Showing papers on "Contourlet published in 2013"

Directive Contrast Based Multimodal Medical Image Fusion in NSCT Domain

Abstract: Multimodal medical image fusion, as a powerful tool for the clinical applications, has developed with the advent of various imaging modalities in medical imaging. The main motivation is to capture most relevant information from sources into a single output, which plays an important role in medical diagnosis. In this paper, a novel fusion framework is proposed for multimodal medical images based on non-subsampled contourlet transform (NSCT). The source medical images are first transformed by NSCT followed by combining low- and high-frequency components. Two different fusion rules based on phase congruency and directive contrast are proposed and used to fuse low- and high-frequency coefficients. Finally, the fused image is constructed by the inverse NSCT with all composite coefficients. Experimental results and comparative study show that the proposed fusion framework provides an effective way to enable more accurate analysis of multimodality images. Further, the applicability of the proposed framework is carried out by the three clinical examples of persons affected with Alzheimer, subacute stroke and recurrent tumor.

A Neuro-Fuzzy Approach for Medical Image Fusion

Abstract: This paper addresses a novel approach to the multimodal medical image fusion (MIF) problem, employing multiscale geometric analysis of the nonsubsampled contourlet transform and fuzzy-adaptive reduced pulse-coupled neural network (RPCNN). The linking strengths of the RPCNNs' neurons are adaptively set by modeling them as the fuzzy membership values, representing their significance in the corresponding source image. Use of the RPCNN with a less complex structure and having less number of parameters leads to computational efficiency-an important requirement of point-of-care health care technologies. The proposed scheme is free from the common shortcomings of the state-of-the-art MIF techniques: contrast reduction, loss of image fine details, and unwanted image degradations, etc. Subjective and objective evaluations show better performance of this new approach compared to the existing techniques.

Image Fusion Algorithm Based on Nonsubsampled Contourlet Transform

Abstract: t is mostly difficult to get an image that contains all relevant objects in focus, because of the limited depth-of-focus of optical lenses. The multifocus image fusion method can solve the problem effectively. Nonsubsampled Contourlet transform has varying directions and multiple scales. When the Nonsubsampled contourlet transform is introduced to image fusion, the characteristics of original images are taken better and more information for fusion is obtained. A new method of multi-focus image fusion based on Nonsubsampled contourlet transform (NSCT) with the fusion rule of region statistics is proposed in this paper. Firstly, different focus images are decomposed using Nonsubsampled contourlet transform. Then low-bands are integrated using the weighted average, high-bands are integrated using region statistics rule. Next the fused image will be obtained by inverse Nonsubsampled contourlet transform. Finally the experimental results are showed and compared with those of method based on Contourlet transform. Experiments show that the approach can achieve better results than the method based on contourlet transform.

A QR Code Based Zero-Watermarking Scheme for Authentication of Medical Images in Teleradiology Cloud

Abstract: Healthcare institutions adapt cloud based archiving of medical images and patient records to share them efficiently. Controlled access to these records and authentication of images must be enforced to mitigate fraudulent activities and medical errors. This paper presents a zero-watermarking scheme implemented in the composite Contourlet Transform (CT)—Singular Value Decomposition (SVD) domain for unambiguous authentication of medical images. Further, a framework is proposed for accessing patient records based on the watermarking scheme. The patient identification details and a link to patient data encoded into a Quick Response (QR) code serves as the watermark. In the proposed scheme, the medical image is not subjected to degradations due to watermarking. Patient authentication and authorized access to patient data are realized on combining a Secret Share with the Master Share constructed from invariant features of the medical image. The Hu's invariant image moments are exploited in creating the Master Share. The proposed system is evaluated with Checkmark software and is found to be robust to both geometric and non geometric attacks.

Multifocus and multispectral image fusion based on pixel significance using multiresolution decomposition

Abstract: Image fusion has been receiving increasing attention in the research community with the aim of investigating general formal solutions to a wide spectrum of applications. The objective of this work is to formulate a method that can efficiently fuse multifocus as well as multispectral images for context enhancement and thus can be used by different applications. We propose a novel pixel fusion rule based on multiresolution decomposition of the source images using wavelet, wavelet-packet, and contourlet transform. To compute fused pixel value, we take weighted average of the source pixels, where the weight to be given to the pixel is adaptively decided based on the significance of the pixel, which in turn is decided by the corresponding children pixels in the finer resolution bands. The fusion performance has been extensively tested on different types of images viz. multifocus images, medical images (CT and MRI), as well as IR − visible surveillance images. Several pairs of images were fused to compare the results quantitatively as well as qualitatively with various recently published methods. The analysis shows that for all the image types into consideration, the proposed method increases the quality of the fused image significantly, both visually and quantitatively, by preserving all the relevant information. The major achievement is average 50% reduction in artifacts.

Image fusion with nonsubsampled contourlet transform and sparse representation

Abstract: Image fusion combines several images of the same scene into a fused image, which contains all important information. Multiscale transform and sparse representation can solve this problem effectively. However, due to the limited number of dictionary atoms, it is difficult to provide an accurate description for image details in the sparse representation–based image fusion method, and it needs a great deal of calculations. In addition, for the multiscale transform–based method, the low-pass subband coefficients are so hard to represent sparsely that they cannot extract significant features from images. In this paper, a nonsubsampled contourlet transform (NSCT) and sparse representation–based image fusion method (NSCTSR) is proposed. NSCT is used to perform a multiscale decomposition of source images to express the details of images, and we present a dictionary learning scheme in NSCT domain, based on which we can represent low-frequency information of the image sparsely in order to extract the salient features of images. Furthermore, it can reduce the calculation cost of the fusion algorithm with sparse representation by the way of nonoverlapping blocking. The experimental results show that the proposed method outperforms both the fusion method based on single sparse representation and multiscale decompositon.

Image Fusion Based on Nonsubsampled Contourlet Transform and Saliency-Motivated Pulse Coupled Neural Networks

Abstract: In the nonsubsampled contourlet transform (NSCT) domain, a novel image fusion algorithm based on the visual attention model and pulse coupled neural networks (PCNNs) is proposed. For the fusion of high-pass subbands in NSCT domain, a saliency-motivated PCNN model is proposed. The main idea is that high-pass subband coefficients are combined with their visual saliency maps as input to motivate PCNN. Coefficients with large firing times are employed as the fused high-pass subband coefficients. Low-pass subband coefficients are merged to develop a weighted fusion rule based on firing times of PCNN. The fused image contains abundant detailed contents from source images and preserves effectively the saliency structure while enhancing the image contrast. The algorithm can preserve the completeness and the sharpness of object regions. The fused image is more natural and can satisfy the requirement of human visual system (HVS). Experiments demonstrate that the proposed algorithm yields better performance.

Contourlet-based active contour model for PET image segmentation

Abstract: Purpose: PET-guided radiation therapy treatment planning, clinical diagnosis, assessment of tumor growth, and therapy response rely on the accurate delineation of the tumor volume and quantification of tracer uptake. Most PET image segmentation techniques proposed thus far are suboptimal in the presence of heterogeneity of tracer uptake within the lesion. This work presents an active contour model approach based on the method of Chan and Vese ["Active contours without edges," IEEE Trans. Image Process. 10, 266-277 (2001)] designed to take into account the high level of statistical uncertainty (noise) and to handle the heterogeneity of tumor uptake typically present in PET images. Methods: In the proposed method, the fitting terms in the Chan-Vese formulation are modified by introducing new input images, including the smoothed version of the original image using anisotropic diffusion filtering (ADF) and the contourlet transform of the image. The advantage of utilizing ADF for image smoothing is that it avoids blurring the object's edges and preserves the average activity within a region, which is important for accurate PET quantification. Moreover, incorporating the contourlet transform of the image into the fitting terms makes the energy functional more effective in directing the evolving curve toward the object boundaries due to the enhancement of the tumor-to-background ratio (TBR). The proper choice of the energy functional parameters has been formulated by making a clear consensus based on tumor heterogeneity and TBR levels. This cautious parameter selection leads to proper handling of heterogeneous lesions. The algorithm was evaluated using simulated phantom and clinical studies, where the ground truth and histology, respectively, were available for accurate quantitative analysis of the segmentation results. The proposed technique was also compared to a number of previously reported image segmentation techniques. Results: The results were quantitatively analyzed using three evaluation metrics, including the spatial overlap index (SOI), the mean relative error (MRE), and the mean classification error (MCE). Although the performance of the proposed method was analogous to other methods for some datasets, overall the proposed algorithm outperforms all other techniques. In the largest clinical group comprising nine datasets, the proposed approach improved the SOI from 0.41 +/- 0.14 obtained using the best-performing algorithm to 0.54 +/- 0.12 and reduced the MRE from 54.23 +/- 103.29 to 0.19 +/- 16.63 and the MCE from 112.86 +/- 69.07 to 60.58 +/- 18.43. Conclusions: The proposed segmentation technique is superior to other representative segmentation techniques in terms of highest overlap between the segmented volume and the ground truth/histology and minimum relative and classification errors. Therefore, the proposed active contour model can result in more accurate tumor volume delineation from PET images. (C) 2013 American Association of Physicists in Medicine.

Speckle reduction of retinal optical coherence tomography based on contourlet shrinkage

Abstract: Speckle reduction of retinal optical coherence tomography (OCT) images helps the diagnosis of ocular diseases. In this Letter, we present a speckle reduction method based on shrinkage in the contourlet domain for retinal OCT images. The algorithm overcomes the disadvantages of the wavelet shrinkage method, which lacks directionality and anisotropy. The trade-off between speckle reduction and edge preservation is controlled by a single adjustable parameter, which determines the threshold in the contourlet domain. Results show substantial reduction of speckle noise and enhanced visualization of layer structures as demonstrated in the image of the central fovea region of the human retina. It is expected to be utilized in a wide range of biomedical imaging applications.

A highly robust two-stage Contourlet-based digital image watermarking method

Abstract: In this paper we propose a blind and highly robust watermarking method consisting of two embedding stages. In the first stage, the odd description of image is divided into non-overlapped fixed size blocks and the signature (watermark) is embedded in the high frequency component of the Contourlet transform (CT) of the blocks. In the second stage, the signature is embedded in the low frequency component of the global CT of the image. The main issue associated with two-stage blind watermarking is the selection of the less affected signature among the two embedded signatures. In this paper a measure is introduced to decide between the two extracted signatures. Simulation results indicate that the proposed method achieves higher robustness compared to other known watermarking methods. Moreover, since watermark is embedded in the local as well as global CT coefficients of two different frequency bands, the proposed method is robust against a wide range of attacks. This is due to the fact that most of the attacks affect either a specific frequency band or a specific location in the watermarked image.

Multimodal Medical Image Fusion Framework Based on Simplified PCNN in Nonsubsampled Contourlet Transform Domain

Abstract: In this paper, we present a new medical image fusion algorithm based on nonsubsampled contourlet transform (NSCT) and spiking cortical model (SCM). The flexible multi-resolution, anisotropy, and directional expansion characteristics of NSCT are associated with global coupling and pulse synchronization features of SCM. Considering the human visual system characteristics, two different fusion rules are used to fuse the low and high frequency sub-bands respectively. Firstly, maximum selection rule (MSR) is used to fuse low frequency coefficients. Secondly, spatial frequency (SF) is applied to motivate SCM network rather than using coefficients value directly, and then the time matrix of SCM is set as criteria to select coefficients of high frequency subband. The effectiveness of the proposed algorithm is achieved by the comparison with existing fusion methods.

Multi-level fuzzy contourlet-based image fusion for medical applications

Abstract: Multi-modal images fusion is one of the most truthful and useful diagnostic techniques in medical imaging system. This study proposes an image fusion system for medical engineering based on contourlet transform and multi-level fuzzy reasoning technique in which useful information from two spatially registered medical images is integrated into a new image that can be used to make clinical diagnosis and treatment more accurate. The system applies pixel-based fuzzy fusion rule to contourlet's coefficients of high-frequency details and feature-based fuzzy fusion to its low-frequency approximations, which can help the development of sophisticated algorithms that consider not only the time cost but also the quality of the fused image. The developed fusion system eliminates undesirable effects such as fusion artefacts and loss of visually vital information that compromise their usefulness by means of taking into account the physical meaning of contourlet coefficients. The experimental results show that the proposed fusion system outperforms the existing fusion algorithms and is effective to fuse medical images from different sensors with applications in brain image processing.

Image fusion scheme based on modified dual pulse coupled neural network

Abstract: Image fusion combines information from multiple images of the same scene to obtain a composite image which is more suitable for further image processing tasks. This study presented an image fusion scheme based on the modified dual pulse coupled neural network (PCNN) in non-subsampled contourlet transform (NSCT) domain. NSCT can overcome the lack of shift invariance in contourlet transform. Original images were decomposed to obtain the coefficients of low-frequency subbands and high-frequency subbands. In this fusion scheme, a new sum-modified Laplacian of the low-frequency subband image, which represents the edge-feature of the low-frequency subband image in NSCT domain, is presented and input to motivate modified dual PCNN. For fusion of high-frequency subband coefficients, spatial frequency will be used as the gradient features of images to motivate dual channel PCNN and to overcome Gibbs phenomena. Experimental results show that the proposed scheme can significantly improve image fusion performance, performs very well in fusion and outperforms conventional methods such as traditional discrete wavelet transform, dual tree complex wavelet and PCNN in terms of objective criteria and visual appearance.

Image denoising algorithm based on contourlet transform for optical coherence tomography heart tube image.

Abstract: Optical coherence tomography (OCT) is becoming an increasingly important imaging technology in the Biomedical field. However, the application of OCT is limited by the ubiquitous noise. In this study, the noise of OCT heart tube image is first verified as being multiplicative based on the local statistics (i.e. the linear relationship between the mean and the standard deviation of certain flat area). The variance of the noise is evaluated in log-domain. Based on these, a joint probability density function is constructed to take the inter-direction dependency in the contourlet domain from the logarithmic transformed image into account. Then, a bivariate shrinkage function is derived to denoise the image by the maximum a posteriori estimation. Systemic comparative experiments are made to synthesis images, OCT heart tube images and other OCT tissue images by subjective assessment and objective metrics. The experiment results are analysed based on the denoising results and the predominance degree of the proposed algorithm with respect to the wavelet-based algorithm. The results show that the proposed algorithm improves the signal-to-noise ratio, whereas preserving the edges and has more advantages on the images containing multi-direction information like OCT heart tube image.

Despeckling of SAR images in contourlet domain using a new adaptive thresholding

Abstract: SAR is a powerful tool for producing high resolution remote sensing images under all weather conditions. But, during the backscattering process, the obtained images are contaminated with a special type of noise, called speckle. Speckle noise appears like a granular pattern in the SAR images and results in degrading the image quality. This work uses contourlet transform to effectively represent the directional information and intrinsic geometrical structures of the SAR image along the smooth contours (edges). Here, the speckled image is decomposed into directional sub-bands using contourlet transform followed by application of adaptive thresholding and finally, they are reconstructed to produce the denoised image. The adaptive thresholding uses a function with increasing and decreasing exponential functions to suppress the speckle content from the SAR image. The finally produced images prove their superiority in terms of speckle suppression and detail preservation.

A Novel Image Fusion Method Based on FRFT-NSCT

Abstract: Nonsubsampled Contourlet transform (NSCT) has properties such as multiscale, localization, multidirection, and shift invariance, but only limits the signal analysis to the time frequency domain. Fractional Fourier transform (FRFT) develops the signal analysis to fractional domain, has many super performances, but is unable to attribute the signal partial characteristic. A novel image fusion algorithm based on FRFT and NSCT is proposed and demonstrated in this paper. Firstly, take FRFT on the two source images to obtain fractional domain matrices. Secondly, the NSCT is performed on the aforementioned matrices to acquire multiscale and multidirection images. Thirdly, take fusion rule for low-frequency subband coefficients and directional bandpass subband coefficients to get the fused coefficients. Finally, the fused image is obtained by performing the inverse NSCT and inverse FRFT on the combined coefficients. Three modes images and three fusion rules are demonstrated in the proposed algorithm test. The simulation results show that the proposed fusion approach is better than the methods based on NSCT at the same parameters.

Fast iterative contourlet thresholding for compressed sensing MRI

Abstract: Proposed is the use of the contourlet as a sparse transform which is combined with the fast iterative shrinkage/threshold algorithm (FISTA) for compressed sensing magnetic resonance imaging reconstruction. The proposed method not only inherits the simplicity and effectiveness of the original FISTA but also has the sparse curve representation ability of the contourlet. Simulation results validate the superior performance of the proposed method in terms of reconstruction accuracy and computation time.

Local thresholding with adaptive window shrinkage in the contourlet domain for image denoising

Abstract: Threshold selection is a challenging job for the image denoising in the contourlet domain. In this paper, a new local threshold with adaptive window shrinkage is proposed. According to the anisotropic energy clusters in contourlet subbands, local adaptive elliptic windows are introduced to determine the neighboring coefficients with strong dependencies for each coefficient. Utilizing the maximum likelihood estimator within the adaptive window, the signal variance is estimated from the noisy neighboring coefficients. Based on the signal variance estimation, the new threshold is obtained in the Bayesian framework. Since it makes full use of the captured directional information of images, the threshold extends to the anisotropic spatial adaptability and behaves reliably. Simulation experiments show that the new method exhibits better performance than other outstanding wavelet and contourlet denoising schemes obviously, both in the PSNR value and the visual appearance.

Textural Feature Extraction and Classification of Mammogram Images using CCCM and PNN

Abstract: This work presents and investigates the discriminatory capability of contourlet coefficient co- occurrence matrix features in the analysis of mammogram images and its classification. It has been revealed that contourlet transform has a remarkable potential for analysis of images representing smooth contours and fine geometrical structures, thus suitable for textural details. Initially the ROI (Region of Interest) is cropped from the original image and its contrast is enhanced using histogram equalization. The ROI is decomposed using contourlet transform and the co-occurrence matrices are generated for four different directions (θ=0°, 45°, 90° and 135°) and distance (d= 1 pixel). For each co-occurrence matrix a variety of second order statistical texture features are extracted and the dimensionality of the features is reduced using Sequential Floating Forward Selection (SFFS) algorithm. A PNN is used for the purpose of classification. For experimental evaluation, 200 images are taken from mini MIAS (Mammographic Image Analysis Society) database. Experimental results show that the proposed methodology is more efficient and maximum classification accuracy of 92.5% is achieved. The results prove that contourlet coefficient co-occurrence matrix texture features can be successfully applied for the classification of mammogram images. Keywords-Contourlet Transform, Mammogram, SFFS, PNN, ROI, MIAS

Algorithm on Contourlet Domain in Detection of Road Cracks for Pavement Images

Abstract: In this paper we present a detection algorithm of road cracks in several directions and each layer for pavement images in contourlet domain Firstly, a pavement image is converted to a grey-scale image which is decomposed by using contourlet transform Then directionality and anisotropy are used to enhance the singular characteristic of the image by expanding all scale detail coefficients to the same size and combining the coefficients The new approach avoids unreasonable evaluation result of classical unified crack index pavement distress evaluation method based on image tile Experimental results show that the algorithm proposed in this paper can detect pavement cracks effectively which is not affected by noise