Showing papers on "Contourlet published in 2017"

Forensics feature analysis in quaternion wavelet domain for distinguishing photographic images and computer graphics

Abstract: In this paper, a novel set of features based on Quaternion Wavelet Transform (QWT) is proposed for digital image forensics. Compared with Discrete Wavelet Transform (DWT) and Contourlet Wavelet Transform (CWT), QWT produces the parameters, i.e., one magnitude and three angles, which provide more valuable information to distinguish photographic (PG) images and computer generated (CG) images. Some theoretical analysis are done and comparative experiments are made. The corresponding results show that the proposed scheme achieves 18 percents’ improvements on the detection accuracy than Farid’s scheme and 12 percents than Ozparlak’s scheme. It may be the first time to introduce QWT to image forensics, but the improvements are encouraging.

Automated characterization of coronary artery disease, myocardial infarction, and congestive heart failure using contourlet and shearlet transforms of electrocardiogram signal

Abstract: Undiagnosed coronary artery disease (CAD) progresses rapidly and leads to myocardial infarction (MI) by reducing the blood flow to the cardiac muscles. Timely diagnosis of MI and its location is significant, else, it expands and may impair the left ventricular (LV) function. Thus, if CAD and MI are not picked up by electrocardiogram (ECG) during diagnostic test, it can lead to congestive heart failure (CHF). Therefore, in this paper, the characterization of three cardiac abnormalities namely, CAD, MI and CHF are compared. Performance of novel algorithms is based on contourlet and shearlet transformations of the ECG signals. Continuous wavelet transform (CWT) is performed on normal, CAD, MI and CHF ECG beat to obtain scalograms. Subsequently, contourlet and shearlet transformations are applied on the scalograms to obtain the respective coefficients. Entropies, first and second order statistical features namely, mean ( M n i ), min ( M i n i ), max ( M x i ), standard deviation ( D s t i ), average power ( P a v g i ), inter-quartile range (IQRi), Shannon entropy ( E s h i ), mean Tsallis entropy ( E m t s i ), kurtosis ( K u r i ), mean absolute deviation ( M A D i ), and mean energy ( Ω m i ), are extracted from each contourlet and shearlet coefficients. Only significant features are selected using improved binary particle swarm optimization (IBPSO) feature selection method. Selected features are ranked using analysis of variance (ANOVA) and relieff techniques. The highly ranked features are subjected to decision tree (DT) and K-nearest neighbor (KNN) classifiers. Proposed method has achieved accuracy, sensitivity and specificity of (i) 99.55%, 99.93% and 99.24% using contourlet transform, and (ii) 99.01%, 99.82% and 98.75% using shearlet transform. Among the two proposed techniques, contourlet transform method performed marginally better than shearlet transform technique in classifying the four classes. The proposed CWT combined with contourlet-based technique can be implemented in hospitals to speed up the diagnosis of three different cardiac abnormalities using a single ECG test. This technique, minimizes the unnecessary diagnostic tests required to confirm the diagnosis.

Image Registration and Fusion of Visible and Infrared Integrated Camera for Medium-Altitude Unmanned Aerial Vehicle Remote Sensing

Abstract: This study proposes a novel method for image registration and fusion via commonly used visible light and infrared integrated cameras mounted on medium-altitude unmanned aerial vehicles (UAVs).The innovation of image registration lies in three aspects. First, it reveals how complex perspective transformation can be converted to simple scale transformation and translation transformation between two sensor images under long-distance and parallel imaging conditions. Second, with the introduction of metadata, a scale calculation algorithm is designed according to spatial geometry, and a coarse translation estimation algorithm is presented based on coordinate transformation. Third, the problem of non-strictly aligned edges in precise translation estimation is solved via edge–distance field transformation. A searching algorithm based on particle swarm optimization is introduced to improve efficiency. Additionally, a new image fusion algorithm is designed based on a pulse coupled neural network and nonsubsampled contourlet transform to meet the special requirements of preserving color information, adding infrared brightness information, improving spatial resolution, and highlighting target areas for unmanned aerial vehicle (UAV) applications. A medium-altitude UAV is employed to collect datasets. The result is promising, especially in applications that involve other medium-altitude or high-altitude UAVs with similar system structures.

Hybrid semi-blind image watermarking in redundant wavelet domain

Abstract: Image watermarking in wavelet domain has been found useful for copyright protection and rightful ownership. Classical wavelet transforms, like discrete wavelet transform (DWT), are shift sensitive and provide information in horizontal, vertical and diagonal directions only. Shift invariance and directional information are required for better reconstruction of images. In this work, we propose a semi-blind gray scale image watermarking technique in redundant wavelet domain. The primary focus of this research is to highlight the usefulness of redundant wavelet transforms in image watermarking. We have used nonsubsampled contourlet transform (NSCT) and redundant discrete wavelet transform (RDWT). These redundant transforms are shift invariant. Also, NSCT provides rich directional information. Thus, they overcome the shortcomings of DWT and are more useful in image watermarking. We have integrated singular value decomposition (SVD) in the proposed method. We use NSCT-RDWT-SVD decomposition for single and dual image watermarking. For single watermark embedding, cover images are sub-sampled followed by one level NSCT and RDWT decomposition. SVD has been applied on obtained RDWT coefficients. In the same way, image watermark has been processed and NSCT-RDWT-SVD decomposition has been applied on it. SVD coefficients of the cover image and watermark image have been combined using scaling factor. Inverse SVD-RDWT-NSCT operation together with reverse sub-sampling provides watermarked image. For extraction of the watermark, we follow the NSCT-RDWT-SVD decomposition and SVD coefficients have been separated by the same scaling factor that was used in embedding. In the dual watermarking, Arnold transform has been used for encryption of the text watermark and rest of the steps are similar to single watermarking. NSCT, RDWT and SVD improve the performance of the proposed method against geometrical and image processing attacks for single and dual image watermarking. Experiments have been carried over standard images and results have been shown for natural and medical images. Qualitative and quantitative evaluations in terms of peak signal to noise ratio (PSNR), correlation coefficient (CC), bit error rate (BER) and structural similarity index metric (SSIM) show that the proposed method is suitable for single and dual image watermarking, and outperforms existing methods.

A robust semi-blind watermarking for color images based on multiple decompositions

Abstract: In this paper, a robust semi-blind watermarking scheme for color images, based on multiple decompositions is proposed to preserve the copyrights of the owner. Using multiple decompositions, the gray watermark is embedded into a host color image. Prior to that, to enhance security the gray watermark is encrypted with Arnold transform and SVD by generating secret keys. The luminance component of the given host image is subjected to discrete wavelet transform(DWT), contourlet transform(CT), Schur decomposition and singular value decomposition(SVD) in sequence and finally the watermark is embedded. In the semi-blind extraction process, the watermark is extracted without the help of the original host image. Experimental results show that the proposed watermarking scheme has better visual imperceptibility and high robustness against image & signal processing attacks compared to other methods.

A novel blind color image watermarking based on Contourlet transform and Hessenberg decomposition

Abstract: In this paper, a novel blind color image watermarking based on Contourlet transform and Hessenberg decomposition is proposed to protect digital copyright of color image. Firstly, each color channel of the host image is transformed by Contourlet transform and its low frequency sub-band is divided into 4ź×ź4 non-overlap coefficient block. Secondly, the coefficient block selected by MD5-based Hash pseudo-random algorithm is decomposed by Hessenberg decomposition. Thirdly, the watermark information permuted by Arnold transform is embedded into the biggest energy element of the upper Hessenberg matrix by quantization technique. In extraction process, the quantization strength is used for blindly extracting watermark information from the attacked host image without the help of any original image. The results show that the proposed scheme has higher imperceptibility and robustness against most common image attacks in comparison with other related methods.

Hybrid NSCT domain multiple watermarking for medical images

Abstract: Medical image watermarking is a challenging area of research. High bandwidth, secure transmission of patient's data among hospitals and hiding capacity are major concerns in medical image watermarking. Recently, wavelet transforms and their hybrid combinations have been widely used for this purpose. The conventional wavelet transforms suffer from shift sensitivity and have low hiding capacity. Therefore, the performance of hybrid combinations of wavelet transforms for image watermarking is limited. The shortcomings of wavelet transforms can be overcome with the use of Nonsubsampled contourlet transform (NSCT) which is shift invariant in nature and provides rich directional information. For these reasons, in this work we propose NSCT based technique for medical image watermarking which combines discrete cosine transform (DCT) along with Multiresolution Singular value decomposition (MSVD) and Arnold transform in order to increase robustness, capacity and imperceptibility. In the proposed work, multiple (three) image watermarks have been used for a single cover medical image. We have embedded three image watermarks into NSCT coefficients of the cover image. Among which two of them are image watermarks and third is encrypted text watermark. By using NSCT, embedding capacity has been increased and it becomes more resistant to geometrical attacks. Also, hybrid combination of NSCT with DCT, MSVD and Arnold transform increases the perceptual quality and security of watermarked image. Experimental results showed that the proposed method provides high robustness against attacks like JPEG compression, Rotation, Resizing, noise, Blurring and found better than existing hybrid methods for medical image watermarking.

Adaptive blind image watermarking using edge pixel concentration

Abstract: In recent years many methods for image watermarking have been proposed to overcome the growing concern of copyright protection. The goal of all these methods is to satisfy the tradeoff between two important characteristics of robustness and imperceptibility. In this paper an adaptive blind watermarking method in the Contourlet transform domain is proposed. In this method we apply a two-level Contourlet transform on the original image. The first level approximate image is partitioned into blocks. Using a novel edge detection algorithm, important edges of each block of the approximate image are detected and the entropy of each block is also computed. Then by concatenating some portions of the second level subbands we form blocks. These formed blocks are transformed into DCT domain. Watermark is embedded by modification of the DCT coefficients. The severity of the embedding is controlled depending on the complexity of the corresponding block in the approximate image. For higher robustness against attacks, we embedded the watermark redundantly and used voting mechanism in extraction stage. Experimental results reveal that our method has high robustness and acceptable imperceptibility.

Scheme for Unsupervised Colour–texture Image Segmentation Using Neutrosophic Set and Non-Subsampled Contourlet Transform

Abstract: The process of partitioning an image into some different meaningful regions with the homogeneous characteristics is called the image segmentation which is a crucial task in image analysis.

Infrared and visible image fusion based on target extraction in the nonsubsampled contourlet transform domain

Abstract: A fusion algorithm based on target extraction for infrared image (IIR) and visible image fusion in the nonsubsampled contourlet transform (NSCT) domain is proposed. Commonly, the target information in IIR is important; in order to fully retain the target information in a final fused image, first, use maximum between-class variance method to segment IIR, such that the target regions with salient objects are extracted to produce the background and target images. Next, the visible and background images are decomposed to a series of low-pass and band-pass images by NSCT, respectively. Then, fuse the obtained images to produce the fused background image by different strategies in each band, in which Gaussian fuzzy logic is used to produce the low-pass coefficient; the spatial frequency of each band-pass image is used to determine the linking strength β value of pulse coupled neural network structure, and the result is used to fuse the band-pass images. Eventually, the fused image is produced combining the target image and the fused background image. The experiments show that this algorithm can retain more background details of the two images and highlight the target in the infrared image more effectively, as well as obviously improve the visual effect of the fusion image.

Performance enhanced image steganography systems using transforms and optimization techniques

Abstract: Image steganography is the art of hiding highly sensitive information onto the cover image. An ideal approach to image steganography must satisfy two factors: high quality of stego image and high embedding capacity. Conventionally, transform based techniques are widely preferred for these applications. The commonly used transforms for steganography applications are Discrete Cosine Transform (DCT), Discrete Wavelet Transform (DWT) etc. In this work, frequency domain transforms such as Fresnelet Transform (FT) and Contourlet Transform (CT) are used for the data hiding process. The secret data is normally hidden in the coefficients of these transforms. However, data hiding in transform coefficients yield less accurate results since the coefficients used for data hiding are selected randomly. Hence, in this work, optimization techniques such as Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) are used for improving the performance of the steganography system. GA and PSO are used to find the best coefficients in order to hide the Quick Response (QR) coded secret data. This approach yields an average PSNR of 52.56 dB and an embedding capacity of 902,136 bits. These experimental results validate the practical feasibility of the proposed methodology for security applications.

A retinal image enhancement technique for blood vessel segmentation algorithm

Abstract: The morphology of blood vessels in retinal fundus images is an important indicator of diseases like glaucoma, hypertension and diabetic retinopathy. The accuracy of retinal blood vessels segmentation affects the quality of retinal image analysis which is used in diagnosis methods in modern ophthalmology. Contrast enhancement is one of the crucial steps in any of retinal blood vessel segmentation approaches. The reliability of the segmentation depends on the consistency of the contrast over the image. This paper presents an assessment of the suitability of a recently invented spatially adaptive contrast enhancement technique for enhancing retinal fundus images for blood vessel segmentation. The enhancement technique was integrated with a variant of Tyler Coye algorithm, which has been improved with Hough line transformation based vessel reconstruction method. The proposed approach was evaluated on two public datasets STARE and DRIVE. The assessment was done by comparing the segmentation performance with five widely used contrast enhancement techniques based on wavelet transform, contrast limited histogram equalization, local normalization, linear un-sharp masking and contourlet transform. The results revealed that the assessed enhancement technique is well suited for the application and also outperforms all compared techniques.

Electroencephalogram signal classification based on shearlet and contourlet transforms

Abstract: Detection of epilepsy patterns in EEG signals with high accuracy.Development of a novel methodology based on curvelet and shearlet transforms.Extraction of a set of discriminative characteristics from the signals.Evaluation on a public data set.Results superior/comparable to the literature. Epilepsy is a disorder that affects approximately 50 million people of all ages, according to World?Health Organization?(2016), which makes it one of the most common neurological diseases worldwide. Electroencephalogram (EEG) signals have been widely used to detect epilepsy and other brain abnormalities. In this work, we propose and evaluate a novel methodology based on shearlet and contourlet transforms to decompose the EEG signals into frequency bands. A set of features are extracted from these time-frequency coefficients and used as input to different classifiers. Experiments are conducted on a public data set to demonstrate the effectiveness of the proposed classification method. The developed system can help neurophysiologists identify EEG patterns in epilepsy diagnostic tasks.

Multi-Focus Image Fusion via Clustering PCA Based Joint Dictionary Learning

Abstract: This paper presents a novel framework based on the non-subsampled contourlet transform (NSCT) and sparse representation (SR) to fuse the multi-focus images. In the proposed fusion method, each source image is first decomposed with NSCT to obtain one low-pass sub-image and a number of high-pass sub-images. Second, an SR-based scheme is put forward to fuse the low-pass sub-images of multiple source images. In the SR-based scheme, a joint dictionary is constructed by integrating many informative and compact sub-dictionaries, in which each sub-dictionary is learned by extracting a few principal component analysis bases from the jointly clustered patches obtained from the low-pass sub-images. Thirdly, we design a multi-scale morphology focus-measure (MSMF) to synthesize the high-pass sub-images. The MSMF is constructed based on the multi-scale morphology structuring elements and the morphology gradient operators, so that it can effectively extract the comprehensive gradient features from the sub-images. The “Max-MSMF” is then defined as the fusion rule to fuse the high-pass sub-images. Finally, the fused image is reconstructed by performing the inverse NSCT on the merged low-pass and high-pass sub-images, respectively. The proposed method is tested on a series of multi-focus images and compared with several well-known fusion methods. Experimental results and analyses indicate that the proposed method is effective and outperforms some existing state-of-the-art methods.

Technique for multi-focus image fusion based on fuzzy-adaptive pulse-coupled neural network

Abstract: Multi-focus image fusion technique can solve the problem that not all the targets in an image are clear in case of imaging in the same scene. In this paper, a novel multi-focus image fusion technique is presented, which is developed by using the nonsubsampled contourlet transform (NSCT) and a proposed fuzzy logic based adaptive pulse-coupled neural network (PCNN) model. In our method, sum-modified Laplacian (SML) is calculated as the motivation for PCNN neurons in NSCT domain. Since the linking strength plays an important role in PCNN, we propose an adaptively fuzzy way to determine it by computing each coefficient’s importance relative to the surrounding coefficients. Combined with human visual perception characteristics, the fuzzy membership value is employed to automatically achieve the degree of importance of each coefficient, which is utilized as the linking strength in PCNN model. Experimental results on simulated and real multi-focus images show that the proposed technique has a superior performance to series of exist fusion methods.

Robust contourlet-based blind watermarking for depth-image-based rendering 3D images

Abstract: The copyright protection for 3D multimedia has attracted considerable attention and depth-image-based rendering (DIBR) has been playing a critical role in 3D content representation due to its numerous advantages. In this paper, we propose a new blind watermarking scheme for DIBR 3D images. Considering the directional multiresolution image representation and convenient tree structures of contourlet transform (CT), we perform the watermark embedding and extraction in contourlet domain. The center view and the depth map are available at the content provider side. After applying contourlet transform to the center view, we embed the watermark into the selected contourlet subbands of the center view by quantization on certain contourlet coefficients. The virtual left and right views are generated from the watermarked center view and the associated depth map using DIBR technique at the receiver side. The statistical differences between quantized and unquantized contourlet coefficients are used for watermark extraction. The watermark can be detected with a low bit error rate (BER) from the center view, the left and right views even when each view is distorted and distributed separately. The simulation results demonstrate that the proposed scheme is very robust to image compression, noise addition and geometric attacks such as rotation, scaling and cropping. Moreover, the proposed scheme has good performance in terms of depth image variation and baseline distance adjustment. HighlightsA novel blind watermarking scheme for depth-image-based rendering 3D images is proposed.The watermark embedding and extraction are performed in contourlet domain.The watermark can be detected with a low bit error rate from the watermarked center view, and the synthesized left and right views even when each view is distorted and distributed separately.The proposed scheme is robust to geometric attacks and common DIBR processing.

Method of Improved Fuzzy Contrast Combined Adaptive Threshold in NSCT for Medical Image Enhancement.

Abstract: Noises and artifacts are introduced to medical images due to acquisition techniques and systems. This interference leads to low contrast and distortion in images, which not only impacts the effectiveness of the medical image but also seriously affects the clinical diagnoses. This paper proposes an algorithm for medical image enhancement based on the nonsubsampled contourlet transform (NSCT), which combines adaptive threshold and an improved fuzzy set. First, the original image is decomposed into the NSCT domain with a low-frequency subband and several high-frequency subbands. Then, a linear transformation is adopted for the coefficients of the low-frequency component. An adaptive threshold method is used for the removal of high-frequency image noise. Finally, the improved fuzzy set is used to enhance the global contrast and the Laplace operator is used to enhance the details of the medical images. Experiments and simulation results show that the proposed method is superior to existing methods of image noise removal, improves the contrast of the image significantly, and obtains a better visual effect.

CT and MRI image compression using wavelet-based contourlet transform and binary array technique

Abstract: Compression techniques are essential for efficient storage and fast transfer of medical image data In this paper, a rapid 2-D lossy compression technique constructed using wavelet-based contourlet transform (WBCT) and binary array technique (BAT) has been proposed for computed tomography (CT) and magnetic resonance imaging (MRI) images In WBCT, the high-frequency subband obtained from wavelet transform is further decomposed into multiple directional subbands by directional filter bank to obtain more directional information The relationship between the coefficients has been changed in WBCT as it has more directions The differences in parent---child relationships are handled by a repositioning algorithm The repositioned coefficients are then subjected to quantization The quantized coefficients are further compressed by BAT where the most frequently occurring value is coded only once The proposed method has been experimented with real-time CT and MRI images, the results indicated that the processing time of the proposed method is less compared to existing wavelet-based set-partitioning in hierarchical trees and set-partitioning embedded block coders The evaluation results obtained from radiologists indicated that the proposed method could reproduce the diagnostic features of CT and MRI images precisely

Combination of wavelet and contourlet transforms for PET and MRI image fusion

Abstract: Image fusion is a widely used technique for enhancing the interpretation quality of images in medical application, which use different medical imaging sensors. This paper presents an image fusion framework for images acquired by using two distinct medical imaging sensor modalities (i.e. PET and MRI) using a combination of Stationary Wavelet Transform (SWT) and Non Sub-sampled Contourlet Transform (NSCT). We use a cascaded combination of SWT and NSCT to benefit advantages of SWT at the first step of the proposed method. Then, to decrease the SWT's drawbacks such as shift variance, poor directionality and absence of phase information, we employ Principal Component Analysis (PCA) algorithm in the SWT domain to minimize the redundancy. In the second step the maximum fusion rule is used in the NSCT domain to enhance the diagnostic features. The experimental results demonstrate that the proposed method is better than various existing transform-based and spatial based fusion methods and some other hybrid methods, in terms of both subjective and objective evaluations.

Image fusion based on multi-scale transform and sparse representation: an image energy approach

Abstract: Image fusion is a process to enhance the human perception of different images from the same scene. Nowadays, two popular methods in the signal/image fusion, namely, multi-scale transform (MST) and sparse representation (SR) are being used. This study uses an image energy approach to enhance a fusion rule based on the combination of MST and SR methods. Each source image is first decomposed to its sub-bands using the selected MST method. Then, SR is applied to the low-pass band and maximum absolute (max-abs) rule merges the high-pass bands. The activity level of the sparse coefficients is measured based on the energy differences of the source images. When the gap energy is high enough, a coefficient with maximum L 2 -norm is selected; otherwise, maximum L 1 -norm is considered. Finally, by applying inverse MST to the attained bands, the fused image is reconstructed. The popular MSTs, such as discrete wavelet transform, dual-tree complex wavelet transform and non-sub-sampled contourlet are used. The experiments are carried out on several standard and real-life images. The measurement results confirm that the proposed method has enhanced the contrast, clarity and visual information of the fused results.

Classification of Region of Interest in Mammograms Using Dual Contourlet Transform and Improved KNN

Abstract: Goal. Breast cancer is becoming one of the most common cancers among women. Early detection can help increase the survival rates. Feature extraction directly affects diagnosis result. In this work, a novel feature extraction method based on Dual Contourlet Transform (Dual-CT) is presented, and improved nearest neighbor (KNN) is employed to improve the classification performance. Method. This presented method includes three main sections: firstly, the Region of Interest (ROI) is cropped manually according to gold standard from Mammographic Image Analysis Society (MIAS) database; secondly, the ROIs are decomposed into different resolution levels using Dual-CT, contourlet, and wavelet; a set of texture features are extracted. Then improved KNN and traditional KNN are implemented for classification. Experiments are performed on 324 ROIs which include 206 normal cases and 118 abnormal cases; the abnormal cases are composed of 66 benign cases and 52 malignant cases. Results. Experimental results prove the validity and superiority of Dual-CT-based feature and improved KNN. In particular, 94.14% and 95.76% classification accuracy is achieved based on Dual-CT domain. Moreover, the proposed method is comparable with state-of-the-art methods in terms of accuracy. Contribution. Dual-CT-based feature is used for analyzing mammogram and can help improve breast cancer diagnosis accuracy.

A hybrid NSCT domain image watermarking scheme

Abstract: At present, dealing with the piracy and tampering of images has become a notable challenge, due to the presence of smart mobile gadgets. In this paper, we propose a novel watermarking algorithm based on non-subsampled contourlet transform (NSCT) for improving the security aspects of such images. Moreover, the fusion of feature searching approach with watermarking methods has gained prominence in the current years. The scale-invariant feature transform (SIFT) is a technique in computer vision for detecting and illustrating the local features in images. Nevertheless, the SIFT algorithm can extract feature points with high invariance that are resilient to several issues like rotation, compression, and scaling. Furthermore, the extracted feature points are embedded with watermark using the NSCT approach. Subsequently, the tree split, voting, rotation searching, and morphology techniques are employed for improving the robustness against the noise. The proposed watermarking algorithm offers superior capability, better capture quality, and tampering resistance, when compared with existing watermarking approaches.

An efficient and automatic glioblastoma brain tumor detection using shift-invariant shearlet transform and neural networks

Abstract: The detection and segmentation of tumor region in brain image is a critical task due to the similarity between abnormal and normal region. In this article, a computer-aided automatic detection and segmentation of brain tumor is proposed. The proposed system consists of enhancement, transformation, feature extraction, and classification. The shift-invariant shearlet transform (SIST) is used to enhance the brain image. Further, nonsubsampled contourlet transform (NSCT) is used as multiresolution transform which transforms the spatial domain enhanced image into multiresolution image. The texture features from grey level co-occurrence matrix (GLCM), Gabor, and discrete wavelet transform (DWT) are extracted with the approximate subband of the NSCT transformed image. These extracted features are trained and classified into either normal or glioblastoma brain image using feed forward back propagation neural networks. Further, K-means clustering algorithm is used to segment the tumor region in classified glioblastoma brain image. The proposed method achieves 89.7% of sensitivity, 99.9% of specificity, and 99.8% of accuracy.