreting remote sensing images by combining manual visual interpretation and computer automatic classification and recognition is an important application of human–computer interaction (HCI) in the field of remote sensing. Remote sensing images with high spatial resolution and high spectral resolution is an important basis for automatic classification and recognition. However, such images are often difficult to obtain directly. In order to solve the problem, a novel pan-sharpening method via multi-scale and multiple deep neural networks is presented. First, the non-subsampled contourlet transform (NSCT) is employed to decompose the high resolution (HR)/low resolution (LR) panchromatic (PAN) images into the high frequency (HF)/low frequency (LF) images, respectively. For pan-sharpening, the training sets are only sampled from the HF images. Then, the DNN is utilized to learn the feature of the HF images in different directions of HR/LR PAN images, which is trained by the image patch pair sampled from HF images of HR/LR PAN images. Moreover, in the fusion stage, NSCT is also employed to decompose the principal component of initially amplified LR multispectral (MS) image obtained by the transformation of adaptive PCA (A-PCA). The HF image patches of LR MS, as the input data of the trained DNN, go through forward propagation to obtain the output HR MS image. Finally, the output HF sub-band images and the original LF sub-band images of LR MS image fuse into a new sub-band set. The inverse transformations of NSCT and A-PCA , residual compensation are conducted to obtain the pan-sharpened HR MS. The experimental results show that our method is better than other well-known pan-sharpening methods.