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One of the challenges that Singular Value Decomposition (SVD)-based algorithms in watermarking face is the false positive problem while maintaining important parameters in watermarking, especially in different attacks. False positives occur when the watermark is placed on the S component of the SVD transformation of the host image. The proposed solutions have problems such as the low-quality watermarked image and its lack of stability against various attacks. To solve such problems, low-frequency bands of the host image, which are obtained by transforming the Contourlet obtained by taking the SVD transform and separating the S component are embedded by multiplying a parameter called delta obtained from the PSO (Particle swarm optimization) algorithm in the watermark image, and the embedding stage ends at this point. The result of this method is a relatively improved NC value, especially for the salt and pepper attack, which is compared with different articles in the results section. In the extraction section, the contourlet transform is taken from both the host image and the watermark, and the results are expressed with and without the denoising convolutional neural network algorithm (DnCNN). The values obtained for the false positive problem in the results section show that this scheme, while not having such a problem, is also resistant to various attacks. [ABSTRACT FROM AUTHOR]

Biometric devices have been indicated to be vulnerable to various threats. In the present study, a novel liveness identification technique is introduced according to the noise analysis of fingerprint images. It has been shown that the textural patterns of real fingertips are different from those of spoof fingertips. In this work, Real images were successfully distinguished from fake ones with an accuracy of approximately 99.50%. The database consists of 200 fingerprints, half of which is the real database (i.e. 100 fingerprints), and the other half is the fake database. Textural features are analyzed using pyramidal mean and PDFB analysis techniques that decompose images into directional sub-bands at several scales. The Shannon entropy, approximate entropy, sample entropy, permutation entropy, fuzzy entropy, multiscale entropy, Log energy entropy, and Sure entropy were estimated for all these sub-bands and were arranged into an array. In the classification stage, an ensemble classifier based on a multiobjective genetic algorithm was employed. The results obtained revealed that the suggested technique could successfully differentiate live fingertips from fake ones. The best accuracy was obtained for the proposed ensemble technique, with an average accuracy of 99.52% compared to other classifiers. After the ensemble method, quadratic SVM achieved the best classification performance with a mean accuracy of 97.34% in discriminating real live fingerprints from fake fingerprints. In comparison to other prior techniques, the suggested method produced good classification performance in real-time fingerprint identification while remaining straightforward. Therefore, the system can be a good option for use in real biometric systems. [ABSTRACT FROM AUTHOR]

This paper aims to develop an automatic feature extraction system for detecting icebergs in Antarctica. Extracting suitable features to discriminate an iceberg from sea ice and land melting based on its content is tedious. Especially in Synthetic Aperture Radar data, high image content is highly affected by speckle noise. Establishing the appropriate spatial relationship between pixels is not producing much accuracy with the standard low-level features. The proposed method introduces the two-level iceberg detection and tracking algorithm. The available samples were used to train the first-level convolution neural network-based features. False-positive predictions have been removed using the multiscale contourlet-based Haralick texture features in the second level. The final detected iceberg movement has been tracked using the temporal image data. The distance moved in both temporal images is computed with the help of latitude and longitude information. The proposed methodology exhibited the best performance over state-of-the-art methods and acquired 79.1% precision and 83.8 F1 score. [ABSTRACT FROM AUTHOR]

Seismic fault interpretation has great importance in characterising subsurface geology. However, it is in general a manual and time-consuming task, thus, adaptive methods to achieve good fault detection results would be valuable. In this paper, we present a new method using combination of Contourlet transform and fuzzy entropy definition to adaptively detect and extract faults from seismic data. Contourlet is a multiscale and multidirectional filter that has high directionality which decomposes an image to various subscales. Our proposed scheme has 3 phases: first, employing Contourlet to pull out fault information using multidirectional and multiscale property of contourlet (feature extraction), second, using differentiation to boost fault information and calculating the correlation coefficient between the input image and subscales, fault information can be isolated from reflectors adaptively (feature selection) and third, applying a multi-level thresholding approach built on fuzzy partition of the histogram and entropy theory to classify image pixels into fault and non-fault (classification). The adaptive hybrid technique was applied to one synthetic and two real datasets containing fault data, reflectors and random noise. According to the results and their assessments, the proposed scheme has desirably located fault features in the data. We also examined the effect of random noise (Signal to Noise Ratio (SNR) = 2) on our adaptive algorithm which showed the success of our designed technique in the presence of noise. [ABSTRACT FROM AUTHOR]

Simultaneous towed-streamer marine acquisition has the advantages of reducing the total time requirements and costs of surveys. However, the seismic records obtained are blended seismic data, so the successful deblending of such data is the key to this method. In this article, we propose an effective deblending method with a new thresholding operator based on the shaping regularization framework in the contourlet domain. The new thresholding operator consists of an adaptive Bayesian threshold and a new thresholding function. Because of its multiresolution, locality, and directionality properties, the contourlet transform can effectively capture geometrical structures, which are the main features in natural images. To make the traditional Bayesian threshold adaptive in the contourlet domain, we propose a scale adjustment factor, a direction adjustment factor, and an attenuation factor to modify the threshold, and we also adopt local adaptive elliptic windows to estimate the standard deviations of useful signals; eventually, we obtain an adaptive Bayesian threshold. Furthermore, the new thresholding function can overcome the shortcomings of the existing soft and hard thresholding functions. Experimental results demonstrate that our method can effectively separate blended data. [ABSTRACT FROM AUTHOR]

Image steganography is one of the promising and popular techniques used to secure the sensitive information. Even though there are numerous steganography techniques for hiding the sensitive information, there are still a lot of challenges to the researchers regarding the effective hiding of the sensitive data. Thus, an effective pixel prediction-based image steganography method is proposed, which uses the error dependent SVNN classifier for effective pixel identification. The suitable pixels are effectively identified from the medical image using the SVNN classifier using the pixel features, such as edge information, pixel coverage, texture, wavelet energy, Gabor, and scattering features. Here, the SVNN is trained optimally using the GA or MS Algorithm based on the minimal error. Then, the CT is applied to the predicted pixel for embedding. Finally, the inverse CT is employed to extract the secret message from the embedded image. The experimentation of the proposed image steganography is performed using the BRATS database depending on the performance metrics, PSNR, SSIM, and correlation coefficient, which acquired 89.3253 dB, 1, and 1, for the image without noise and 48.5778 dB, 0.6123, and 0.9933, for the image affected by noise, respectively. [ABSTRACT FROM AUTHOR]

This study presents two new approaches based on Weighted Contourlet Parametric (WCP) images for the classification of breast tumors from B-mode ultrasound images. The Rician Inverse Gaussian (RiIG) distribution is considered for modeling the statistics of ultrasound images in the Contourlet transform domain. The WCP images are obtained by weighting the RiIG modeled Contourlet sub-band coefficient images. In the feature-based approach, various geometrical, statistical, and texture features are shown to have low ANOVA p-value, thus indicating a good capacity for class discrimination. Using three publicly available datasets (Mendeley, UDIAT, and BUSI), it is shown that the classical feature-based approach can yield more than 97% accuracy across the datasets for breast tumor classification using WCP images while the custom-made convolutional neural network (CNN) can deliver more than 98% accuracy, sensitivity, specificity, NPV, and PPV values utilizing the same WCP images. Both methods provide superior classification performance, better than those of several existing techniques on the same datasets. [ABSTRACT FROM AUTHOR]

Deep learning-based automatic classification of breast tumors using parametric imaging techniques from ultrasound (US) B-mode images is still an exciting research area. The Rician inverse Gaussian (RiIG) distribution is currently emerging as an appropriate example of statistical modeling. This study presents a new approach of correlated-weighted contourlet-transformed RiIG (CWCtr-RiIG) and curvelet-transformed RiIG (CWCrv-RiIG) image-based deep convolutional neural network (CNN) architecture for breast tumor classification from B-mode ultrasound images. A comparative study with other statistical models, such as Nakagami and normal inverse Gaussian (NIG) distributions, is also experienced here. The weighted entitled here is for weighting the contourlet and curvelet sub-band coefficient images by correlation with their corresponding RiIG statistically modeled images. By taking into account three freely accessible datasets (Mendeley, UDIAT, and BUSI), it is demonstrated that the proposed approach can provide more than 98 percent accuracy, sensitivity, specificity, NPV, and PPV values using the CWCtr-RiIG images. On the same datasets, the suggested method offers superior classification performance to several other existing strategies.

Many cities in developing countries are facing rapid growth of dynamic slum areas but often lack detailed information and analysis on these informal settlements. Multiresolution analysis (MRA) has been successfully used in texture analysis. Texture analysis is widely discussed in literature, but most of the methods which do not employ multiresolution strategy cannot exploit the fact that texture occurs at various spatial scales. This paper proposes a texture-based segmentation scheme using newly developed multiresolution methods for slum area identification. The proposed method is tested on remotely sensed images where textural information in terms of statistical moments and energy are extracted at various scales and in different directions with the help of curvelet and contourlet transforms. The results are compared with wavelet-based MRA method of segmentation. Accuracy assessment is performed for segmented images, and comparative analysis is carried out in terms of class-wise and overall accuracies. It is found that the proposed method shows better class-discriminating power as compared to existing methods and overall classification accuracy of 91.4–95.4%.

Melanoma or skin cancer is the most dangerous and deadliest disease. As the incidence and mortality rate of skin cancer increases worldwide, an automated skin cancer detection/classification system is required for early detection and prevention of skin cancer. In this study, a Hybrid Artificial Intelligence Model (HAIM) is designed for skin cancer classification. It uses diverse multi-directional representation systems for feature extraction and an efficient Exponentially Weighted and Heaped Multi-Layer Perceptron (EWHMLP) for the classification. Though the wavelet transform is a powerful tool for signal and image processing, it is unable to detect the intermediate dimensional structures of a medical image. Thus the proposed HAIM uses Curvelet (CurT), Contourlet (ConT) and Shearlet (SheT) transforms as feature extraction techniques. Though MLP is very flexible and well suitable for the classification problem, the learning of weights is a challenging task. Also, the optimization process does not converge, and the model may not be stable. To overcome these drawbacks, EWHMLP is developed. Results show that the combined qualities of each transform in a hybrid approach provides an accuracy of 98.33% in a multi-class approach on PH2 database. [ABSTRACT FROM AUTHOR]

For the problem that the gear fault feature is difficult to extract in actual working condition,a fault feature extraction method based on vibration signal time-frequency image is proposed,which is based on the global texture of the contourlet transform and the local texture of the local binary pattern. Firstly,the vibration signal is transformed into the time-frequency domain by wavelet transform and then the time-frequency gray image is obtained. Then,contourlet transform is performed on the gray image,the low frequency and high frequency subbands are gotten,the low-frequency mean,standard deviation and the high frequency subbands of each layer of the average energy are extracted as part of a feature vector. At the same time,the feature values of the local binary pattern of the time-frequency gray image are extracted and another feature vector is obtained,and the final feature vectors are obtained by the combination of the two feature vectors. Finally,the data of different conditions from a gearbox and rolling bearing are classified and tested by using SVM,the experimental results show the effectiveness of the method,an effective method for the pattern recognition of mechanical equipment is provided.

This study presents two new approaches based on Weighted Contourlet Parametric (WCP) images for the classification of breast tumors from B-mode ultrasound images. The Rician Inverse Gaussian (RiIG) distribution is considered for modeling the statistics of ultrasound images in the Contourlet transform domain. The WCP images are obtained by weighting the RiIG modeled Contourlet sub-band coefficient images. In the feature-based approach, various geometrical, statistical, and texture features are shown to have low ANOVA p-value, thus indicating a good capacity for class discrimination. Using three publicly available datasets (Mendeley, UDIAT, and BUSI), it is shown that the classical feature-based approach can yield more than 97% accuracy across the datasets for breast tumor classification using WCP images while the custom-made convolutional neural network (CNN) can deliver more than 98% accuracy, sensitivity, specificity, NPV, and PPV values utilizing the same WCP images. Both methods provide superior classification performance, better than those of several existing techniques on the same datasets.

Multiresolution analysis (MRA) methods have been successfully used in texture analysis. Texture analysis is widely discussed in literature, but most of the methods which do not employ multiresolution strategy cannot exploit the fact that texture occurs at various spatial scales. This paper proposes a methodology to identify different classes in satellite images using texture features from newly developed multiresolution methods. The proposed method is tested on remotely sensed optical images and a Pauli RGB decomposed version of synthetic aperture radar image. The textural information is extracted at various scales and in different directions from curvelet and contourlet transforms. The results are compared with wavelet-based features. Accuracy assessment is performed and comparative analysis is carried out using minimum distance to mean, support vector machine and random forest classifiers. It is found that the proposed method shows better class discriminating power and classification capability as compared to existing wavelet-based method. [ABSTRACT FROM AUTHOR]

Image denoising, a significant research area in the field of medical image processing, makes an effort to recover the original image from its noise corrupted image. The Pulse Coupled Neural Networks (PCNN) works well against denoising a noisy image. Generally, image denoising techniques are directly applied on the pixels. From the literature review, it is reported that denoising after frequency domain transformation is performing better since noise removal is applied over the coefficients. Motivated by this, in this paper, a new technique called the Static Thresholded Pulse Coupled Neural Network (ST-PCNN) is proposed by combining PCNN with traditional filtering or threshold shrinkage technique in Contourlet Transform domain. Four different existing PCNN architectures, such as Neuromime Structure, Intersecting Cortical Model, Unit-Linking Model and Multichannel Model are considered for comparative analysis. The filters such as Wiener, Median, Average, Gaussian and threshold shrinkage techniques such as Sure Shrink, HeurShrink, Neigh Shrink, BayesShrink are used. For noise removal, a mixture of Speckle and Gaussian noise is considered for a CT skull image. A mixture of Rician and Gaussian noise is considered for MRI brain image. A mixture of Speckle and Salt and Pepper noise is considered for a Mammogram image. The Performance Metrics such as Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Index (SSIM), Image Quality Index (IQI), Universal Image Quality Index (UQI), Image Enhancement Filter (IEF), Structural Content (SC), Correlation Coefficient (CC), and Weighted Signal-to-Noise Ratio (WSNR) and Visual Signal-to-Noise Ratio (VSNR) are used to evaluate the performance of denoising. [ABSTRACT FROM AUTHOR]

The advancements in salient object detection have attracted many researchers and are significant in several computer vision applications. However, efficient salient object detection using still images is a major challenge. This paper proposes salient object detection technique using the proposed Spider-Gray Wolf Optimiser (S-GWO) algorithm that is designed by combining Gray Wolf Optimiser (GWO) and Spider Monkey Optimisation (SMO). The technique undergoes three steps, which involves keyframe extraction, saliency mapping, contourlet mapping, and then, fusion of obtained outputs using optimal coefficients. Initially, the extracted frames are subjected to saliency mapping and contourlet mapping simultaneously in order to determine the quality of each pixel. Then, the outputs obtained from the saliency mapping and contourlet mapping is fused using the arbitrary coefficients for obtaining the final result that is employed for detecting the salient objects. Here, the proposed S-GWO is employed for selecting the optimal coefficients for detecting the salient objects. The experimental evaluation of the proposed S-GWO based on the performance metrics reveals that the proposed S-GWO attained a maximal accuracy, sensitivity and specificity with 0.914, 0.861 and 0.929, respectively. [ABSTRACT FROM AUTHOR]

In the today Internet era, protection of digital content during transmission is an indigent. Alphanumeric watermarking is a resolution to the copyright defense than the endorsement of information into the system. In exhibit watermarking calculation, wellbeing of such watermarking process is moderately low. For expanding the soundness, an approach is presented, which is contourlet change with neuro-fuzzy-based watermark inserting process. The conventional approaches having loss during data recovery, this situation will be resolved using proposed watermarking scheme and also increase the security of watermarked image. The proposed color image watermarking scheme binary image is embedded over the shading image which utilizes contourlet and converse contourlet calculation for preprocessing of an image and neuro-fuzzy calculation to implant the bits in the green plane of an image. After completing the watermarking process, the results are analyzing using the quality assessment metrics like Peak Signal to Noise Ratio (PSNR) and Mean Square Error (MSE) etc., It is implemented using MATLAB R2013 software. The developed MATLAB code is converted into Hardware Description Language (HDL) and then implemented for Virtex-5 L110T Field Programmable Gate Array (FPGA) kit. [ABSTRACT FROM AUTHOR]

In order to better preserve the local contrast of the original color image and obtain the perceptual accurate grayscale image, a grayscale algorithm based on local contrast enhancement in contourlet transform domain is proposed. Firstly, in CIE Lab space, the conjugate gradient algorithm is used to optimize the objective function to get the global mapping function parameters, and preliminary grayscale image is obtained. Then, the multi-scale and multi-direction decomposition of color image and preliminary grayscale image are carried out by contourlet transform, local chromatic contrast ratio is used to enhance the contrast of directional detail image, and the enhanced detail image is obtained by inverse contourlet transform. Finally, the enhanced detail image is superimposed with the preliminary grayscale image to get the final grayscale image. Experiments on COLOR250 and ?adik database show that the proposed algorithm in this paper is superior to some typical algorithms in the existing literature, which can effectively preserve the contrast and structure information of the original image. The visual perception is natural for the grayscale image, whose subjectivity and objective evaluations are optimal. [ABSTRACT FROM AUTHOR]

Machine learning and statistical modeling techniques has drawn much interest within the medical imaging research community. However, clinically-relevant modeling of anatomical structures continues to be a challenging task. This paper presents a novel method for multiscale sparse appearance modeling in medical images with application to simulation of pathological deformations in X-ray images of human spine. The proposed appearance model benefits from the non-linear approximation power of Contourlets and its ability to capture higher order singularities to achieve a sparse representation while preserving the accuracy of the statistical model. Independent Component Analysis is used to extract statistical independent modes of variations from the sparse Contourlet-based domain. The new model is then used to simulate clinically-relevant pathological deformations in radiographic images.

Este artículo se discute la aplicación de tres transformadas, a saber, Contourlet, Curvelet y Ridgelet, que están destinadas a la mejora de bordes en una imagen, las cuales se aplicaron a las imágenes de satélite Worldview 2 fusionadas. La fusión se realizó sobre las imágenes de satélite WorldView 2 mediante la aplicación de varias transformada wavelet, entre ellas, Daubechies, Bior, rbior, Coiflet y Symlet 5 con diferentes niveles de descomposición. Los mejores resultados se obtuvieron con la transformada Symlet 5 en el nivel de descomposición cinco. Las imágenes fusionadas con la transformada wavelet (symlet 5) y las imágenes generadas (usando las transformadas Contourlet, Curvelet y Ridgelet) se evaluaron y analizaron cuantitativamente. Los métodos cuantitativos en el presente análisis incluyen ERGAS, RASE, Calidad Universal (Qu) y coeficiente de correlación (CC). La fusión de imágenes y la implementación de las transformadas se realizaron con el software MatLab®, que proporciona los siguientes Toolbox: Toolbox de Wavelet, Toolbox de procesamiento de imágenes, Toolbox Contourlet y código fuente para Curvelet y Ridgelet. Las transformadas Curvelet y Ridgelet producen mejores resultados en términos de mejora de bordes tanto para la imagen fusionada como para la imagen original.

It is important to extract texture feature from the ground-base cloud image for cloud type automatic detection. In this paper, a new method is presented to capture the contour edge, texture and geometric structure of cloud images by using Contourlet and the power spectrum analysis algorithm. More abundant texture information is extracted. Cloud images can be obtained a multiscale and multidirection decomposition. The coefficient matrix from Contourlet transform of ground nephogram is calculated. The energy, mean and variance characteristics calculated from coefficient matrix are composed of the feature information. The frequency information of the data series from the feature vector values is obtained by the power spectrum analysis. Then Support Vector Machines (SVM) classifier is used to classify according to the frequency information of the trend graph of data series. It is shown that altocumulus and stratus with different texture frequencies can be effectively recognized and further subdivided the types of clouds. [ABSTRACT FROM AUTHOR]

Noise-level estimation remains one of the most critical issues related to the contourlet-based approaches. In this paper, an investigation of an effective solution is directed from any redundant contourlet expansion. This is going to be addressed for the first time in that domain. In this proposition, the noise level is estimated as the median absolute value (MAD) of the finest multi-scale coefficients, calibrated by three correction parameters. This is done according to some visual classification of the natural images. The present estimator provides a better compromise between the image and the contourlet expansion nature, which makes the estimation results more accurate for a wide range of natural images, when compared to the best state-of-the-art methods. Therefore, it is extensively recommended for most of the contourlet-based image applications (Thresholding, filtering, etc.) thanks to its accuracy, simplicity, and rapidity. [ABSTRACT FROM AUTHOR]

Motion estimation is a means, which consists in studying the displacement of objects in a video sequence, seeking the correlation between two successive frames, to predict the change in the contents position. Motion estimation is becoming a progressively significant requirement in a variety of applications such as medicine, robotics and video compression. In recent years, wavelets are effective tools for motion estimation, but the DWT (Discrete Wavelet Transform) will suffer from problems like translation sensitivity, poor directionality and absence of phase information. These three disadvantages make classical wavelets incapable of calculating motion in complex sequences (contain several directions.). In order to improve these negative aspects, we will choose geometric wavelet. Therefore, our objective is to propose a method capable of estimating the motion in terms of performance (speed and accuracy). This method will be based on the geometric wavelet transform and more precisely on the Contourlet transform. This work consists of two parts: in the first stage, the denoising process is examined by the Contourlet transform to ensure the precision of motion; in the second phase, we applied the iterative method of Horn and Schunck to calculate the motion in order to guarantee good speed. Comparative experimental results of artificial sequences show that the proposed algorithm obtains considerably better performance than several state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Many cities in developing countries are facing rapid growth of dynamic slum areas but often lack detailed information and analysis on these informal settlements. Multiresolution analysis (MRA) has been successfully used in texture analysis. Texture analysis is widely discussed in literature, but most of the methods which do not employ multiresolution strategy cannot exploit the fact that texture occurs at various spatial scales. This paper proposes a texture-based segmentation scheme using newly developed multiresolution methods for slum area identification. The proposed method is tested on remotely sensed images where textural information in terms of statistical moments and energy are extracted at various scales and in different directions with the help of curvelet and contourlet transforms. The results are compared with wavelet-based MRA method of segmentation. Accuracy assessment is performed for segmented images, and comparative analysis is carried out in terms of class-wise and overall accuracies. It is found that the proposed method shows better class-discriminating power as compared to existing methods and overall classification accuracy of 91.4–95.4%. [ABSTRACT FROM AUTHOR]

A multiscale and multidirectional network named the Contourlet convolutional neural network (CCNN) is proposed for synthetic aperture radar (SAR) image despeckling. SAR image resolution is not higher than that of optical images. If the network depth is increased blindly, the SAR image detail information flow will become quite weak, resulting in severe vanishing/exploding gradients. In this paper, a multiscale and multidirectional convolutional neural network is constructed, in which a single-stream structure of convolutional layers is replaced with a multiple-stream structure to extract image features with multidirectional and multiscale properties, thus significantly improving the despeckling performance. With the help of the Contourlet, the CCNN is designed with multiple independent subnetworks to respectively capture abstract features of an image in a certain frequency and direction band. The CCNN can increase the number of convolutional layers by increasing the number of subnetworks, which makes the CCNN not only have enough convolutional layers to capture the SAR image features, but also overcome the problem of vanishing/exploding gradients caused by deepening the networks. Extensive quantitative and qualitative evaluations of synthetic and real SAR images show the superiority of our proposed method over the state-of-the-art speckle reduction method.

Image denoising is a very important step in cryo-transmission electron microscopy (cryo-TEM) and the energy filtering TEM images before the 3D tomography reconstruction, as it addresses the problem of high noise in these images, that leads to a loss of the contained information. High noise levels contribute in particular to difficulties in the alignment required for 3D tomography reconstruction. This paper investigates the denoising of TEM images that are acquired with a very low exposure time, with the primary objectives of enhancing the quality of these low-exposure time TEM images and improving the alignment process. We propose denoising structures to combine multiple noisy copies of the TEM images. The structures are based on Bayesian estimation in the transform domains instead of the spatial domain to build a novel feature preserving image denoising structures; namely: wavelet domain, the contourlet transform domain and the contourlet transform with sharp frequency localization. Numerical image denoising experiments demonstrate the performance of the Bayesian approach in the contourlet transform domain in terms of improving the signal to noise ratio (SNR) and recovering fine details that may be hidden in the data. The SNR and the visual quality of the denoised images are considerably enhanced using these denoising structures that combine multiple noisy copies. The proposed methods also enable a reduction in the exposure time.

Highlights • To enhance the brain image using Non-Sub sampled Contourlet Transform (NSCT). • To implement Adaptive Neuro Fuzzy Inference System (ANFIS) approach to classify the brain image into normal and Glioma brain image. • To improve the performance of the brain tumor detection system. Abstract The detection of tumor regions in Glioma brain image is a challenging task due to its low sensitive boundary pixels. In this paper, Non-Sub sampled Contourlet Transform (NSCT) is used to enhance the brain image and then texture features are extracted from the enhanced brain image. These extracted features are trained and classified using Adaptive Neuro Fuzzy Inference System (ANFIS) approach to classify the brain image into normal and Glioma brain image. Then, the tumor regions in Glioma brain image is segmented using morphological functions. The proposed Glioma brain tumor detection methodology is applied on the Brain Tumor image Segmentation challenge (BRATS) open access dataset in order to evaluate the performance. [ABSTRACT FROM AUTHOR]

To protect the copyrights of the digital multimedia in the information security domain, digital watermarking is still a solution. But recently, most of the researchers focused on the zero-watermarking technology where the original image is undisturbed by watermark embedding. In mission-critical surveillance applications (intrusion detection and disaster management scenarios) where the mobile robots are used, it will be a good idea to watermark the images captured by the robots to prevent the copyright infringement. If we watermark robot captured images (RCI) visual quality degradation of the image will be of main concern. So a zero-watermarking scheme using non sub-sampled contourlet transform and quadtree decomposition is proposed for RCI in this article. The approximated sub-band obtained by contourlet transform is undergone quadtree decomposition, to split the sub-band into blocks of different sizes. The sparse features of these blocks and the statistical distribution of the sub-band coefficients are exploited to generate a zero-watermark which is used for ownership authentication. The experimental results show that the proposed method outperforms the existing scheme, and resists most of the geometrical attacks and the signal processing attacks effectively. [ABSTRACT FROM AUTHOR]

In the digital holographic measurement of complex surfaces, phase unwrapping is a critical step for accurate reconstruction. The phases of the complex amplitudes calculated from interferometric holograms are disturbed by speckle noise, thus reliable unwrapping results are difficult to be obtained. Most of existing unwrapping algorithms implement denoising operations first to obtain noise-free phases and then conduct phase unwrapping pixel by pixel. This approach is sensitive to spikes and prone to unreliable results in practice. In this paper, a robust unwrapping algorithm based on the non-subsampled contourlet transform (NSCT) is developed. The multiscale and directional decomposition of NSCT enhances the boundary between adjacent phase levels and henceforth the influence of local noise can be eliminated in the transform domain. The wrapped phase map is segmented into several regions corresponding to different phase levels. Finally, an unwrapped phase map is obtained by elevating the phases of a whole segment instead of individual pixels to avoid unwrapping errors caused by local spikes. This algorithm is suitable for dealing with complex and noisy wavefronts. Its universality and superiority in the digital holographic interferometry have been demonstrated by both numerical analysis and practical experiments. [ABSTRACT FROM AUTHOR]

This article discusses the implementation of three transforms, namely Contourlet, Curvelet and Ridgelet, which are intended for image edge enhancement. These transforms were applied to fused Worldview 2 satellite images. The fusion was performed over the WorldView 2 satellite images applying various types of wavelet transforms, such as Daubechies, Bior, rbior, Coiflet and Symlet 5, with different levels of decomposition. The best results were obtained for the case of Symlet 5, level 5. Wavelet fused images and the generated images (using Contourlet, Curvelet and transformed Ridgelet) were evaluated and analyzed quantitatively. Quantitative methods in the present analysis include ERGAS, RASE, Universal Quality (Qu) and correlation coefficient (CC). Image merging and the implementation of transforms were performed with MatLab®, which supplies the following tools: Wavelet toolbox, Image processing toolbox, Contourlet toolbox, and Curvelet and Ridgelet source code. The results show that the Curvelet and Ridgelet transforms yield better results in terms of edge enhancement for both the merged image and the original image. [ABSTRACT FROM AUTHOR]