Nonsubsampled Contourlet Research Articles

MRI and PET image fusion using the nonparametric density model and the theory of variable-weight

Medical image fusion is important in the field of clinical diagnosis because it can improve the availability of information contained in images. Magnetic Resonance Imaging (MRI) provides excellent anatomical details as well as functional information on regional changes in physiology, hemodynamics, and tissue composition. In contrast, although the spatial resolution of Positron Emission Tomography (PET) provides is lower than that an MRI, PET is capable of depicting the tissue's molecular and pathological activities that are not available from MRI. Fusion of MRI and PET may allow us to combine the advantages of both imaging modalities and achieve more precise localization and characterization of abnormalities. Previous image fusion algorithms, based on the estimation theory, assume that all distortions follow Gaussian distribution and are therefore susceptible to the model mismatch problem. To overcome this mismatch problem, we propose a new image fusion method with multi-resolution and nonparametric density models (MRNDM). The RGB space registered from the source multi-modal medical images is first transformed into a generalized intensity-hue-saturation space (GIHS), and then is decomposed into the low- and high-frequency components using the non-subsampled contourlet transform (NSCT). Two different fusion rules, which are based on the nonparametric density model and the theory of variable-weight, are developed and used to fuse low- and high-frequency coefficients. The fused images are constructed by performing the inverse of the NSCT operation with all composite coefficients. Our experimental results demonstrate that the quality of images fused from PET and MRI brain images using our proposed method MRNDM is higher than that of those fused using six previous fusion methods.

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.

Change Detection of Water Resources via Remote Sensing: An L-V-NSCT Approach

Aiming at the change detection of water resources via remote sensing, the non-subsampling contour transformation method combining a log-vari model and the Stractural Similarity of Variogram (VSSIM) model, namely log-vari and VSSIM based non-subsampled contourlet transform (L-V-NSCT) approach, is proposed. Firstly, a differential image construction method based on non-subsampled contourlet transform (NSCT) texture analysis is designed to extract the low-frequency and high-frequency texture features of the objects in the images. Secondly, the texture features of rivers, lakes and other objects in the images are accurately classified. Finally, the change detection results of regions of interest are extracted and evaluated. In this experiment, the L-V-NSCT approach is compared with other methods with the results showing the effectiveness of this method. The change in Dongting Lake is also analyzed, which can be used as a reference for relevant administrative departments.

Feature Extraction of Foam Nickel Surface Based on Multi-Scale Texture Analysis

In the foam nickel process, texture is the indicator of foam nickel performance. In order to recognize foam nickel surface defects accurately and provide guidance for production operations, this paper proposes a method for extracting foam nickel image textures based on multi-scale texture analysis. First, nonsubsampled contourlet (NSCT) is used to carry out foam nickel image multi-scale decomposition, and the low-frequency and high-frequency components following decomposition are used to characterize different defect details. Then, the Haralick eigenvalue, which measures the foam nickel image texture information at each sub-band, is calculated. The KPCA and optimal DAG-SVM are adopted in order to reduce the parameter dimension and clarify defects. Tests are carried out on the foam nickel surface image samples, including crack, scratch, pollution, leakage, and indentation tests. The results indicate that the method proposed in this paper can extract different pieces of detailed texture information and can achieve a defect-identifying accuracy of up to 88.9%.

A New Pulse Coupled Neural Network (PCNN) for Brain Medical Image Fusion Empowered by Shuffled Frog Leaping Algorithm.

Recent research has reported the application of image fusion technologies in medical images in a wide range of aspects, such as in the diagnosis of brain diseases, the detection of glioma and the diagnosis of Alzheimer’s disease. In our study, a new fusion method based on the combination of the shuffled frog leaping algorithm (SFLA) and the pulse coupled neural network (PCNN) is proposed for the fusion of SPECT and CT images to improve the quality of fused brain images. First, the intensity-hue-saturation (IHS) of a SPECT and CT image are decomposed using a non-subsampled contourlet transform (NSCT) independently, where both low-frequency and high-frequency images, using NSCT, are obtained. We then used the combined SFLA and PCNN to fuse the high-frequency sub-band images and low-frequency images. The SFLA is considered to optimize the PCNN network parameters. Finally, the fused image was produced from the reversed NSCT and reversed IHS transforms. We evaluated our algorithms against standard deviation (SD), mean gradient (Ḡ), spatial frequency (SF) and information entropy (E) using three different sets of brain images. The experimental results demonstrated the superior performance of the proposed fusion method to enhance both precision and spatial resolution significantly.

A multi-scale data fusion framework for bone age assessment with convolutional neural networks

Bone age assessment (BAA) has various clinical applications such as diagnosis of endocrine disorders and prediction of final adult height for adolescents. Recent studies indicate that deep learning techniques have great potential in developing automated BAA methods with significant advantages over the conventional methods based on handcrafted features. In this paper, we propose a multi-scale data fusion framework for bone age assessment with X-ray images based on non-subsampled contourlet transform (NSCT) and convolutional neural networks (CNNs). Unlike the existing CNN-based BAA methods that adopt the original spatial domain image as network input directly, we pre-extract a rich set of features for the input image by performing NSCT to obtain its multi-scale and multi-direction representations. This feature pre-extraction strategy could be beneficial to network training as the number of annotated examples in the problem of BAA is typically quite limited. The obtained NSCT coefficient maps at each scale are fed into a convolutional network individually and the information from different scales are then merged to achieve the final prediction. Specifically, two CNN models with different data fusion strategies are presented for BAA: a regression model with feature-level fusion and a classification model with decision-level fusion. Experiments on the public BAA dataset Digital Hand Atlas demonstrate that the proposed method can obtain promising results and outperform many state-of-the-art BAA methods. In particular, the proposed approaches exhibit obvious advantages over the corresponding spatial domain approaches (generally with an improvement of more than 0.1 years on the mean absolute error), showing great potential in the future study of this field.

A Contrast Enhancement Method for Improving Surface Defects Detection of Elevator Compensation Chain

A contrast enhancement method based on the adaptive noise threshold estimation and logarithmic function in nonsubsampled contourlet transform (NSCT) domain is proposed, which can improve the defects segmentation accuracy of the elevator compensation chain. After extracting region of interest (ROI) according to the spatial location of defects, it is transformed into NSCT domain, where the high-frequency subband coefficients corresponding to noise is suppressed by the adaptive noise threshold estimation. Then, the logarithmic function transformation is used to enhance the image edges to a variable extent. Finally, the enhanced image is segmented by watershed algorithm based on laws texture analysis. Experimental results demonstrate that the performance of the proposed method is superior to the existing methods in terms of both the quality of contrast enhancement and the segmentation accuracy.

An improved multimodal medical image fusion scheme based on hybrid combination of nonsubsampled contourlet transform and stationary wavelet transform

AbstractThis research proposes an improved hybrid fusion scheme for non‐subsampled contourlet transform (NSCT) and stationary wavelet transform (SWT). Initially, the source images are decomposed into different sub‐bands using NSCT. The locally weighted sum of square of the coefficients based fusion rule with consistency verification is used to fuse the detailed coefficients of NSCT. The SWT is employed to decompose approximation coefficients of NSCT into different sub‐bands. The entropy of square of the coefficients and weighted sum‐modified Laplacian is employed as the fusion rules with SWT. The final output is obtained using inverse NSCT. The proposed research is compared with existing fusion schemes visually and quantitatively. From the visual analysis, it is observed that the proposed scheme retained important complementary information of source images in a better way. From the quantitative comparison, it is seen that this scheme gave improved edge information, clarity, contrast, texture, and brightness in the fused image.

A parallel fusion method of remote sensing image based on NSCT

Remote sensing image fusion is very important for playing the advantages of a variety of remote sensing data. However, remote sensing image fusion is large in computing capacity and time consuming. In this paper, in order to fuse remote sensing images accurately and quickly, a parallel fusion algorithm of remote sensing image based on NSCT (nonsubsampled contourlet transform) is proposed. In the method, two important kinds of remote sensing image, multispectral image and panchromatic image are used, and the advantages of parallel computing in high performance computing and the advantages of NSCT in information processing are combined. In the method, based on parallel computing, some processes with large amount of calculation including IHS (Intensity, Hue, Saturation) transform, NSCT, inverse NSCT, inverse IHS transform, etc., are done. To realize the method, multispectral image is processed with IHS transform, and the three components, I, H, and S are gotten. The component I and the panchromatic image are decomposed with NSCT. The obtained low frequency components of NSCT are fused with the fusion rule based on the neighborhood energy feature matching, and the obtained high frequency components are fused with the fusion rule based on the subregion variance. Then the low frequency components and the high frequency components after fusion are processed with the inverse NSCT, and the fused component is gotten. Finally, the fused component, the component H and the component S are processed with the inverse IHS transform, and the fusion image is obtained. The experiment results show that the proposed method can get better fusion results and faster computing speed for multispectral image and panchromatic image.

基于非下采样双树复轮廓波与自适应分块的红外与可见光图像融合

基于非下采样双树复轮廓波与自适应分块的红外与可见光图像融合

High-Resolution SAR Change Detection Based on ROI and SPP Net

With the resolution increasing, the structure information becomes more and more abundant in Synthetic Aperture Radar (SAR) images. The speckle noise generated by the coherent imaging mechanism, has a great influence on the detection accuracy and detection difficulty accordingly in high-resolution SAR change detection. In this paper, a multivariate change detection framework based on non-subsampled contourlet transform (NSCT), deep belief networks (DBN), fuzzy c-means (FCM) clustering, and global-local spatial pyramid pooling (SPP) net is proposed. NSCT decomposes the image into multiple scales and DBN is used for extracting feature of the decomposed coefficient matrix. FCM converges the similarity matrix of the initial features by DBN into two classes as a pseudo-label for global-local SPP net training data. The global-local SPP net consists of a large-scale region of interest (ROI) SPP net and a small-scale change detection SPP net. The combination of ROI and the SPP net, as well as the fusion between different scales, weakens the interference of the unchanged information and effectively eliminates a large number of redundant information. The experimental results show that our proposed method can effectively remove speckle noise and improve the robustness of high-resolution SAR change detection.

非下采样Contourlet变换域内结合模糊逻辑和自适应脉冲耦合神经网络的图像融合

非下采样Contourlet变换域内结合模糊逻辑和自适应脉冲耦合神经网络的图像融合

CT and MRI image fusion based on multiscale decomposition method and hybrid approach

In the fusion process of medical computed tomography (CT) and magnetic resonance image (MRI), traditional multiscale methods often reduce the contrast of fused images. Although sparse representation (SR) methods overcome this shortcoming, they are often too smooth along the strong edges of the fusion image. To overcome these shortcomings, CT and MRI image fusion based on multiscale decomposition method and hybrid approach is proposed. There are three main steps. First, the cartoon parts and texture parts of CT and MRI are obtained by the improved image decomposition method using global sparse gradients. Second, the large structure cartoon parts are fused using the specific cartoon dictionary and the `L1-max norm' principle. The textured parts are fused using non-subsampled contourlet transformation (NSCT) and the maximum energy rule. Finally, the final result is obtained by superimposing the fused cartoon part and the fused texture part. The experimental results demonstrate that the proposed method outperforms the state-of-the-art method SR and NSCT in terms of visual effect and objective quality.

Particle swarm optimisation aided weighted averaging fusion strategy for CT and MRI medical images

Multimodal medical image fusion is a technique that combines two or more images into a single output image in order to enhance the accuracy of clinical diagnosis. In this paper, a non-subsampled contourlet transform (NSCT) image fusion framework that combines CT and MRI images is proposed. The proposed method decomposes the source images into low and high frequency bands using NSCT and the information across the bands are combined using weighted averaging fusion rule. The weights are optimised by particle swarm optimisation (PSO) with an objective function that jointly maximises the entropy and minimises root mean square error to give improved image quality, which makes different from existing fusion methods in NSCT domain. The performance of the proposed fusion framework is illustrated using five sets of CT and MRI images and various performance metrics indicate that the proposed method is highly efficient and suitable for medical application in better decision making.

Palm Vein Verification System based on Nonsubsampled Contourlet Transform

This document presents a new approach in verification system to verify the identity of person by his intrinsic characteristics “Palm vein” which is unique, universal and easy to captured. The first step in this system is to extract the region of interest (ROI) which represent the most informative region of palm, then coding step based on nonsubsampled contourlet transform (NSCT) is presented to produce a binary vector for each ROI, next a matching step of different representative vectors is given and finally a decision is made for both identification and verification mode. This approach is tested on CASIA multispectral database; the experimentations have proved the effectiveness of this coding system in verification modes to gives 0.19% of Equal Error Rate (EER).

A Phase Congruency and Local Laplacian Energy Based Multi-Modality Medical Image Fusion Method in NSCT Domain

Multi-modality image fusion provides more comprehensive and sophisticated information in modern medical diagnosis, remote sensing, video surveillance, and so on. This paper presents a novel multi-modality medical image fusion method based on phase congruency and local Laplacian energy. In the proposed method, the non-subsampled contourlet transform is performed on medical image pairs to decompose the source images into high-pass and low-pass subbands. The high-pass subbands are integrated by a phase congruency-based fusion rule that can enhance the detailed features of the fused image for medical diagnosis. A local Laplacian energy-based fusion rule is proposed for low-pass subbands. The local Laplacian energy consists of weighted local energy and the weighted sum of Laplacian coefficients that describe the structured information and the detailed features of source image pairs, respectively. Thus, the proposed fusion rule can simultaneously integrate two key components for the fusion of low-pass subbands. The fused high-pass and low-pass subbands are inversely transformed to obtain the fused image. In the comparative experiments, three categories of multi-modality medical image pairs are used to verify the effectiveness of the proposed method. The experiment results show that the proposed method achieves competitive performance in both the image quantity and computational costs.

Image Automatic Categorisation using Selected Features Attained from Integrated Non-Subsampled Contourlet with Multiphase Level Sets

A framework of automatic detection and categorization of Breast Cancer (BC) biopsy images utilizing significant interpretable features is initially considered in discussed work. Appropriate efficient techniques are engaged in layout steps of the discussed framework. Different steps include 1.To emphasize the edge particulars of tissue structure; the distinguished Non-Subsampled Contourlet (NSC) transform is implemented. 2. For the demarcation of cells from background, k-means, Adaptive Size Marker Controlled Watershed, two proposed integrated methodologies were discussed. Proposed Method-II, an integrated approach of NSC and Multiphase Level Sets is preferred to other segmentation practices as it proves better performance 3. In feature extraction phase, extracted 13 shape morphology, 33 textural (includes 6 histogram, 22 Haralick’s, 3 Tamura’s, 2 Graylevel Run-Length Matrix,) and 2 intensity features from partitioned tissue images for 96 trained images

NSCT domain–based secure multiple‐watermarking technique through lightweight encryption for medical images

SummaryThis paper discusses a lightweight encryption–based secure digital watermarking technique for medical applications. The technique uses redundant discrete wavelet transform (RDWT) and singular value decomposition (SVD) along with nonsubsampled contourlet transform (NSCT) to improve robustness and imperceptibility. The security of the proposed technique is further improved by incorporating a lightweight (low‐complexity) cryptographic mechanism that is applied after embedding multiple watermarks. The proposed scheme first partitions the host image into subcomponents and then calculates the entropy values for it. To the maximum entropy value, the NSCT is applied, followed by RDWT decomposition. Finally, SVD is applied to obtain a singular vector of RDWT‐decomposed components. The watermark images are also processed using the same procedure mentioned above. The method uses singular values to hide watermarks into a host image. The experimental outcome shows that the combined technique makes our proposed approach more robust and imperceptible, while it is evaluated for various wavelet filters and 10 different types of medical and five different types of nonmedical cover images. Furthermore, the strength of the cryptographic mechanism is tested using standard performance measures and confirms its effectiveness in security. Moreover, it is evident from the results that our method shows improvement in robustness in comparison to previously reported techniques under consideration.

Speech and music classification using spectrogram based statistical descriptors and extreme learning machine

This article proposes a novel feature extraction approach for speech/music classification based on generalized Gaussian distribution descriptors extracted from IIR-CQT spectrogram representation. IIR-CQT spectrogram visual representation provides superior temporal resolution at high frequencies and better spectral resolution for low frequencies compared to the conventional short-time Fourier transform analysis which provides uniform frequency resolution. Multi-level decomposition of the spectrogram image is then performed using the Nonsubsampled Contourlet Transform (NSCT) which a fully shift-invariant, multi-scale, and multi-direction expansion that can preserve the edges of the textural pattern of speech and music. The generalized Gaussian distribution (GGD) parameters are produced using maximum likelihood estimation (MLE) from the NSCT subbands to create the image feature descriptor. Chaos crow search algorithm is employed to chose the most relevant feature sub-set and to discard redundant features and finally the extreme learning machine classifier categorizes input audio segment into speech/music. The experimental results show that the proposed feature descriptor is effective and performs better compared to the existing approaches in the speech/music classification. In addition, mismatched training and testing results are also presented.

Remote Sensing Image Fusion Based on Nonlinear IHS and Fast Nonsubsampled Contourlet Transform

The purpose of remote sensing image fusion is to inject the detail image extracted from the panchromatic (PAN) image into the low spatial resolution multispectral (MS) image. A novel remote sensing image fusion method based on fast nonsubsampled contourlet transform (FNSCT) and Nonlinear intensity-hue-saturation (IHS) is presented in this paper. Firstly, the Nonlinear IHS transform is performed on the multispectral image, and then the I-component representing the spatial resolution and the panchromatic image is transformed by NSCT to obtain the low frequency and high frequency. Finally, the coefficients are selected using the improved sum-modified-Laplacian (SML) method and the improved Log-Gabor filter in the low frequency and the high frequency, respectively. Experimental results show that the proposed method is the most advanced fusion method in subjective and objective evaluation, can provide more spatial information, and retain more spectral information compared with several other methods.