Content Based Image Retrieval based on Wavelet Transform coefficients distribution

PubMed Central

Lamard, Mathieu; Cazuguel, Guy; Quellec, Gwénolé; Bekri, Lynda; Roux, Christian; Cochener, Béatrice

2007-01-01

In this paper we propose a content based image retrieval method for diagnosis aid in medical fields. We characterize images without extracting significant features by using distribution of coefficients obtained by building signatures from the distribution of wavelet transform. The research is carried out by computing signature distances between the query and database images. Several signatures are proposed; they use a model of wavelet coefficient distribution. To enhance results, a weighted distance between signatures is used and an adapted wavelet base is proposed. Retrieval efficiency is given for different databases including a diabetic retinopathy, a mammography and a face database. Results are promising: the retrieval efficiency is higher than 95% for some cases using an optimization process. PMID:18003013

Combining a wavelet transform with a channelized Hotelling observer for tumor detection in 3D PET oncology imaging

NASA Astrophysics Data System (ADS)

Lartizien, Carole; Tomei, Sandrine; Maxim, Voichita; Odet, Christophe

2007-03-01

This study evaluates new observer models for 3D whole-body Positron Emission Tomography (PET) imaging based on a wavelet sub-band decomposition and compares them with the classical constant-Q CHO model. Our final goal is to develop an original method that performs guided detection of abnormal activity foci in PET oncology imaging based on these new observer models. This computer-aided diagnostic method would highly benefit to clinicians for diagnostic purpose and to biologists for massive screening of rodents populations in molecular imaging. Method: We have previously shown good correlation of the channelized Hotelling observer (CHO) using a constant-Q model with human observer performance for 3D PET oncology imaging. We propose an alternate method based on combining a CHO observer with a wavelet sub-band decomposition of the image and we compare it to the standard CHO implementation. This method performs an undecimated transform using a biorthogonal B-spline 4/4 wavelet basis to extract the features set for input to the Hotelling observer. This work is based on simulated 3D PET images of an extended MCAT phantom with randomly located lesions. We compare three evaluation criteria: classification performance using the signal-to-noise ratio (SNR), computation efficiency and visual quality of the derived 3D maps of the decision variable λ. The SNR is estimated on a series of test images for a variable number of training images for both observers. Results: Results show that the maximum SNR is higher with the constant-Q CHO observer, especially for targets located in the liver, and that it is reached with a smaller number of training images. However, preliminary analysis indicates that the visual quality of the 3D maps of the decision variable λ is higher with the wavelet-based CHO and the computation time to derive a 3D λ-map is about 350 times shorter than for the standard CHO. This suggests that the wavelet-CHO observer is a good candidate for use in our guided detection method.

A wavelet-based Bayesian framework for 3D object segmentation in microscopy

NASA Astrophysics Data System (ADS)

Pan, Kangyu; Corrigan, David; Hillebrand, Jens; Ramaswami, Mani; Kokaram, Anil

2012-03-01

In confocal microscopy, target objects are labeled with fluorescent markers in the living specimen, and usually appear with irregular brightness in the observed images. Also, due to the existence of out-of-focus objects in the image, the segmentation of 3-D objects in the stack of image slices captured at different depth levels of the specimen is still heavily relied on manual analysis. In this paper, a novel Bayesian model is proposed for segmenting 3-D synaptic objects from given image stack. In order to solve the irregular brightness and out-offocus problems, the segmentation model employs a likelihood using the luminance-invariant 'wavelet features' of image objects in the dual-tree complex wavelet domain as well as a likelihood based on the vertical intensity profile of the image stack in 3-D. Furthermore, a smoothness 'frame' prior based on the a priori knowledge of the connections of the synapses is introduced to the model for enhancing the connectivity of the synapses. As a result, our model can successfully segment the in-focus target synaptic object from a 3D image stack with irregular brightness.

Highly efficient codec based on significance-linked connected-component analysis of wavelet coefficients

NASA Astrophysics Data System (ADS)

Chai, Bing-Bing; Vass, Jozsef; Zhuang, Xinhua

1997-04-01

Recent success in wavelet coding is mainly attributed to the recognition of importance of data organization. There has been several very competitive wavelet codecs developed, namely, Shapiro's Embedded Zerotree Wavelets (EZW), Servetto et. al.'s Morphological Representation of Wavelet Data (MRWD), and Said and Pearlman's Set Partitioning in Hierarchical Trees (SPIHT). In this paper, we propose a new image compression algorithm called Significant-Linked Connected Component Analysis (SLCCA) of wavelet coefficients. SLCCA exploits both within-subband clustering of significant coefficients and cross-subband dependency in significant fields. A so-called significant link between connected components is designed to reduce the positional overhead of MRWD. In addition, the significant coefficients' magnitude are encoded in bit plane order to match the probability model of the adaptive arithmetic coder. Experiments show that SLCCA outperforms both EZW and MRWD, and is tied with SPIHT. Furthermore, it is observed that SLCCA generally has the best performance on images with large portion of texture. When applied to fingerprint image compression, it outperforms FBI's wavelet scalar quantization by about 1 dB.

Wavelet analysis of polarization azimuths maps for laser images of myocardial tissue for the purpose of diagnosing acute coronary insufficiency

NASA Astrophysics Data System (ADS)

Wanchuliak, O. Ya.; Peresunko, A. P.; Bakko, Bouzan Adel; Kushnerick, L. Ya.

2011-09-01

This paper presents the foundations of a large scale - localized wavelet - polarization analysis - inhomogeneous laser images of histological sections of myocardial tissue. Opportunities were identified defining relations between the structures of wavelet coefficients and causes of death. The optical model of polycrystalline networks of myocardium protein fibrils is presented. The technique of determining the coordinate distribution of polarization azimuth of the points of laser images of myocardium histological sections is suggested. The results of investigating the interrelation between the values of statistical (statistical moments of the 1st-4th order) parameters are presented which characterize distributions of wavelet - coefficients polarization maps of myocardium layers and death reasons.

Image restoration by minimizing zero norm of wavelet frame coefficients

NASA Astrophysics Data System (ADS)

Bao, Chenglong; Dong, Bin; Hou, Likun; Shen, Zuowei; Zhang, Xiaoqun; Zhang, Xue

2016-11-01

In this paper, we propose two algorithms, namely the extrapolated proximal iterative hard thresholding (EPIHT) algorithm and the EPIHT algorithm with line-search, for solving the {{\\ell }}0-norm regularized wavelet frame balanced approach for image restoration. Under the theoretical framework of Kurdyka-Łojasiewicz property, we show that the sequences generated by the two algorithms converge to a local minimizer with linear convergence rate. Moreover, extensive numerical experiments on sparse signal reconstruction and wavelet frame based image restoration problems including CT reconstruction, image deblur, demonstrate the improvement of {{\\ell }}0-norm based regularization models over some prevailing ones, as well as the computational efficiency of the proposed algorithms.

A Multiscale Vision Model and Applications to Astronomical Image and Data Analyses

NASA Astrophysics Data System (ADS)

Bijaoui, A.; Slezak, E.; Vandame, B.

Many researches were carried out on the automated identification of the astrophy sical sources, and their relevant measurements. Some vision models have been developed for this task, their use depending on the image content. We have developed a multiscale vision model (MVM) \\cite{BR95} well suited for analyzing complex structures such like interstellar clouds, galaxies, or cluster of galaxies. Our model is based on a redundant wavelet transform. For each scale we detect significant wavelet coefficients by application of a decision rule based on their probability density functions (PDF) under the hypothesis of a uniform distribution. In the case of a Poisson noise, this PDF can be determined from the autoconvolution of the wavelet function histogram \\cite{SLB93}. We may also apply Anscombe's transform, scale by scale in order to take into account the integrated number of events at each scale \\cite{FSB98}. Our aim is to compute an image of all detected structural features. MVM allows us to build oriented trees from the neighbouring of significant wavelet coefficients. Each tree is also divided into subtrees taking into account the maxima along the scale axis. This leads to identify objects in the scale space, and then to restore their images by classical inverse methods. This model works only if the sampling is correct at each scale. It is not generally the case for the orthogonal wavelets, so that we apply the so-called `a trous algorithm \\cite{BSM94} or a specific pyramidal one \\cite{RBV98}. It leads to ext ract superimposed objets of different size, and it gives for each of them a separate image, from which we can obtain position, flux and p attern parameters. We have applied these methods to different kinds of images, photographic plates, CCD frames or X-ray images. We have only to change the statistical rule for extr acting significant coefficients to adapt the model from an image class to another one. We have also applied this model to extract clusters hierarchically distributed or to identify regions devoid of objects from galaxy counts.

A multiscale Markov random field model in wavelet domain for image segmentation

NASA Astrophysics Data System (ADS)

Dai, Peng; Cheng, Yu; Wang, Shengchun; Du, Xinyu; Wu, Dan

2017-07-01

The human vision system has abilities for feature detection, learning and selective attention with some properties of hierarchy and bidirectional connection in the form of neural population. In this paper, a multiscale Markov random field model in the wavelet domain is proposed by mimicking some image processing functions of vision system. For an input scene, our model provides its sparse representations using wavelet transforms and extracts its topological organization using MRF. In addition, the hierarchy property of vision system is simulated using a pyramid framework in our model. There are two information flows in our model, i.e., a bottom-up procedure to extract input features and a top-down procedure to provide feedback controls. The two procedures are controlled simply by two pyramidal parameters, and some Gestalt laws are also integrated implicitly. Equipped with such biological inspired properties, our model can be used to accomplish different image segmentation tasks, such as edge detection and region segmentation.

Analysis of autostereoscopic three-dimensional images using multiview wavelets.

PubMed

Saveljev, Vladimir; Palchikova, Irina

2016-08-10

We propose that multiview wavelets can be used in processing multiview images. The reference functions for the synthesis/analysis of multiview images are described. The synthesized binary images were observed experimentally as three-dimensional visual images. The symmetric multiview B-spline wavelets are proposed. The locations recognized in the continuous wavelet transform correspond to the layout of the test objects. The proposed wavelets can be applied to the multiview, integral, and plenoptic images.

Wavelet-domain de-noising of OCT images of human brain malignant glioma

NASA Astrophysics Data System (ADS)

Dolganova, I. N.; Aleksandrova, P. V.; Beshplav, S.-I. T.; Chernomyrdin, N. V.; Dubyanskaya, E. N.; Goryaynov, S. A.; Kurlov, V. N.; Reshetov, I. V.; Potapov, A. A.; Tuchin, V. V.; Zaytsev, K. I.

2018-04-01

We have proposed a wavelet-domain de-noising technique for imaging of human brain malignant glioma by optical coherence tomography (OCT). It implies OCT image decomposition using the direct fast wavelet transform, thresholding of the obtained wavelet spectrum and further inverse fast wavelet transform for image reconstruction. By selecting both wavelet basis and thresholding procedure, we have found an optimal wavelet filter, which application improves differentiation of the considered brain tissue classes - i.e. malignant glioma and normal/intact tissue. Namely, it allows reducing the scattering noise in the OCT images and retaining signal decrement for each tissue class. Therefore, the observed results reveals the wavelet-domain de-noising as a prospective tool for improved characterization of biological tissue using the OCT.

Double Density Dual Tree Discrete Wavelet Transform implementation for Degraded Image Enhancement

NASA Astrophysics Data System (ADS)

Vimala, C.; Aruna Priya, P.

2018-04-01

Wavelet transform is a main tool for image processing applications in modern existence. A Double Density Dual Tree Discrete Wavelet Transform is used and investigated for image denoising. Images are considered for the analysis and the performance is compared with discrete wavelet transform and the Double Density DWT. Peak Signal to Noise Ratio values and Root Means Square error are calculated in all the three wavelet techniques for denoised images and the performance has evaluated. The proposed techniques give the better performance when comparing other two wavelet techniques.

Wavelet packets for multi- and hyper-spectral imagery

NASA Astrophysics Data System (ADS)

Benedetto, J. J.; Czaja, W.; Ehler, M.; Flake, C.; Hirn, M.

2010-01-01

State of the art dimension reduction and classification schemes in multi- and hyper-spectral imaging rely primarily on the information contained in the spectral component. To better capture the joint spatial and spectral data distribution we combine the Wavelet Packet Transform with the linear dimension reduction method of Principal Component Analysis. Each spectral band is decomposed by means of the Wavelet Packet Transform and we consider a joint entropy across all the spectral bands as a tool to exploit the spatial information. Dimension reduction is then applied to the Wavelet Packets coefficients. We present examples of this technique for hyper-spectral satellite imaging. We also investigate the role of various shrinkage techniques to model non-linearity in our approach.

Wavelet tree structure based speckle noise removal for optical coherence tomography

NASA Astrophysics Data System (ADS)

Yuan, Xin; Liu, Xuan; Liu, Yang

2018-02-01

We report a new speckle noise removal algorithm in optical coherence tomography (OCT). Though wavelet domain thresholding algorithms have demonstrated superior advantages in suppressing noise magnitude and preserving image sharpness in OCT, the wavelet tree structure has not been investigated in previous applications. In this work, we propose an adaptive wavelet thresholding algorithm via exploiting the tree structure in wavelet coefficients to remove the speckle noise in OCT images. The threshold for each wavelet band is adaptively selected following a special rule to retain the structure of the image across different wavelet layers. Our results demonstrate that the proposed algorithm outperforms conventional wavelet thresholding, with significant advantages in preserving image features.

Dependency of Optimal Parameters of the IRIS Template on Image Quality and Border Detection Error

NASA Astrophysics Data System (ADS)

Matveev, I. A.; Novik, V. P.

2017-05-01

Generation of a template containing spatial-frequency features of iris is an important stage of identification. The template is obtained by a wavelet transform in an image region specified by iris borders. One of the main characteristics of the identification system is the value of recognition error, equal error rate (EER) is used as criterion here. The optimal values (in sense of minimizing the EER) of wavelet transform parameters depend on many factors: image quality, sharpness, size of characteristic objects, etc. It is hard to isolate these factors and their influences. The work presents an attempt to study an influence of following factors to EER: iris segmentation precision, defocus level, noise level. Several public domain iris image databases were involved in experiments. The images were subjected to modelled distortions of said types. The dependencies of wavelet parameter and EER values from the distortion levels were build. It is observed that the increase of the segmentation error and image noise leads to the increase of the optimal wavelength of the wavelets, whereas the increase of defocus level leads to decreasing of this value.

Evidence for asymmetric inertial instability in the FIRE satellite dataset

NASA Technical Reports Server (NTRS)

Stevens, Duane E.; Ciesielski, Paul E.

1990-01-01

One of the main goals of the First ISCCP Regional Experiment (FIRE) is obtaining the basic knowledge to better interpret satellite image of clouds on regional and smaller scales. An analysis of a mesoscale circulation phenomenon as observed in hourly FIRE satellite images is presented. Specifically, the phenomenon of interest appeared on satellite images as a group of propagating cloud wavelets located on the edge of a cirrus canopy on the anticylonic side of a strong, upper-level subtropical jet. These wavelets, which were observed between 1300 and 2200 GMT on 25 February 1987, are seen most distinctly in the GOES-West infrared satellite picture at 1800 GMT. The purpose is to document that these wavelets were a manifestation of asymmetric inertial instability. During their lifetime, the wavelets were located over the North American synoptic sounding network, so that the meteorological conditions surrounding their occurrence could be examined. A particular emphasis of the analysis is on the jet streak in which the wavelets were imbedded. The characteristics of the wavelets are examined using hourly satellite imagery. The hypothesis that inertial instability is the dynamical mechanism responsible for generating the observed cloud wavelets was examined. To further substantiate this contention, the observed characteristics of the wavelets are compared to, and found to be consistent with, a theoretical model of inertia instability by Stevens and Ciesielski.

Wavelet analysis for wind fields estimation.

PubMed

Leite, Gladeston C; Ushizima, Daniela M; Medeiros, Fátima N S; de Lima, Gilson G

2010-01-01

Wind field analysis from synthetic aperture radar images allows the estimation of wind direction and speed based on image descriptors. In this paper, we propose a framework to automate wind direction retrieval based on wavelet decomposition associated with spectral processing. We extend existing undecimated wavelet transform approaches, by including à trous with B(3) spline scaling function, in addition to other wavelet bases as Gabor and Mexican-hat. The purpose is to extract more reliable directional information, when wind speed values range from 5 to 10 ms(-1). Using C-band empirical models, associated with the estimated directional information, we calculate local wind speed values and compare our results with QuikSCAT scatterometer data. The proposed approach has potential application in the evaluation of oil spills and wind farms.

Skin image retrieval using Gabor wavelet texture feature.

PubMed

Ou, X; Pan, W; Zhang, X; Xiao, P

2016-12-01

Skin imaging plays a key role in many clinical studies. We have used many skin imaging techniques, including the recently developed capacitive contact skin imaging based on fingerprint sensors. The aim of this study was to develop an effective skin image retrieval technique using Gabor wavelet transform, which can be used on different types of skin images, but with a special focus on skin capacitive contact images. Content-based image retrieval (CBIR) is a useful technology to retrieve stored images from database by supplying query images. In a typical CBIR, images are retrieved based on colour, shape, texture, etc. In this study, texture feature is used for retrieving skin images, and Gabor wavelet transform is used for texture feature description and extraction. The results show that the Gabor wavelet texture features can work efficiently on different types of skin images. Although Gabor wavelet transform is slower compared with other image retrieval techniques, such as principal component analysis (PCA) and grey-level co-occurrence matrix (GLCM), Gabor wavelet transform is the best for retrieving skin capacitive contact images and facial images with different orientations. Gabor wavelet transform can also work well on facial images with different expressions and skin cancer/disease images. We have developed an effective skin image retrieval method based on Gabor wavelet transform, that it is useful for retrieving different types of images, namely digital colour face images, digital colour skin cancer and skin disease images, and particularly greyscale skin capacitive contact images. Gabor wavelet transform can also be potentially useful for face recognition (with different orientation and expressions) and skin cancer/disease diagnosis. © 2016 Society of Cosmetic Scientists and the Société Française de Cosmétologie.

Agile Multi-Scale Decompositions for Automatic Image Registration

NASA Technical Reports Server (NTRS)

Murphy, James M.; Leija, Omar Navarro; Le Moigne, Jacqueline

2016-01-01

In recent works, the first and third authors developed an automatic image registration algorithm based on a multiscale hybrid image decomposition with anisotropic shearlets and isotropic wavelets. This prototype showed strong performance, improving robustness over registration with wavelets alone. However, this method imposed a strict hierarchy on the order in which shearlet and wavelet features were used in the registration process, and also involved an unintegrated mixture of MATLAB and C code. In this paper, we introduce a more agile model for generating features, in which a flexible and user-guided mix of shearlet and wavelet features are computed. Compared to the previous prototype, this method introduces a flexibility to the order in which shearlet and wavelet features are used in the registration process. Moreover, the present algorithm is now fully coded in C, making it more efficient and portable than the MATLAB and C prototype. We demonstrate the versatility and computational efficiency of this approach by performing registration experiments with the fully-integrated C algorithm. In particular, meaningful timing studies can now be performed, to give a concrete analysis of the computational costs of the flexible feature extraction. Examples of synthetically warped and real multi-modal images are analyzed.

Use of the wavelet transform to investigate differences in brain PET images between patient groups

NASA Astrophysics Data System (ADS)

Ruttimann, Urs E.; Unser, Michael A.; Rio, Daniel E.; Rawlings, Robert R.

1993-06-01

Suitability of the wavelet transform was studied for the analysis of glucose utilization differences between subject groups as displayed in PET images. To strengthen statistical inference, it was of particular interest investigating the tradeoff between signal localization and image decomposition into uncorrelated components. This tradeoff is shown to be controlled by wavelet regularity, with the optimal compromise attained by third-order orthogonal spline wavelets. Testing of the ensuing wavelet coefficients identified only about 1.5% as statistically different (p < .05) from noise, which then served to resynthesize the difference images by the inverse wavelet transform. The resulting images displayed relatively uniform, noise-free regions of significant differences with, due to the good localization maintained by the wavelets, very little reconstruction artifacts.

Adaptive multifocus image fusion using block compressed sensing with smoothed projected Landweber integration in the wavelet domain.

PubMed

V S, Unni; Mishra, Deepak; Subrahmanyam, G R K S

2016-12-01

The need for image fusion in current image processing systems is increasing mainly due to the increased number and variety of image acquisition techniques. Image fusion is the process of combining substantial information from several sensors using mathematical techniques in order to create a single composite image that will be more comprehensive and thus more useful for a human operator or other computer vision tasks. This paper presents a new approach to multifocus image fusion based on sparse signal representation. Block-based compressive sensing integrated with a projection-driven compressive sensing (CS) recovery that encourages sparsity in the wavelet domain is used as a method to get the focused image from a set of out-of-focus images. Compression is achieved during the image acquisition process using a block compressive sensing method. An adaptive thresholding technique within the smoothed projected Landweber recovery process reconstructs high-resolution focused images from low-dimensional CS measurements of out-of-focus images. Discrete wavelet transform and dual-tree complex wavelet transform are used as the sparsifying basis for the proposed fusion. The main finding lies in the fact that sparsification enables a better selection of the fusion coefficients and hence better fusion. A Laplacian mixture model fit is done in the wavelet domain and estimation of the probability density function (pdf) parameters by expectation maximization leads us to the proper selection of the coefficients of the fused image. Using the proposed method compared with the fusion scheme without employing the projected Landweber (PL) scheme and the other existing CS-based fusion approaches, it is observed that with fewer samples itself, the proposed method outperforms other approaches.

Quantification of localized vertebral deformities using a sparse wavelet-based shape model.

PubMed

Zewail, R; Elsafi, A; Durdle, N

2008-01-01

Medical experts often examine hundreds of spine x-ray images to determine existence of various pathologies. Common pathologies of interest are anterior osteophites, disc space narrowing, and wedging. By careful inspection of the outline shapes of the vertebral bodies, experts are able to identify and assess vertebral abnormalities with respect to the pathology under investigation. In this paper, we present a novel method for quantification of vertebral deformation using a sparse shape model. Using wavelets and Independent component analysis (ICA), we construct a sparse shape model that benefits from the approximation power of wavelets and the capability of ICA to capture higher order statistics in wavelet space. The new model is able to capture localized pathology-related shape deformations, hence it allows for quantification of vertebral shape variations. We investigate the capability of the model to predict localized pathology related deformations. Next, using support-vector machines, we demonstrate the diagnostic capabilities of the method through the discrimination of anterior osteophites in lumbar vertebrae. Experiments were conducted using a set of 150 contours from digital x-ray images of lumbar spine. Each vertebra is labeled as normal or abnormal. Results reported in this work focus on anterior osteophites as the pathology of interest.

Preprocessing of 2-Dimensional Gel Electrophoresis Images Applied to Proteomic Analysis: A Review.

PubMed

Goez, Manuel Mauricio; Torres-Madroñero, Maria Constanza; Röthlisberger, Sarah; Delgado-Trejos, Edilson

2018-02-01

Various methods and specialized software programs are available for processing two-dimensional gel electrophoresis (2-DGE) images. However, due to the anomalies present in these images, a reliable, automated, and highly reproducible system for 2-DGE image analysis has still not been achieved. The most common anomalies found in 2-DGE images include vertical and horizontal streaking, fuzzy spots, and background noise, which greatly complicate computational analysis. In this paper, we review the preprocessing techniques applied to 2-DGE images for noise reduction, intensity normalization, and background correction. We also present a quantitative comparison of non-linear filtering techniques applied to synthetic gel images, through analyzing the performance of the filters under specific conditions. Synthetic proteins were modeled into a two-dimensional Gaussian distribution with adjustable parameters for changing the size, intensity, and degradation. Three types of noise were added to the images: Gaussian, Rayleigh, and exponential, with signal-to-noise ratios (SNRs) ranging 8-20 decibels (dB). We compared the performance of wavelet, contourlet, total variation (TV), and wavelet-total variation (WTTV) techniques using parameters SNR and spot efficiency. In terms of spot efficiency, contourlet and TV were more sensitive to noise than wavelet and WTTV. Wavelet worked the best for images with SNR ranging 10-20 dB, whereas WTTV performed better with high noise levels. Wavelet also presented the best performance with any level of Gaussian noise and low levels (20-14 dB) of Rayleigh and exponential noise in terms of SNR. Finally, the performance of the non-linear filtering techniques was evaluated using a real 2-DGE image with previously identified proteins marked. Wavelet achieved the best detection rate for the real image. Copyright © 2018 Beijing Institute of Genomics, Chinese Academy of Sciences and Genetics Society of China. Production and hosting by Elsevier B.V. All rights reserved.

Wavelet-space correlation imaging for high-speed MRI without motion monitoring or data segmentation.

PubMed

Li, Yu; Wang, Hui; Tkach, Jean; Roach, David; Woods, Jason; Dumoulin, Charles

2015-12-01

This study aims to (i) develop a new high-speed MRI approach by implementing correlation imaging in wavelet-space, and (ii) demonstrate the ability of wavelet-space correlation imaging to image human anatomy with involuntary or physiological motion. Correlation imaging is a high-speed MRI framework in which image reconstruction relies on quantification of data correlation. The presented work integrates correlation imaging with a wavelet transform technique developed originally in the field of signal and image processing. This provides a new high-speed MRI approach to motion-free data collection without motion monitoring or data segmentation. The new approach, called "wavelet-space correlation imaging", is investigated in brain imaging with involuntary motion and chest imaging with free-breathing. Wavelet-space correlation imaging can exceed the speed limit of conventional parallel imaging methods. Using this approach with high acceleration factors (6 for brain MRI, 16 for cardiac MRI, and 8 for lung MRI), motion-free images can be generated in static brain MRI with involuntary motion and nonsegmented dynamic cardiac/lung MRI with free-breathing. Wavelet-space correlation imaging enables high-speed MRI in the presence of involuntary motion or physiological dynamics without motion monitoring or data segmentation. © 2014 Wiley Periodicals, Inc.

Wavelet Analysis for Wind Fields Estimation

PubMed Central

Leite, Gladeston C.; Ushizima, Daniela M.; Medeiros, Fátima N. S.; de Lima, Gilson G.

2010-01-01

Wind field analysis from synthetic aperture radar images allows the estimation of wind direction and speed based on image descriptors. In this paper, we propose a framework to automate wind direction retrieval based on wavelet decomposition associated with spectral processing. We extend existing undecimated wavelet transform approaches, by including à trous with B3 spline scaling function, in addition to other wavelet bases as Gabor and Mexican-hat. The purpose is to extract more reliable directional information, when wind speed values range from 5 to 10 ms−1. Using C-band empirical models, associated with the estimated directional information, we calculate local wind speed values and compare our results with QuikSCAT scatterometer data. The proposed approach has potential application in the evaluation of oil spills and wind farms. PMID:22219699

Wavelet and Multiresolution Analysis for Finite Element Networking Paradigms

NASA Technical Reports Server (NTRS)

Kurdila, Andrew J.; Sharpley, Robert C.

1999-01-01

This paper presents a final report on Wavelet and Multiresolution Analysis for Finite Element Networking Paradigms. The focus of this research is to derive and implement: 1) Wavelet based methodologies for the compression, transmission, decoding, and visualization of three dimensional finite element geometry and simulation data in a network environment; 2) methodologies for interactive algorithm monitoring and tracking in computational mechanics; and 3) Methodologies for interactive algorithm steering for the acceleration of large scale finite element simulations. Also included in this report are appendices describing the derivation of wavelet based Particle Image Velocity algorithms and reduced order input-output models for nonlinear systems by utilizing wavelet approximations.

Wavelet-space Correlation Imaging for High-speed MRI without Motion Monitoring or Data Segmentation

PubMed Central

Li, Yu; Wang, Hui; Tkach, Jean; Roach, David; Woods, Jason; Dumoulin, Charles

2014-01-01

Purpose This study aims to 1) develop a new high-speed MRI approach by implementing correlation imaging in wavelet-space, and 2) demonstrate the ability of wavelet-space correlation imaging to image human anatomy with involuntary or physiological motion. Methods Correlation imaging is a high-speed MRI framework in which image reconstruction relies on quantification of data correlation. The presented work integrates correlation imaging with a wavelet transform technique developed originally in the field of signal and image processing. This provides a new high-speed MRI approach to motion-free data collection without motion monitoring or data segmentation. The new approach, called “wavelet-space correlation imaging”, is investigated in brain imaging with involuntary motion and chest imaging with free-breathing. Results Wavelet-space correlation imaging can exceed the speed limit of conventional parallel imaging methods. Using this approach with high acceleration factors (6 for brain MRI, 16 for cardiac MRI and 8 for lung MRI), motion-free images can be generated in static brain MRI with involuntary motion and nonsegmented dynamic cardiac/lung MRI with free-breathing. Conclusion Wavelet-space correlation imaging enables high-speed MRI in the presence of involuntary motion or physiological dynamics without motion monitoring or data segmentation. PMID:25470230

Wavelet extractor: A Bayesian well-tie and wavelet extraction program

NASA Astrophysics Data System (ADS)

Gunning, James; Glinsky, Michael E.

2006-06-01

We introduce a new open-source toolkit for the well-tie or wavelet extraction problem of estimating seismic wavelets from seismic data, time-to-depth information, and well-log suites. The wavelet extraction model is formulated as a Bayesian inverse problem, and the software will simultaneously estimate wavelet coefficients, other parameters associated with uncertainty in the time-to-depth mapping, positioning errors in the seismic imaging, and useful amplitude-variation-with-offset (AVO) related parameters in multi-stack extractions. It is capable of multi-well, multi-stack extractions, and uses continuous seismic data-cube interpolation to cope with the problem of arbitrary well paths. Velocity constraints in the form of checkshot data, interpreted markers, and sonic logs are integrated in a natural way. The Bayesian formulation allows computation of full posterior uncertainties of the model parameters, and the important problem of the uncertain wavelet span is addressed uses a multi-model posterior developed from Bayesian model selection theory. The wavelet extraction tool is distributed as part of the Delivery seismic inversion toolkit. A simple log and seismic viewing tool is included in the distribution. The code is written in Java, and thus platform independent, but the Seismic Unix (SU) data model makes the inversion particularly suited to Unix/Linux environments. It is a natural companion piece of software to Delivery, having the capacity to produce maximum likelihood wavelet and noise estimates, but will also be of significant utility to practitioners wanting to produce wavelet estimates for other inversion codes or purposes. The generation of full parameter uncertainties is a crucial function for workers wishing to investigate questions of wavelet stability before proceeding to more advanced inversion studies.

Wavelet denoising during optical coherence tomography of the prostate nerves using the complex wavelet transform.

PubMed

Chitchian, Shahab; Fiddy, Michael; Fried, Nathaniel M

2008-01-01

Preservation of the cavernous nerves during prostate cancer surgery is critical in preserving sexual function after surgery. Optical coherence tomography (OCT) of the prostate nerves has recently been studied for potential use in nerve-sparing prostate surgery. In this study, the discrete wavelet transform and complex dual-tree wavelet transform are implemented for wavelet shrinkage denoising in OCT images of the rat prostate. Applying the complex dual-tree wavelet transform provides improved results for speckle noise reduction in the OCT prostate image. Image quality metrics of the cavernous nerves and signal-to-noise ratio (SNR) were improved significantly using this complex wavelet denoising technique.

Using wavelet denoising and mathematical morphology in the segmentation technique applied to blood cells images.

PubMed

Boix, Macarena; Cantó, Begoña

2013-04-01

Accurate image segmentation is used in medical diagnosis since this technique is a noninvasive pre-processing step for biomedical treatment. In this work we present an efficient segmentation method for medical image analysis. In particular, with this method blood cells can be segmented. For that, we combine the wavelet transform with morphological operations. Moreover, the wavelet thresholding technique is used to eliminate the noise and prepare the image for suitable segmentation. In wavelet denoising we determine the best wavelet that shows a segmentation with the largest area in the cell. We study different wavelet families and we conclude that the wavelet db1 is the best and it can serve for posterior works on blood pathologies. The proposed method generates goods results when it is applied on several images. Finally, the proposed algorithm made in MatLab environment is verified for a selected blood cells.

Visibility of wavelet quantization noise

NASA Technical Reports Server (NTRS)

Watson, A. B.; Yang, G. Y.; Solomon, J. A.; Villasenor, J.

1997-01-01

The discrete wavelet transform (DWT) decomposes an image into bands that vary in spatial frequency and orientation. It is widely used for image compression. Measures of the visibility of DWT quantization errors are required to achieve optimal compression. Uniform quantization of a single band of coefficients results in an artifact that we call DWT uniform quantization noise; it is the sum of a lattice of random amplitude basis functions of the corresponding DWT synthesis filter. We measured visual detection thresholds for samples of DWT uniform quantization noise in Y, Cb, and Cr color channels. The spatial frequency of a wavelet is r 2-lambda, where r is display visual resolution in pixels/degree, and lambda is the wavelet level. Thresholds increase rapidly with wavelet spatial frequency. Thresholds also increase from Y to Cr to Cb, and with orientation from lowpass to horizontal/vertical to diagonal. We construct a mathematical model for DWT noise detection thresholds that is a function of level, orientation, and display visual resolution. This allows calculation of a "perceptually lossless" quantization matrix for which all errors are in theory below the visual threshold. The model may also be used as the basis for adaptive quantization schemes.

Noise adaptive wavelet thresholding for speckle noise removal in optical coherence tomography.

PubMed

Zaki, Farzana; Wang, Yahui; Su, Hao; Yuan, Xin; Liu, Xuan

2017-05-01

Optical coherence tomography (OCT) is based on coherence detection of interferometric signals and hence inevitably suffers from speckle noise. To remove speckle noise in OCT images, wavelet domain thresholding has demonstrated significant advantages in suppressing noise magnitude while preserving image sharpness. However, speckle noise in OCT images has different characteristics in different spatial scales, which has not been considered in previous applications of wavelet domain thresholding. In this study, we demonstrate a noise adaptive wavelet thresholding (NAWT) algorithm that exploits the difference of noise characteristics in different wavelet sub-bands. The algorithm is simple, fast, effective and is closely related to the physical origin of speckle noise in OCT image. Our results demonstrate that NAWT outperforms conventional wavelet thresholding.

Identification and classification of similar looking food grains

NASA Astrophysics Data System (ADS)

Anami, B. S.; Biradar, Sunanda D.; Savakar, D. G.; Kulkarni, P. V.

2013-01-01

This paper describes the comparative study of Artificial Neural Network (ANN) and Support Vector Machine (SVM) classifiers by taking a case study of identification and classification of four pairs of similar looking food grains namely, Finger Millet, Mustard, Soyabean, Pigeon Pea, Aniseed, Cumin-seeds, Split Greengram and Split Blackgram. Algorithms are developed to acquire and process color images of these grains samples. The developed algorithms are used to extract 18 colors-Hue Saturation Value (HSV), and 42 wavelet based texture features. Back Propagation Neural Network (BPNN)-based classifier is designed using three feature sets namely color - HSV, wavelet-texture and their combined model. SVM model for color- HSV model is designed for the same set of samples. The classification accuracies ranging from 93% to 96% for color-HSV, ranging from 78% to 94% for wavelet texture model and from 92% to 97% for combined model are obtained for ANN based models. The classification accuracy ranging from 80% to 90% is obtained for color-HSV based SVM model. Training time required for the SVM based model is substantially lesser than ANN for the same set of images.

Wavelet SVM in Reproducing Kernel Hilbert Space for hyperspectral remote sensing image classification

NASA Astrophysics Data System (ADS)

Du, Peijun; Tan, Kun; Xing, Xiaoshi

2010-12-01

Combining Support Vector Machine (SVM) with wavelet analysis, we constructed wavelet SVM (WSVM) classifier based on wavelet kernel functions in Reproducing Kernel Hilbert Space (RKHS). In conventional kernel theory, SVM is faced with the bottleneck of kernel parameter selection which further results in time-consuming and low classification accuracy. The wavelet kernel in RKHS is a kind of multidimensional wavelet function that can approximate arbitrary nonlinear functions. Implications on semiparametric estimation are proposed in this paper. Airborne Operational Modular Imaging Spectrometer II (OMIS II) hyperspectral remote sensing image with 64 bands and Reflective Optics System Imaging Spectrometer (ROSIS) data with 115 bands were used to experiment the performance and accuracy of the proposed WSVM classifier. The experimental results indicate that the WSVM classifier can obtain the highest accuracy when using the Coiflet Kernel function in wavelet transform. In contrast with some traditional classifiers, including Spectral Angle Mapping (SAM) and Minimum Distance Classification (MDC), and SVM classifier using Radial Basis Function kernel, the proposed wavelet SVM classifier using the wavelet kernel function in Reproducing Kernel Hilbert Space is capable of improving classification accuracy obviously.

Wavelet Transforms in Parallel Image Processing

DTIC Science & Technology

1994-01-27

NUMBER OF PAGES Object Segmentation, Texture Segmentation, Image Compression, Image 137 Halftoning , Neural Network, Parallel Algorithms, 2D and 3D...Vector Quantization of Wavelet Transform Coefficients ........ ............................. 57 B.1.f Adaptive Image Halftoning based on Wavelet...application has been directed to the adaptive image halftoning . The gray information at a pixel, including its gray value and gradient, is represented by

Just Noticeable Distortion Model and Its Application in Color Image Watermarking

NASA Astrophysics Data System (ADS)

Liu, Kuo-Cheng

In this paper, a perceptually adaptive watermarking scheme for color images is proposed in order to achieve robustness and transparency. A new just noticeable distortion (JND) estimator for color images is first designed in the wavelet domain. The key issue of the JND model is to effectively integrate visual masking effects. The estimator is an extension to the perceptual model that is used in image coding for grayscale images. Except for the visual masking effects given coefficient by coefficient by taking into account the luminance content and the texture of grayscale images, the crossed masking effect given by the interaction between luminance and chrominance components and the effect given by the variance within the local region of the target coefficient are investigated such that the visibility threshold for the human visual system (HVS) can be evaluated. In a locally adaptive fashion based on the wavelet decomposition, the estimator applies to all subbands of luminance and chrominance components of color images and is used to measure the visibility of wavelet quantization errors. The subband JND profiles are then incorporated into the proposed color image watermarking scheme. Performance in terms of robustness and transparency of the watermarking scheme is obtained by means of the proposed approach to embed the maximum strength watermark while maintaining the perceptually lossless quality of the watermarked color image. Simulation results show that the proposed scheme with inserting watermarks into luminance and chrominance components is more robust than the existing scheme while retaining the watermark transparency.

QWT: Retrospective and New Applications

NASA Astrophysics Data System (ADS)

Xu, Yi; Yang, Xiaokang; Song, Li; Traversoni, Leonardo; Lu, Wei

Quaternion wavelet transform (QWT) achieves much attention in recent years as a new image analysis tool. In most cases, it is an extension of the real wavelet transform and complex wavelet transform (CWT) by using the quaternion algebra and the 2D Hilbert transform of filter theory, where analytic signal representation is desirable to retrieve phase-magnitude description of intrinsically 2D geometric structures in a grayscale image. In the context of color image processing, however, it is adapted to analyze the image pattern and color information as a whole unit by mapping sequential color pixels to a quaternion-valued vector signal. This paper provides a retrospective of QWT and investigates its potential use in the domain of image registration, image fusion, and color image recognition. It is indicated that it is important for QWT to induce the mechanism of adaptive scale representation of geometric features, which is further clarified through two application instances of uncalibrated stereo matching and optical flow estimation. Moreover, quaternionic phase congruency model is defined based on analytic signal representation so as to operate as an invariant feature detector for image registration. To achieve better localization of edges and textures in image fusion task, we incorporate directional filter bank (DFB) into the quaternion wavelet decomposition scheme to greatly enhance the direction selectivity and anisotropy of QWT. Finally, the strong potential use of QWT in color image recognition is materialized in a chromatic face recognition system by establishing invariant color features. Extensive experimental results are presented to highlight the exciting properties of QWT.

Perceptual compression of magnitude-detected synthetic aperture radar imagery

NASA Technical Reports Server (NTRS)

Gorman, John D.; Werness, Susan A.

1994-01-01

A perceptually-based approach for compressing synthetic aperture radar (SAR) imagery is presented. Key components of the approach are a multiresolution wavelet transform, a bit allocation mask based on an empirical human visual system (HVS) model, and hybrid scalar/vector quantization. Specifically, wavelet shrinkage techniques are used to segregate wavelet transform coefficients into three components: local means, edges, and texture. Each of these three components is then quantized separately according to a perceptually-based bit allocation scheme. Wavelet coefficients associated with local means and edges are quantized using high-rate scalar quantization while texture information is quantized using low-rate vector quantization. The impact of the perceptually-based multiresolution compression algorithm on visual image quality, impulse response, and texture properties is assessed for fine-resolution magnitude-detected SAR imagery; excellent image quality is found at bit rates at or above 1 bpp along with graceful performance degradation at rates below 1 bpp.

Wavelet-based compression of M-FISH images.

PubMed

Hua, Jianping; Xiong, Zixiang; Wu, Qiang; Castleman, Kenneth R

2005-05-01

Multiplex fluorescence in situ hybridization (M-FISH) is a recently developed technology that enables multi-color chromosome karyotyping for molecular cytogenetic analysis. Each M-FISH image set consists of a number of aligned images of the same chromosome specimen captured at different optical wavelength. This paper presents embedded M-FISH image coding (EMIC), where the foreground objects/chromosomes and the background objects/images are coded separately. We first apply critically sampled integer wavelet transforms to both the foreground and the background. We then use object-based bit-plane coding to compress each object and generate separate embedded bitstreams that allow continuous lossy-to-lossless compression of the foreground and the background. For efficient arithmetic coding of bit planes, we propose a method of designing an optimal context model that specifically exploits the statistical characteristics of M-FISH images in the wavelet domain. Our experiments show that EMIC achieves nearly twice as much compression as Lempel-Ziv-Welch coding. EMIC also performs much better than JPEG-LS and JPEG-2000 for lossless coding. The lossy performance of EMIC is significantly better than that of coding each M-FISH image with JPEG-2000.

Efficient random access high resolution region-of-interest (ROI) image retrieval using backward coding of wavelet trees (BCWT)

NASA Astrophysics Data System (ADS)

Corona, Enrique; Nutter, Brian; Mitra, Sunanda; Guo, Jiangling; Karp, Tanja

2008-03-01

Efficient retrieval of high quality Regions-Of-Interest (ROI) from high resolution medical images is essential for reliable interpretation and accurate diagnosis. Random access to high quality ROI from codestreams is becoming an essential feature in many still image compression applications, particularly in viewing diseased areas from large medical images. This feature is easier to implement in block based codecs because of the inherent spatial independency of the code blocks. This independency implies that the decoding order of the blocks is unimportant as long as the position for each is properly identified. In contrast, wavelet-tree based codecs naturally use some interdependency that exploits the decaying spectrum model of the wavelet coefficients. Thus one must keep track of the decoding order from level to level with such codecs. We have developed an innovative multi-rate image subband coding scheme using "Backward Coding of Wavelet Trees (BCWT)" which is fast, memory efficient, and resolution scalable. It offers far less complexity than many other existing codecs including both, wavelet-tree, and block based algorithms. The ROI feature in BCWT is implemented through a transcoder stage that generates a new BCWT codestream containing only the information associated with the user-defined ROI. This paper presents an efficient technique that locates a particular ROI within the BCWT coded domain, and decodes it back to the spatial domain. This technique allows better access and proper identification of pathologies in high resolution images since only a small fraction of the codestream is required to be transmitted and analyzed.

Local wavelet transform: a cost-efficient custom processor for space image compression

NASA Astrophysics Data System (ADS)

Masschelein, Bart; Bormans, Jan G.; Lafruit, Gauthier

2002-11-01

Thanks to its intrinsic scalability features, the wavelet transform has become increasingly popular as decorrelator in image compression applications. Throuhgput, memory requirements and complexity are important parameters when developing hardware image compression modules. An implementation of the classical, global wavelet transform requires large memory sizes and implies a large latency between the availability of the input image and the production of minimal data entities for entropy coding. Image tiling methods, as proposed by JPEG2000, reduce the memory sizes and the latency, but inevitably introduce image artefacts. The Local Wavelet Transform (LWT), presented in this paper, is a low-complexity wavelet transform architecture using a block-based processing that results in the same transformed images as those obtained by the global wavelet transform. The architecture minimizes the processing latency with a limited amount of memory. Moreover, as the LWT is an instruction-based custom processor, it can be programmed for specific tasks, such as push-broom processing of infinite-length satelite images. The features of the LWT makes it appropriate for use in space image compression, where high throughput, low memory sizes, low complexity, low power and push-broom processing are important requirements.

ICER-3D Hyperspectral Image Compression Software

NASA Technical Reports Server (NTRS)

Xie, Hua; Kiely, Aaron; Klimesh, matthew; Aranki, Nazeeh

2010-01-01

Software has been developed to implement the ICER-3D algorithm. ICER-3D effects progressive, three-dimensional (3D), wavelet-based compression of hyperspectral images. If a compressed data stream is truncated, the progressive nature of the algorithm enables reconstruction of hyperspectral data at fidelity commensurate with the given data volume. The ICER-3D software is capable of providing either lossless or lossy compression, and incorporates an error-containment scheme to limit the effects of data loss during transmission. The compression algorithm, which was derived from the ICER image compression algorithm, includes wavelet-transform, context-modeling, and entropy coding subalgorithms. The 3D wavelet decomposition structure used by ICER-3D exploits correlations in all three dimensions of sets of hyperspectral image data, while facilitating elimination of spectral ringing artifacts, using a technique summarized in "Improving 3D Wavelet-Based Compression of Spectral Images" (NPO-41381), NASA Tech Briefs, Vol. 33, No. 3 (March 2009), page 7a. Correlation is further exploited by a context-modeling subalgorithm, which exploits spectral dependencies in the wavelet-transformed hyperspectral data, using an algorithm that is summarized in "Context Modeler for Wavelet Compression of Hyperspectral Images" (NPO-43239), which follows this article. An important feature of ICER-3D is a scheme for limiting the adverse effects of loss of data during transmission. In this scheme, as in the similar scheme used by ICER, the spatial-frequency domain is partitioned into rectangular error-containment regions. In ICER-3D, the partitions extend through all the wavelength bands. The data in each partition are compressed independently of those in the other partitions, so that loss or corruption of data from any partition does not affect the other partitions. Furthermore, because compression is progressive within each partition, when data are lost, any data from that partition received prior to the loss can be used to reconstruct that partition at lower fidelity. By virtue of the compression improvement it achieves relative to previous means of onboard data compression, this software enables (1) increased return of hyperspectral scientific data in the presence of limits on the rates of transmission of data from spacecraft to Earth via radio communication links and/or (2) reduction in spacecraft radio-communication power and/or cost through reduction in the amounts of data required to be downlinked and stored onboard prior to downlink. The software is also suitable for compressing hyperspectral images for ground storage or archival purposes.

Directional Multi-scale Modeling of High-Resolution Computed Tomography (HRCT) Lung Images for Diffuse Lung Disease Classification

NASA Astrophysics Data System (ADS)

Vo, Kiet T.; Sowmya, Arcot

A directional multi-scale modeling scheme based on wavelet and contourlet transforms is employed to describe HRCT lung image textures for classifying four diffuse lung disease patterns: normal, emphysema, ground glass opacity (GGO) and honey-combing. Generalized Gaussian density parameters are used to represent the detail sub-band features obtained by wavelet and contourlet transforms. In addition, support vector machines (SVMs) with excellent performance in a variety of pattern classification problems are used as classifier. The method is tested on a collection of 89 slices from 38 patients, each slice of size 512x512, 16 bits/pixel in DICOM format. The dataset contains 70,000 ROIs of those slices marked by experienced radiologists. We employ this technique at different wavelet and contourlet transform scales for diffuse lung disease classification. The technique presented here has best overall sensitivity 93.40% and specificity 98.40%.

Scope and applications of translation invariant wavelets to image registration

NASA Technical Reports Server (NTRS)

Chettri, Samir; LeMoigne, Jacqueline; Campbell, William

1997-01-01

The first part of this article introduces the notion of translation invariance in wavelets and discusses several wavelets that have this property. The second part discusses the possible applications of such wavelets to image registration. In the case of registration of affinely transformed images, we would conclude that the notion of translation invariance is not really necessary. What is needed is affine invariance and one way to do this is via the method of moment invariants. Wavelets or, in general, pyramid processing can then be combined with the method of moment invariants to reduce the computational load.

Automatic Image Registration of Multimodal Remotely Sensed Data with Global Shearlet Features

NASA Technical Reports Server (NTRS)

Murphy, James M.; Le Moigne, Jacqueline; Harding, David J.

2015-01-01

Automatic image registration is the process of aligning two or more images of approximately the same scene with minimal human assistance. Wavelet-based automatic registration methods are standard, but sometimes are not robust to the choice of initial conditions. That is, if the images to be registered are too far apart relative to the initial guess of the algorithm, the registration algorithm does not converge or has poor accuracy, and is thus not robust. These problems occur because wavelet techniques primarily identify isotropic textural features and are less effective at identifying linear and curvilinear edge features. We integrate the recently developed mathematical construction of shearlets, which is more effective at identifying sparse anisotropic edges, with an existing automatic wavelet-based registration algorithm. Our shearlet features algorithm produces more distinct features than wavelet features algorithms; the separation of edges from textures is even stronger than with wavelets. Our algorithm computes shearlet and wavelet features for the images to be registered, then performs least squares minimization on these features to compute a registration transformation. Our algorithm is two-staged and multiresolution in nature. First, a cascade of shearlet features is used to provide a robust, though approximate, registration. This is then refined by registering with a cascade of wavelet features. Experiments across a variety of image classes show an improved robustness to initial conditions, when compared to wavelet features alone.

Automatic Image Registration of Multi-Modal Remotely Sensed Data with Global Shearlet Features

PubMed Central

Murphy, James M.; Le Moigne, Jacqueline; Harding, David J.

2017-01-01

Automatic image registration is the process of aligning two or more images of approximately the same scene with minimal human assistance. Wavelet-based automatic registration methods are standard, but sometimes are not robust to the choice of initial conditions. That is, if the images to be registered are too far apart relative to the initial guess of the algorithm, the registration algorithm does not converge or has poor accuracy, and is thus not robust. These problems occur because wavelet techniques primarily identify isotropic textural features and are less effective at identifying linear and curvilinear edge features. We integrate the recently developed mathematical construction of shearlets, which is more effective at identifying sparse anisotropic edges, with an existing automatic wavelet-based registration algorithm. Our shearlet features algorithm produces more distinct features than wavelet features algorithms; the separation of edges from textures is even stronger than with wavelets. Our algorithm computes shearlet and wavelet features for the images to be registered, then performs least squares minimization on these features to compute a registration transformation. Our algorithm is two-staged and multiresolution in nature. First, a cascade of shearlet features is used to provide a robust, though approximate, registration. This is then refined by registering with a cascade of wavelet features. Experiments across a variety of image classes show an improved robustness to initial conditions, when compared to wavelet features alone. PMID:29123329

Shearlet Features for Registration of Remotely Sensed Multitemporal Images

NASA Technical Reports Server (NTRS)

Murphy, James M.; Le Moigne, Jacqueline

2015-01-01

We investigate the role of anisotropic feature extraction methods for automatic image registration of remotely sensed multitemporal images. Building on the classical use of wavelets in image registration, we develop an algorithm based on shearlets, a mathematical generalization of wavelets that offers increased directional sensitivity. Initial experimental results on LANDSAT images are presented, which indicate superior performance of the shearlet algorithm when compared to classical wavelet algorithms.

Application of Time-Frequency Domain Transform to Three-Dimensional Interpolation of Medical Images.

PubMed

Lv, Shengqing; Chen, Yimin; Li, Zeyu; Lu, Jiahui; Gao, Mingke; Lu, Rongrong

2017-11-01

Medical image three-dimensional (3D) interpolation is an important means to improve the image effect in 3D reconstruction. In image processing, the time-frequency domain transform is an efficient method. In this article, several time-frequency domain transform methods are applied and compared in 3D interpolation. And a Sobel edge detection and 3D matching interpolation method based on wavelet transform is proposed. We combine wavelet transform, traditional matching interpolation methods, and Sobel edge detection together in our algorithm. What is more, the characteristics of wavelet transform and Sobel operator are used. They deal with the sub-images of wavelet decomposition separately. Sobel edge detection 3D matching interpolation method is used in low-frequency sub-images under the circumstances of ensuring high frequency undistorted. Through wavelet reconstruction, it can get the target interpolation image. In this article, we make 3D interpolation of the real computed tomography (CT) images. Compared with other interpolation methods, our proposed method is verified to be effective and superior.

Improved medical image fusion based on cascaded PCA and shift invariant wavelet transforms.

PubMed

Reena Benjamin, J; Jayasree, T

2018-02-01

In the medical field, radiologists need more informative and high-quality medical images to diagnose diseases. Image fusion plays a vital role in the field of biomedical image analysis. It aims to integrate the complementary information from multimodal images, producing a new composite image which is expected to be more informative for visual perception than any of the individual input images. The main objective of this paper is to improve the information, to preserve the edges and to enhance the quality of the fused image using cascaded principal component analysis (PCA) and shift invariant wavelet transforms. A novel image fusion technique based on cascaded PCA and shift invariant wavelet transforms is proposed in this paper. PCA in spatial domain extracts relevant information from the large dataset based on eigenvalue decomposition, and the wavelet transform operating in the complex domain with shift invariant properties brings out more directional and phase details of the image. The significance of maximum fusion rule applied in dual-tree complex wavelet transform domain enhances the average information and morphological details. The input images of the human brain of two different modalities (MRI and CT) are collected from whole brain atlas data distributed by Harvard University. Both MRI and CT images are fused using cascaded PCA and shift invariant wavelet transform method. The proposed method is evaluated based on three main key factors, namely structure preservation, edge preservation, contrast preservation. The experimental results and comparison with other existing fusion methods show the superior performance of the proposed image fusion framework in terms of visual and quantitative evaluations. In this paper, a complex wavelet-based image fusion has been discussed. The experimental results demonstrate that the proposed method enhances the directional features as well as fine edge details. Also, it reduces the redundant details, artifacts, distortions.

Segmentation of dermoscopy images using wavelet networks.

PubMed

Sadri, Amir Reza; Zekri, Maryam; Sadri, Saeed; Gheissari, Niloofar; Mokhtari, Mojgan; Kolahdouzan, Farzaneh

2013-04-01

This paper introduces a new approach for the segmentation of skin lesions in dermoscopic images based on wavelet network (WN). The WN presented here is a member of fixed-grid WNs that is formed with no need of training. In this WN, after formation of wavelet lattice, determining shift and scale parameters of wavelets with two screening stage and selecting effective wavelets, orthogonal least squares algorithm is used to calculate the network weights and to optimize the network structure. The existence of two stages of screening increases globality of the wavelet lattice and provides a better estimation of the function especially for larger scales. R, G, and B values of a dermoscopy image are considered as the network inputs and the network structure formation. Then, the image is segmented and the skin lesions exact boundary is determined accordingly. The segmentation algorithm were applied to 30 dermoscopic images and evaluated with 11 different metrics, using the segmentation result obtained by a skilled pathologist as the ground truth. Experimental results show that our method acts more effectively in comparison with some modern techniques that have been successfully used in many medical imaging problems.

Visual information processing; Proceedings of the Meeting, Orlando, FL, Apr. 20-22, 1992

NASA Technical Reports Server (NTRS)

Huck, Friedrich O. (Editor); Juday, Richard D. (Editor)

1992-01-01

Topics discussed in these proceedings include nonlinear processing and communications; feature extraction and recognition; image gathering, interpolation, and restoration; image coding; and wavelet transform. Papers are presented on noise reduction for signals from nonlinear systems; driving nonlinear systems with chaotic signals; edge detection and image segmentation of space scenes using fractal analyses; a vision system for telerobotic operation; a fidelity analysis of image gathering, interpolation, and restoration; restoration of images degraded by motion; and information, entropy, and fidelity in visual communication. Attention is also given to image coding methods and their assessment, hybrid JPEG/recursive block coding of images, modified wavelets that accommodate causality, modified wavelet transform for unbiased frequency representation, and continuous wavelet transform of one-dimensional signals by Fourier filtering.

Built-Up Area Detection from High-Resolution Satellite Images Using Multi-Scale Wavelet Transform and Local Spatial Statistics

NASA Astrophysics Data System (ADS)

Chen, Y.; Zhang, Y.; Gao, J.; Yuan, Y.; Lv, Z.

2018-04-01

Recently, built-up area detection from high-resolution satellite images (HRSI) has attracted increasing attention because HRSI can provide more detailed object information. In this paper, multi-resolution wavelet transform and local spatial autocorrelation statistic are introduced to model the spatial patterns of built-up areas. First, the input image is decomposed into high- and low-frequency subbands by wavelet transform at three levels. Then the high-frequency detail information in three directions (horizontal, vertical and diagonal) are extracted followed by a maximization operation to integrate the information in all directions. Afterward, a cross-scale operation is implemented to fuse different levels of information. Finally, local spatial autocorrelation statistic is introduced to enhance the saliency of built-up features and an adaptive threshold algorithm is used to achieve the detection of built-up areas. Experiments are conducted on ZY-3 and Quickbird panchromatic satellite images, and the results show that the proposed method is very effective for built-up area detection.

Image Retrieval using Integrated Features of Binary Wavelet Transform

NASA Astrophysics Data System (ADS)

Agarwal, Megha; Maheshwari, R. P.

2011-12-01

In this paper a new approach for image retrieval is proposed with the application of binary wavelet transform. This new approach facilitates the feature calculation with the integration of histogram and correlogram features extracted from binary wavelet subbands. Experiments are performed to evaluate and compare the performance of proposed method with the published literature. It is verified that average precision and average recall of proposed method (69.19%, 41.78%) is significantly improved compared to optimal quantized wavelet correlogram (OQWC) [6] (64.3%, 38.00%) and Gabor wavelet correlogram (GWC) [10] (64.1%, 40.6%). All the experiments are performed on Corel 1000 natural image database [20].

Wavelet Types Comparison for Extracting Iris Feature Based on Energy Compaction

NASA Astrophysics Data System (ADS)

Rizal Isnanto, R.

2015-06-01

Human iris has a very unique pattern which is possible to be used as a biometric recognition. To identify texture in an image, texture analysis method can be used. One of method is wavelet that extract the image feature based on energy. Wavelet transforms used are Haar, Daubechies, Coiflets, Symlets, and Biorthogonal. In the research, iris recognition based on five mentioned wavelets was done and then comparison analysis was conducted for which some conclusions taken. Some steps have to be done in the research. First, the iris image is segmented from eye image then enhanced with histogram equalization. The features obtained is energy value. The next step is recognition using normalized Euclidean distance. Comparison analysis is done based on recognition rate percentage with two samples stored in database for reference images. After finding the recognition rate, some tests are conducted using Energy Compaction for all five types of wavelets above. As the result, the highest recognition rate is achieved using Haar, whereas for coefficients cutting for C(i) < 0.1, Haar wavelet has a highest percentage, therefore the retention rate or significan coefficient retained for Haaris lower than other wavelet types (db5, coif3, sym4, and bior2.4)

Wavelet-based compression of pathological images for telemedicine applications

NASA Astrophysics Data System (ADS)

Chen, Chang W.; Jiang, Jianfei; Zheng, Zhiyong; Wu, Xue G.; Yu, Lun

2000-05-01

In this paper, we present the performance evaluation of wavelet-based coding techniques as applied to the compression of pathological images for application in an Internet-based telemedicine system. We first study how well suited the wavelet-based coding is as it applies to the compression of pathological images, since these images often contain fine textures that are often critical to the diagnosis of potential diseases. We compare the wavelet-based compression with the DCT-based JPEG compression in the DICOM standard for medical imaging applications. Both objective and subjective measures have been studied in the evaluation of compression performance. These studies are performed in close collaboration with expert pathologists who have conducted the evaluation of the compressed pathological images and communication engineers and information scientists who designed the proposed telemedicine system. These performance evaluations have shown that the wavelet-based coding is suitable for the compression of various pathological images and can be integrated well with the Internet-based telemedicine systems. A prototype of the proposed telemedicine system has been developed in which the wavelet-based coding is adopted for the compression to achieve bandwidth efficient transmission and therefore speed up the communications between the remote terminal and the central server of the telemedicine system.

Comparative Analysis of Haar and Daubechies Wavelet for Hyper Spectral Image Classification

NASA Astrophysics Data System (ADS)

Sharif, I.; Khare, S.

2014-11-01

With the number of channels in the hundreds instead of in the tens Hyper spectral imagery possesses much richer spectral information than multispectral imagery. The increased dimensionality of such Hyper spectral data provides a challenge to the current technique for analyzing data. Conventional classification methods may not be useful without dimension reduction pre-processing. So dimension reduction has become a significant part of Hyper spectral image processing. This paper presents a comparative analysis of the efficacy of Haar and Daubechies wavelets for dimensionality reduction in achieving image classification. Spectral data reduction using Wavelet Decomposition could be useful because it preserves the distinction among spectral signatures. Daubechies wavelets optimally capture the polynomial trends while Haar wavelet is discontinuous and resembles a step function. The performance of these wavelets are compared in terms of classification accuracy and time complexity. This paper shows that wavelet reduction has more separate classes and yields better or comparable classification accuracy. In the context of the dimensionality reduction algorithm, it is found that the performance of classification of Daubechies wavelets is better as compared to Haar wavelet while Daubechies takes more time compare to Haar wavelet. The experimental results demonstrate the classification system consistently provides over 84% classification accuracy.

Enhancing hyperspectral spatial resolution using multispectral image fusion: A wavelet approach

NASA Astrophysics Data System (ADS)

Jazaeri, Amin

High spectral and spatial resolution images have a significant impact in remote sensing applications. Because both spatial and spectral resolutions of spaceborne sensors are fixed by design and it is not possible to further increase the spatial or spectral resolution, techniques such as image fusion must be applied to achieve such goals. This dissertation introduces the concept of wavelet fusion between hyperspectral and multispectral sensors in order to enhance the spectral and spatial resolution of a hyperspectral image. To test the robustness of this concept, images from Hyperion (hyperspectral sensor) and Advanced Land Imager (multispectral sensor) were first co-registered and then fused using different wavelet algorithms. A regression-based fusion algorithm was also implemented for comparison purposes. The results show that the fused images using a combined bi-linear wavelet-regression algorithm have less error than other methods when compared to the ground truth. In addition, a combined regression-wavelet algorithm shows more immunity to misalignment of the pixels due to the lack of proper registration. The quantitative measures of average mean square error show that the performance of wavelet-based methods degrades when the spatial resolution of hyperspectral images becomes eight times less than its corresponding multispectral image. Regardless of what method of fusion is utilized, the main challenge in image fusion is image registration, which is also a very time intensive process. Because the combined regression wavelet technique is computationally expensive, a hybrid technique based on regression and wavelet methods was also implemented to decrease computational overhead. However, the gain in faster computation was offset by the introduction of more error in the outcome. The secondary objective of this dissertation is to examine the feasibility and sensor requirements for image fusion for future NASA missions in order to be able to perform onboard image fusion. In this process, the main challenge of image registration was resolved by registering the input images using transformation matrices of previously acquired data. The composite image resulted from the fusion process remarkably matched the ground truth, indicating the possibility of real time onboard fusion processing.

Identification of important image features for pork and turkey ham classification using colour and wavelet texture features and genetic selection.

PubMed

Jackman, Patrick; Sun, Da-Wen; Allen, Paul; Valous, Nektarios A; Mendoza, Fernando; Ward, Paddy

2010-04-01

A method to discriminate between various grades of pork and turkey ham was developed using colour and wavelet texture features. Image analysis methods originally developed for predicting the palatability of beef were applied to rapidly identify the ham grade. With high quality digital images of 50-94 slices per ham it was possible to identify the greyscale that best expressed the differences between the various ham grades. The best 10 discriminating image features were then found with a genetic algorithm. Using the best 10 image features, simple linear discriminant analysis models produced 100% correct classifications for both pork and turkey on both calibration and validation sets. 2009 Elsevier Ltd. All rights reserved.

A Lossless hybrid wavelet-fractal compression for welding radiographic images.

PubMed

Mekhalfa, Faiza; Avanaki, Mohammad R N; Berkani, Daoud

2016-01-01

In this work a lossless wavelet-fractal image coder is proposed. The process starts by compressing and decompressing the original image using wavelet transformation and fractal coding algorithm. The decompressed image is removed from the original one to obtain a residual image which is coded by using Huffman algorithm. Simulation results show that with the proposed scheme, we achieve an infinite peak signal to noise ratio (PSNR) with higher compression ratio compared to typical lossless method. Moreover, the use of wavelet transform speeds up the fractal compression algorithm by reducing the size of the domain pool. The compression results of several welding radiographic images using the proposed scheme are evaluated quantitatively and compared with the results of Huffman coding algorithm.

Wavelet analysis in two-dimensional tomography

NASA Astrophysics Data System (ADS)

Burkovets, Dimitry N.

2002-02-01

The diagnostic possibilities of wavelet-analysis of coherent images of connective tissue in its pathological changes diagnostics. The effectiveness of polarization selection in obtaining wavelet-coefficients' images is also shown. The wavelet structures, characterizing the process of skin psoriasis, bone-tissue osteoporosis have been analyzed. The histological sections of physiological normal and pathologically changed samples of connective tissue of human skin and spongy bone tissue have been analyzed.

Wavelet Fusion for Concealed Object Detection Using Passive Millimeter Wave Sequence Images

NASA Astrophysics Data System (ADS)

Chen, Y.; Pang, L.; Liu, H.; Xu, X.

2018-04-01

PMMW imaging system can create interpretable imagery on the objects concealed under clothing, which gives the great advantage to the security check system. Paper addresses wavelet fusion to detect concealed objects using passive millimeter wave (PMMW) sequence images. According to PMMW real-time imager acquired image characteristics and storage methods firstly, using the sum of squared difference (SSD) as the image-related parameters to screen the sequence images. Secondly, the selected images are optimized using wavelet fusion algorithm. Finally, the concealed objects are detected by mean filter, threshold segmentation and edge detection. The experimental results show that this method improves the detection effect of concealed objects by selecting the most relevant images from PMMW sequence images and using wavelet fusion to enhance the information of the concealed objects. The method can be effectively applied to human body concealed object detection in millimeter wave video.

Multiresolution edge detection using enhanced fuzzy c-means clustering for ultrasound image speckle reduction

DOE Office of Scientific and Technical Information (OSTI.GOV)

Tsantis, Stavros; Spiliopoulos, Stavros; Karnabatidis, Dimitrios

Purpose: Speckle suppression in ultrasound (US) images of various anatomic structures via a novel speckle noise reduction algorithm. Methods: The proposed algorithm employs an enhanced fuzzy c-means (EFCM) clustering and multiresolution wavelet analysis to distinguish edges from speckle noise in US images. The edge detection procedure involves a coarse-to-fine strategy with spatial and interscale constraints so as to classify wavelet local maxima distribution at different frequency bands. As an outcome, an edge map across scales is derived whereas the wavelet coefficients that correspond to speckle are suppressed in the inverse wavelet transform acquiring the denoised US image. Results: A totalmore » of 34 thyroid, liver, and breast US examinations were performed on a Logiq 9 US system. Each of these images was subjected to the proposed EFCM algorithm and, for comparison, to commercial speckle reduction imaging (SRI) software and another well-known denoising approach, Pizurica's method. The quantification of the speckle suppression performance in the selected set of US images was carried out via Speckle Suppression Index (SSI) with results of 0.61, 0.71, and 0.73 for EFCM, SRI, and Pizurica's methods, respectively. Peak signal-to-noise ratios of 35.12, 33.95, and 29.78 and edge preservation indices of 0.94, 0.93, and 0.86 were found for the EFCM, SIR, and Pizurica's method, respectively, demonstrating that the proposed method achieves superior speckle reduction performance and edge preservation properties. Based on two independent radiologists’ qualitative evaluation the proposed method significantly improved image characteristics over standard baseline B mode images, and those processed with the Pizurica's method. Furthermore, it yielded results similar to those for SRI for breast and thyroid images significantly better results than SRI for liver imaging, thus improving diagnostic accuracy in both superficial and in-depth structures. Conclusions: A new wavelet-based EFCM clustering model was introduced toward noise reduction and detail preservation. The proposed method improves the overall US image quality, which in turn could affect the decision-making on whether additional imaging and/or intervention is needed.« less

Multiresolution edge detection using enhanced fuzzy c-means clustering for ultrasound image speckle reduction.

PubMed

Tsantis, Stavros; Spiliopoulos, Stavros; Skouroliakou, Aikaterini; Karnabatidis, Dimitrios; Hazle, John D; Kagadis, George C

2014-07-01

Speckle suppression in ultrasound (US) images of various anatomic structures via a novel speckle noise reduction algorithm. The proposed algorithm employs an enhanced fuzzy c-means (EFCM) clustering and multiresolution wavelet analysis to distinguish edges from speckle noise in US images. The edge detection procedure involves a coarse-to-fine strategy with spatial and interscale constraints so as to classify wavelet local maxima distribution at different frequency bands. As an outcome, an edge map across scales is derived whereas the wavelet coefficients that correspond to speckle are suppressed in the inverse wavelet transform acquiring the denoised US image. A total of 34 thyroid, liver, and breast US examinations were performed on a Logiq 9 US system. Each of these images was subjected to the proposed EFCM algorithm and, for comparison, to commercial speckle reduction imaging (SRI) software and another well-known denoising approach, Pizurica's method. The quantification of the speckle suppression performance in the selected set of US images was carried out via Speckle Suppression Index (SSI) with results of 0.61, 0.71, and 0.73 for EFCM, SRI, and Pizurica's methods, respectively. Peak signal-to-noise ratios of 35.12, 33.95, and 29.78 and edge preservation indices of 0.94, 0.93, and 0.86 were found for the EFCM, SIR, and Pizurica's method, respectively, demonstrating that the proposed method achieves superior speckle reduction performance and edge preservation properties. Based on two independent radiologists' qualitative evaluation the proposed method significantly improved image characteristics over standard baseline B mode images, and those processed with the Pizurica's method. Furthermore, it yielded results similar to those for SRI for breast and thyroid images significantly better results than SRI for liver imaging, thus improving diagnostic accuracy in both superficial and in-depth structures. A new wavelet-based EFCM clustering model was introduced toward noise reduction and detail preservation. The proposed method improves the overall US image quality, which in turn could affect the decision-making on whether additional imaging and/or intervention is needed.

The effects of wavelet compression on Digital Elevation Models (DEMs)

USGS Publications Warehouse

Oimoen, M.J.

2004-01-01

This paper investigates the effects of lossy compression on floating-point digital elevation models using the discrete wavelet transform. The compression of elevation data poses a different set of problems and concerns than does the compression of images. Most notably, the usefulness of DEMs depends largely in the quality of their derivatives, such as slope and aspect. Three areas extracted from the U.S. Geological Survey's National Elevation Dataset were transformed to the wavelet domain using the third order filters of the Daubechies family (DAUB6), and were made sparse by setting 95 percent of the smallest wavelet coefficients to zero. The resulting raster is compressible to a corresponding degree. The effects of the nulled coefficients on the reconstructed DEM are noted as residuals in elevation, derived slope and aspect, and delineation of drainage basins and streamlines. A simple masking technique also is presented, that maintains the integrity and flatness of water bodies in the reconstructed DEM.

Image superresolution of cytology images using wavelet based patch search

NASA Astrophysics Data System (ADS)

Vargas, Carlos; García-Arteaga, Juan D.; Romero, Eduardo

2015-01-01

Telecytology is a new research area that holds the potential of significantly reducing the number of deaths due to cervical cancer in developing countries. This work presents a novel super-resolution technique that couples high and low frequency information in order to reduce the bandwidth consumption of cervical image transmission. The proposed approach starts by decomposing into wavelets the high resolution images and transmitting only the lower frequency coefficients. The transmitted coefficients are used to reconstruct an image of the original size. Additional details are added by iteratively replacing patches of the wavelet reconstructed image with equivalent high resolution patches from a previously acquired image database. Finally, the original transmitted low frequency coefficients are used to correct the final image. Results show a higher signal to noise ratio in the proposed method over simply discarding high frequency wavelet coefficients or replacing directly down-sampled patches from the image-database.

The wavelet/scalar quantization compression standard for digital fingerprint images

DOE Office of Scientific and Technical Information (OSTI.GOV)

Bradley, J.N.; Brislawn, C.M.

1994-04-01

A new digital image compression standard has been adopted by the US Federal Bureau of Investigation for use on digitized gray-scale fingerprint images. The algorithm is based on adaptive uniform scalar quantization of a discrete wavelet transform image decomposition and is referred to as the wavelet/scalar quantization standard. The standard produces archival quality images at compression ratios of around 20:1 and will allow the FBI to replace their current database of paper fingerprint cards with digital imagery.

Joint Inversion of Body-Wave Arrival Times and Surface-Wave Dispersion Data in the Wavelet Domain Constrained by Sparsity Regularization

NASA Astrophysics Data System (ADS)

Zhang, H.; Fang, H.; Yao, H.; Maceira, M.; van der Hilst, R. D.

2014-12-01

Recently, Zhang et al. (2014, Pure and Appiled Geophysics) have developed a joint inversion code incorporating body-wave arrival times and surface-wave dispersion data. The joint inversion code was based on the regional-scale version of the double-difference tomography algorithm tomoDD. The surface-wave inversion part uses the propagator matrix solver in the algorithm DISPER80 (Saito, 1988) for forward calculation of dispersion curves from layered velocity models and the related sensitivities. The application of the joint inversion code to the SAFOD site in central California shows that the fault structure is better imaged in the new model, which is able to fit both the body-wave and surface-wave observations adequately. Here we present a new joint inversion method that solves the model in the wavelet domain constrained by sparsity regularization. Compared to the previous method, it has the following advantages: (1) The method is both data- and model-adaptive. For the velocity model, it can be represented by different wavelet coefficients at different scales, which are generally sparse. By constraining the model wavelet coefficients to be sparse, the inversion in the wavelet domain can inherently adapt to the data distribution so that the model has higher spatial resolution in the good data coverage zone. Fang and Zhang (2014, Geophysical Journal International) have showed the superior performance of the wavelet-based double-difference seismic tomography method compared to the conventional method. (2) For the surface wave inversion, the joint inversion code takes advantage of the recent development of direct inversion of surface wave dispersion data for 3-D variations of shear wave velocity without the intermediate step of phase or group velocity maps (Fang et al., 2014, Geophysical Journal International). A fast marching method is used to compute, at each period, surface wave traveltimes and ray paths between sources and receivers. We will test the new joint inversion code at the SAFOD site to compare its performance over the previous code. We will also select another fault zone such as the San Jacinto Fault Zone to better image its structure.

Exploring an optimal wavelet-based filter for cryo-ET imaging.

PubMed

Huang, Xinrui; Li, Sha; Gao, Song

2018-02-07

Cryo-electron tomography (cryo-ET) is one of the most advanced technologies for the in situ visualization of molecular machines by producing three-dimensional (3D) biological structures. However, cryo-ET imaging has two serious disadvantages-low dose and low image contrast-which result in high-resolution information being obscured by noise and image quality being degraded, and this causes errors in biological interpretation. The purpose of this research is to explore an optimal wavelet denoising technique to reduce noise in cryo-ET images. We perform tests using simulation data and design a filter using the optimum selected wavelet parameters (three-level decomposition, level-1 zeroed out, subband-dependent threshold, a soft-thresholding and spline-based discrete dyadic wavelet transform (DDWT)), which we call a modified wavelet shrinkage filter; this filter is suitable for noisy cryo-ET data. When testing using real cryo-ET experiment data, higher quality images and more accurate measures of a biological structure can be obtained with the modified wavelet shrinkage filter processing compared with conventional processing. Because the proposed method provides an inherent advantage when dealing with cryo-ET images, it can therefore extend the current state-of-the-art technology in assisting all aspects of cryo-ET studies: visualization, reconstruction, structural analysis, and interpretation.

Appearance and characterization of fruit image textures for quality sorting using wavelet transform and genetic algorithms.

PubMed

Khoje, Suchitra

2018-02-01

Images of four qualities of mangoes and guavas are evaluated for color and textural features to characterize and classify them, and to model the fruit appearance grading. The paper discusses three approaches to identify most discriminating texture features of both the fruits. In the first approach, fruit's color and texture features are selected using Mahalanobis distance. A total of 20 color features and 40 textural features are extracted for analysis. Using Mahalanobis distance and feature intercorrelation analyses, one best color feature (mean of a* [L*a*b* color space]) and two textural features (energy a*, contrast of H*) are selected as features for Guava while two best color features (R std, H std) and one textural features (energy b*) are selected as features for mangoes with the highest discriminate power. The second approach studies some common wavelet families for searching the best classification model for fruit quality grading. The wavelet features extracted from five basic mother wavelets (db, bior, rbior, Coif, Sym) are explored to characterize fruits texture appearance. In third approach, genetic algorithm is used to select only those color and wavelet texture features that are relevant to the separation of the class, from a large universe of features. The study shows that image color and texture features which were identified using a genetic algorithm can distinguish between various qualities classes of fruits. The experimental results showed that support vector machine classifier is elected for Guava grading with an accuracy of 97.61% and artificial neural network is elected from Mango grading with an accuracy of 95.65%. The proposed method is nondestructive fruit quality assessment method. The experimental results has proven that Genetic algorithm along with wavelet textures feature has potential to discriminate fruit quality. Finally, it can be concluded that discussed method is an accurate, reliable, and objective tool to determine fruit quality namely Mango and Guava, and might be applicable to in-line sorting systems. © 2017 Wiley Periodicals, Inc.

Bayesian demosaicing using Gaussian scale mixture priors with local adaptivity in the dual tree complex wavelet packet transform domain

NASA Astrophysics Data System (ADS)

Goossens, Bart; Aelterman, Jan; Luong, Hiep; Pizurica, Aleksandra; Philips, Wilfried

2013-02-01

In digital cameras and mobile phones, there is an ongoing trend to increase the image resolution, decrease the sensor size and to use lower exposure times. Because smaller sensors inherently lead to more noise and a worse spatial resolution, digital post-processing techniques are required to resolve many of the artifacts. Color filter arrays (CFAs), which use alternating patterns of color filters, are very popular because of price and power consumption reasons. However, color filter arrays require the use of a post-processing technique such as demosaicing to recover full resolution RGB images. Recently, there has been some interest in techniques that jointly perform the demosaicing and denoising. This has the advantage that the demosaicing and denoising can be performed optimally (e.g. in the MSE sense) for the considered noise model, while avoiding artifacts introduced when using demosaicing and denoising sequentially. In this paper, we will continue the research line of the wavelet-based demosaicing techniques. These approaches are computationally simple and very suited for combination with denoising. Therefore, we will derive Bayesian Minimum Squared Error (MMSE) joint demosaicing and denoising rules in the complex wavelet packet domain, taking local adaptivity into account. As an image model, we will use Gaussian Scale Mixtures, thereby taking advantage of the directionality of the complex wavelets. Our results show that this technique is well capable of reconstructing fine details in the image, while removing all of the noise, at a relatively low computational cost. In particular, the complete reconstruction (including color correction, white balancing etc) of a 12 megapixel RAW image takes 3.5 sec on a recent mid-range GPU.

Fusion of multiscale wavelet-based fractal analysis on retina image for stroke prediction.

PubMed

Che Azemin, M Z; Kumar, Dinesh K; Wong, T Y; Wang, J J; Kawasaki, R; Mitchell, P; Arjunan, Sridhar P

2010-01-01

In this paper, we present a novel method of analyzing retinal vasculature using Fourier Fractal Dimension to extract the complexity of the retinal vasculature enhanced at different wavelet scales. Logistic regression was used as a fusion method to model the classifier for 5-year stroke prediction. The efficacy of this technique has been tested using standard pattern recognition performance evaluation, Receivers Operating Characteristics (ROC) analysis and medical prediction statistics, odds ratio. Stroke prediction model was developed using the proposed system.

WAVELET-DOMAIN REGRESSION AND PREDICTIVE INFERENCE IN PSYCHIATRIC NEUROIMAGING

PubMed Central

Reiss, Philip T.; Huo, Lan; Zhao, Yihong; Kelly, Clare; Ogden, R. Todd

2016-01-01

An increasingly important goal of psychiatry is the use of brain imaging data to develop predictive models. Here we present two contributions to statistical methodology for this purpose. First, we propose and compare a set of wavelet-domain procedures for fitting generalized linear models with scalar responses and image predictors: sparse variants of principal component regression and of partial least squares, and the elastic net. Second, we consider assessing the contribution of image predictors over and above available scalar predictors, in particular via permutation tests and an extension of the idea of confounding to the case of functional or image predictors. Using the proposed methods, we assess whether maps of a spontaneous brain activity measure, derived from functional magnetic resonance imaging, can meaningfully predict presence or absence of attention deficit/hyperactivity disorder (ADHD). Our results shed light on the role of confounding in the surprising outcome of the recent ADHD-200 Global Competition, which challenged researchers to develop algorithms for automated image-based diagnosis of the disorder. PMID:27330652

Optical asymmetric image encryption using gyrator wavelet transform

NASA Astrophysics Data System (ADS)

Mehra, Isha; Nishchal, Naveen K.

2015-11-01

In this paper, we propose a new optical information processing tool termed as gyrator wavelet transform to secure a fully phase image, based on amplitude- and phase-truncation approach. The gyrator wavelet transform constitutes four basic parameters; gyrator transform order, type and level of mother wavelet, and position of different frequency bands. These parameters are used as encryption keys in addition to the random phase codes to the optical cryptosystem. This tool has also been applied for simultaneous compression and encryption of an image. The system's performance and its sensitivity to the encryption parameters, such as, gyrator transform order, and robustness has also been analyzed. It is expected that this tool will not only update current optical security systems, but may also shed some light on future developments. The computer simulation results demonstrate the abilities of the gyrator wavelet transform as an effective tool, which can be used in various optical information processing applications, including image encryption, and image compression. Also this tool can be applied for securing the color image, multispectral, and three-dimensional images.

Vector coding of wavelet-transformed images

NASA Astrophysics Data System (ADS)

Zhou, Jun; Zhi, Cheng; Zhou, Yuanhua

1998-09-01

Wavelet, as a brand new tool in signal processing, has got broad recognition. Using wavelet transform, we can get octave divided frequency band with specific orientation which combines well with the properties of Human Visual System. In this paper, we discuss the classified vector quantization method for multiresolution represented image.

3D Segmentation of Maxilla in Cone-beam Computed Tomography Imaging Using Base Invariant Wavelet Active Shape Model on Customized Two-manifold Topology

PubMed Central

Chang, Yu-Bing; Xia, James J.; Yuan, Peng; Kuo, Tai-Hong; Xiong, Zixiang; Gateno, Jaime; Zhou, Xiaobo

2013-01-01

Recent advances in cone-beam computed tomography (CBCT) have rapidly enabled widepsread applications of dentomaxillofacial imaging and orthodontic practices in the past decades due to its low radiation dose, high spatial resolution, and accessibility. However, low contrast resolution in CBCT image has become its major limitation in building skull models. Intensive hand-segmentation is usually required to reconstruct the skull models. One of the regions affected by this limitation the most is the thin bone images. This paper presents a novel segmentation approach based on wavelet density model (WDM) for a particular interest in the outer surface of anterior wall of maxilla. Nineteen CBCT datasets are used to conduct two experiments. This mode-based segmentation approach is validated and compared with three different segmentation approaches. The results show that the performance of this model-based segmentation approach is better than those of the other approaches. It can achieve 0.25 ± 0.2mm of surface error from ground truth of bone surface. PMID:23694914

Clinical utility of wavelet compression for resolution-enhanced chest radiography

NASA Astrophysics Data System (ADS)

Andriole, Katherine P.; Hovanes, Michael E.; Rowberg, Alan H.

2000-05-01

This study evaluates the usefulness of wavelet compression for resolution-enhanced storage phosphor chest radiographs in the detection of subtle interstitial disease, pneumothorax and other abnormalities. A wavelet compression technique, MrSIDTM (LizardTech, Inc., Seattle, WA), is implemented which compresses the images from their original 2,000 by 2,000 (2K) matrix size, and then decompresses the image data for display at optimal resolution by matching the spatial frequency characteristics of image objects using a 4,000- square matrix. The 2K-matrix computed radiography (CR) chest images are magnified to a 4K-matrix using wavelet series expansion. The magnified images are compared with the original uncompressed 2K radiographs and with two-times magnification of the original images. Preliminary results show radiologist preference for MrSIDTM wavelet-based magnification over magnification of original data, and suggest that the compressed/decompressed images may provide an enhancement to the original. Data collection for clinical trials of 100 chest radiographs including subtle interstitial abnormalities and/or subtle pneumothoraces and normal cases, are in progress. Three experienced thoracic radiologists will view images side-by- side on calibrated softcopy workstations under controlled viewing conditions, and rank order preference tests will be performed. This technique combines image compression with image enhancement, and suggests that compressed/decompressed images can actually improve the originals.

Two-Layer Fragile Watermarking Method Secured with Chaotic Map for Authentication of Digital Holy Quran

PubMed Central

Khalil, Mohammed S.; Khan, Muhammad Khurram; Alginahi, Yasser M.

2014-01-01

This paper presents a novel watermarking method to facilitate the authentication and detection of the image forgery on the Quran images. Two layers of embedding scheme on wavelet and spatial domain are introduced to enhance the sensitivity of fragile watermarking and defend the attacks. Discrete wavelet transforms are applied to decompose the host image into wavelet prior to embedding the watermark in the wavelet domain. The watermarked wavelet coefficient is inverted back to spatial domain then the least significant bits is utilized to hide another watermark. A chaotic map is utilized to blur the watermark to make it secure against the local attack. The proposed method allows high watermark payloads, while preserving good image quality. Experiment results confirm that the proposed methods are fragile and have superior tampering detection even though the tampered area is very small. PMID:25028681

Two-layer fragile watermarking method secured with chaotic map for authentication of digital Holy Quran.

PubMed

Khalil, Mohammed S; Kurniawan, Fajri; Khan, Muhammad Khurram; Alginahi, Yasser M

2014-01-01

This paper presents a novel watermarking method to facilitate the authentication and detection of the image forgery on the Quran images. Two layers of embedding scheme on wavelet and spatial domain are introduced to enhance the sensitivity of fragile watermarking and defend the attacks. Discrete wavelet transforms are applied to decompose the host image into wavelet prior to embedding the watermark in the wavelet domain. The watermarked wavelet coefficient is inverted back to spatial domain then the least significant bits is utilized to hide another watermark. A chaotic map is utilized to blur the watermark to make it secure against the local attack. The proposed method allows high watermark payloads, while preserving good image quality. Experiment results confirm that the proposed methods are fragile and have superior tampering detection even though the tampered area is very small.

Onboard image compression schemes for modular airborne imaging spectrometer (MAIS) based on wavelet transform

NASA Astrophysics Data System (ADS)

Zhu, Zhenyu; Wang, Jianyu

1996-11-01

In this paper, two compression schemes are presented to meet the urgent needs of compressing the huge volume and high data rate of imaging spectrometer images. According to the multidimensional feature of the images and the high fidelity requirement of the reconstruction, both schemes were devised to exploit the high redundancy in both spatial and spectral dimension based on the mature wavelet transform technology. Wavelet transform was applied here in two ways: First, with the spatial wavelet transform and the spectral DPCM decorrelation, a ratio up to 84.3 with PSNR > 48db's near-lossless result was attained. This is based ont he fact that the edge structure among all the spectral bands are similar while WT has higher resolution in high frequency components. Secondly, with the wavelet's high efficiency in processing the 'wideband transient' signals, it was used to transform the raw nonstationary signals in the spectral dimension. A good result was also attained.

Block-based scalable wavelet image codec

NASA Astrophysics Data System (ADS)

Bao, Yiliang; Kuo, C.-C. Jay

1999-10-01

This paper presents a high performance block-based wavelet image coder which is designed to be of very low implementational complexity yet with rich features. In this image coder, the Dual-Sliding Wavelet Transform (DSWT) is first applied to image data to generate wavelet coefficients in fixed-size blocks. Here, a block only consists of wavelet coefficients from a single subband. The coefficient blocks are directly coded with the Low Complexity Binary Description (LCBiD) coefficient coding algorithm. Each block is encoded using binary context-based bitplane coding. No parent-child correlation is exploited in the coding process. There is also no intermediate buffering needed in between DSWT and LCBiD. The compressed bit stream generated by the proposed coder is both SNR and resolution scalable, as well as highly resilient to transmission errors. Both DSWT and LCBiD process the data in blocks whose size is independent of the size of the original image. This gives more flexibility in the implementation. The codec has a very good coding performance even the block size is (16,16).

3D image restoration for confocal microscopy: toward a wavelet deconvolution for the study of complex biological structures

NASA Astrophysics Data System (ADS)

Boutet de Monvel, Jacques; Le Calvez, Sophie; Ulfendahl, Mats

2000-05-01

Image restoration algorithms provide efficient tools for recovering part of the information lost in the imaging process of a microscope. We describe recent progress in the application of deconvolution to confocal microscopy. The point spread function of a Biorad-MRC1024 confocal microscope was measured under various imaging conditions, and used to process 3D-confocal images acquired in an intact preparation of the inner ear developed at Karolinska Institutet. Using these experiments we investigate the application of denoising methods based on wavelet analysis as a natural regularization of the deconvolution process. Within the Bayesian approach to image restoration, we compare wavelet denoising with the use of a maximum entropy constraint as another natural regularization method. Numerical experiments performed with test images show a clear advantage of the wavelet denoising approach, allowing to `cool down' the image with respect to the signal, while suppressing much of the fine-scale artifacts appearing during deconvolution due to the presence of noise, incomplete knowledge of the point spread function, or undersampling problems. We further describe a natural development of this approach, which consists of performing the Bayesian inference directly in the wavelet domain.

Sparsity prediction and application to a new steganographic technique

NASA Astrophysics Data System (ADS)

Phillips, David; Noonan, Joseph

2004-10-01

Steganography is a technique of embedding information in innocuous data such that only the innocent data is visible. The wavelet transform lends itself to image steganography because it generates a large number of coefficients representing the information in the image. Altering a small set of these coefficients allows embedding of information (payload) into an image (cover) without noticeably altering the original image. We propose a novel, dual-wavelet steganographic technique, using transforms selected such that the transform of the cover image has low sparsity, while the payload transform has high sparsity. Maximizing the sparsity of the payload transform reduces the amount of information embedded in the cover, and minimizing the sparsity of the cover increases the locations that can be altered without significantly altering the image. Making this system effective on any given image pair requires a metric to indicate the best (maximum sparsity) and worst (minimum sparsity) wavelet transforms to use. This paper develops the first stage of this metric, which can predict, averaged across many wavelet families, which of two images will have a higher sparsity. A prototype implementation of the dual-wavelet system as a proof of concept is also developed.

Medical Image Compression Based on Vector Quantization with Variable Block Sizes in Wavelet Domain

PubMed Central

Jiang, Huiyan; Ma, Zhiyuan; Hu, Yang; Yang, Benqiang; Zhang, Libo

2012-01-01

An optimized medical image compression algorithm based on wavelet transform and improved vector quantization is introduced. The goal of the proposed method is to maintain the diagnostic-related information of the medical image at a high compression ratio. Wavelet transformation was first applied to the image. For the lowest-frequency subband of wavelet coefficients, a lossless compression method was exploited; for each of the high-frequency subbands, an optimized vector quantization with variable block size was implemented. In the novel vector quantization method, local fractal dimension (LFD) was used to analyze the local complexity of each wavelet coefficients, subband. Then an optimal quadtree method was employed to partition each wavelet coefficients, subband into several sizes of subblocks. After that, a modified K-means approach which is based on energy function was used in the codebook training phase. At last, vector quantization coding was implemented in different types of sub-blocks. In order to verify the effectiveness of the proposed algorithm, JPEG, JPEG2000, and fractal coding approach were chosen as contrast algorithms. Experimental results show that the proposed method can improve the compression performance and can achieve a balance between the compression ratio and the image visual quality. PMID:23049544

Medical image compression based on vector quantization with variable block sizes in wavelet domain.

PubMed

Jiang, Huiyan; Ma, Zhiyuan; Hu, Yang; Yang, Benqiang; Zhang, Libo

2012-01-01

An optimized medical image compression algorithm based on wavelet transform and improved vector quantization is introduced. The goal of the proposed method is to maintain the diagnostic-related information of the medical image at a high compression ratio. Wavelet transformation was first applied to the image. For the lowest-frequency subband of wavelet coefficients, a lossless compression method was exploited; for each of the high-frequency subbands, an optimized vector quantization with variable block size was implemented. In the novel vector quantization method, local fractal dimension (LFD) was used to analyze the local complexity of each wavelet coefficients, subband. Then an optimal quadtree method was employed to partition each wavelet coefficients, subband into several sizes of subblocks. After that, a modified K-means approach which is based on energy function was used in the codebook training phase. At last, vector quantization coding was implemented in different types of sub-blocks. In order to verify the effectiveness of the proposed algorithm, JPEG, JPEG2000, and fractal coding approach were chosen as contrast algorithms. Experimental results show that the proposed method can improve the compression performance and can achieve a balance between the compression ratio and the image visual quality.

SU-F-J-27: Segmentation of Prostate CBCT Images with Implanted Calypso Transponders Using Double Haar Wavelet Transform

DOE Office of Scientific and Technical Information (OSTI.GOV)

Liu, Y; Saleh, Z; Tang, X

Purpose: Segmentation of prostate CBCT images is an essential step towards real-time adaptive radiotherapy. It is challenging For Calypso patients, as more artifacts are generated by the beacon transponders. We herein propose a novel wavelet-based segmentation algorithm for rectum, bladder, and prostate of CBCT images with implanted Calypso transponders. Methods: Five hypofractionated prostate patients with daily CBCT were studied. Each patient had 3 Calypso transponder beacons implanted, and the patients were setup and treated with Calypso tracking system. Two sets of CBCT images from each patient were studied. The structures (i.e. rectum, bladder, and prostate) were contoured by a trainedmore » expert, and these served as ground truth. For a given CBCT, the moving window-based Double Haar transformation is applied first to obtain the wavelet coefficients. Based on a user defined point in the object of interest, a cluster algorithm based adaptive thresholding is applied to the low frequency components of the wavelet coefficients, and a Lee filter theory based adaptive thresholding is applied to the high frequency components. For the next step, the wavelet reconstruction is applied to the thresholded wavelet coefficients. A binary/segmented image of the object of interest is therefore obtained. DICE, sensitivity, inclusiveness and ΔV were used to evaluate the segmentation result. Results: Considering all patients, the bladder has the DICE, sensitivity, inclusiveness, and ΔV ranges of [0.81–0.95], [0.76–0.99], [0.83–0.94], [0.02–0.21]. For prostate, the ranges are [0.77–0.93], [0.84–0.97], [0.68–0.92], [0.1–0.46]. For rectum, the ranges are [0.72–0.93], [0.57–0.99], [0.73–0.98], [0.03–0.42]. Conclusion: The proposed algorithm appeared effective segmenting prostate CBCT images with the present of the Calypso artifacts. However, it is not robust in two scenarios: 1) rectum with significant amount of gas; 2) prostate with very low contrast. Model based algorithm might improve the segmentation in these two scenarios.« less

Wavelet transform: fundamentals, applications, and implementation using acousto-optic correlators

NASA Astrophysics Data System (ADS)

DeCusatis, Casimer M.; Koay, J.; Litynski, Daniel M.; Das, Pankaj K.

1995-10-01

In recent years there has been a great deal of interest in the use of wavelets to supplement or replace conventional Fourier transform signal processing. This paper provides a review of wavelet transforms for signal processing applications, and discusses several emerging applications which benefit from the advantages of wavelets. The wavelet transform can be implemented as an acousto-optic correlator; perfect reconstruction of digital signals may also be achieved using acousto-optic finite impulse response filter banks. Acousto-optic image correlators are discussed as a potential implementation of the wavelet transform, since a 1D wavelet filter bank may be encoded as a 2D image. We discuss applications of the wavelet transform including nondestructive testing of materials, biomedical applications in the analysis of EEG signals, and interference excision in spread spectrum communication systems. Computer simulations and experimental results for these applications are also provided.

Alcoholism detection in magnetic resonance imaging by Haar wavelet transform and back propagation neural network

NASA Astrophysics Data System (ADS)

Yu, Yali; Wang, Mengxia; Lima, Dimas

2018-04-01

In order to develop a novel alcoholism detection method, we proposed a magnetic resonance imaging (MRI)-based computer vision approach. We first use contrast equalization to increase the contrast of brain slices. Then, we perform Haar wavelet transform and principal component analysis. Finally, we use back propagation neural network (BPNN) as the classification tool. Our method yields a sensitivity of 81.71±4.51%, a specificity of 81.43±4.52%, and an accuracy of 81.57±2.18%. The Haar wavelet gives better performance than db4 wavelet and sym3 wavelet.

Reconstruction of color images via Haar wavelet based on digital micromirror device

NASA Astrophysics Data System (ADS)

Liu, Xingjiong; He, Weiji; Gu, Guohua

2015-10-01

A digital micro mirror device( DMD) is introduced to form Haar wavelet basis , projecting on the color target image by making use of structured illumination, including red, green and blue light. The light intensity signals reflected from the target image are received synchronously by the bucket detector which has no spatial resolution, converted into voltage signals and then transferred into PC[1] .To reach the aim of synchronization, several synchronization processes are added during data acquisition. In the data collection process, according to the wavelet tree structure, the locations of significant coefficients at the finer scale are predicted by comparing the coefficients sampled at the coarsest scale with the threshold. The monochrome grayscale images are obtained under red , green and blue structured illumination by using Haar wavelet inverse transform algorithm, respectively. The color fusion algorithm is carried on the three monochrome grayscale images to obtain the final color image. According to the imaging principle, the experimental demonstration device is assembled. The letter "K" and the X-rite Color Checker Passport are projected and reconstructed as target images, and the final reconstructed color images have good qualities. This article makes use of the method of Haar wavelet reconstruction, reducing the sampling rate considerably. It provides color information without compromising the resolution of the final image.

Quality of reconstruction of compressed off-axis digital holograms by frequency filtering and wavelets.

PubMed

Cheremkhin, Pavel A; Kurbatova, Ekaterina A

2018-01-01

Compression of digital holograms can significantly help with the storage of objects and data in 2D and 3D form, its transmission, and its reconstruction. Compression of standard images by methods based on wavelets allows high compression ratios (up to 20-50 times) with minimum losses of quality. In the case of digital holograms, application of wavelets directly does not allow high values of compression to be obtained. However, additional preprocessing and postprocessing can afford significant compression of holograms and the acceptable quality of reconstructed images. In this paper application of wavelet transforms for compression of off-axis digital holograms are considered. The combined technique based on zero- and twin-order elimination, wavelet compression of the amplitude and phase components of the obtained Fourier spectrum, and further additional compression of wavelet coefficients by thresholding and quantization is considered. Numerical experiments on reconstruction of images from the compressed holograms are performed. The comparative analysis of applicability of various wavelets and methods of additional compression of wavelet coefficients is performed. Optimum parameters of compression of holograms by the methods can be estimated. Sizes of holographic information were decreased up to 190 times.

JPEG and wavelet compression of ophthalmic images

NASA Astrophysics Data System (ADS)

Eikelboom, Robert H.; Yogesan, Kanagasingam; Constable, Ian J.; Barry, Christopher J.

1999-05-01

This study was designed to determine the degree and methods of digital image compression to produce ophthalmic imags of sufficient quality for transmission and diagnosis. The photographs of 15 subjects, which inclined eyes with normal, subtle and distinct pathologies, were digitized to produce 1.54MB images and compressed to five different methods: (i) objectively by calculating the RMS error between the uncompressed and compressed images, (ii) semi-subjectively by assessing the visibility of blood vessels, and (iii) subjectively by asking a number of experienced observers to assess the images for quality and clinical interpretation. Results showed that as a function of compressed image size, wavelet compressed images produced less RMS error than JPEG compressed images. Blood vessel branching could be observed to a greater extent after Wavelet compression compared to JPEG compression produced better images then a JPEG compression for a given image size. Overall, it was shown that images had to be compressed to below 2.5 percent for JPEG and 1.7 percent for Wavelet compression before fine detail was lost, or when image quality was too poor to make a reliable diagnosis.

EBLAST: an efficient high-compression image transformation 3. application to Internet image and video transmission

NASA Astrophysics Data System (ADS)

Schmalz, Mark S.; Ritter, Gerhard X.; Caimi, Frank M.

2001-12-01

A wide variety of digital image compression transforms developed for still imaging and broadcast video transmission are unsuitable for Internet video applications due to insufficient compression ratio, poor reconstruction fidelity, or excessive computational requirements. Examples include hierarchical transforms that require all, or large portion of, a source image to reside in memory at one time, transforms that induce significant locking effect at operationally salient compression ratios, and algorithms that require large amounts of floating-point computation. The latter constraint holds especially for video compression by small mobile imaging devices for transmission to, and compression on, platforms such as palmtop computers or personal digital assistants (PDAs). As Internet video requirements for frame rate and resolution increase to produce more detailed, less discontinuous motion sequences, a new class of compression transforms will be needed, especially for small memory models and displays such as those found on PDAs. In this, the third series of papers, we discuss the EBLAST compression transform and its application to Internet communication. Leading transforms for compression of Internet video and still imagery are reviewed and analyzed, including GIF, JPEG, AWIC (wavelet-based), wavelet packets, and SPIHT, whose performance is compared with EBLAST. Performance analysis criteria include time and space complexity and quality of the decompressed image. The latter is determined by rate-distortion data obtained from a database of realistic test images. Discussion also includes issues such as robustness of the compressed format to channel noise. EBLAST has been shown to perform superiorly to JPEG and, unlike current wavelet compression transforms, supports fast implementation on embedded processors with small memory models.

Hyperspectral imaging with wavelet transform for classification of colon tissue biopsy samples

NASA Astrophysics Data System (ADS)

Masood, Khalid

2008-08-01

Automatic classification of medical images is a part of our computerised medical imaging programme to support the pathologists in their diagnosis. Hyperspectral data has found its applications in medical imagery. Its usage is increasing significantly in biopsy analysis of medical images. In this paper, we present a histopathological analysis for the classification of colon biopsy samples into benign and malignant classes. The proposed study is based on comparison between 3D spectral/spatial analysis and 2D spatial analysis. Wavelet textural features in the wavelet domain are used in both these approaches for classification of colon biopsy samples. Experimental results indicate that the incorporation of wavelet textural features using a support vector machine, in 2D spatial analysis, achieve best classification accuracy.

High-performance wavelet engine

NASA Astrophysics Data System (ADS)

Taylor, Fred J.; Mellot, Jonathon D.; Strom, Erik; Koren, Iztok; Lewis, Michael P.

1993-11-01

Wavelet processing has shown great promise for a variety of image and signal processing applications. Wavelets are also among the most computationally expensive techniques in signal processing. It is demonstrated that a wavelet engine constructed with residue number system arithmetic elements offers significant advantages over commercially available wavelet accelerators based upon conventional arithmetic elements. Analysis is presented predicting the dynamic range requirements of the reported residue number system based wavelet accelerator.

Combined self-learning based single-image super-resolution and dual-tree complex wavelet transform denoising for medical images

NASA Astrophysics Data System (ADS)

Yang, Guang; Ye, Xujiong; Slabaugh, Greg; Keegan, Jennifer; Mohiaddin, Raad; Firmin, David

2016-03-01

In this paper, we propose a novel self-learning based single-image super-resolution (SR) method, which is coupled with dual-tree complex wavelet transform (DTCWT) based denoising to better recover high-resolution (HR) medical images. Unlike previous methods, this self-learning based SR approach enables us to reconstruct HR medical images from a single low-resolution (LR) image without extra training on HR image datasets in advance. The relationships between the given image and its scaled down versions are modeled using support vector regression with sparse coding and dictionary learning, without explicitly assuming reoccurrence or self-similarity across image scales. In addition, we perform DTCWT based denoising to initialize the HR images at each scale instead of simple bicubic interpolation. We evaluate our method on a variety of medical images. Both quantitative and qualitative results show that the proposed approach outperforms bicubic interpolation and state-of-the-art single-image SR methods while effectively removing noise.

Impulse Noise Cancellation of Medical Images Using Wavelet Networks and Median Filters

PubMed Central

Sadri, Amir Reza; Zekri, Maryam; Sadri, Saeid; Gheissari, Niloofar

2012-01-01

This paper presents a new two-stage approach to impulse noise removal for medical images based on wavelet network (WN). The first step is noise detection, in which the so-called gray-level difference and average background difference are considered as the inputs of a WN. Wavelet Network is used as a preprocessing for the second stage. The second step is removing impulse noise with a median filter. The wavelet network presented here is a fixed one without learning. Experimental results show that our method acts on impulse noise effectively, and at the same time preserves chromaticity and image details very well. PMID:23493998

Texture feature extraction based on wavelet transform and gray-level co-occurrence matrices applied to osteosarcoma diagnosis.

PubMed

Hu, Shan; Xu, Chao; Guan, Weiqiao; Tang, Yong; Liu, Yana

2014-01-01

Osteosarcoma is the most common malignant bone tumor among children and adolescents. In this study, image texture analysis was made to extract texture features from bone CR images to evaluate the recognition rate of osteosarcoma. To obtain the optimal set of features, Sym4 and Db4 wavelet transforms and gray-level co-occurrence matrices were applied to the image, with statistical methods being used to maximize the feature selection. To evaluate the performance of these methods, a support vector machine algorithm was used. The experimental results demonstrated that the Sym4 wavelet had a higher classification accuracy (93.44%) than the Db4 wavelet with respect to osteosarcoma occurrence in the epiphysis, whereas the Db4 wavelet had a higher classification accuracy (96.25%) for osteosarcoma occurrence in the diaphysis. Results including accuracy, sensitivity, specificity and ROC curves obtained using the wavelets were all higher than those obtained using the features derived from the GLCM method. It is concluded that, a set of texture features can be extracted from the wavelets and used in computer-aided osteosarcoma diagnosis systems. In addition, this study also confirms that multi-resolution analysis is a useful tool for texture feature extraction during bone CR image processing.

Image-adaptive and robust digital wavelet-domain watermarking for images

NASA Astrophysics Data System (ADS)

Zhao, Yi; Zhang, Liping

2018-03-01

We propose a new frequency domain wavelet based watermarking technique. The key idea of our scheme is twofold: multi-tier solution representation of image and odd-even quantization embedding/extracting watermark. Because many complementary watermarks need to be hidden, the watermark image designed is image-adaptive. The meaningful and complementary watermark images was embedded into the original image (host image) by odd-even quantization modifying coefficients, which was selected from the detail wavelet coefficients of the original image, if their magnitudes are larger than their corresponding Just Noticeable Difference thresholds. The tests show good robustness against best-known attacks such as noise addition, image compression, median filtering, clipping as well as geometric transforms. Further research may improve the performance by refining JND thresholds.

Visual information processing II; Proceedings of the Meeting, Orlando, FL, Apr. 14-16, 1993

NASA Technical Reports Server (NTRS)

Huck, Friedrich O. (Editor); Juday, Richard D. (Editor)

1993-01-01

Various papers on visual information processing are presented. Individual topics addressed include: aliasing as noise, satellite image processing using a hammering neural network, edge-detetion method using visual perception, adaptive vector median filters, design of a reading test for low-vision image warping, spatial transformation architectures, automatic image-enhancement method, redundancy reduction in image coding, lossless gray-scale image compression by predictive GDF, information efficiency in visual communication, optimizing JPEG quantization matrices for different applications, use of forward error correction to maintain image fidelity, effect of peanoscanning on image compression. Also discussed are: computer vision for autonomous robotics in space, optical processor for zero-crossing edge detection, fractal-based image edge detection, simulation of the neon spreading effect by bandpass filtering, wavelet transform (WT) on parallel SIMD architectures, nonseparable 2D wavelet image representation, adaptive image halftoning based on WT, wavelet analysis of global warming, use of the WT for signal detection, perfect reconstruction two-channel rational filter banks, N-wavelet coding for pattern classification, simulation of image of natural objects, number-theoretic coding for iconic systems.

Fingerprint recognition of wavelet-based compressed images by neuro-fuzzy clustering

NASA Astrophysics Data System (ADS)

Liu, Ti C.; Mitra, Sunanda

1996-06-01

Image compression plays a crucial role in many important and diverse applications requiring efficient storage and transmission. This work mainly focuses on a wavelet transform (WT) based compression of fingerprint images and the subsequent classification of the reconstructed images. The algorithm developed involves multiresolution wavelet decomposition, uniform scalar quantization, entropy and run- length encoder/decoder and K-means clustering of the invariant moments as fingerprint features. The performance of the WT-based compression algorithm has been compared with JPEG current image compression standard. Simulation results show that WT outperforms JPEG in high compression ratio region and the reconstructed fingerprint image yields proper classification.

Experimental Studies on a Compact Storage Scheme for Wavelet-based Multiresolution Subregion Retrieval

NASA Technical Reports Server (NTRS)

Poulakidas, A.; Srinivasan, A.; Egecioglu, O.; Ibarra, O.; Yang, T.

1996-01-01

Wavelet transforms, when combined with quantization and a suitable encoding, can be used to compress images effectively. In order to use them for image library systems, a compact storage scheme for quantized coefficient wavelet data must be developed with a support for fast subregion retrieval. We have designed such a scheme and in this paper we provide experimental studies to demonstrate that it achieves good image compression ratios, while providing a natural indexing mechanism that facilitates fast retrieval of portions of the image at various resolutions.

Wavelet-Based Signal and Image Processing for Target Recognition

NASA Astrophysics Data System (ADS)

Sherlock, Barry G.

2002-11-01

The PI visited NSWC Dahlgren, VA, for six weeks in May-June 2002 and collaborated with scientists in the G33 TEAMS facility, and with Marilyn Rudzinsky of T44 Technology and Photonic Systems Branch. During this visit the PI also presented six educational seminars to NSWC scientists on various aspects of signal processing. Several items from the grant proposal were completed, including (1) wavelet-based algorithms for interpolation of 1-d signals and 2-d images; (2) Discrete Wavelet Transform domain based algorithms for filtering of image data; (3) wavelet-based smoothing of image sequence data originally obtained for the CRITTIR (Clutter Rejection Involving Temporal Techniques in the Infra-Red) project. The PI visited the University of Stellenbosch, South Africa to collaborate with colleagues Prof. B.M. Herbst and Prof. J. du Preez on the use of wavelet image processing in conjunction with pattern recognition techniques. The University of Stellenbosch has offered the PI partial funding to support a sabbatical visit in Fall 2003, the primary purpose of which is to enable the PI to develop and enhance his expertise in Pattern Recognition. During the first year, the grant supported publication of 3 referred papers, presentation of 9 seminars and an intensive two-day course on wavelet theory. The grant supported the work of two students who functioned as research assistants.

Speckle noise reduction in quantitative optical metrology techniques by application of the discrete wavelet transformation

NASA Astrophysics Data System (ADS)

Furlong, Cosme; Pryputniewicz, Ryszard J.

2002-06-01

Effective suppression of speckle noise content in interferometric data images can help in improving accuracy and resolution of the results obtained with interferometric optical metrology techniques. In this paper, novel speckle noise reduction algorithms based on the discrete wavelet transformation are presented. The algorithms proceed by: (a) estimating the noise level contained in the interferograms of interest, (b) selecting wavelet families, (c) applying the wavelet transformation using the selected families, (d) wavelet thresholding, and (e) applying the inverse wavelet transformation, producing denoised interferograms. The algorithms are applied to the different stages of the processing procedures utilized for generation of quantitative speckle correlation interferometry data of fiber-optic based opto-electronic holography (FOBOEH) techniques, allowing identification of optimal processing conditions. It is shown that wavelet algorithms are effective for speckle noise reduction while preserving image features otherwise faded with other algorithms.

Entropy-aware projected Landweber reconstruction for quantized block compressive sensing of aerial imagery

NASA Astrophysics Data System (ADS)

Liu, Hao; Li, Kangda; Wang, Bing; Tang, Hainie; Gong, Xiaohui

2017-01-01

A quantized block compressive sensing (QBCS) framework, which incorporates the universal measurement, quantization/inverse quantization, entropy coder/decoder, and iterative projected Landweber reconstruction, is summarized. Under the QBCS framework, this paper presents an improved reconstruction algorithm for aerial imagery, QBCS, with entropy-aware projected Landweber (QBCS-EPL), which leverages the full-image sparse transform without Wiener filter and an entropy-aware thresholding model for wavelet-domain image denoising. Through analyzing the functional relation between the soft-thresholding factors and entropy-based bitrates for different quantization methods, the proposed model can effectively remove wavelet-domain noise of bivariate shrinkage and achieve better image reconstruction quality. For the overall performance of QBCS reconstruction, experimental results demonstrate that the proposed QBCS-EPL algorithm significantly outperforms several existing algorithms. With the experiment-driven methodology, the QBCS-EPL algorithm can obtain better reconstruction quality at a relatively moderate computational cost, which makes it more desirable for aerial imagery applications.

Design of compactly supported wavelet to match singularities in medical images

NASA Astrophysics Data System (ADS)

Fung, Carrson C.; Shi, Pengcheng

2002-11-01

Analysis and understanding of medical images has important clinical values for patient diagnosis and treatment, as well as technical implications for computer vision and pattern recognition. One of the most fundamental issues is the detection of object boundaries or singularities, which is often the basis for further processes such as organ/tissue recognition, image registration, motion analysis, measurement of anatomical and physiological parameters, etc. The focus of this work involved taking a correlation based approach toward edge detection, by exploiting some of desirable properties of wavelet analysis. This leads to the possibility of constructing a bank of detectors, consisting of multiple wavelet basis functions of different scales which are optimal for specific types of edges, in order to optimally detect all the edges in an image. Our work involved developing a set of wavelet functions which matches the shape of the ramp and pulse edges. The matching algorithm used focuses on matching the edges in the frequency domain. It was proven that this technique could create matching wavelets applicable at all scales. Results have shown that matching wavelets can be obtained for the pulse edge while the ramp edge requires another matching algorithm.

Gabor-based kernel PCA with fractional power polynomial models for face recognition.

PubMed

Liu, Chengjun

2004-05-01

This paper presents a novel Gabor-based kernel Principal Component Analysis (PCA) method by integrating the Gabor wavelet representation of face images and the kernel PCA method for face recognition. Gabor wavelets first derive desirable facial features characterized by spatial frequency, spatial locality, and orientation selectivity to cope with the variations due to illumination and facial expression changes. The kernel PCA method is then extended to include fractional power polynomial models for enhanced face recognition performance. A fractional power polynomial, however, does not necessarily define a kernel function, as it might not define a positive semidefinite Gram matrix. Note that the sigmoid kernels, one of the three classes of widely used kernel functions (polynomial kernels, Gaussian kernels, and sigmoid kernels), do not actually define a positive semidefinite Gram matrix either. Nevertheless, the sigmoid kernels have been successfully used in practice, such as in building support vector machines. In order to derive real kernel PCA features, we apply only those kernel PCA eigenvectors that are associated with positive eigenvalues. The feasibility of the Gabor-based kernel PCA method with fractional power polynomial models has been successfully tested on both frontal and pose-angled face recognition, using two data sets from the FERET database and the CMU PIE database, respectively. The FERET data set contains 600 frontal face images of 200 subjects, while the PIE data set consists of 680 images across five poses (left and right profiles, left and right half profiles, and frontal view) with two different facial expressions (neutral and smiling) of 68 subjects. The effectiveness of the Gabor-based kernel PCA method with fractional power polynomial models is shown in terms of both absolute performance indices and comparative performance against the PCA method, the kernel PCA method with polynomial kernels, the kernel PCA method with fractional power polynomial models, the Gabor wavelet-based PCA method, and the Gabor wavelet-based kernel PCA method with polynomial kernels.

Face recognition by applying wavelet subband representation and kernel associative memory.

PubMed

Zhang, Bai-Ling; Zhang, Haihong; Ge, Shuzhi Sam

2004-01-01

In this paper, we propose an efficient face recognition scheme which has two features: 1) representation of face images by two-dimensional (2-D) wavelet subband coefficients and 2) recognition by a modular, personalised classification method based on kernel associative memory models. Compared to PCA projections and low resolution "thumb-nail" image representations, wavelet subband coefficients can efficiently capture substantial facial features while keeping computational complexity low. As there are usually very limited samples, we constructed an associative memory (AM) model for each person and proposed to improve the performance of AM models by kernel methods. Specifically, we first applied kernel transforms to each possible training pair of faces sample and then mapped the high-dimensional feature space back to input space. Our scheme using modular autoassociative memory for face recognition is inspired by the same motivation as using autoencoders for optical character recognition (OCR), for which the advantages has been proven. By associative memory, all the prototypical faces of one particular person are used to reconstruct themselves and the reconstruction error for a probe face image is used to decide if the probe face is from the corresponding person. We carried out extensive experiments on three standard face recognition datasets, the FERET data, the XM2VTS data, and the ORL data. Detailed comparisons with earlier published results are provided and our proposed scheme offers better recognition accuracy on all of the face datasets.

Retinal optical coherence tomography image enhancement via shrinkage denoising using double-density dual-tree complex wavelet transform

PubMed Central

Mayer, Markus A.; Boretsky, Adam R.; van Kuijk, Frederik J.; Motamedi, Massoud

2012-01-01

Abstract. Image enhancement of retinal structures, in optical coherence tomography (OCT) scans through denoising, has the potential to aid in the diagnosis of several eye diseases. In this paper, a locally adaptive denoising algorithm using double-density dual-tree complex wavelet transform, a combination of the double-density wavelet transform and the dual-tree complex wavelet transform, is applied to reduce speckle noise in OCT images of the retina. The algorithm overcomes the limitations of commonly used multiple frame averaging technique, namely the limited number of frames that can be recorded due to eye movements, by providing a comparable image quality in significantly less acquisition time equal to an order of magnitude less time compared to the averaging method. In addition, improvements of image quality metrics and 5 dB increase in the signal-to-noise ratio are attained. PMID:23117804

Retinal optical coherence tomography image enhancement via shrinkage denoising using double-density dual-tree complex wavelet transform.

PubMed

Chitchian, Shahab; Mayer, Markus A; Boretsky, Adam R; van Kuijk, Frederik J; Motamedi, Massoud

2012-11-01

ABSTRACT. Image enhancement of retinal structures, in optical coherence tomography (OCT) scans through denoising, has the potential to aid in the diagnosis of several eye diseases. In this paper, a locally adaptive denoising algorithm using double-density dual-tree complex wavelet transform, a combination of the double-density wavelet transform and the dual-tree complex wavelet transform, is applied to reduce speckle noise in OCT images of the retina. The algorithm overcomes the limitations of commonly used multiple frame averaging technique, namely the limited number of frames that can be recorded due to eye movements, by providing a comparable image quality in significantly less acquisition time equal to an order of magnitude less time compared to the averaging method. In addition, improvements of image quality metrics and 5 dB increase in the signal-to-noise ratio are attained.

Segmentation-based wavelet transform for still-image compression

NASA Astrophysics Data System (ADS)

Mozelle, Gerard; Seghier, Abdellatif; Preteux, Francoise J.

1996-10-01

In order to address simultaneously the two functionalities, content-based scalability required by MPEG-4, we introduce a segmentation-based wavelet transform (SBWT). SBWT takes into account both the mathematical properties of multiresolution analysis and the flexibility of region-based approaches for image compression. The associated methodology has two stages: 1) image segmentation into convex and polygonal regions; 2) 2D-wavelet transform of the signal corresponding to each region. In this paper, we have mathematically studied a method for constructing a multiresolution analysis (VjOmega)j (epsilon) N adapted to a polygonal region which provides an adaptive region-based filtering. The explicit construction of scaling functions, pre-wavelets and orthonormal wavelets bases defined on a polygon is carried out by using scaling functions is established by using the theory of Toeplitz operators. The corresponding expression can be interpreted as a location property which allow defining interior and boundary scaling functions. Concerning orthonormal wavelets and pre-wavelets, a similar expansion is obtained by taking advantage of the properties of the orthogonal projector P(V(j(Omega )) perpendicular from the space Vj(Omega ) + 1 onto the space (Vj(Omega )) perpendicular. Finally the mathematical results provide a simple and fast algorithm adapted to polygonal regions.

Real-time modeling of primitive environments through wavelet sensors and Hebbian learning

NASA Astrophysics Data System (ADS)

Vaccaro, James M.; Yaworsky, Paul S.

1999-06-01

Modeling the world through sensory input necessarily provides a unique perspective for the observer. Given a limited perspective, objects and events cannot always be encoded precisely but must involve crude, quick approximations to deal with sensory information in a real- time manner. As an example, when avoiding an oncoming car, a pedestrian needs to identify the fact that a car is approaching before ascertaining the model or color of the vehicle. In our methodology, we use wavelet-based sensors with self-organized learning to encode basic sensory information in real-time. The wavelet-based sensors provide necessary transformations while a rank-based Hebbian learning scheme encodes a self-organized environment through translation, scale and orientation invariant sensors. Such a self-organized environment is made possible by combining wavelet sets which are orthonormal, log-scale with linear orientation and have automatically generated membership functions. In earlier work we used Gabor wavelet filters, rank-based Hebbian learning and an exponential modulation function to encode textural information from images. Many different types of modulation are possible, but based on biological findings the exponential modulation function provided a good approximation of first spike coding of `integrate and fire' neurons. These types of Hebbian encoding schemes (e.g., exponential modulation, etc.) are useful for quick response and learning, provide several advantages over contemporary neural network learning approaches, and have been found to quantize data nonlinearly. By combining wavelets with Hebbian learning we can provide a real-time front-end for modeling an intelligent process, such as the autonomous control of agents in a simulated environment.

A study on multiresolution lossless video coding using inter/intra frame adaptive prediction

NASA Astrophysics Data System (ADS)

Nakachi, Takayuki; Sawabe, Tomoko; Fujii, Tetsuro

2003-06-01

Lossless video coding is required in the fields of archiving and editing digital cinema or digital broadcasting contents. This paper combines a discrete wavelet transform and adaptive inter/intra-frame prediction in the wavelet transform domain to create multiresolution lossless video coding. The multiresolution structure offered by the wavelet transform facilitates interchange among several video source formats such as Super High Definition (SHD) images, HDTV, SDTV, and mobile applications. Adaptive inter/intra-frame prediction is an extension of JPEG-LS, a state-of-the-art lossless still image compression standard. Based on the image statistics of the wavelet transform domains in successive frames, inter/intra frame adaptive prediction is applied to the appropriate wavelet transform domain. This adaptation offers superior compression performance. This is achieved with low computational cost and no increase in additional information. Experiments on digital cinema test sequences confirm the effectiveness of the proposed algorithm.

Three-dimensional object recognitions from two-dimensional images using wavelet transforms and neural networks

NASA Astrophysics Data System (ADS)

Deschenes, Sylvain; Sheng, Yunlong; Chevrette, Paul C.

1998-03-01

3D object classification from 2D IR images is shown. The wavelet transform is used for edge detection. Edge tracking is used for removing noise effectively int he wavelet transform. The invariant Fourier descriptor is used to describe the contour curves. Invariance under out-of-plane rotation is achieved by the feature space trajectory neural network working as a classifier.

Some uses of wavelets for imaging dynamic processes in live cochlear structures

NASA Astrophysics Data System (ADS)

Boutet de Monvel, J.

2007-09-01

A variety of image and signal processing algorithms based on wavelet filtering tools have been developed during the last few decades, that are well adapted to the experimental variability typically encountered in live biological microscopy. A number of processing tools are reviewed, that use wavelets for adaptive image restoration and for motion or brightness variation analysis by optical flow computation. The usefulness of these tools for biological imaging is illustrated in the context of the restoration of images of the inner ear and the analysis of cochlear motion patterns in two and three dimensions. I also report on recent work that aims at capturing fluorescence intensity changes associated with vesicle dynamics at synaptic zones of sensory hair cells. This latest application requires one to separate the intensity variations associated with the physiological process under study from the variations caused by motion of the observed structures. A wavelet optical flow algorithm for doing this is presented, and its effectiveness is demonstrated on artificial and experimental image sequences.

MR image denoising method for brain surface 3D modeling

NASA Astrophysics Data System (ADS)

Zhao, De-xin; Liu, Peng-jie; Zhang, De-gan

2014-11-01

Three-dimensional (3D) modeling of medical images is a critical part of surgical simulation. In this paper, we focus on the magnetic resonance (MR) images denoising for brain modeling reconstruction, and exploit a practical solution. We attempt to remove the noise existing in the MR imaging signal and preserve the image characteristics. A wavelet-based adaptive curve shrinkage function is presented in spherical coordinates system. The comparative experiments show that the denoising method can preserve better image details and enhance the coefficients of contours. Using these denoised images, the brain 3D visualization is given through surface triangle mesh model, which demonstrates the effectiveness of the proposed method.

Investigation of the scaling characteristics of LANDSAT temperature and vegetation data: a wavelet-based approach

NASA Astrophysics Data System (ADS)

Rathinasamy, Maheswaran; Bindhu, V. M.; Adamowski, Jan; Narasimhan, Balaji; Khosa, Rakesh

2017-10-01

An investigation of the scaling characteristics of vegetation and temperature data derived from LANDSAT data was undertaken for a heterogeneous area in Tamil Nadu, India. A wavelet-based multiresolution technique decomposed the data into large-scale mean vegetation and temperature fields and fluctuations in horizontal, diagonal, and vertical directions at hierarchical spatial resolutions. In this approach, the wavelet coefficients were used to investigate whether the normalized difference vegetation index (NDVI) and land surface temperature (LST) fields exhibited self-similar scaling behaviour. In this study, l-moments were used instead of conventional simple moments to understand scaling behaviour. Using the first six moments of the wavelet coefficients through five levels of dyadic decomposition, the NDVI data were shown to be statistically self-similar, with a slope of approximately -0.45 in each of the horizontal, vertical, and diagonal directions of the image, over scales ranging from 30 to 960 m. The temperature data were also shown to exhibit self-similarity with slopes ranging from -0.25 in the diagonal direction to -0.20 in the vertical direction over the same scales. These findings can help develop appropriate up- and down-scaling schemes of remotely sensed NDVI and LST data for various hydrologic and environmental modelling applications. A sensitivity analysis was also undertaken to understand the effect of mother wavelets on the scaling characteristics of LST and NDVI images.

Multivariate analysis of PRISMA optimized TLC image for predicting antioxidant activity and identification of contributing compounds from Pereskia bleo.

PubMed

Sharif, K M; Rahman, M M; Azmir, J; Khatib, A; Sabina, E; Shamsudin, S H; Zaidul, I S M

2015-12-01

Multivariate analysis of thin-layer chromatography (TLC) images was modeled to predict antioxidant activity of Pereskia bleo leaves and to identify the contributing compounds of the activity. TLC was developed in optimized mobile phase using the 'PRISMA' optimization method and the image was then converted to wavelet signals and imported for multivariate analysis. An orthogonal partial least square (OPLS) model was developed consisting of a wavelet-converted TLC image and 2,2-diphynyl-picrylhydrazyl free radical scavenging activity of 24 different preparations of P. bleo as the x- and y-variables, respectively. The quality of the constructed OPLS model (1 + 1 + 0) with one predictive and one orthogonal component was evaluated by internal and external validity tests. The validated model was then used to identify the contributing spot from the TLC plate that was then analyzed by GC-MS after trimethylsilyl derivatization. Glycerol and amine compounds were mainly found to contribute to the antioxidant activity of the sample. An alternative method to predict the antioxidant activity of a new sample of P. bleo leaves has been developed. Copyright © 2015 John Wiley & Sons, Ltd.

Adaptive Filtering in the Wavelet Transform Domain Via Genetic Algorithms

DTIC Science & Technology

2004-08-01

inverse transform process. 2. BACKGROUND The image processing research conducted at the AFRL/IFTA Reconfigurable Computing Laboratory has been...coefficients from the wavelet domain back into the original signal domain. In other words, the inverse transform produces the original signal x(t) from the...coefficients for an inverse wavelet transform, such that the MSE of images reconstructed by this inverse transform is significantly less than the mean squared

Lifting wavelet method of target detection

NASA Astrophysics Data System (ADS)

Han, Jun; Zhang, Chi; Jiang, Xu; Wang, Fang; Zhang, Jin

2009-11-01

Image target recognition plays a very important role in the areas of scientific exploration, aeronautics and space-to-ground observation, photography and topographic mapping. Complex environment of the image noise, fuzzy, all kinds of interference has always been to affect the stability of recognition algorithm. In this paper, the existence of target detection in real-time, accuracy problems, as well as anti-interference ability, using lifting wavelet image target detection methods. First of all, the use of histogram equalization, the goal difference method to obtain the region, on the basis of adaptive threshold and mathematical morphology operations to deal with the elimination of the background error. Secondly, the use of multi-channel wavelet filter wavelet transform of the original image de-noising and enhancement, to overcome the general algorithm of the noise caused by the sensitive issue of reducing the rate of miscarriage of justice will be the multi-resolution characteristics of wavelet and promotion of the framework can be designed directly in the benefits of space-time region used in target detection, feature extraction of targets. The experimental results show that the design of lifting wavelet has solved the movement of the target due to the complexity of the context of the difficulties caused by testing, which can effectively suppress noise, and improve the efficiency and speed of detection.

Efficacy Evaluation of Different Wavelet Feature Extraction Methods on Brain MRI Tumor Detection

NASA Astrophysics Data System (ADS)

Nabizadeh, Nooshin; John, Nigel; Kubat, Miroslav

2014-03-01

Automated Magnetic Resonance Imaging brain tumor detection and segmentation is a challenging task. Among different available methods, feature-based methods are very dominant. While many feature extraction techniques have been employed, it is still not quite clear which of feature extraction methods should be preferred. To help improve the situation, we present the results of a study in which we evaluate the efficiency of using different wavelet transform features extraction methods in brain MRI abnormality detection. Applying T1-weighted brain image, Discrete Wavelet Transform (DWT), Discrete Wavelet Packet Transform (DWPT), Dual Tree Complex Wavelet Transform (DTCWT), and Complex Morlet Wavelet Transform (CMWT) methods are applied to construct the feature pool. Three various classifiers as Support Vector Machine, K Nearest Neighborhood, and Sparse Representation-Based Classifier are applied and compared for classifying the selected features. The results show that DTCWT and CMWT features classified with SVM, result in the highest classification accuracy, proving of capability of wavelet transform features to be informative in this application.

Multiscale Medical Image Fusion in Wavelet Domain

PubMed Central

Khare, Ashish

2013-01-01

Wavelet transforms have emerged as a powerful tool in image fusion. However, the study and analysis of medical image fusion is still a challenging area of research. Therefore, in this paper, we propose a multiscale fusion of multimodal medical images in wavelet domain. Fusion of medical images has been performed at multiple scales varying from minimum to maximum level using maximum selection rule which provides more flexibility and choice to select the relevant fused images. The experimental analysis of the proposed method has been performed with several sets of medical images. Fusion results have been evaluated subjectively and objectively with existing state-of-the-art fusion methods which include several pyramid- and wavelet-transform-based fusion methods and principal component analysis (PCA) fusion method. The comparative analysis of the fusion results has been performed with edge strength (Q), mutual information (MI), entropy (E), standard deviation (SD), blind structural similarity index metric (BSSIM), spatial frequency (SF), and average gradient (AG) metrics. The combined subjective and objective evaluations of the proposed fusion method at multiple scales showed the effectiveness and goodness of the proposed approach. PMID:24453868

Devil's vortex Fresnel lens phase masks on an asymmetric cryptosystem based on phase-truncation in gyrator wavelet transform domain

NASA Astrophysics Data System (ADS)

Singh, Hukum

2016-06-01

An asymmetric scheme has been proposed for optical double images encryption in the gyrator wavelet transform (GWT) domain. Grayscale and binary images are encrypted separately using double random phase encoding (DRPE) in the GWT domain. Phase masks based on devil's vortex Fresnel Lens (DVFLs) and random phase masks (RPMs) are jointly used in spatial as well as in the Fourier plane. The images to be encrypted are first gyrator transformed and then single-level discrete wavelet transformed (DWT) to decompose LL , HL , LH and HH matrices of approximation, horizontal, vertical and diagonal coefficients. The resulting coefficients from the DWT are multiplied by other RPMs and the results are applied to inverse discrete wavelet transform (IDWT) for obtaining the encrypted images. The images are recovered from their corresponding encrypted images by using the correct parameters of the GWT, DVFL and its digital implementation has been performed using MATLAB 7.6.0 (R2008a). The mother wavelet family, DVFL and gyrator transform orders associated with the GWT are extra keys that cause difficulty to an attacker. Thus, the scheme is more secure as compared to conventional techniques. The efficacy of the proposed scheme is verified by computing mean-squared-error (MSE) between recovered and the original images. The sensitivity of the proposed scheme is verified with encryption parameters and noise attacks.

A new method of Quickbird own image fusion

NASA Astrophysics Data System (ADS)

Han, Ying; Jiang, Hong; Zhang, Xiuying

2009-10-01

With the rapid development of remote sensing technology, the means of accessing to remote sensing data become increasingly abundant, thus the same area can form a large number of multi-temporal, different resolution image sequence. At present, the fusion methods are mainly: HPF, IHS transform method, PCA method, Brovey, Mallat algorithm and wavelet transform and so on. There exists a serious distortion of the spectrums in the IHS transform, Mallat algorithm omits low-frequency information of the high spatial resolution images, the integration results of which has obvious blocking effects. Wavelet multi-scale decomposition for different sizes, the directions, details and the edges can have achieved very good results, but different fusion rules and algorithms can achieve different effects. This article takes the Quickbird own image fusion as an example, basing on wavelet transform and HVS, wavelet transform and IHS integration. The result shows that the former better. This paper introduces the correlation coefficient, the relative average spectral error index and usual index to evaluate the quality of image.

Exploiting the wavelet structure in compressed sensing MRI.

PubMed

Chen, Chen; Huang, Junzhou

2014-12-01

Sparsity has been widely utilized in magnetic resonance imaging (MRI) to reduce k-space sampling. According to structured sparsity theories, fewer measurements are required for tree sparse data than the data only with standard sparsity. Intuitively, more accurate image reconstruction can be achieved with the same number of measurements by exploiting the wavelet tree structure in MRI. A novel algorithm is proposed in this article to reconstruct MR images from undersampled k-space data. In contrast to conventional compressed sensing MRI (CS-MRI) that only relies on the sparsity of MR images in wavelet or gradient domain, we exploit the wavelet tree structure to improve CS-MRI. This tree-based CS-MRI problem is decomposed into three simpler subproblems then each of the subproblems can be efficiently solved by an iterative scheme. Simulations and in vivo experiments demonstrate the significant improvement of the proposed method compared to conventional CS-MRI algorithms, and the feasibleness on MR data compared to existing tree-based imaging algorithms. Copyright © 2014 Elsevier Inc. All rights reserved.

A quality quantitative method of silicon direct bonding based on wavelet image analysis

NASA Astrophysics Data System (ADS)

Tan, Xiao; Tao, Zhi; Li, Haiwang; Xu, Tiantong; Yu, Mingxing

2018-04-01

The rapid development of MEMS (micro-electro-mechanical systems) has received significant attention from researchers in various fields and subjects. In particular, the MEMS fabrication process is elaborate and, as such, has been the focus of extensive research inquiries. However, in MEMS fabrication, component bonding is difficult to achieve and requires a complex approach. Thus, improvements in bonding quality are relatively important objectives. A higher quality bond can only be achieved with improved measurement and testing capabilities. In particular, the traditional testing methods mainly include infrared testing, tensile testing, and strength testing, despite the fact that using these methods to measure bond quality often results in low efficiency or destructive analysis. Therefore, this paper focuses on the development of a precise, nondestructive visual testing method based on wavelet image analysis that is shown to be highly effective in practice. The process of wavelet image analysis includes wavelet image denoising, wavelet image enhancement, and contrast enhancement, and as an end result, can display an image with low background noise. In addition, because the wavelet analysis software was developed with MATLAB, it can reveal the bonding boundaries and bonding rates to precisely indicate the bond quality at all locations on the wafer. This work also presents a set of orthogonal experiments that consist of three prebonding factors, the prebonding temperature, the positive pressure value and the prebonding time, which are used to analyze the prebonding quality. This method was used to quantify the quality of silicon-to-silicon wafer bonding, yielding standard treatment quantities that could be practical for large-scale use.

Fast and robust wavelet-based dynamic range compression and contrast enhancement model with color restoration

NASA Astrophysics Data System (ADS)

Unaldi, Numan; Asari, Vijayan K.; Rahman, Zia-ur

2009-05-01

Recently we proposed a wavelet-based dynamic range compression algorithm to improve the visual quality of digital images captured from high dynamic range scenes with non-uniform lighting conditions. The fast image enhancement algorithm that provides dynamic range compression, while preserving the local contrast and tonal rendition, is also a good candidate for real time video processing applications. Although the colors of the enhanced images produced by the proposed algorithm are consistent with the colors of the original image, the proposed algorithm fails to produce color constant results for some "pathological" scenes that have very strong spectral characteristics in a single band. The linear color restoration process is the main reason for this drawback. Hence, a different approach is required for the final color restoration process. In this paper the latest version of the proposed algorithm, which deals with this issue is presented. The results obtained by applying the algorithm to numerous natural images show strong robustness and high image quality.

Microseismic imaging using a source function independent full waveform inversion method

NASA Astrophysics Data System (ADS)

Wang, Hanchen; Alkhalifah, Tariq

2018-07-01

At the heart of microseismic event measurements is the task to estimate the location of the source microseismic events, as well as their ignition times. The accuracy of locating the sources is highly dependent on the velocity model. On the other hand, the conventional microseismic source locating methods require, in many cases, manual picking of traveltime arrivals, which do not only lead to manual effort and human interaction, but also prone to errors. Using full waveform inversion (FWI) to locate and image microseismic events allows for an automatic process (free of picking) that utilizes the full wavefield. However, FWI of microseismic events faces incredible nonlinearity due to the unknown source locations (space) and functions (time). We developed a source function independent FWI of microseismic events to invert for the source image, source function and the velocity model. It is based on convolving reference traces with these observed and modelled to mitigate the effect of an unknown source ignition time. The adjoint-state method is used to derive the gradient for the source image, source function and velocity updates. The extended image for the source wavelet in Z axis is extracted to check the accuracy of the inverted source image and velocity model. Also, angle gathers are calculated to assess the quality of the long wavelength component of the velocity model. By inverting for the source image, source wavelet and the velocity model simultaneously, the proposed method produces good estimates of the source location, ignition time and the background velocity for synthetic examples used here, like those corresponding to the Marmousi model and the SEG/EAGE overthrust model.

Optical Coherence Tomography Noise Reduction Using Anisotropic Local Bivariate Gaussian Mixture Prior in 3D Complex Wavelet Domain.

PubMed

Rabbani, Hossein; Sonka, Milan; Abramoff, Michael D

2013-01-01

In this paper, MMSE estimator is employed for noise-free 3D OCT data recovery in 3D complex wavelet domain. Since the proposed distribution for noise-free data plays a key role in the performance of MMSE estimator, a priori distribution for the pdf of noise-free 3D complex wavelet coefficients is proposed which is able to model the main statistical properties of wavelets. We model the coefficients with a mixture of two bivariate Gaussian pdfs with local parameters which are able to capture the heavy-tailed property and inter- and intrascale dependencies of coefficients. In addition, based on the special structure of OCT images, we use an anisotropic windowing procedure for local parameters estimation that results in visual quality improvement. On this base, several OCT despeckling algorithms are obtained based on using Gaussian/two-sided Rayleigh noise distribution and homomorphic/nonhomomorphic model. In order to evaluate the performance of the proposed algorithm, we use 156 selected ROIs from 650 × 512 × 128 OCT dataset in the presence of wet AMD pathology. Our simulations show that the best MMSE estimator using local bivariate mixture prior is for the nonhomomorphic model in the presence of Gaussian noise which results in an improvement of 7.8 ± 1.7 in CNR.

Development of a classification method for a crack on a pavement surface images using machine learning

NASA Astrophysics Data System (ADS)

Hizukuri, Akiyoshi; Nagata, Takeshi

2017-03-01

The purpose of this study is to develop a classification method for a crack on a pavement surface image using machine learning to reduce a maintenance fee. Our database consists of 3500 pavement surface images. This includes 800 crack and 2700 normal pavement surface images. The pavement surface images first are decomposed into several sub-images using a discrete wavelet transform (DWT) decomposition. We then calculate the wavelet sub-band histogram from each several sub-images at each level. The support vector machine (SVM) with computed wavelet sub-band histogram is employed for distinguishing between a crack and normal pavement surface images. The accuracies of the proposed classification method are 85.3% for crack and 84.4% for normal pavement images. The proposed classification method achieved high performance. Therefore, the proposed method would be useful in maintenance inspection.

Image sharpness assessment based on wavelet energy of edge area

NASA Astrophysics Data System (ADS)

Li, Jin; Zhang, Hong; Zhang, Lei; Yang, Yifan; He, Lei; Sun, Mingui

2018-04-01

Image quality assessment is needed in multiple image processing areas and blur is one of the key reasons of image deterioration. Although great full-reference image quality assessment metrics have been proposed in the past few years, no-reference method is still an area of current research. Facing this problem, this paper proposes a no-reference sharpness assessment method based on wavelet transformation which focuses on the edge area of image. Based on two simple characteristics of human vision system, weights are introduced to calculate weighted log-energy of each wavelet sub band. The final score is given by the ratio of high-frequency energy to the total energy. The algorithm is tested on multiple databases. Comparing with several state-of-the-art metrics, proposed algorithm has better performance and less runtime consumption.

Adaptive zero-tree structure for curved wavelet image coding

NASA Astrophysics Data System (ADS)

Zhang, Liang; Wang, Demin; Vincent, André

2006-02-01

We investigate the issue of efficient data organization and representation of the curved wavelet coefficients [curved wavelet transform (WT)]. We present an adaptive zero-tree structure that exploits the cross-subband similarity of the curved wavelet transform. In the embedded zero-tree wavelet (EZW) and the set partitioning in hierarchical trees (SPIHT), the parent-child relationship is defined in such a way that a parent has four children, restricted to a square of 2×2 pixels, the parent-child relationship in the adaptive zero-tree structure varies according to the curves along which the curved WT is performed. Five child patterns were determined based on different combinations of curve orientation. A new image coder was then developed based on this adaptive zero-tree structure and the set-partitioning technique. Experimental results using synthetic and natural images showed the effectiveness of the proposed adaptive zero-tree structure for encoding of the curved wavelet coefficients. The coding gain of the proposed coder can be up to 1.2 dB in terms of peak SNR (PSNR) compared to the SPIHT coder. Subjective evaluation shows that the proposed coder preserves lines and edges better than the SPIHT coder.

Wavelet-based de-noising algorithm for images acquired with parallel magnetic resonance imaging (MRI).

PubMed

Delakis, Ioannis; Hammad, Omer; Kitney, Richard I

2007-07-07

Wavelet-based de-noising has been shown to improve image signal-to-noise ratio in magnetic resonance imaging (MRI) while maintaining spatial resolution. Wavelet-based de-noising techniques typically implemented in MRI require that noise displays uniform spatial distribution. However, images acquired with parallel MRI have spatially varying noise levels. In this work, a new algorithm for filtering images with parallel MRI is presented. The proposed algorithm extracts the edges from the original image and then generates a noise map from the wavelet coefficients at finer scales. The noise map is zeroed at locations where edges have been detected and directional analysis is also used to calculate noise in regions of low-contrast edges that may not have been detected. The new methodology was applied on phantom and brain images and compared with other applicable de-noising techniques. The performance of the proposed algorithm was shown to be comparable with other techniques in central areas of the images, where noise levels are high. In addition, finer details and edges were maintained in peripheral areas, where noise levels are low. The proposed methodology is fully automated and can be applied on final reconstructed images without requiring sensitivity profiles or noise matrices of the receiver coils, therefore making it suitable for implementation in a clinical MRI setting.

Wavelet-based associative memory

NASA Astrophysics Data System (ADS)

Jones, Katharine J.

2004-04-01

Faces provide important characteristics of a person"s identification. In security checks, face recognition still remains the method in continuous use despite other approaches (i.e. fingerprints, voice recognition, pupil contraction, DNA scanners). With an associative memory, the output data is recalled directly using the input data. This can be achieved with a Nonlinear Holographic Associative Memory (NHAM). This approach can also distinguish between strongly correlated images and images that are partially or totally enclosed by others. Adaptive wavelet lifting has been used for Content-Based Image Retrieval. In this paper, adaptive wavelet lifting will be applied to face recognition to achieve an associative memory.

Optimal wavelet transform for the detection of microaneurysms in retina photographs.

PubMed

Quellec, Gwénolé; Lamard, Mathieu; Josselin, Pierre Marie; Cazuguel, Guy; Cochener, Béatrice; Roux, Christian

2008-09-01

In this paper, we propose an automatic method to detect microaneurysms in retina photographs. Microaneurysms are the most frequent and usually the first lesions to appear as a consequence of diabetic retinopathy. So, their detection is necessary for both screening the pathology and follow up (progression measurement). Automating this task, which is currently performed manually, would bring more objectivity and reproducibility. We propose to detect them by locally matching a lesion template in subbands of wavelet transformed images. To improve the method performance, we have searched for the best adapted wavelet within the lifting scheme framework. The optimization process is based on a genetic algorithm followed by Powell's direction set descent. Results are evaluated on 120 retinal images analyzed by an expert and the optimal wavelet is compared to different conventional mother wavelets. These images are of three different modalities: there are color photographs, green filtered photographs, and angiographs. Depending on the imaging modality, microaneurysms were detected with a sensitivity of respectively 89.62%, 90.24%, and 93.74% and a positive predictive value of respectively 89.50%, 89.75%, and 91.67%, which is better than previously published methods.

Optimal wavelet transform for the detection of microaneurysms in retina photographs

PubMed Central

Quellec, Gwénolé; Lamard, Mathieu; Josselin, Pierre Marie; Cazuguel, Guy; Cochener, Béatrice; Roux, Christian

2008-01-01

In this article, we propose an automatic method to detect microaneurysms in retina photographs. Microaneurysms are the most frequent and usually the first lesions to appear as a consequence of diabetic retinopathy. So, their detection is necessary for both screening the pathology and follow up (progression measurement). Automating this task, which is currently performed manually, would bring more objectivity and reproducibility. We propose to detect them by locally matching a lesion template in subbands of wavelet transformed images. To improve the method performance, we have searched for the best adapted wavelet within the lifting scheme framework. The optimization process is based on a genetic algorithm followed by Powell’s direction set descent. Results are evaluated on 120 retinal images analyzed by an expert and the optimal wavelet is compared to different conventional mother wavelets. These images are of three different modalites: there are color photographs, green filtered photographs and angiographs. Depending on the imaging modality, microaneurysms were detected with a sensitivity of respectively 89.62%, 90.24% and 93.74% and a positive predictive value of respectively 89.50%, 89.75% and 91.67%, which is better than previously published methods. PMID:18779064

Reversible wavelet filter banks with side informationless spatially adaptive low-pass filters

NASA Astrophysics Data System (ADS)

Abhayaratne, Charith

2011-07-01

Wavelet transforms that have an adaptive low-pass filter are useful in applications that require the signal singularities, sharp transitions, and image edges to be left intact in the low-pass signal. In scalable image coding, the spatial resolution scalability is achieved by reconstructing the low-pass signal subband, which corresponds to the desired resolution level, and discarding other high-frequency wavelet subbands. In such applications, it is vital to have low-pass subbands that are not affected by smoothing artifacts associated with low-pass filtering. We present the mathematical framework for achieving 1-D wavelet transforms that have a spatially adaptive low-pass filter (SALP) using the prediction-first lifting scheme. The adaptivity decisions are computed using the wavelet coefficients, and no bookkeeping is required for the perfect reconstruction. Then, 2-D wavelet transforms that have a spatially adaptive low-pass filter are designed by extending the 1-D SALP framework. Because the 2-D polyphase decompositions are used in this case, the 2-D adaptivity decisions are made nonseparable as opposed to the separable 2-D realization using 1-D transforms. We present examples using the 2-D 5/3 wavelet transform and their lossless image coding and scalable decoding performances in terms of quality and resolution scalability. The proposed 2-D-SALP scheme results in better performance compared to the existing adaptive update lifting schemes.

Retinal vessel segmentation using the 2-D Gabor wavelet and supervised classification.

PubMed

Soares, João V B; Leandro, Jorge J G; Cesar Júnior, Roberto M; Jelinek, Herbert F; Cree, Michael J

2006-09-01

We present a method for automated segmentation of the vasculature in retinal images. The method produces segmentations by classifying each image pixel as vessel or nonvessel, based on the pixel's feature vector. Feature vectors are composed of the pixel's intensity and two-dimensional Gabor wavelet transform responses taken at multiple scales. The Gabor wavelet is capable of tuning to specific frequencies, thus allowing noise filtering and vessel enhancement in a single step. We use a Bayesian classifier with class-conditional probability density functions (likelihoods) described as Gaussian mixtures, yielding a fast classification, while being able to model complex decision surfaces. The probability distributions are estimated based on a training set of labeled pixels obtained from manual segmentations. The method's performance is evaluated on publicly available DRIVE (Staal et al., 2004) and STARE (Hoover et al., 2000) databases of manually labeled images. On the DRIVE database, it achieves an area under the receiver operating characteristic curve of 0.9614, being slightly superior than that presented by state-of-the-art approaches. We are making our implementation available as open source MATLAB scripts for researchers interested in implementation details, evaluation, or development of methods.

Wavelet-based adaptive thresholding method for image segmentation

NASA Astrophysics Data System (ADS)

Chen, Zikuan; Tao, Yang; Chen, Xin; Griffis, Carl

2001-05-01

A nonuniform background distribution may cause a global thresholding method to fail to segment objects. One solution is using a local thresholding method that adapts to local surroundings. In this paper, we propose a novel local thresholding method for image segmentation, using multiscale threshold functions obtained by wavelet synthesis with weighted detail coefficients. In particular, the coarse-to- fine synthesis with attenuated detail coefficients produces a threshold function corresponding to a high-frequency- reduced signal. This wavelet-based local thresholding method adapts to both local size and local surroundings, and its implementation can take advantage of the fast wavelet algorithm. We applied this technique to physical contaminant detection for poultry meat inspection using x-ray imaging. Experiments showed that inclusion objects in deboned poultry could be extracted at multiple resolutions despite their irregular sizes and uneven backgrounds.

Implementation in an FPGA circuit of Edge detection algorithm based on the Discrete Wavelet Transforms

NASA Astrophysics Data System (ADS)

Bouganssa, Issam; Sbihi, Mohamed; Zaim, Mounia

2017-07-01

The 2D Discrete Wavelet Transform (DWT) is a computationally intensive task that is usually implemented on specific architectures in many imaging systems in real time. In this paper, a high throughput edge or contour detection algorithm is proposed based on the discrete wavelet transform. A technique for applying the filters on the three directions (Horizontal, Vertical and Diagonal) of the image is used to present the maximum of the existing contours. The proposed architectures were designed in VHDL and mapped to a Xilinx Sparten6 FPGA. The results of the synthesis show that the proposed architecture has a low area cost and can operate up to 100 MHz, which can perform 2D wavelet analysis for a sequence of images while maintaining the flexibility of the system to support an adaptive algorithm.

Wavelet Filter Banks for Super-Resolution SAR Imaging

NASA Technical Reports Server (NTRS)

Sheybani, Ehsan O.; Deshpande, Manohar; Memarsadeghi, Nargess

2011-01-01

This paper discusses Innovative wavelet-based filter banks designed to enhance the analysis of super resolution Synthetic Aperture Radar (SAR) images using parametric spectral methods and signal classification algorithms, SAR finds applications In many of NASA's earth science fields such as deformation, ecosystem structure, and dynamics of Ice, snow and cold land processes, and surface water and ocean topography. Traditionally, standard methods such as Fast-Fourier Transform (FFT) and Inverse Fast-Fourier Transform (IFFT) have been used to extract Images from SAR radar data, Due to non-parametric features of these methods and their resolution limitations and observation time dependence, use of spectral estimation and signal pre- and post-processing techniques based on wavelets to process SAR radar data has been proposed. Multi-resolution wavelet transforms and advanced spectral estimation techniques have proven to offer efficient solutions to this problem.

Detection of small bowel tumors in capsule endoscopy frames using texture analysis based on the discrete wavelet transform.

PubMed

Barbosa, Daniel J C; Ramos, Jaime; Lima, Carlos S

2008-01-01

Capsule endoscopy is an important tool to diagnose tumor lesions in the small bowel. The capsule endoscopic images possess vital information expressed by color and texture. This paper presents an approach based in the textural analysis of the different color channels, using the wavelet transform to select the bands with the most significant texture information. A new image is then synthesized from the selected wavelet bands, trough the inverse wavelet transform. The features of each image are based on second-order textural information, and they are used in a classification scheme using a multilayer perceptron neural network. The proposed methodology has been applied in real data taken from capsule endoscopic exams and reached 98.7% sensibility and 96.6% specificity. These results support the feasibility of the proposed algorithm.

A new approach to pre-processing digital image for wavelet-based watermark

NASA Astrophysics Data System (ADS)

Agreste, Santa; Andaloro, Guido

2008-11-01

The growth of the Internet has increased the phenomenon of digital piracy, in multimedia objects, like software, image, video, audio and text. Therefore it is strategic to individualize and to develop methods and numerical algorithms, which are stable and have low computational cost, that will allow us to find a solution to these problems. We describe a digital watermarking algorithm for color image protection and authenticity: robust, not blind, and wavelet-based. The use of Discrete Wavelet Transform is motivated by good time-frequency features and a good match with Human Visual System directives. These two combined elements are important for building an invisible and robust watermark. Moreover our algorithm can work with any image, thanks to the step of pre-processing of the image that includes resize techniques that adapt to the size of the original image for Wavelet transform. The watermark signal is calculated in correlation with the image features and statistic properties. In the detection step we apply a re-synchronization between the original and watermarked image according to the Neyman-Pearson statistic criterion. Experimentation on a large set of different images has been shown to be resistant against geometric, filtering, and StirMark attacks with a low rate of false alarm.

Statistical characterization of portal images and noise from portal imaging systems.

PubMed

González-López, Antonio; Morales-Sánchez, Juan; Verdú-Monedero, Rafael; Larrey-Ruiz, Jorge

2013-06-01

In this paper, we consider the statistical characteristics of the so-called portal images, which are acquired prior to the radiotherapy treatment, as well as the noise that present the portal imaging systems, in order to analyze whether the well-known noise and image features in other image modalities, such as natural image, can be found in the portal imaging modality. The study is carried out in the spatial image domain, in the Fourier domain, and finally in the wavelet domain. The probability density of the noise in the spatial image domain, the power spectral densities of the image and noise, and the marginal, joint, and conditional statistical distributions of the wavelet coefficients are estimated. Moreover, the statistical dependencies between noise and signal are investigated. The obtained results are compared with practical and useful references, like the characteristics of the natural image and the white noise. Finally, we discuss the implication of the results obtained in several noise reduction methods that operate in the wavelet domain.

A new wavelet transform to sparsely represent cortical current densities for EEG/MEG inverse problems.

PubMed

Liao, Ke; Zhu, Min; Ding, Lei

2013-08-01

The present study investigated the use of transform sparseness of cortical current density on human brain surface to improve electroencephalography/magnetoencephalography (EEG/MEG) inverse solutions. Transform sparseness was assessed by evaluating compressibility of cortical current densities in transform domains. To do that, a structure compression method from computer graphics was first adopted to compress cortical surface structure, either regular or irregular, into hierarchical multi-resolution meshes. Then, a new face-based wavelet method based on generated multi-resolution meshes was proposed to compress current density functions defined on cortical surfaces. Twelve cortical surface models were built by three EEG/MEG softwares and their structural compressibility was evaluated and compared by the proposed method. Monte Carlo simulations were implemented to evaluate the performance of the proposed wavelet method in compressing various cortical current density distributions as compared to other two available vertex-based wavelet methods. The present results indicate that the face-based wavelet method can achieve higher transform sparseness than vertex-based wavelet methods. Furthermore, basis functions from the face-based wavelet method have lower coherence against typical EEG and MEG measurement systems than vertex-based wavelet methods. Both high transform sparseness and low coherent measurements suggest that the proposed face-based wavelet method can improve the performance of L1-norm regularized EEG/MEG inverse solutions, which was further demonstrated in simulations and experimental setups using MEG data. Thus, this new transform on complicated cortical structure is promising to significantly advance EEG/MEG inverse source imaging technologies. Copyright © 2013 Elsevier Ireland Ltd. All rights reserved.

Wavelet-based energy features for glaucomatous image classification.

PubMed

Dua, Sumeet; Acharya, U Rajendra; Chowriappa, Pradeep; Sree, S Vinitha

2012-01-01

Texture features within images are actively pursued for accurate and efficient glaucoma classification. Energy distribution over wavelet subbands is applied to find these important texture features. In this paper, we investigate the discriminatory potential of wavelet features obtained from the daubechies (db3), symlets (sym3), and biorthogonal (bio3.3, bio3.5, and bio3.7) wavelet filters. We propose a novel technique to extract energy signatures obtained using 2-D discrete wavelet transform, and subject these signatures to different feature ranking and feature selection strategies. We have gauged the effectiveness of the resultant ranked and selected subsets of features using a support vector machine, sequential minimal optimization, random forest, and naïve Bayes classification strategies. We observed an accuracy of around 93% using tenfold cross validations to demonstrate the effectiveness of these methods.

Optical image encryption using chaos-based compressed sensing and phase-shifting interference in fractional wavelet domain

NASA Astrophysics Data System (ADS)

Liu, Qi; Wang, Ying; Wang, Jun; Wang, Qiong-Hua

2018-02-01

In this paper, a novel optical image encryption system combining compressed sensing with phase-shifting interference in fractional wavelet domain is proposed. To improve the encryption efficiency, the volume data of original image are decreased by compressed sensing. Then the compacted image is encoded through double random phase encoding in asymmetric fractional wavelet domain. In the encryption system, three pseudo-random sequences, generated by three-dimensional chaos map, are used as the measurement matrix of compressed sensing and two random-phase masks in the asymmetric fractional wavelet transform. It not only simplifies the keys to storage and transmission, but also enhances our cryptosystem nonlinearity to resist some common attacks. Further, holograms make our cryptosystem be immune to noises and occlusion attacks, which are obtained by two-step-only quadrature phase-shifting interference. And the compression and encryption can be achieved in the final result simultaneously. Numerical experiments have verified the security and validity of the proposed algorithm.

Neural network face recognition using wavelets

NASA Astrophysics Data System (ADS)

Karunaratne, Passant V.; Jouny, Ismail I.

1997-04-01

The recognition of human faces is a phenomenon that has been mastered by the human visual system and that has been researched extensively in the domain of computer neural networks and image processing. This research is involved in the study of neural networks and wavelet image processing techniques in the application of human face recognition. The objective of the system is to acquire a digitized still image of a human face, carry out pre-processing on the image as required, an then, given a prior database of images of possible individuals, be able to recognize the individual in the image. The pre-processing segment of the system includes several procedures, namely image compression, denoising, and feature extraction. The image processing is carried out using Daubechies wavelets. Once the images have been passed through the wavelet-based image processor they can be efficiently analyzed by means of a neural network. A back- propagation neural network is used for the recognition segment of the system. The main constraints of the system is with regard to the characteristics of the images being processed. The system should be able to carry out effective recognition of the human faces irrespective of the individual's facial-expression, presence of extraneous objects such as head-gear or spectacles, and face/head orientation. A potential application of this face recognition system would be as a secondary verification method in an automated teller machine.

Wavelets in medical imaging

NASA Astrophysics Data System (ADS)

Zahra, Noor e.; Sevindir, Hulya Kodal; Aslan, Zafer; Siddiqi, A. H.

2012-07-01

The aim of this study is to provide emerging applications of wavelet methods to medical signals and images, such as electrocardiogram, electroencephalogram, functional magnetic resonance imaging, computer tomography, X-ray and mammography. Interpretation of these signals and images are quite important. Nowadays wavelet methods have a significant impact on the science of medical imaging and the diagnosis of disease and screening protocols. Based on our initial investigations, future directions include neurosurgical planning and improved assessment of risk for individual patients, improved assessment and strategies for the treatment of chronic pain, improved seizure localization, and improved understanding of the physiology of neurological disorders. We look ahead to these and other emerging applications as the benefits of this technology become incorporated into current and future patient care. In this chapter by applying Fourier transform and wavelet transform, analysis and denoising of one of the important biomedical signals like EEG is carried out. The presence of rhythm, template matching, and correlation is discussed by various method. Energy of EEG signal is used to detect seizure in an epileptic patient. We have also performed denoising of EEG signals by SWT.

Wavelet domain textual coding of Ottoman script images

NASA Astrophysics Data System (ADS)

Gerek, Oemer N.; Cetin, Enis A.; Tewfik, Ahmed H.

1996-02-01

Image coding using wavelet transform, DCT, and similar transform techniques is well established. On the other hand, these coding methods neither take into account the special characteristics of the images in a database nor are they suitable for fast database search. In this paper, the digital archiving of Ottoman printings is considered. Ottoman documents are printed in Arabic letters. Witten et al. describes a scheme based on finding the characters in binary document images and encoding the positions of the repeated characters This method efficiently compresses document images and is suitable for database research, but it cannot be applied to Ottoman or Arabic documents as the concept of character is different in Ottoman or Arabic. Typically, one has to deal with compound structures consisting of a group of letters. Therefore, the matching criterion will be according to those compound structures. Furthermore, the text images are gray tone or color images for Ottoman scripts for the reasons that are described in the paper. In our method the compound structure matching is carried out in wavelet domain which reduces the search space and increases the compression ratio. In addition to the wavelet transformation which corresponds to the linear subband decomposition, we also used nonlinear subband decomposition. The filters in the nonlinear subband decomposition have the property of preserving edges in the low resolution subband image.

Segmentation of Polarimetric SAR Images Usig Wavelet Transformation and Texture Features

NASA Astrophysics Data System (ADS)

Rezaeian, A.; Homayouni, S.; Safari, A.

2015-12-01

Polarimetric Synthetic Aperture Radar (PolSAR) sensors can collect useful observations from earth's surfaces and phenomena for various remote sensing applications, such as land cover mapping, change and target detection. These data can be acquired without the limitations of weather conditions, sun illumination and dust particles. As result, SAR images, and in particular Polarimetric SAR (PolSAR) are powerful tools for various environmental applications. Unlike the optical images, SAR images suffer from the unavoidable speckle, which causes the segmentation of this data difficult. In this paper, we use the wavelet transformation for segmentation of PolSAR images. Our proposed method is based on the multi-resolution analysis of texture features is based on wavelet transformation. Here, we use the information of gray level value and the information of texture. First, we produce coherency or covariance matrices and then generate span image from them. In the next step of proposed method is texture feature extraction from sub-bands is generated from discrete wavelet transform (DWT). Finally, PolSAR image are segmented using clustering methods as fuzzy c-means (FCM) and k-means clustering. We have applied the proposed methodology to full polarimetric SAR images acquired by the Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR) L-band system, during July, in 2012 over an agricultural area in Winnipeg, Canada.

Sonar target enhancement by shrinkage of incoherent wavelet coefficients.

PubMed

Hunter, Alan J; van Vossen, Robbert

2014-01-01

Background reverberation can obscure useful features of the target echo response in broadband low-frequency sonar images, adversely affecting detection and classification performance. This paper describes a resolution and phase-preserving means of separating the target response from the background reverberation noise using a coherence-based wavelet shrinkage method proposed recently for de-noising magnetic resonance images. The algorithm weights the image wavelet coefficients in proportion to their coherence between different looks under the assumption that the target response is more coherent than the background. The algorithm is demonstrated successfully on experimental synthetic aperture sonar data from a broadband low-frequency sonar developed for buried object detection.

Wavelet optimization for content-based image retrieval in medical databases.

PubMed

Quellec, G; Lamard, M; Cazuguel, G; Cochener, B; Roux, C

2010-04-01

We propose in this article a content-based image retrieval (CBIR) method for diagnosis aid in medical fields. In the proposed system, images are indexed in a generic fashion, without extracting domain-specific features: a signature is built for each image from its wavelet transform. These image signatures characterize the distribution of wavelet coefficients in each subband of the decomposition. A distance measure is then defined to compare two image signatures and thus retrieve the most similar images in a database when a query image is submitted by a physician. To retrieve relevant images from a medical database, the signatures and the distance measure must be related to the medical interpretation of images. As a consequence, we introduce several degrees of freedom in the system so that it can be tuned to any pathology and image modality. In particular, we propose to adapt the wavelet basis, within the lifting scheme framework, and to use a custom decomposition scheme. Weights are also introduced between subbands. All these parameters are tuned by an optimization procedure, using the medical grading of each image in the database to define a performance measure. The system is assessed on two medical image databases: one for diabetic retinopathy follow up and one for screening mammography, as well as a general purpose database. Results are promising: a mean precision of 56.50%, 70.91% and 96.10% is achieved for these three databases, when five images are returned by the system. Copyright 2009 Elsevier B.V. All rights reserved.

An Application of Rotation- and Translation-Invariant Overcomplete Wavelets to the registration of Remotely Sensed Imagery

NASA Technical Reports Server (NTRS)

LeMoigne, Jacqueline; Zavorine, Ilya

1999-01-01

A wavelet-based image registration approach has previously been proposed by the authors. In this work, wavelet coefficient maxima obtained from an orthogonal wavelet decomposition using Daubechies filters were utilized to register images in a multi-resolution fashion. Tested on several remote sensing datasets, this method gave very encouraging results. Despite the lack of translation-invariance of these filters, we showed that when using cross-correlation as a feature matching technique, features of size larger than twice the size of the filters are correctly registered by using the low-frequency subbands of the Daubechies wavelet decomposition. Nevertheless, high-frequency subbands are still sensitive to translation effects. In this work, we are considering a rotation- and translation-invariant representation developed by E. Simoncelli and integrate it in our image registration scheme. The two types of filters, Daubechies and Simoncelli filters, are then being compared from a registration point of view, utilizing synthetic data as well as data from the Landsat/ Thematic Mapper (TM) and from the NOAA Advanced Very High Resolution Radiometer (AVHRR).

An Application of Rotation- and Translation-Invariant Overcomplete Wavelets to the Registration of Remotely Sensed Imagery

NASA Technical Reports Server (NTRS)

LeMoigne, Jacqueline; Zavorine, Ilya

1999-01-01

A wavelet-based image registration approach has previously been proposed by the authors. In this work, wavelet coefficient maxima obtained from an orthogonal wavelet decomposition using Daubechies filters were utilized to register images in a multi-resolution fashion. Tested on several remote sensing datasets, this method gave very encouraging results. Despite the lack of translation-invariance of these filters, we showed that when using cross-correlation as a feature matching technique, features of size larger than twice the size of the filters are correctly registered by using the low-frequency subbands of the Daubechies wavelet decomposition. Nevertheless, high-frequency subbands are still sensitive to translation effects. In this work, we are considering a rotation- and translation-invariant representation developed by E. Simoncelli and integrate it in our image registration scheme. The two types of filters, Daubechies and Simoncelli filters, are then being compared from a registration point of view, utilizing synthetic data as well as data from the Landsat/ Thematic Mapper (TM) and from the NOAA Advanced Very High Resolution Radiometer (AVHRR).

Wavelet Analysis of SAR Images for Coastal Monitoring

NASA Technical Reports Server (NTRS)

Liu, Antony K.; Wu, Sunny Y.; Tseng, William Y.; Pichel, William G.

1998-01-01

The mapping of mesoscale ocean features in the coastal zone is a major potential application for satellite data. The evolution of mesoscale features such as oil slicks, fronts, eddies, and ice edge can be tracked by the wavelet analysis using satellite data from repeating paths. The wavelet transform has been applied to satellite images, such as those from Synthetic Aperture Radar (SAR), Advanced Very High-Resolution Radiometer (AVHRR), and ocean color sensor for feature extraction. In this paper, algorithms and techniques for automated detection and tracking of mesoscale features from satellite SAR imagery employing wavelet analysis have been developed. Case studies on two major coastal oil spills have been investigated using wavelet analysis for tracking along the coast of Uruguay (February 1997), and near Point Barrow, Alaska (November 1997). Comparison of SAR images with SeaWiFS (Sea-viewing Wide Field-of-view Sensor) data for coccolithophore bloom in the East Bering Sea during the fall of 1997 shows a good match on bloom boundary. This paper demonstrates that this technique is a useful and promising tool for monitoring of coastal waters.

Wavelet domain image restoration with adaptive edge-preserving regularization.

PubMed

Belge, M; Kilmer, M E; Miller, E L

2000-01-01

In this paper, we consider a wavelet based edge-preserving regularization scheme for use in linear image restoration problems. Our efforts build on a collection of mathematical results indicating that wavelets are especially useful for representing functions that contain discontinuities (i.e., edges in two dimensions or jumps in one dimension). We interpret the resulting theory in a statistical signal processing framework and obtain a highly flexible framework for adapting the degree of regularization to the local structure of the underlying image. In particular, we are able to adapt quite easily to scale-varying and orientation-varying features in the image while simultaneously retaining the edge preservation properties of the regularizer. We demonstrate a half-quadratic algorithm for obtaining the restorations from observed data.

Real-time human versus animal classification using pyro-electric sensor array and Hidden Markov Model

NASA Astrophysics Data System (ADS)

Hossen, Jakir; Jacobs, Eddie L.; Chari, Srikant

2014-03-01

In this paper, we propose a real-time human versus animal classification technique using a pyro-electric sensor array and Hidden Markov Model. The technique starts with the variational energy functional level set segmentation technique to separate the object from background. After segmentation, we convert the segmented object to a signal by considering column-wise pixel values and then finding the wavelet coefficients of the signal. HMMs are trained to statistically model the wavelet features of individuals through an expectation-maximization learning process. Human versus animal classifications are made by evaluating a set of new wavelet feature data against the trained HMMs using the maximum-likelihood criterion. Human and animal data acquired-using a pyro-electric sensor in different terrains are used for performance evaluation of the algorithms. Failures of the computationally effective SURF feature based approach that we develop in our previous research are because of distorted images produced when the object runs very fast or if the temperature difference between target and background is not sufficient to accurately profile the object. We show that wavelet based HMMs work well for handling some of the distorted profiles in the data set. Further, HMM achieves improved classification rate over the SURF algorithm with almost the same computational time.

Daily water level forecasting using wavelet decomposition and artificial intelligence techniques

NASA Astrophysics Data System (ADS)

Seo, Youngmin; Kim, Sungwon; Kisi, Ozgur; Singh, Vijay P.

2015-01-01

Reliable water level forecasting for reservoir inflow is essential for reservoir operation. The objective of this paper is to develop and apply two hybrid models for daily water level forecasting and investigate their accuracy. These two hybrid models are wavelet-based artificial neural network (WANN) and wavelet-based adaptive neuro-fuzzy inference system (WANFIS). Wavelet decomposition is employed to decompose an input time series into approximation and detail components. The decomposed time series are used as inputs to artificial neural networks (ANN) and adaptive neuro-fuzzy inference system (ANFIS) for WANN and WANFIS models, respectively. Based on statistical performance indexes, the WANN and WANFIS models are found to produce better efficiency than the ANN and ANFIS models. WANFIS7-sym10 yields the best performance among all other models. It is found that wavelet decomposition improves the accuracy of ANN and ANFIS. This study evaluates the accuracy of the WANN and WANFIS models for different mother wavelets, including Daubechies, Symmlet and Coiflet wavelets. It is found that the model performance is dependent on input sets and mother wavelets, and the wavelet decomposition using mother wavelet, db10, can further improve the efficiency of ANN and ANFIS models. Results obtained from this study indicate that the conjunction of wavelet decomposition and artificial intelligence models can be a useful tool for accurate forecasting daily water level and can yield better efficiency than the conventional forecasting models.

Micro-seismic imaging using a source function independent full waveform inversion method

NASA Astrophysics Data System (ADS)

Wang, Hanchen; Alkhalifah, Tariq

2018-03-01

At the heart of micro-seismic event measurements is the task to estimate the location of the source micro-seismic events, as well as their ignition times. The accuracy of locating the sources is highly dependent on the velocity model. On the other hand, the conventional micro-seismic source locating methods require, in many cases manual picking of traveltime arrivals, which do not only lead to manual effort and human interaction, but also prone to errors. Using full waveform inversion (FWI) to locate and image micro-seismic events allows for an automatic process (free of picking) that utilizes the full wavefield. However, full waveform inversion of micro-seismic events faces incredible nonlinearity due to the unknown source locations (space) and functions (time). We developed a source function independent full waveform inversion of micro-seismic events to invert for the source image, source function and the velocity model. It is based on convolving reference traces with these observed and modeled to mitigate the effect of an unknown source ignition time. The adjoint-state method is used to derive the gradient for the source image, source function and velocity updates. The extended image for the source wavelet in Z axis is extracted to check the accuracy of the inverted source image and velocity model. Also, angle gathers is calculated to assess the quality of the long wavelength component of the velocity model. By inverting for the source image, source wavelet and the velocity model simultaneously, the proposed method produces good estimates of the source location, ignition time and the background velocity for synthetic examples used here, like those corresponding to the Marmousi model and the SEG/EAGE overthrust model.

Fast frequency domain method to detect skew in a document image

NASA Astrophysics Data System (ADS)

Mehta, Sunita; Walia, Ekta; Dutta, Maitreyee

2015-12-01

In this paper, a new fast frequency domain method based on Discrete Wavelet Transform and Fast Fourier Transform has been implemented for the determination of the skew angle in a document image. Firstly, image size reduction is done by using two-dimensional Discrete Wavelet Transform and then skew angle is computed using Fast Fourier Transform. Skew angle error is almost negligible. The proposed method is experimented using a large number of documents having skew between -90° and +90° and results are compared with Moments with Discrete Wavelet Transform method and other commonly used existing methods. It has been determined that this method works more efficiently than the existing methods. Also, it works with typed, picture documents having different fonts and resolutions. It overcomes the drawback of the recently proposed method of Moments with Discrete Wavelet Transform that does not work with picture documents.

Anisotropic analysis of trabecular architecture in human femur bone radiographs using quaternion wavelet transforms.

PubMed

Sangeetha, S; Sujatha, C M; Manamalli, D

2014-01-01

In this work, anisotropy of compressive and tensile strength regions of femur trabecular bone are analysed using quaternion wavelet transforms. The normal and abnormal femur trabecular bone radiographic images are considered for this study. The sub-anatomic regions, which include compressive and tensile regions, are delineated using pre-processing procedures. These delineated regions are subjected to quaternion wavelet transforms and statistical parameters are derived from the transformed images. These parameters are correlated with apparent porosity, which is derived from the strength regions. Further, anisotropy is also calculated from the transformed images and is analyzed. Results show that the anisotropy values derived from second and third phase components of quaternion wavelet transform are found to be distinct for normal and abnormal samples with high statistical significance for both compressive and tensile regions. These investigations demonstrate that architectural anisotropy derived from QWT analysis is able to differentiate normal and abnormal samples.

A fast method for the detection of vascular structure in images, based on the continuous wavelet transform with the Morlet wavelet having a low central frequency

NASA Astrophysics Data System (ADS)

Postnikov, Eugene B.; Tsoy, Maria O.; Kurochkin, Maxim A.; Postnov, Dmitry E.

2017-04-01

A manual measurement of blood vessels diameter is a conventional component of routine visual assessment of microcirculation, say, during optical capillaroscopy. However, many modern optical methods for blood flow measurements demand the reliable procedure for a fully automated detection of vessels and estimation of their diameter that is a challenging task. Specifically, if one measure the velocity of red blood cells by means of laser speckle imaging, then visual measurements become impossible, while the velocity-based estimation has their own limitations. One of promising approaches is based on fast switching of illumination type, but it drastically reduces the observation time, and hence, the achievable quality of images. In the present work we address this problem proposing an alternative method for the processing of noisy images of vascular structure, which extracts the mask denoting locations of vessels, based on the application of the continuous wavelet transform with the Morlet wavelet having small central frequencies. Such a method combines a reasonable accuracy with the possibility of fast direct implementation to images. Discussing the latter, we describe in details a new MATLAB program code realization for the CWT with the Morlet wavelet, which does not use loops completely replaced with element-by-element operations that drastically reduces the computation time.

Research of generalized wavelet transformations of Haar correctness in remote sensing of the Earth

NASA Astrophysics Data System (ADS)

Kazaryan, Maretta; Shakhramanyan, Mihail; Nedkov, Roumen; Richter, Andrey; Borisova, Denitsa; Stankova, Nataliya; Ivanova, Iva; Zaharinova, Mariana

2017-10-01

In this paper, Haar's generalized wavelet functions are applied to the problem of ecological monitoring by the method of remote sensing of the Earth. We study generalized Haar wavelet series and suggest the use of Tikhonov's regularization method for investigating them for correctness. In the solution of this problem, an important role is played by classes of functions that were introduced and described in detail by I.M. Sobol for studying multidimensional quadrature formulas and it contains functions with rapidly convergent series of wavelet Haar. A theorem on the stability and uniform convergence of the regularized summation function of the generalized wavelet-Haar series of a function from this class with approximate coefficients is proved. The article also examines the problem of using orthogonal transformations in Earth remote sensing technologies for environmental monitoring. Remote sensing of the Earth allows to receive from spacecrafts information of medium, high spatial resolution and to conduct hyperspectral measurements. Spacecrafts have tens or hundreds of spectral channels. To process the images, the device of discrete orthogonal transforms, and namely, wavelet transforms, was used. The aim of the work is to apply the regularization method in one of the problems associated with remote sensing of the Earth and subsequently to process the satellite images through discrete orthogonal transformations, in particular, generalized Haar wavelet transforms. General methods of research. In this paper, Tikhonov's regularization method, the elements of mathematical analysis, the theory of discrete orthogonal transformations, and methods for decoding of satellite images are used. Scientific novelty. The task of processing of archival satellite snapshots (images), in particular, signal filtering, was investigated from the point of view of an incorrectly posed problem. The regularization parameters for discrete orthogonal transformations were determined.

Wavelet analysis of hemispheroid flow separation toward understanding human vocal fold pathologies

NASA Astrophysics Data System (ADS)

Plesniak, Daniel H.; Carr, Ian A.; Bulusu, Kartik V.; Plesniak, Michael W.

2014-11-01

Physiological flows observed in human vocal fold pathologies, such as polyps and nodules, can be modeled by flow over a wall-mounted protuberance. The experimental investigation of flow separation over a surface-mounted hemispheroid was performed using particle image velocimetry (PIV) and measurements of surface pressure in a low-speed wind tunnel. This study builds on the hypothesis that the signatures of vortical structures associated with flow separation are imprinted on the surface pressure distributions. Wavelet decomposition methods in one- and two-dimensions were utilized to elucidate the flow behavior. First, a complex Gaussian wavelet was used for the reconstruction of surface pressure time series from static pressure measurements acquired from ports upstream, downstream, and on the surface of the hemispheroid. This was followed by the application of a novel continuous wavelet transform algorithm (PIVlet 1.2) using a 2D-Ricker wavelet for coherent structure detection on instantaneous PIV-data. The goal of this study is to correlate phase shifts in surface pressure with Strouhal numbers associated with the vortex shedding. Ultimately, the wavelet-based analytical framework will be aimed at addressing pulsatile flows. This material is based in part upon work supported by the National Science Foundation under Grant Number CBET-1236351, and GW Center for Biomimetics and Bioinspired Engineering (COBRE).

Improving 3D Wavelet-Based Compression of Hyperspectral Images

NASA Technical Reports Server (NTRS)

Klimesh, Matthew; Kiely, Aaron; Xie, Hua; Aranki, Nazeeh

2009-01-01

Two methods of increasing the effectiveness of three-dimensional (3D) wavelet-based compression of hyperspectral images have been developed. (As used here, images signifies both images and digital data representing images.) The methods are oriented toward reducing or eliminating detrimental effects of a phenomenon, referred to as spectral ringing, that is described below. In 3D wavelet-based compression, an image is represented by a multiresolution wavelet decomposition consisting of several subbands obtained by applying wavelet transforms in the two spatial dimensions corresponding to the two spatial coordinate axes of the image plane, and by applying wavelet transforms in the spectral dimension. Spectral ringing is named after the more familiar spatial ringing (spurious spatial oscillations) that can be seen parallel to and near edges in ordinary images reconstructed from compressed data. These ringing phenomena are attributable to effects of quantization. In hyperspectral data, the individual spectral bands play the role of edges, causing spurious oscillations to occur in the spectral dimension. In the absence of such corrective measures as the present two methods, spectral ringing can manifest itself as systematic biases in some reconstructed spectral bands and can reduce the effectiveness of compression of spatially-low-pass subbands. One of the two methods is denoted mean subtraction. The basic idea of this method is to subtract mean values from spatial planes of spatially low-pass subbands prior to encoding, because (a) such spatial planes often have mean values that are far from zero and (b) zero-mean data are better suited for compression by methods that are effective for subbands of two-dimensional (2D) images. In this method, after the 3D wavelet decomposition is performed, mean values are computed for and subtracted from each spatial plane of each spatially-low-pass subband. The resulting data are converted to sign-magnitude form and compressed in a manner similar to that of a baseline hyperspectral- image-compression method. The mean values are encoded in the compressed bit stream and added back to the data at the appropriate decompression step. The overhead incurred by encoding the mean values only a few bits per spectral band is negligible with respect to the huge size of a typical hyperspectral data set. The other method is denoted modified decomposition. This method is so named because it involves a modified version of a commonly used multiresolution wavelet decomposition, known in the art as the 3D Mallat decomposition, in which (a) the first of multiple stages of a 3D wavelet transform is applied to the entire dataset and (b) subsequent stages are applied only to the horizontally-, vertically-, and spectrally-low-pass subband from the preceding stage. In the modified decomposition, in stages after the first, not only is the spatially-low-pass, spectrally-low-pass subband further decomposed, but also spatially-low-pass, spectrally-high-pass subbands are further decomposed spatially. Either method can be used alone to improve the quality of a reconstructed image (see figure). Alternatively, the two methods can be combined by first performing modified decomposition, then subtracting the mean values from spatial planes of spatially-low-pass subbands.

Fabric wrinkle characterization and classification using modified wavelet coefficients and optimized support-vector-machine classifier

USDA-ARS?s Scientific Manuscript database

This paper presents a novel wrinkle evaluation method that uses modified wavelet coefficients and an optimized support-vector-machine (SVM) classification scheme to characterize and classify wrinkle appearance of fabric. Fabric images were decomposed with the wavelet transform (WT), and five parame...

[An improved medical image fusion algorithm and quality evaluation].

PubMed

Chen, Meiling; Tao, Ling; Qian, Zhiyu

2009-08-01

Medical image fusion is of very important value for application in medical image analysis and diagnosis. In this paper, the conventional method of wavelet fusion is improved,so a new algorithm of medical image fusion is presented and the high frequency and low frequency coefficients are studied respectively. When high frequency coefficients are chosen, the regional edge intensities of each sub-image are calculated to realize adaptive fusion. The choice of low frequency coefficient is based on the edges of images, so that the fused image preserves all useful information and appears more distinctly. We apply the conventional and the improved fusion algorithms based on wavelet transform to fuse two images of human body and also evaluate the fusion results through a quality evaluation method. Experimental results show that this algorithm can effectively retain the details of information on original images and enhance their edge and texture features. This new algorithm is better than the conventional fusion algorithm based on wavelet transform.

Deriving leaf mass per area (LMA) from foliar reflectance across a variety of plant species using continuous wavelet analysis

NASA Astrophysics Data System (ADS)

Cheng, Tao; Rivard, Benoit; Sánchez-Azofeifa, Arturo G.; Féret, Jean-Baptiste; Jacquemoud, Stéphane; Ustin, Susan L.

2014-01-01

Leaf mass per area (LMA), the ratio of leaf dry mass to leaf area, is a trait of central importance to the understanding of plant light capture and carbon gain. It can be estimated from leaf reflectance spectroscopy in the infrared region, by making use of information about the absorption features of dry matter. This study reports on the application of continuous wavelet analysis (CWA) to the estimation of LMA across a wide range of plant species. We compiled a large database of leaf reflectance spectra acquired within the framework of three independent measurement campaigns (ANGERS, LOPEX and PANAMA) and generated a simulated database using the PROSPECT leaf optical properties model. CWA was applied to the measured and simulated databases to extract wavelet features that correlate with LMA. These features were assessed in terms of predictive capability and robustness while transferring predictive models from the simulated database to the measured database. The assessment was also conducted with two existing spectral indices, namely the Normalized Dry Matter Index (NDMI) and the Normalized Difference index for LMA (NDLMA). Five common wavelet features were determined from the two databases, which showed significant correlations with LMA (R2: 0.51-0.82, p < 0.0001). The best robustness (R2 = 0.74, RMSE = 18.97 g/m2 and Bias = 0.12 g/m2) was obtained using a combination of two low-scale features (1639 nm, scale 4) and (2133 nm, scale 5), the first being predominantly important. The transferability of the wavelet-based predictive model to the whole measured database was either better than or comparable to those based on spectral indices. Additionally, only the wavelet-based model showed consistent predictive capabilities among the three measured data sets. In comparison, the models based on spectral indices were sensitive to site-specific data sets. Integrating the NDLMA spectral index and the two robust wavelet features improved the LMA prediction. One of the bands used by this spectral index, 1368 nm, was located in a strong atmospheric water absorption region and replacing it with the next available band (1340 nm) led to lower predictive accuracies. However, the two wavelet features were not affected by data quality in the atmospheric absorption regions and therefore showed potential for canopy-level investigations. The wavelet approach provides a different perspective into spectral responses to LMA variation than the traditional spectral indices and holds greater promise for implementation with airborne or spaceborne imaging spectroscopy data for mapping canopy foliar dry biomass.

The Brera Multiscale Wavelet ROSAT HRI Source Catalog. I. The Algorithm

NASA Astrophysics Data System (ADS)

Lazzati, Davide; Campana, Sergio; Rosati, Piero; Panzera, Maria Rosa; Tagliaferri, Gianpiero

1999-10-01

We present a new detection algorithm based on the wavelet transform for the analysis of high-energy astronomical images. The wavelet transform, because of its multiscale structure, is suited to the optimal detection of pointlike as well as extended sources, regardless of any loss of resolution with the off-axis angle. Sources are detected as significant enhancements in the wavelet space, after the subtraction of the nonflat components of the background. Detection thresholds are computed through Monte Carlo simulations in order to establish the expected number of spurious sources per field. The source characterization is performed through a multisource fitting in the wavelet space. The procedure is designed to correctly deal with very crowded fields, allowing for the simultaneous characterization of nearby sources. To obtain a fast and reliable estimate of the source parameters and related errors, we apply a novel decimation technique that, taking into account the correlation properties of the wavelet transform, extracts a subset of almost independent coefficients. We test the performance of this algorithm on synthetic fields, analyzing with particular care the characterization of sources in poor background situations, where the assumption of Gaussian statistics does not hold. In these cases, for which standard wavelet algorithms generally provide underestimated errors, we infer errors through a procedure that relies on robust basic statistics. Our algorithm is well suited to the analysis of images taken with the new generation of X-ray instruments equipped with CCD technology, which will produce images with very low background and/or high source density.

Optical Coherence Tomography Noise Reduction Using Anisotropic Local Bivariate Gaussian Mixture Prior in 3D Complex Wavelet Domain

PubMed Central

Sonka, Milan; Abramoff, Michael D.

2013-01-01

In this paper, MMSE estimator is employed for noise-free 3D OCT data recovery in 3D complex wavelet domain. Since the proposed distribution for noise-free data plays a key role in the performance of MMSE estimator, a priori distribution for the pdf of noise-free 3D complex wavelet coefficients is proposed which is able to model the main statistical properties of wavelets. We model the coefficients with a mixture of two bivariate Gaussian pdfs with local parameters which are able to capture the heavy-tailed property and inter- and intrascale dependencies of coefficients. In addition, based on the special structure of OCT images, we use an anisotropic windowing procedure for local parameters estimation that results in visual quality improvement. On this base, several OCT despeckling algorithms are obtained based on using Gaussian/two-sided Rayleigh noise distribution and homomorphic/nonhomomorphic model. In order to evaluate the performance of the proposed algorithm, we use 156 selected ROIs from 650 × 512 × 128 OCT dataset in the presence of wet AMD pathology. Our simulations show that the best MMSE estimator using local bivariate mixture prior is for the nonhomomorphic model in the presence of Gaussian noise which results in an improvement of 7.8 ± 1.7 in CNR. PMID:24222760

Wavelet/scalar quantization compression standard for fingerprint images

DOE Office of Scientific and Technical Information (OSTI.GOV)

Brislawn, C.M.

1996-06-12

US Federal Bureau of Investigation (FBI) has recently formulated a national standard for digitization and compression of gray-scale fingerprint images. Fingerprints are scanned at a spatial resolution of 500 dots per inch, with 8 bits of gray-scale resolution. The compression algorithm for the resulting digital images is based on adaptive uniform scalar quantization of a discrete wavelet transform subband decomposition (wavelet/scalar quantization method). The FBI standard produces archival-quality images at compression ratios of around 15 to 1 and will allow the current database of paper fingerprint cards to be replaced by digital imagery. The compression standard specifies a class ofmore » potential encoders and a universal decoder with sufficient generality to reconstruct compressed images produced by any compliant encoder, allowing flexibility for future improvements in encoder technology. A compliance testing program is also being implemented to ensure high standards of image quality and interchangeability of data between different implementations.« less

Target Identification Using Harmonic Wavelet Based ISAR Imaging

NASA Astrophysics Data System (ADS)

Shreyamsha Kumar, B. K.; Prabhakar, B.; Suryanarayana, K.; Thilagavathi, V.; Rajagopal, R.

2006-12-01

A new approach has been proposed to reduce the computations involved in the ISAR imaging, which uses harmonic wavelet-(HW) based time-frequency representation (TFR). Since the HW-based TFR falls into a category of nonparametric time-frequency (T-F) analysis tool, it is computationally efficient compared to parametric T-F analysis tools such as adaptive joint time-frequency transform (AJTFT), adaptive wavelet transform (AWT), and evolutionary AWT (EAWT). Further, the performance of the proposed method of ISAR imaging is compared with the ISAR imaging by other nonparametric T-F analysis tools such as short-time Fourier transform (STFT) and Choi-Williams distribution (CWD). In the ISAR imaging, the use of HW-based TFR provides similar/better results with significant (92%) computational advantage compared to that obtained by CWD. The ISAR images thus obtained are identified using a neural network-based classification scheme with feature set invariant to translation, rotation, and scaling.

Design of almost symmetric orthogonal wavelet filter bank via direct optimization.

PubMed

Murugesan, Selvaraaju; Tay, David B H

2012-05-01

It is a well-known fact that (compact-support) dyadic wavelets [based on the two channel filter banks (FBs)] cannot be simultaneously orthogonal and symmetric. Although orthogonal wavelets have the energy preservation property, biorthogonal wavelets are preferred in image processing applications because of their symmetric property. In this paper, a novel method is presented for the design of almost symmetric orthogonal wavelet FB. Orthogonality is structurally imposed by using the unnormalized lattice structure, and this leads to an objective function, which is relatively simple to optimize. The designed filters have good frequency response, flat group delay, almost symmetric filter coefficients, and symmetric wavelet function.

Visual communications and image processing '92; Proceedings of the Meeting, Boston, MA, Nov. 18-20, 1992

NASA Astrophysics Data System (ADS)

Maragos, Petros

The topics discussed at the conference include hierarchical image coding, motion analysis, feature extraction and image restoration, video coding, and morphological and related nonlinear filtering. Attention is also given to vector quantization, morphological image processing, fractals and wavelets, architectures for image and video processing, image segmentation, biomedical image processing, and model-based analysis. Papers are presented on affine models for motion and shape recovery, filters for directly detecting surface orientation in an image, tracking of unresolved targets in infrared imagery using a projection-based method, adaptive-neighborhood image processing, and regularized multichannel restoration of color images using cross-validation. (For individual items see A93-20945 to A93-20951)

Wavelet-based image compression using shuffling and bit plane correlation

NASA Astrophysics Data System (ADS)

Kim, Seungjong; Jeong, Jechang

2000-12-01

In this paper, we propose a wavelet-based image compression method using shuffling and bit plane correlation. The proposed method improves coding performance in two steps: (1) removing the sign bit plane by shuffling process on quantized coefficients, (2) choosing the arithmetic coding context according to maximum correlation direction. The experimental results are comparable or superior for some images with low correlation, to existing coders.

Basic Investigation on Medical Ultrasonic Echo Image Compression by JPEG2000 - Availability of Wavelet Transform and ROI Method

DTIC Science & Technology

2001-10-25

Table III. In spite of the same quality in ROI, it is decided that the images in the cases where QF is 1.3, 1.5 or 2.0 are not good for diagnosis. Of...but (b) is not good for diagnosis by decision of ultrasonographer. Results reveal that wavelet transform achieves higher quality of image compared

Super-resolution algorithm based on sparse representation and wavelet preprocessing for remote sensing imagery

NASA Astrophysics Data System (ADS)

Ren, Ruizhi; Gu, Lingjia; Fu, Haoyang; Sun, Chenglin

2017-04-01

An effective super-resolution (SR) algorithm is proposed for actual spectral remote sensing images based on sparse representation and wavelet preprocessing. The proposed SR algorithm mainly consists of dictionary training and image reconstruction. Wavelet preprocessing is used to establish four subbands, i.e., low frequency, horizontal, vertical, and diagonal high frequency, for an input image. As compared to the traditional approaches involving the direct training of image patches, the proposed approach focuses on the training of features derived from these four subbands. The proposed algorithm is verified using different spectral remote sensing images, e.g., moderate-resolution imaging spectroradiometer (MODIS) images with different bands, and the latest Chinese Jilin-1 satellite images with high spatial resolution. According to the visual experimental results obtained from the MODIS remote sensing data, the SR images using the proposed SR algorithm are superior to those using a conventional bicubic interpolation algorithm or traditional SR algorithms without preprocessing. Fusion algorithms, e.g., standard intensity-hue-saturation, principal component analysis, wavelet transform, and the proposed SR algorithms are utilized to merge the multispectral and panchromatic images acquired by the Jilin-1 satellite. The effectiveness of the proposed SR algorithm is assessed by parameters such as peak signal-to-noise ratio, structural similarity index, correlation coefficient, root-mean-square error, relative dimensionless global error in synthesis, relative average spectral error, spectral angle mapper, and the quality index Q4, and its performance is better than that of the standard image fusion algorithms.

Implementation of the 2-D Wavelet Transform into FPGA for Image

NASA Astrophysics Data System (ADS)

León, M.; Barba, L.; Vargas, L.; Torres, C. O.

2011-01-01

This paper presents a hardware system implementation of the of discrete wavelet transform algoritm in two dimensions for FPGA, using the Daubechies filter family of order 2 (db2). The decomposition algorithm of this transform is designed and simulated with the Hardware Description Language VHDL and is implemented in a programmable logic device (FPGA) XC3S1200E reference, Spartan IIIE family, by Xilinx, take advantage the parallels properties of these gives us and speeds processing that can reach them. The architecture is evaluated using images input of different sizes. This implementation is done with the aim of developing a future images encryption hardware system using wavelet transform for security information.

Embedded wavelet-based face recognition under variable position

NASA Astrophysics Data System (ADS)

Cotret, Pascal; Chevobbe, Stéphane; Darouich, Mehdi

2015-02-01

For several years, face recognition has been a hot topic in the image processing field: this technique is applied in several domains such as CCTV, electronic devices delocking and so on. In this context, this work studies the efficiency of a wavelet-based face recognition method in terms of subject position robustness and performance on various systems. The use of wavelet transform has a limited impact on the position robustness of PCA-based face recognition. This work shows, for a well-known database (Yale face database B*), that subject position in a 3D space can vary up to 10% of the original ROI size without decreasing recognition rates. Face recognition is performed on approximation coefficients of the image wavelet transform: results are still satisfying after 3 levels of decomposition. Furthermore, face database size can be divided by a factor 64 (22K with K = 3). In the context of ultra-embedded vision systems, memory footprint is one of the key points to be addressed; that is the reason why compression techniques such as wavelet transform are interesting. Furthermore, it leads to a low-complexity face detection stage compliant with limited computation resources available on such systems. The approach described in this work is tested on three platforms from a standard x86-based computer towards nanocomputers such as RaspberryPi and SECO boards. For K = 3 and a database with 40 faces, the execution mean time for one frame is 0.64 ms on a x86-based computer, 9 ms on a SECO board and 26 ms on a RaspberryPi (B model).

Pigmented skin lesion detection using random forest and wavelet-based texture

NASA Astrophysics Data System (ADS)

Hu, Ping; Yang, Tie-jun

2016-10-01

The incidence of cutaneous malignant melanoma, a disease of worldwide distribution and is the deadliest form of skin cancer, has been rapidly increasing over the last few decades. Because advanced cutaneous melanoma is still incurable, early detection is an important step toward a reduction in mortality. Dermoscopy photographs are commonly used in melanoma diagnosis and can capture detailed features of a lesion. A great variability exists in the visual appearance of pigmented skin lesions. Therefore, in order to minimize the diagnostic errors that result from the difficulty and subjectivity of visual interpretation, an automatic detection approach is required. The objectives of this paper were to propose a hybrid method using random forest and Gabor wavelet transformation to accurately differentiate which part belong to lesion area and the other is not in a dermoscopy photographs and analyze segmentation accuracy. A random forest classifier consisting of a set of decision trees was used for classification. Gabor wavelets transformation are the mathematical model of visual cortical cells of mammalian brain and an image can be decomposed into multiple scales and multiple orientations by using it. The Gabor function has been recognized as a very useful tool in texture analysis, due to its optimal localization properties in both spatial and frequency domain. Texture features based on Gabor wavelets transformation are found by the Gabor filtered image. Experiment results indicate the following: (1) the proposed algorithm based on random forest outperformed the-state-of-the-art in pigmented skin lesions detection (2) and the inclusion of Gabor wavelet transformation based texture features improved segmentation accuracy significantly.

Filtering and left ventricle segmentation of the fetal heart in ultrasound images

NASA Astrophysics Data System (ADS)

Vargas-Quintero, Lorena; Escalante-Ramírez, Boris

2013-11-01

In this paper, we propose to use filtering methods and a segmentation algorithm for the analysis of fetal heart in ultrasound images. Since noise speckle makes difficult the analysis of ultrasound images, the filtering process becomes a useful task in these types of applications. The filtering techniques consider in this work assume that the speckle noise is a random variable with a Rayleigh distribution. We use two multiresolution methods: one based on wavelet decomposition and the another based on the Hermite transform. The filtering process is used as way to strengthen the performance of the segmentation tasks. For the wavelet-based approach, a Bayesian estimator at subband level for pixel classification is employed. The Hermite method computes a mask to find those pixels that are corrupted by speckle. On the other hand, we picked out a method based on a deformable model or "snake" to evaluate the influence of the filtering techniques in the segmentation task of left ventricle in fetal echocardiographic images.

Compression of multispectral Landsat imagery using the Embedded Zerotree Wavelet (EZW) algorithm

NASA Technical Reports Server (NTRS)

Shapiro, Jerome M.; Martucci, Stephen A.; Czigler, Martin

1994-01-01

The Embedded Zerotree Wavelet (EZW) algorithm has proven to be an extremely efficient and flexible compression algorithm for low bit rate image coding. The embedding algorithm attempts to order the bits in the bit stream in numerical importance and thus a given code contains all lower rate encodings of the same algorithm. Therefore, precise bit rate control is achievable and a target rate or distortion metric can be met exactly. Furthermore, the technique is fully image adaptive. An algorithm for multispectral image compression which combines the spectral redundancy removal properties of the image-dependent Karhunen-Loeve Transform (KLT) with the efficiency, controllability, and adaptivity of the embedded zerotree wavelet algorithm is presented. Results are shown which illustrate the advantage of jointly encoding spectral components using the KLT and EZW.

Multiresolution With Super-Compact Wavelets

NASA Technical Reports Server (NTRS)

Lee, Dohyung

2000-01-01

The solution data computed from large scale simulations are sometimes too big for main memory, for local disks, and possibly even for a remote storage disk, creating tremendous processing time as well as technical difficulties in analyzing the data. The excessive storage demands a corresponding huge penalty in I/O time, rendering time and transmission time between different computer systems. In this paper, a multiresolution scheme is proposed to compress field simulation or experimental data without much loss of important information in the representation. Originally, the wavelet based multiresolution scheme was introduced in image processing, for the purposes of data compression and feature extraction. Unlike photographic image data which has rather simple settings, computational field simulation data needs more careful treatment in applying the multiresolution technique. While the image data sits on a regular spaced grid, the simulation data usually resides on a structured curvilinear grid or unstructured grid. In addition to the irregularity in grid spacing, the other difficulty is that the solutions consist of vectors instead of scalar values. The data characteristics demand more restrictive conditions. In general, the photographic images have very little inherent smoothness with discontinuities almost everywhere. On the other hand, the numerical solutions have smoothness almost everywhere and discontinuities in local areas (shock, vortices, and shear layers). The wavelet bases should be amenable to the solution of the problem at hand and applicable to constraints such as numerical accuracy and boundary conditions. In choosing a suitable wavelet basis for simulation data among a variety of wavelet families, the supercompact wavelets designed by Beam and Warming provide one of the most effective multiresolution schemes. Supercompact multi-wavelets retain the compactness of Haar wavelets, are piecewise polynomial and orthogonal, and can have arbitrary order of approximation. The advantages of the multiresolution algorithm are that no special treatment is required at the boundaries of the interval, and that the application to functions which are only piecewise continuous (internal boundaries) can be efficiently implemented. In this presentation, Beam's supercompact wavelets are generalized to higher dimensions using multidimensional scaling and wavelet functions rather than alternating the directions as in the 1D version. As a demonstration of actual 3D data compression, supercompact wavelet transforms are applied to a 3D data set for wing tip vortex flow solutions (2.5 million grid points). It is shown that high data compression ratio can be achieved (around 50:1 ratio) in both vector and scalar data set.

Multispectral multisensor image fusion using wavelet transforms

USGS Publications Warehouse

Lemeshewsky, George P.

1999-01-01

Fusion techniques can be applied to multispectral and higher spatial resolution panchromatic images to create a composite image that is easier to interpret than the individual images. Wavelet transform-based multisensor, multiresolution fusion (a type of band sharpening) was applied to Landsat thematic mapper (TM) multispectral and coregistered higher resolution SPOT panchromatic images. The objective was to obtain increased spatial resolution, false color composite products to support the interpretation of land cover types wherein the spectral characteristics of the imagery are preserved to provide the spectral clues needed for interpretation. Since the fusion process should not introduce artifacts, a shift invariant implementation of the discrete wavelet transform (SIDWT) was used. These results were compared with those using the shift variant, discrete wavelet transform (DWT). Overall, the process includes a hue, saturation, and value color space transform to minimize color changes, and a reported point-wise maximum selection rule to combine transform coefficients. The performance of fusion based on the SIDWT and DWT was evaluated with a simulated TM 30-m spatial resolution test image and a higher resolution reference. Simulated imagery was made by blurring higher resolution color-infrared photography with the TM sensors' point spread function. The SIDWT based technique produced imagery with fewer artifacts and lower error between fused images and the full resolution reference. Image examples with TM and SPOT 10-m panchromatic illustrate the reduction in artifacts due to the SIDWT based fusion.

Use of multiresolution wavelet feature pyramids for automatic registration of multisensor imagery

NASA Technical Reports Server (NTRS)

Zavorin, Ilya; Le Moigne, Jacqueline

2005-01-01

The problem of image registration, or the alignment of two or more images representing the same scene or object, has to be addressed in various disciplines that employ digital imaging. In the area of remote sensing, just like in medical imaging or computer vision, it is necessary to design robust, fast, and widely applicable algorithms that would allow automatic registration of images generated by various imaging platforms at the same or different times and that would provide subpixel accuracy. One of the main issues that needs to be addressed when developing a registration algorithm is what type of information should be extracted from the images being registered, to be used in the search for the geometric transformation that best aligns them. The main objective of this paper is to evaluate several wavelet pyramids that may be used both for invariant feature extraction and for representing images at multiple spatial resolutions to accelerate registration. We find that the bandpass wavelets obtained from the steerable pyramid due to Simoncelli performs best in terms of accuracy and consistency, while the low-pass wavelets obtained from the same pyramid give the best results in terms of the radius of convergence. Based on these findings, we propose a modification of a gradient-based registration algorithm that has recently been developed for medical data. We test the modified algorithm on several sets of real and synthetic satellite imagery.

Automated Diagnosis of Glaucoma Using Empirical Wavelet Transform and Correntropy Features Extracted From Fundus Images.

PubMed

Maheshwari, Shishir; Pachori, Ram Bilas; Acharya, U Rajendra

2017-05-01

Glaucoma is an ocular disorder caused due to increased fluid pressure in the optic nerve. It damages the optic nerve and subsequently causes loss of vision. The available scanning methods are Heidelberg retinal tomography, scanning laser polarimetry, and optical coherence tomography. These methods are expensive and require experienced clinicians to use them. So, there is a need to diagnose glaucoma accurately with low cost. Hence, in this paper, we have presented a new methodology for an automated diagnosis of glaucoma using digital fundus images based on empirical wavelet transform (EWT). The EWT is used to decompose the image, and correntropy features are obtained from decomposed EWT components. These extracted features are ranked based on t value feature selection algorithm. Then, these features are used for the classification of normal and glaucoma images using least-squares support vector machine (LS-SVM) classifier. The LS-SVM is employed for classification with radial basis function, Morlet wavelet, and Mexican-hat wavelet kernels. The classification accuracy of the proposed method is 98.33% and 96.67% using threefold and tenfold cross validation, respectively.

EIT Imaging Regularization Based on Spectral Graph Wavelets.

PubMed

Gong, Bo; Schullcke, Benjamin; Krueger-Ziolek, Sabine; Vauhkonen, Marko; Wolf, Gerhard; Mueller-Lisse, Ullrich; Moeller, Knut

2017-09-01

The objective of electrical impedance tomographic reconstruction is to identify the distribution of tissue conductivity from electrical boundary conditions. This is an ill-posed inverse problem usually solved under the finite-element method framework. In previous studies, standard sparse regularization was used for difference electrical impedance tomography to achieve a sparse solution. However, regarding elementwise sparsity, standard sparse regularization interferes with the smoothness of conductivity distribution between neighboring elements and is sensitive to noise. As an effect, the reconstructed images are spiky and depict a lack of smoothness. Such unexpected artifacts are not realistic and may lead to misinterpretation in clinical applications. To eliminate such artifacts, we present a novel sparse regularization method that uses spectral graph wavelet transforms. Single-scale or multiscale graph wavelet transforms are employed to introduce local smoothness on different scales into the reconstructed images. The proposed approach relies on viewing finite-element meshes as undirected graphs and applying wavelet transforms derived from spectral graph theory. Reconstruction results from simulations, a phantom experiment, and patient data suggest that our algorithm is more robust to noise and produces more reliable images.

Improved Denoising via Poisson Mixture Modeling of Image Sensor Noise.

PubMed

Zhang, Jiachao; Hirakawa, Keigo

2017-04-01

This paper describes a study aimed at comparing the real image sensor noise distribution to the models of noise often assumed in image denoising designs. A quantile analysis in pixel, wavelet transform, and variance stabilization domains reveal that the tails of Poisson, signal-dependent Gaussian, and Poisson-Gaussian models are too short to capture real sensor noise behavior. A new Poisson mixture noise model is proposed to correct the mismatch of tail behavior. Based on the fact that noise model mismatch results in image denoising that undersmoothes real sensor data, we propose a mixture of Poisson denoising method to remove the denoising artifacts without affecting image details, such as edge and textures. Experiments with real sensor data verify that denoising for real image sensor data is indeed improved by this new technique.

Riding the Right Wavelet: Detecting Fracture and Fault Orientation Scale Transitions Using Morlet Wavelets

NASA Astrophysics Data System (ADS)

Rizzo, R. E.; Healy, D.; Farrell, N. J.; Smith, M.

2016-12-01

The analysis of images through two-dimensional (2D) continuous wavelet transforms makes it possible to acquire local information at different scales of resolution. This characteristic allows us to use wavelet analysis to quantify anisotropic random fields such as networks of fractures. Previous studies [1] have used 2D anisotropic Mexican hat wavelets to analyse the organisation of fracture networks from cm- to km-scales. However, Antoine et al. [2] explained that this technique can have a relatively poor directional selectivity. This suggests the use of a wavelet whose transform is more sensitive to directions of linear features, i.e. 2D Morlet wavelets [3]. In this work, we use a fully-anisotropic Morlet wavelet as implemented by Neupauer & Powell [4], which is anisotropic in its real and imaginary parts and also in its magnitude. We demonstrate the validity of this analytical technique by application to both synthetic - generated according to known distributions of orientations and lengths - and experimentally produced fracture networks. We have analysed SEM Back Scattered Electron images of thin sections of Hopeman Sandstone (Scotland, UK) deformed under triaxial conditions. We find that the Morlet wavelet, compared to the Mexican hat, is more precise in detecting dominant orientations in fracture scale transition at every scale from intra-grain fractures (µm-scale) up to the faults cutting the whole thin section (cm-scale). Through this analysis we can determine the relationship between the initial orientation of tensile microcracks and the final geometry of the through-going shear fault, with total areal coverage of the analysed image. By comparing thin sections from experiments at different confining pressures, we can quantitatively explore the relationship between the observed geometry and the inferred mechanical processes. [1] Ouillon et al., Nonlinear Processes in Geophysics (1995) 2:158 - 177. [2] Antoine et al., Cambridge University Press (2008) 192-194. [3] Antoine et al., Signal Processing (1993) 31:241 - 272. [4] Neupauer & Powell, Computer & Geosciences (2005) 31:456 - 471.

Adjusting Wavelet-based Multiresolution Analysis Boundary Conditions for Robust Long-term Streamflow Forecasting Model

NASA Astrophysics Data System (ADS)

Maslova, I.; Ticlavilca, A. M.; McKee, M.

2012-12-01

There has been an increased interest in wavelet-based streamflow forecasting models in recent years. Often overlooked in this approach are the circularity assumptions of the wavelet transform. We propose a novel technique for minimizing the wavelet decomposition boundary condition effect to produce long-term, up to 12 months ahead, forecasts of streamflow. A simulation study is performed to evaluate the effects of different wavelet boundary rules using synthetic and real streamflow data. A hybrid wavelet-multivariate relevance vector machine model is developed for forecasting the streamflow in real-time for Yellowstone River, Uinta Basin, Utah, USA. The inputs of the model utilize only the past monthly streamflow records. They are decomposed into components formulated in terms of wavelet multiresolution analysis. It is shown that the model model accuracy can be increased by using the wavelet boundary rule introduced in this study. This long-term streamflow modeling and forecasting methodology would enable better decision-making and managing water availability risk.

Developing a multi-Kinect-system for monitoring in dairy cows: object recognition and surface analysis using wavelets.

PubMed

Salau, J; Haas, J H; Thaller, G; Leisen, M; Junge, W

2016-09-01

Camera-based systems in dairy cattle were intensively studied over the last years. Different from this study, single camera systems with a limited range of applications were presented, mostly using 2D cameras. This study presents current steps in the development of a camera system comprising multiple 3D cameras (six Microsoft Kinect cameras) for monitoring purposes in dairy cows. An early prototype was constructed, and alpha versions of software for recording, synchronizing, sorting and segmenting images and transforming the 3D data in a joint coordinate system have already been implemented. This study introduced the application of two-dimensional wavelet transforms as method for object recognition and surface analyses. The method was explained in detail, and four differently shaped wavelets were tested with respect to their reconstruction error concerning Kinect recorded depth maps from different camera positions. The images' high frequency parts reconstructed from wavelet decompositions using the haar and the biorthogonal 1.5 wavelet were statistically analyzed with regard to the effects of image fore- or background and of cows' or persons' surface. Furthermore, binary classifiers based on the local high frequencies have been implemented to decide whether a pixel belongs to the image foreground and if it was located on a cow or a person. Classifiers distinguishing between image regions showed high (⩾0.8) values of Area Under reciever operation characteristic Curve (AUC). The classifications due to species showed maximal AUC values of 0.69.

Embedded wavelet packet transform technique for texture compression

NASA Astrophysics Data System (ADS)

Li, Jin; Cheng, Po-Yuen; Kuo, C.-C. Jay

1995-09-01

A highly efficient texture compression scheme is proposed in this research. With this scheme, energy compaction of texture images is first achieved by the wavelet packet transform, and an embedding approach is then adopted for the coding of the wavelet packet transform coefficients. By comparing the proposed algorithm with the JPEG standard, FBI wavelet/scalar quantization standard and the EZW scheme with extensive experimental results, we observe a significant improvement in the rate-distortion performance and visual quality.

Image processing for quantifying fracture orientation and length scale transitions during brittle deformation

NASA Astrophysics Data System (ADS)

Rizzo, R. E.; Healy, D.; Farrell, N. J.

2017-12-01

We have implemented a novel image processing tool, namely two-dimensional (2D) Morlet wavelet analysis, capable of detecting changes occurring in fracture patterns at different scales of observation, and able of recognising the dominant fracture orientations and the spatial configurations for progressively larger (or smaller) scale of analysis. Because of its inherited anisotropy, the Morlet wavelet is proved to be an excellent choice for detecting directional linear features, i.e. regions where the amplitude of the signal is regular along one direction and has sharp variation along the perpendicular direction. Performances of the Morlet wavelet are tested against the 'classic' Mexican hat wavelet, deploying a complex synthetic fracture network. When applied to a natural fracture network, formed triaxially (σ1>σ2=σ3) deforming a core sample of the Hopeman sandstone, the combination of 2D Morlet wavelet and wavelet coefficient maps allows for the detection of characteristic scale orientation and length transitions, associated with the shifts from distributed damage to the growth of localised macroscopic shear fracture. A complementary outcome arises from the wavelet coefficient maps produced by increasing the wavelet scale parameter. These maps can be used to chart the variations in the spatial distribution of the analysed entities, meaning that it is possible to retrieve information on the density of fracture patterns at specific length scales during deformation.

Adaptive tight frame based medical image reconstruction: a proof-of-concept study for computed tomography

NASA Astrophysics Data System (ADS)

Zhou, Weifeng; Cai, Jian-Feng; Gao, Hao

2013-12-01

A popular approach for medical image reconstruction has been through the sparsity regularization, assuming the targeted image can be well approximated by sparse coefficients under some properly designed system. The wavelet tight frame is such a widely used system due to its capability for sparsely approximating piecewise-smooth functions, such as medical images. However, using a fixed system may not always be optimal for reconstructing a variety of diversified images. Recently, the method based on the adaptive over-complete dictionary that is specific to structures of the targeted images has demonstrated its superiority for image processing. This work is to develop the adaptive wavelet tight frame method image reconstruction. The proposed scheme first constructs the adaptive wavelet tight frame that is task specific, and then reconstructs the image of interest by solving an l1-regularized minimization problem using the constructed adaptive tight frame system. The proof-of-concept study is performed for computed tomography (CT), and the simulation results suggest that the adaptive tight frame method improves the reconstructed CT image quality from the traditional tight frame method.

Multi-threshold de-noising of electrical imaging logging data based on the wavelet packet transform

NASA Astrophysics Data System (ADS)

Xie, Fang; Xiao, Chengwen; Liu, Ruilin; Zhang, Lili

2017-08-01

A key problem of effectiveness evaluation for fractured-vuggy carbonatite reservoir is how to accurately extract fracture and vug information from electrical imaging logging data. Drill bits quaked during drilling and resulted in rugged surfaces of borehole walls and thus conductivity fluctuations in electrical imaging logging data. The occurrence of the conductivity fluctuations (formation background noise) directly affects the fracture/vug information extraction and reservoir effectiveness evaluation. We present a multi-threshold de-noising method based on wavelet packet transform to eliminate the influence of rugged borehole walls. The noise is present as fluctuations in button-electrode conductivity curves and as pockmarked responses in electrical imaging logging static images. The noise has responses in various scales and frequency ranges and has low conductivity compared with fractures or vugs. Our de-noising method is to decompose the data into coefficients with wavelet packet transform on a quadratic spline basis, then shrink high-frequency wavelet packet coefficients in different resolutions with minimax threshold and hard-threshold function, and finally reconstruct the thresholded coefficients. We use electrical imaging logging data collected from fractured-vuggy Ordovician carbonatite reservoir in Tarim Basin to verify the validity of the multi-threshold de-noising method. Segmentation results and extracted parameters are shown as well to prove the effectiveness of the de-noising procedure.

LiveWire interactive boundary extraction algorithm based on Haar wavelet transform and control point set direction search

NASA Astrophysics Data System (ADS)

Cheng, Jun; Zhang, Jun; Tian, Jinwen

2015-12-01

Based on deep analysis of the LiveWire interactive boundary extraction algorithm, a new algorithm focusing on improving the speed of LiveWire algorithm is proposed in this paper. Firstly, the Haar wavelet transform is carried on the input image, and the boundary is extracted on the low resolution image obtained by the wavelet transform of the input image. Secondly, calculating LiveWire shortest path is based on the control point set direction search by utilizing the spatial relationship between the two control points users provide in real time. Thirdly, the search order of the adjacent points of the starting node is set in advance. An ordinary queue instead of a priority queue is taken as the storage pool of the points when optimizing their shortest path value, thus reducing the complexity of the algorithm from O[n2] to O[n]. Finally, A region iterative backward projection method based on neighborhood pixel polling has been used to convert dual-pixel boundary of the reconstructed image to single-pixel boundary after Haar wavelet inverse transform. The algorithm proposed in this paper combines the advantage of the Haar wavelet transform and the advantage of the optimal path searching method based on control point set direction search. The former has fast speed of image decomposition and reconstruction and is more consistent with the texture features of the image and the latter can reduce the time complexity of the original algorithm. So that the algorithm can improve the speed in interactive boundary extraction as well as reflect the boundary information of the image more comprehensively. All methods mentioned above have a big role in improving the execution efficiency and the robustness of the algorithm.

An improved wavelet neural network medical image segmentation algorithm with combined maximum entropy

NASA Astrophysics Data System (ADS)

Hu, Xiaoqian; Tao, Jinxu; Ye, Zhongfu; Qiu, Bensheng; Xu, Jinzhang

2018-05-01

In order to solve the problem of medical image segmentation, a wavelet neural network medical image segmentation algorithm based on combined maximum entropy criterion is proposed. Firstly, we use bee colony algorithm to optimize the network parameters of wavelet neural network, get the parameters of network structure, initial weights and threshold values, and so on, we can quickly converge to higher precision when training, and avoid to falling into relative extremum; then the optimal number of iterations is obtained by calculating the maximum entropy of the segmented image, so as to achieve the automatic and accurate segmentation effect. Medical image segmentation experiments show that the proposed algorithm can reduce sample training time effectively and improve convergence precision, and segmentation effect is more accurate and effective than traditional BP neural network (back propagation neural network : a multilayer feed forward neural network which trained according to the error backward propagation algorithm.

3D Gabor wavelet based vessel filtering of photoacoustic images.

PubMed

Haq, Israr Ul; Nagoaka, Ryo; Makino, Takahiro; Tabata, Takuya; Saijo, Yoshifumi

2016-08-01

Filtering and segmentation of vasculature is an important issue in medical imaging. The visualization of vasculature is crucial for the early diagnosis and therapy in numerous medical applications. This paper investigates the use of Gabor wavelet to enhance the effect of vasculature while eliminating the noise due to size, sensitivity and aperture of the detector in 3D Optical Resolution Photoacoustic Microscopy (OR-PAM). A detailed multi-scale analysis of wavelet filtering and Hessian based method is analyzed for extracting vessels of different sizes since the blood vessels usually vary with in a range of radii. The proposed algorithm first enhances the vasculature in the image and then tubular structures are classified by eigenvalue decomposition of the local Hessian matrix at each voxel in the image. The algorithm is tested on non-invasive experiments, which shows appreciable results to enhance vasculature in photo-acoustic images.

Fusion of GFP and phase contrast images with complex shearlet transform and Haar wavelet-based energy rule.

PubMed

Qiu, Chenhui; Wang, Yuanyuan; Guo, Yanen; Xia, Shunren

2018-03-14

Image fusion techniques can integrate the information from different imaging modalities to get a composite image which is more suitable for human visual perception and further image processing tasks. Fusing green fluorescent protein (GFP) and phase contrast images is very important for subcellular localization, functional analysis of protein and genome expression. The fusion method of GFP and phase contrast images based on complex shearlet transform (CST) is proposed in this paper. Firstly the GFP image is converted to IHS model and its intensity component is obtained. Secondly the CST is performed on the intensity component and the phase contrast image to acquire the low-frequency subbands and the high-frequency subbands. Then the high-frequency subbands are merged by the absolute-maximum rule while the low-frequency subbands are merged by the proposed Haar wavelet-based energy (HWE) rule. Finally the fused image is obtained by performing the inverse CST on the merged subbands and conducting IHS-to-RGB conversion. The proposed fusion method is tested on a number of GFP and phase contrast images and compared with several popular image fusion methods. The experimental results demonstrate that the proposed fusion method can provide better fusion results in terms of subjective quality and objective evaluation. © 2018 Wiley Periodicals, Inc.

Multispectral code excited linear prediction coding and its application in magnetic resonance images.

PubMed

Hu, J H; Wang, Y; Cahill, P T

1997-01-01

This paper reports a multispectral code excited linear prediction (MCELP) method for the compression of multispectral images. Different linear prediction models and adaptation schemes have been compared. The method that uses a forward adaptive autoregressive (AR) model has been proven to achieve a good compromise between performance, complexity, and robustness. This approach is referred to as the MFCELP method. Given a set of multispectral images, the linear predictive coefficients are updated over nonoverlapping three-dimensional (3-D) macroblocks. Each macroblock is further divided into several 3-D micro-blocks, and the best excitation signal for each microblock is determined through an analysis-by-synthesis procedure. The MFCELP method has been applied to multispectral magnetic resonance (MR) images. To satisfy the high quality requirement for medical images, the error between the original image set and the synthesized one is further specified using a vector quantizer. This method has been applied to images from 26 clinical MR neuro studies (20 slices/study, three spectral bands/slice, 256x256 pixels/band, 12 b/pixel). The MFCELP method provides a significant visual improvement over the discrete cosine transform (DCT) based Joint Photographers Expert Group (JPEG) method, the wavelet transform based embedded zero-tree wavelet (EZW) coding method, and the vector tree (VT) coding method, as well as the multispectral segmented autoregressive moving average (MSARMA) method we developed previously.

Performance of the Wavelet Decomposition on Massively Parallel Architectures

NASA Technical Reports Server (NTRS)

El-Ghazawi, Tarek A.; LeMoigne, Jacqueline; Zukor, Dorothy (Technical Monitor)

2001-01-01

Traditionally, Fourier Transforms have been utilized for performing signal analysis and representation. But although it is straightforward to reconstruct a signal from its Fourier transform, no local description of the signal is included in its Fourier representation. To alleviate this problem, Windowed Fourier transforms and then wavelet transforms have been introduced, and it has been proven that wavelets give a better localization than traditional Fourier transforms, as well as a better division of the time- or space-frequency plane than Windowed Fourier transforms. Because of these properties and after the development of several fast algorithms for computing the wavelet representation of any signal, in particular the Multi-Resolution Analysis (MRA) developed by Mallat, wavelet transforms have increasingly been applied to signal analysis problems, especially real-life problems, in which speed is critical. In this paper we present and compare efficient wavelet decomposition algorithms on different parallel architectures. We report and analyze experimental measurements, using NASA remotely sensed images. Results show that our algorithms achieve significant performance gains on current high performance parallel systems, and meet scientific applications and multimedia requirements. The extensive performance measurements collected over a number of high-performance computer systems have revealed important architectural characteristics of these systems, in relation to the processing demands of the wavelet decomposition of digital images.

Multidimensional, mapping-based complex wavelet transforms.

PubMed

Fernandes, Felix C A; van Spaendonck, Rutger L C; Burrus, C Sidney

2005-01-01

Although the discrete wavelet transform (DWT) is a powerful tool for signal and image processing, it has three serious disadvantages: shift sensitivity, poor directionality, and lack of phase information. To overcome these disadvantages, we introduce multidimensional, mapping-based, complex wavelet transforms that consist of a mapping onto a complex function space followed by a DWT of the complex mapping. Unlike other popular transforms that also mitigate DWT shortcomings, the decoupled implementation of our transforms has two important advantages. First, the controllable redundancy of the mapping stage offers a balance between degree of shift sensitivity and transform redundancy. This allows us to create a directional, nonredundant, complex wavelet transform with potential benefits for image coding systems. To the best of our knowledge, no other complex wavelet transform is simultaneously directional and nonredundant. The second advantage of our approach is the flexibility to use any DWT in the transform implementation. As an example, we exploit this flexibility to create the complex double-density DWT: a shift-insensitive, directional, complex wavelet transform with a low redundancy of (3M - 1)/(2M - 1) in M dimensions. No other transform achieves all these properties at a lower redundancy, to the best of our knowledge. By exploiting the advantages of our multidimensional, mapping-based complex wavelet transforms in seismic signal-processing applications, we have demonstrated state-of-the-art results.

PET-CT image fusion using random forest and à-trous wavelet transform.

PubMed

Seal, Ayan; Bhattacharjee, Debotosh; Nasipuri, Mita; Rodríguez-Esparragón, Dionisio; Menasalvas, Ernestina; Gonzalo-Martin, Consuelo

2018-03-01

New image fusion rules for multimodal medical images are proposed in this work. Image fusion rules are defined by random forest learning algorithm and a translation-invariant à-trous wavelet transform (AWT). The proposed method is threefold. First, source images are decomposed into approximation and detail coefficients using AWT. Second, random forest is used to choose pixels from the approximation and detail coefficients for forming the approximation and detail coefficients of the fused image. Lastly, inverse AWT is applied to reconstruct fused image. All experiments have been performed on 198 slices of both computed tomography and positron emission tomography images of a patient. A traditional fusion method based on Mallat wavelet transform has also been implemented on these slices. A new image fusion performance measure along with 4 existing measures has been presented, which helps to compare the performance of 2 pixel level fusion methods. The experimental results clearly indicate that the proposed method outperforms the traditional method in terms of visual and quantitative qualities and the new measure is meaningful. Copyright © 2017 John Wiley & Sons, Ltd.

Fusion of digital breast tomosynthesis images via wavelet synthesis for improved lesion conspicuity

NASA Astrophysics Data System (ADS)

Hariharan, Harishwaran; Pomponiu, Victor; Zheng, Bin; Whiting, Bruce; Gur, David

2014-03-01

Full-field digital mammography (FFDM) is the most common screening procedure for detecting early breast cancer. However, due to complications such as overlapping breast tissue in projection images, the efficacy of FFDM reading is reduced. Recent studies have shown that digital breast tomosynthesis (DBT), in combination with FFDM, increases detection sensitivity considerably while decreasing false-positive, recall rates. There is a huge interest in creating diagnostically accurate 2-D interpretations from the DBT slices. Most of the 2-D syntheses rely on visualizing the maximum intensities (brightness) from each slice through different methods. We propose a wavelet based fusion method, where we focus on preserving holistic information from larger structures such as masses while adding high frequency information that is relevant and helpful for diagnosis. This method enables the spatial generation of a 2D image from a series of DBT images, each of which contains both smooth and coarse structures distributed in the wavelet domain. We believe that the wavelet-synthesized images, generated from their DBT image datasets, provide radiologists with improved lesion and micro-calcification conspicuity as compared with FFDM images. The potential impact of this fusion method is (1) Conception of a device-independent, data-driven modality that increases the conspicuity of lesions, thereby facilitating early detection and potentially reducing recall rates; (2) Reduction of the accompanying radiation dose to the patient.

Fast reversible wavelet image compressor

NASA Astrophysics Data System (ADS)

Kim, HyungJun; Li, Ching-Chung

1996-10-01

We present a unified image compressor with spline biorthogonal wavelets and dyadic rational filter coefficients which gives high computational speed and excellent compression performance. Convolutions with these filters can be preformed by using only arithmetic shifting and addition operations. Wavelet coefficients can be encoded with an arithmetic coder which also uses arithmetic shifting and addition operations. Therefore, from the beginning to the end, the while encoding/decoding process can be done within a short period of time. The proposed method naturally extends form the lossless compression to the lossy but high compression range and can be easily adapted to the progressive reconstruction.

3D Wavelet-Based Filter and Method

DOEpatents

Moss, William C.; Haase, Sebastian; Sedat, John W.

2008-08-12

A 3D wavelet-based filter for visualizing and locating structural features of a user-specified linear size in 2D or 3D image data. The only input parameter is a characteristic linear size of the feature of interest, and the filter output contains only those regions that are correlated with the characteristic size, thus denoising the image.

An optimized digital watermarking algorithm in wavelet domain based on differential evolution for color image.

PubMed

Cui, Xinchun; Niu, Yuying; Zheng, Xiangwei; Han, Yingshuai

2018-01-01

In this paper, a new color watermarking algorithm based on differential evolution is proposed. A color host image is first converted from RGB space to YIQ space, which is more suitable for the human visual system. Then, apply three-level discrete wavelet transformation to luminance component Y and generate four different frequency sub-bands. After that, perform singular value decomposition on these sub-bands. In the watermark embedding process, apply discrete wavelet transformation to a watermark image after the scrambling encryption processing. Our new algorithm uses differential evolution algorithm with adaptive optimization to choose the right scaling factors. Experimental results show that the proposed algorithm has a better performance in terms of invisibility and robustness.

Application of wavelet techniques for cancer diagnosis using ultrasound images: A Review.

PubMed

Sudarshan, Vidya K; Mookiah, Muthu Rama Krishnan; Acharya, U Rajendra; Chandran, Vinod; Molinari, Filippo; Fujita, Hamido; Ng, Kwan Hoong

2016-02-01

Ultrasound is an important and low cost imaging modality used to study the internal organs of human body and blood flow through blood vessels. It uses high frequency sound waves to acquire images of internal organs. It is used to screen normal, benign and malignant tissues of various organs. Healthy and malignant tissues generate different echoes for ultrasound. Hence, it provides useful information about the potential tumor tissues that can be analyzed for diagnostic purposes before therapeutic procedures. Ultrasound images are affected with speckle noise due to an air gap between the transducer probe and the body. The challenge is to design and develop robust image preprocessing, segmentation and feature extraction algorithms to locate the tumor region and to extract subtle information from isolated tumor region for diagnosis. This information can be revealed using a scale space technique such as the Discrete Wavelet Transform (DWT). It decomposes an image into images at different scales using low pass and high pass filters. These filters help to identify the detail or sudden changes in intensity in the image. These changes are reflected in the wavelet coefficients. Various texture, statistical and image based features can be extracted from these coefficients. The extracted features are subjected to statistical analysis to identify the significant features to discriminate normal and malignant ultrasound images using supervised classifiers. This paper presents a review of wavelet techniques used for preprocessing, segmentation and feature extraction of breast, thyroid, ovarian and prostate cancer using ultrasound images. Copyright © 2015 Elsevier Ltd. All rights reserved.

Enhanced CT images by the wavelet transform improving diagnostic accuracy of chest nodules.

PubMed

Guo, Xiuhua; Liu, Xiangye; Wang, Huan; Liang, Zhigang; Wu, Wei; He, Qian; Li, Kuncheng; Wang, Wei

2011-02-01

The objective of this study was to compare the diagnostic accuracy in the interpretation of chest nodules using original CT images versus enhanced CT images based on the wavelet transform. The CT images of 118 patients with cancers and 60 with benign nodules were used in this study. All images were enhanced through an algorithm based on the wavelet transform. Two experienced radiologists interpreted all the images in two reading sessions. The reading sessions were separated by a minimum of 1 month in order to minimize the effect of observer's recall. The Mann-Whitney U nonparametric test was used to analyze the interpretation results between original and enhanced images. The Kruskal-Wallis H nonparametric test of K independent samples was used to investigate the related factors which could affect the diagnostic accuracy of observers. The area under the ROC curves for the original and enhanced images was 0.681 and 0.736, respectively. There is significant difference in diagnosing the malignant nodules between the original and enhanced images (z = 7.122, P < 0.001), whereas there is no significant difference in diagnosing the benign nodules (z = 0.894, P = 0.371). The results showed that there is significant difference between original and enhancement images when the size of nodules was larger than 2 cm (Z = -2.509, P = 0.012, indicating the size of the nodules is a critical evaluating factor of the diagnostic accuracy of observers). This study indicated that the image enhancement based on wavelet transform could improve the diagnostic accuracy of radiologists for the malignant chest nodules.

Content Based Image Retrieval by Using Color Descriptor and Discrete Wavelet Transform.

PubMed

Ashraf, Rehan; Ahmed, Mudassar; Jabbar, Sohail; Khalid, Shehzad; Ahmad, Awais; Din, Sadia; Jeon, Gwangil

2018-01-25

Due to recent development in technology, the complexity of multimedia is significantly increased and the retrieval of similar multimedia content is a open research problem. Content-Based Image Retrieval (CBIR) is a process that provides a framework for image search and low-level visual features are commonly used to retrieve the images from the image database. The basic requirement in any image retrieval process is to sort the images with a close similarity in term of visually appearance. The color, shape and texture are the examples of low-level image features. The feature plays a significant role in image processing. The powerful representation of an image is known as feature vector and feature extraction techniques are applied to get features that will be useful in classifying and recognition of images. As features define the behavior of an image, they show its place in terms of storage taken, efficiency in classification and obviously in time consumption also. In this paper, we are going to discuss various types of features, feature extraction techniques and explaining in what scenario, which features extraction technique will be better. The effectiveness of the CBIR approach is fundamentally based on feature extraction. In image processing errands like object recognition and image retrieval feature descriptor is an immense among the most essential step. The main idea of CBIR is that it can search related images to an image passed as query from a dataset got by using distance metrics. The proposed method is explained for image retrieval constructed on YCbCr color with canny edge histogram and discrete wavelet transform. The combination of edge of histogram and discrete wavelet transform increase the performance of image retrieval framework for content based search. The execution of different wavelets is additionally contrasted with discover the suitability of specific wavelet work for image retrieval. The proposed algorithm is prepared and tried to implement for Wang image database. For Image Retrieval Purpose, Artificial Neural Networks (ANN) is used and applied on standard dataset in CBIR domain. The execution of the recommended descriptors is assessed by computing both Precision and Recall values and compared with different other proposed methods with demonstrate the predominance of our method. The efficiency and effectiveness of the proposed approach outperforms the existing research in term of average precision and recall values.

Imaging The Shallow Velocity Structure Of The Hikurangi Megathrust Using Full-Waveform Inversion

NASA Astrophysics Data System (ADS)

Gray, M.; Bell, R. E.; Morgan, J. V.

2017-12-01

The Hikurangi margin, offshore North Island, New Zealand, exhibits a number of different slip behaviours, including shallow slow slip events (SSEs) (<2km to 15 km). There is also a strong contrast in geodetic coupling along the margin. While reflection data provides an image of the structure, no information about physical properties is provided. Full-waveform inversion (FWI) is an imaging technique which incorporates the full seismic wavelet rather than just the first arrivals, as in traditional tomography. By propagating synthetic seismic waves through a velocity model and comparing the synthetic wavelets to the field data, we update the velocity model until the real and synthetic wavelets match. In this way, we can resolve high-resolution physical property variations which influence the seismic wavefield. In our study, FWI was used to resolve the P-wave velocity structure at the Hikurangi megathrust up to 2km. This method enables investigation of how upper-plate structure may influence plate boundary slip behaviour. In 2005, a seismic survey was carried out over the Hikurangi megathrust. The data was acquired from a 12km streamer, allowing FWI analysis up to 2km below the seabed. The results show low velocity zones correlating to faults interpreted from reflection seismic imaging. We believe these low velocity zones, particularly near the frontal thrust resolve faulting in the area, and present these faults as possible fluid conduits. As the dataset was not collected specifically for FWI, the results show promise in resolving more information at depth. As such, both a 3D seismic survey and two drilling expeditions have been approved for the period November 2017 - May 2018. The seismic survey will be carried out with parameters optimal for FWI, allow imaging of the fault boundary, which is not possible with the current 2D data. The cores will provide direct geological evidence which can be used in conjunction with velocity models to discern lithology and structure. The current result identifies the existence of overpressure and aids in drilling safety when collecting these cores. In conjunction with the new IODP cores, the FWI model will improve understanding of properties of the shallow structure of the megathrust.

Spatial compression algorithm for the analysis of very large multivariate images

DOEpatents

Keenan, Michael R [Albuquerque, NM

2008-07-15

A method for spatially compressing data sets enables the efficient analysis of very large multivariate images. The spatial compression algorithms use a wavelet transformation to map an image into a compressed image containing a smaller number of pixels that retain the original image's information content. Image analysis can then be performed on a compressed data matrix consisting of a reduced number of significant wavelet coefficients. Furthermore, a block algorithm can be used for performing common operations more efficiently. The spatial compression algorithms can be combined with spectral compression algorithms to provide further computational efficiencies.

A Wavelet-Based Algorithm for the Spatial Analysis of Poisson Data

NASA Astrophysics Data System (ADS)

Freeman, P. E.; Kashyap, V.; Rosner, R.; Lamb, D. Q.

2002-01-01

Wavelets are scalable, oscillatory functions that deviate from zero only within a limited spatial regime and have average value zero, and thus may be used to simultaneously characterize the shape, location, and strength of astronomical sources. But in addition to their use as source characterizers, wavelet functions are rapidly gaining currency within the source detection field. Wavelet-based source detection involves the correlation of scaled wavelet functions with binned, two-dimensional image data. If the chosen wavelet function exhibits the property of vanishing moments, significantly nonzero correlation coefficients will be observed only where there are high-order variations in the data; e.g., they will be observed in the vicinity of sources. Source pixels are identified by comparing each correlation coefficient with its probability sampling distribution, which is a function of the (estimated or a priori known) background amplitude. In this paper, we describe the mission-independent, wavelet-based source detection algorithm ``WAVDETECT,'' part of the freely available Chandra Interactive Analysis of Observations (CIAO) software package. Our algorithm uses the Marr, or ``Mexican Hat'' wavelet function, but may be adapted for use with other wavelet functions. Aspects of our algorithm include: (1) the computation of local, exposure-corrected normalized (i.e., flat-fielded) background maps; (2) the correction for exposure variations within the field of view (due to, e.g., telescope support ribs or the edge of the field); (3) its applicability within the low-counts regime, as it does not require a minimum number of background counts per pixel for the accurate computation of source detection thresholds; (4) the generation of a source list in a manner that does not depend upon a detailed knowledge of the point spread function (PSF) shape; and (5) error analysis. These features make our algorithm considerably more general than previous methods developed for the analysis of X-ray image data, especially in the low count regime. We demonstrate the robustness of WAVDETECT by applying it to an image from an idealized detector with a spatially invariant Gaussian PSF and an exposure map similar to that of the Einstein IPC; to Pleiades Cluster data collected by the ROSAT PSPC; and to simulated Chandra ACIS-I image of the Lockman Hole region.

A New Method for Computed Tomography Angiography (CTA) Imaging via Wavelet Decomposition-Dependented Edge Matching Interpolation.

PubMed

Li, Zeyu; Chen, Yimin; Zhao, Yan; Zhu, Lifeng; Lv, Shengqing; Lu, Jiahui

2016-08-01

The interpolation technique of computed tomography angiography (CTA) image provides the ability for 3D reconstruction, as well as reduces the detect cost and the amount of radiation. However, most of the image interpolation algorithms cannot take the automation and accuracy into account. This study provides a new edge matching interpolation algorithm based on wavelet decomposition of CTA. It includes mark, scale and calculation (MSC). Combining the real clinical image data, this study mainly introduces how to search for proportional factor and use the root mean square operator to find a mean value. Furthermore, we re- synthesize the high frequency and low frequency parts of the processed image by wavelet inverse operation, and get the final interpolation image. MSC can make up for the shortage of the conventional Computed Tomography (CT) and Magnetic Resonance Imaging(MRI) examination. The radiation absorption and the time to check through the proposed synthesized image were significantly reduced. In clinical application, it can help doctor to find hidden lesions in time. Simultaneously, the patients get less economic burden as well as less radiation exposure absorbed.

Semi-regular remeshing based trust region spherical geometry image for 3D deformed mesh used MLWNN

NASA Astrophysics Data System (ADS)

Dhibi, Naziha; Elkefi, Akram; Bellil, Wajdi; Ben Amar, Chokri

2017-03-01

Triangular surface are now widely used for modeling three-dimensional object, since these models are very high resolution and the geometry of the mesh is often very dense, it is then necessary to remesh this object to reduce their complexity, the mesh quality (connectivity regularity) must be ameliorated. In this paper, we review the main methods of semi-regular remeshing of the state of the art, given the semi-regular remeshing is mainly relevant for wavelet-based compression, then we present our method for re-meshing based trust region spherical geometry image to have good scheme of 3d mesh compression used to deform 3D meh based on Multi library Wavelet Neural Network structure (MLWNN). Experimental results show that the progressive re-meshing algorithm capable of obtaining more compact representations and semi-regular objects and yield an efficient compression capabilities with minimal set of features used to have good 3D deformation scheme.

Value-at-risk estimation with wavelet-based extreme value theory: Evidence from emerging markets

NASA Astrophysics Data System (ADS)

Cifter, Atilla

2011-06-01

This paper introduces wavelet-based extreme value theory (EVT) for univariate value-at-risk estimation. Wavelets and EVT are combined for volatility forecasting to estimate a hybrid model. In the first stage, wavelets are used as a threshold in generalized Pareto distribution, and in the second stage, EVT is applied with a wavelet-based threshold. This new model is applied to two major emerging stock markets: the Istanbul Stock Exchange (ISE) and the Budapest Stock Exchange (BUX). The relative performance of wavelet-based EVT is benchmarked against the Riskmetrics-EWMA, ARMA-GARCH, generalized Pareto distribution, and conditional generalized Pareto distribution models. The empirical results show that the wavelet-based extreme value theory increases predictive performance of financial forecasting according to number of violations and tail-loss tests. The superior forecasting performance of the wavelet-based EVT model is also consistent with Basel II requirements, and this new model can be used by financial institutions as well.

The use of wavelet filters for reducing noise in posterior fossa Computed Tomography images

DOE Office of Scientific and Technical Information (OSTI.GOV)

Pita-Machado, Reinado; Perez-Diaz, Marlen, E-mail: mperez@uclv.edu.cu; Lorenzo-Ginori, Juan V., E-mail: mperez@uclv.edu.cu

Wavelet transform based de-noising like wavelet shrinkage, gives the good results in CT. This procedure affects very little the spatial resolution. Some applications are reconstruction methods, while others are a posteriori de-noising methods. De-noising after reconstruction is very difficult because the noise is non-stationary and has unknown distribution. Therefore, methods which work on the sinogram-space don’t have this problem, because they always work over a known noise distribution at this point. On the other hand, the posterior fossa in a head CT is a very complex region for physicians, because it is commonly affected by artifacts and noise which aremore » not eliminated during the reconstruction procedure. This can leads to some false positive evaluations. The purpose of our present work is to compare different wavelet shrinkage de-noising filters to reduce noise, particularly in images of the posterior fossa within CT scans in the sinogram-space. This work describes an experimental search for the best wavelets, to reduce Poisson noise in Computed Tomography (CT) scans. Results showed that de-noising with wavelet filters improved the quality of posterior fossa region in terms of an increased CNR, without noticeable structural distortions.« less

Wavelets analysis for differentiating solid, non-macroscopic fat containing, enhancing renal masses: a pilot study

NASA Astrophysics Data System (ADS)

Varghese, Bino; Hwang, Darryl; Mohamed, Passant; Cen, Steven; Deng, Christopher; Chang, Michael; Duddalwar, Vinay

2017-11-01

Purpose: To evaluate potential use of wavelets analysis in discriminating benign and malignant renal masses (RM) Materials and Methods: Regions of interest of the whole lesion were manually segmented and co-registered from multiphase CT acquisitions of 144 patients (98 malignant RM: renal cell carcinoma (RCC) and 46 benign RM: oncocytoma, lipid-poor angiomyolipoma). Here, the Haar wavelet was used to analyze the grayscale images of the largest segmented tumor in the axial direction. Six metrics (energy, entropy, homogeneity, contrast, standard deviation (SD) and variance) derived from 3-levels of image decomposition in 3 directions (horizontal, vertical and diagonal) respectively, were used to quantify tumor texture. Independent t-test or Wilcoxon rank sum test depending on data normality were used as exploratory univariate analysis. Stepwise logistic regression and receiver operator characteristics (ROC) curve analysis were used to select predictors and assess prediction accuracy, respectively. Results: Consistently, 5 out of 6 wavelet-based texture measures (except homogeneity) were higher for malignant tumors compared to benign, when accounting for individual texture direction. Homogeneity was consistently lower in malignant than benign tumors irrespective of direction. SD and variance measured in the diagonal direction on the corticomedullary phase showed significant (p<0.05) difference between benign versus malignant tumors. The multivariate model with variance (3 directions) and SD (vertical direction) extracted from the excretory and pre-contrast phase, respectively showed an area under the ROC curve (AUC) of 0.78 (p < 0.05) in discriminating malignant from benign. Conclusion: Wavelet analysis is a valuable texture evaluation tool to add to a radiomics platforms geared at reliably characterizing and stratifying renal masses.

Comparison of 2D and 3D wavelet features for TLE lateralization

NASA Astrophysics Data System (ADS)

Jafari-Khouzani, Kourosh; Soltanian-Zadeh, Hamid; Elisevich, Kost; Patel, Suresh

2004-04-01

Intensity and volume features of the hippocampus from MR images of the brain are known to be useful in detecting the abnormality and consequently candidacy of the hippocampus for temporal lobe epilepsy surgery. However, currently, intracranial EEG exams are required to determine the abnormal hippocampus. These exams are lengthy, painful and costly. The aim of this study is to evaluate texture characteristics of the hippocampi from MR images to help physicians determine the candidate hippocampus for surgery. We studied the MR images of 20 epileptic patients. Intracranial EEG results as well as surgery outcome were used as gold standard. The hippocampi were manually segmented by an expert from T1-weighted MR images. Then the segmented regions were mapped on the corresponding FLAIR images for texture analysis. We calculate the average energy features from 2D wavelet transform of each slice of hippocampus as well as the energy features produced by 3D wavelet transform of the whole hippocampus volume. The 2D wavelet transform is calculated both from the original slices as well as from the slices perpendicular to the principal axis of the hippocampus. In order to calculate the 3D wavelet transform we first rotate each hippocampus to fit it in a rectangular prism and then fill the empty area by extrapolating the intensity values. We combine the resulting features with volume feature and compare their ability to distinguish between normal and abnormal hippocampi using linear classifier and fuzzy c-means clustering algorithm. Experimental results show that the texture features can correctly classify the hippocampi.

Rate-distortion analysis of directional wavelets.

PubMed

Maleki, Arian; Rajaei, Boshra; Pourreza, Hamid Reza

2012-02-01

The inefficiency of separable wavelets in representing smooth edges has led to a great interest in the study of new 2-D transformations. The most popular criterion for analyzing these transformations is the approximation power. Transformations with near-optimal approximation power are useful in many applications such as denoising and enhancement. However, they are not necessarily good for compression. Therefore, most of the nearly optimal transformations such as curvelets and contourlets have not found any application in image compression yet. One of the most promising schemes for image compression is the elegant idea of directional wavelets (DIWs). While these algorithms outperform the state-of-the-art image coders in practice, our theoretical understanding of them is very limited. In this paper, we adopt the notion of rate-distortion and calculate the performance of the DIW on a class of edge-like images. Our theoretical analysis shows that if the edges are not "sharp," the DIW will compress them more efficiently than the separable wavelets. It also demonstrates the inefficiency of the quadtree partitioning that is often used with the DIW. To solve this issue, we propose a new partitioning scheme called megaquad partitioning. Our simulation results on real-world images confirm the benefits of the proposed partitioning algorithm, promised by our theoretical analysis. © 2011 IEEE

Automated segmentation of retinal blood vessels and identification of proliferative diabetic retinopathy

NASA Astrophysics Data System (ADS)

Jelinek, Herbert F.; Cree, Michael J.; Leandro, Jorge J. G.; Soares, João V. B.; Cesar, Roberto M.; Luckie, A.

2007-05-01

Proliferative diabetic retinopathy can lead to blindness. However, early recognition allows appropriate, timely intervention. Fluorescein-labeled retinal blood vessels of 27 digital images were automatically segmented using the Gabor wavelet transform and classified using traditional features such as area, perimeter, and an additional five morphological features based on the derivatives-of-Gaussian wavelet-derived data. Discriminant analysis indicated that traditional features do not detect early proliferative retinopathy. The best single feature for discrimination was the wavelet curvature with an area under the curve (AUC) of 0.76. Linear discriminant analysis with a selection of six features achieved an AUC of 0.90 (0.73-0.97, 95% confidence interval). The wavelet method was able to segment retinal blood vessels and classify the images according to the presence or absence of proliferative retinopathy.

Dual tree fractional quaternion wavelet transform for disparity estimation.

PubMed

Kumar, Sanoj; Kumar, Sanjeev; Sukavanam, Nagarajan; Raman, Balasubramanian

2014-03-01

This paper proposes a novel phase based approach for computing disparity as the optical flow from the given pair of consecutive images. A new dual tree fractional quaternion wavelet transform (FrQWT) is proposed by defining the 2D Fourier spectrum upto a single quadrant. In the proposed FrQWT, each quaternion wavelet consists of a real part (a real DWT wavelet) and three imaginary parts that are organized according to the quaternion algebra. First two FrQWT phases encode the shifts of image features in the absolute horizontal and vertical coordinate system, while the third phase has the texture information. The FrQWT allowed a multi-scale framework for calculating and adjusting local disparities and executing phase unwrapping from coarse to fine scales with linear computational efficiency. Copyright © 2013 ISA. Published by Elsevier Ltd. All rights reserved.

Automatic multiresolution age-related macular degeneration detection from fundus images

NASA Astrophysics Data System (ADS)

Garnier, Mickaël.; Hurtut, Thomas; Ben Tahar, Houssem; Cheriet, Farida

2014-03-01

Age-related Macular Degeneration (AMD) is a leading cause of legal blindness. As the disease progress, visual loss occurs rapidly, therefore early diagnosis is required for timely treatment. Automatic, fast and robust screening of this widespread disease should allow an early detection. Most of the automatic diagnosis methods in the literature are based on a complex segmentation of the drusen, targeting a specific symptom of the disease. In this paper, we present a preliminary study for AMD detection from color fundus photographs using a multiresolution texture analysis. We analyze the texture at several scales by using a wavelet decomposition in order to identify all the relevant texture patterns. Textural information is captured using both the sign and magnitude components of the completed model of Local Binary Patterns. An image is finally described with the textural pattern distributions of the wavelet coefficient images obtained at each level of decomposition. We use a Linear Discriminant Analysis for feature dimension reduction, to avoid the curse of dimensionality problem, and image classification. Experiments were conducted on a dataset containing 45 images (23 healthy and 22 diseased) of variable quality and captured by different cameras. Our method achieved a recognition rate of 93:3%, with a specificity of 95:5% and a sensitivity of 91:3%. This approach shows promising results at low costs that in agreement with medical experts as well as robustness to both image quality and fundus camera model.

Unified heat kernel regression for diffusion, kernel smoothing and wavelets on manifolds and its application to mandible growth modeling in CT images.

PubMed

Chung, Moo K; Qiu, Anqi; Seo, Seongho; Vorperian, Houri K

2015-05-01

We present a novel kernel regression framework for smoothing scalar surface data using the Laplace-Beltrami eigenfunctions. Starting with the heat kernel constructed from the eigenfunctions, we formulate a new bivariate kernel regression framework as a weighted eigenfunction expansion with the heat kernel as the weights. The new kernel method is mathematically equivalent to isotropic heat diffusion, kernel smoothing and recently popular diffusion wavelets. The numerical implementation is validated on a unit sphere using spherical harmonics. As an illustration, the method is applied to characterize the localized growth pattern of mandible surfaces obtained in CT images between ages 0 and 20 by regressing the length of displacement vectors with respect to a surface template. Copyright © 2015 Elsevier B.V. All rights reserved.

Reliable structural information from multiscale decomposition with the Mellor-Brady filter

NASA Astrophysics Data System (ADS)

Szilágyi, Tünde; Brady, Michael

2009-08-01

Image-based medical diagnosis typically relies on the (poorly reproducible) subjective classification of textures in order to differentiate between diseased and healthy pathology. Clinicians claim that significant benefits would arise from quantitative measures to inform clinical decision making. The first step in generating such measures is to extract local image descriptors - from noise corrupted and often spatially and temporally coarse resolution medical signals - that are invariant to illumination, translation, scale and rotation of the features. The Dual-Tree Complex Wavelet Transform (DT-CWT) provides a wavelet multiresolution analysis (WMRA) tool e.g. in 2D with good properties, but has limited rotational selectivity. Also, it requires computationally-intensive steering due to the inherently 1D operations performed. The monogenic signal, which is defined in n >= 2D with the Riesz transform gives excellent orientation information without the need for steering. Recent work has suggested the Monogenic Riesz-Laplace wavelet transform as a possible tool for integrating these two concepts into a coherent mathematical framework. We have found that the proposed construction suffers from a lack of rotational invariance and is not optimal for retrieving local image descriptors. In this paper we show: 1. Local frequency and local phase from the monogenic signal are not equivalent, especially in the phase congruency model of a "feature", and so they are not interchangeable for medical image applications. 2. The accuracy of local phase computation may be improved by estimating the denoising parameters while maximizing a new measure of "featureness".

Analysis of two dimensional signals via curvelet transform

NASA Astrophysics Data System (ADS)

Lech, W.; Wójcik, W.; Kotyra, A.; Popiel, P.; Duk, M.

2007-04-01

This paper describes an application of curvelet transform analysis problem of interferometric images. Comparing to two-dimensional wavelet transform, curvelet transform has higher time-frequency resolution. This article includes numerical experiments, which were executed on random interferometric image. In the result of nonlinear approximations, curvelet transform obtains matrix with smaller number of coefficients than is guaranteed by wavelet transform. Additionally, denoising simulations show that curvelet could be a very good tool to remove noise from images.

Using component technologies for web based wavelet enhanced mammographic image visualization.

PubMed

Sakellaropoulos, P; Costaridou, L; Panayiotakis, G

2000-01-01

The poor contrast detectability of mammography can be dealt with by domain specific software visualization tools. Remote desktop client access and time performance limitations of a previously reported visualization tool are addressed, aiming at more efficient visualization of mammographic image resources existing in web or PACS image servers. This effort is also motivated by the fact that at present, web browsers do not support domain-specific medical image visualization. To deal with desktop client access the tool was redesigned by exploring component technologies, enabling the integration of stand alone domain specific mammographic image functionality in a web browsing environment (web adaptation). The integration method is based on ActiveX Document Server technology. ActiveX Document is a part of Object Linking and Embedding (OLE) extensible systems object technology, offering new services in existing applications. The standard DICOM 3.0 part 10 compatible image-format specification Papyrus 3.0 is supported, in addition to standard digitization formats such as TIFF. The visualization functionality of the tool has been enhanced by including a fast wavelet transform implementation, which allows for real time wavelet based contrast enhancement and denoising operations. Initial use of the tool with mammograms of various breast structures demonstrated its potential in improving visualization of diagnostic mammographic features. Web adaptation and real time wavelet processing enhance the potential of the previously reported tool in remote diagnosis and education in mammography.

Reversible integer wavelet transform for blind image hiding method

PubMed Central

Bibi, Nargis; Mahmood, Zahid; Akram, Tallha; Naqvi, Syed Rameez

2017-01-01

In this article, a blind data hiding reversible methodology to embed the secret data for hiding purpose into cover image is proposed. The key advantage of this research work is to resolve the privacy and secrecy issues raised during the data transmission over the internet. Firstly, data is decomposed into sub-bands using the integer wavelets. For decomposition, the Fresnelet transform is utilized which encrypts the secret data by choosing a unique key parameter to construct a dummy pattern. The dummy pattern is then embedded into an approximated sub-band of the cover image. Our proposed method reveals high-capacity and great imperceptibility of the secret embedded data. With the utilization of family of integer wavelets, the proposed novel approach becomes more efficient for hiding and retrieving process. It retrieved the secret hidden data from the embedded data blindly, without the requirement of original cover image. PMID:28498855

Classification of endoscopic capsule images by using color wavelet features, higher order statistics and radial basis functions.

PubMed

Lima, C S; Barbosa, D; Ramos, J; Tavares, A; Monteiro, L; Carvalho, L

2008-01-01

This paper presents a system to support medical diagnosis and detection of abnormal lesions by processing capsule endoscopic images. Endoscopic images possess rich information expressed by texture. Texture information can be efficiently extracted from medium scales of the wavelet transform. The set of features proposed in this paper to code textural information is named color wavelet covariance (CWC). CWC coefficients are based on the covariances of second order textural measures, an optimum subset of them is proposed. Third and forth order moments are added to cope with distributions that tend to become non-Gaussian, especially in some pathological cases. The proposed approach is supported by a classifier based on radial basis functions procedure for the characterization of the image regions along the video frames. The whole methodology has been applied on real data containing 6 full endoscopic exams and reached 95% specificity and 93% sensitivity.

Automated identification of diabetic type 2 subjects with and without neuropathy using wavelet transform on pedobarograph.

PubMed

Acharya, Rajendra; Tan, Peck Ha; Subramaniam, Tavintharan; Tamura, Toshiyo; Chua, Kuang Chua; Goh, Seach Chyr Ernest; Lim, Choo Min; Goh, Shu Yi Diana; Chung, Kang Rui Conrad; Law, Chelsea

2008-02-01

Diabetes is a disorder of metabolism-the way our bodies use digested food for growth and energy. The most common form of diabetes is Type 2 diabetes. Abnormal plantar pressures are considered to play a major role in the pathologies of neuropathic ulcers in the diabetic foot. The purpose of this study was to examine the plantar pressure distribution in normal, diabetic Type 2 with and without neuropathy subjects. Foot scans were obtained using the F-scan (Tekscan USA) pressure measurement system. Various discrete wavelet coefficients were evaluated from the foot images. These extracted parameters were extracted using the discrete wavelet transform (DWT) and presented to the Gaussian mixture model (GMM) and a four-layer feed forward neural network for classification. We demonstrated a sensitivity of 100% and a specificity of more than 85% for the classifiers.

Measurement of in-plane displacements using the phase singularities generated by directional wavelet transforms of speckle pattern images.

PubMed

Vadnjal, Ana Laura; Etchepareborda, Pablo; Federico, Alejandro; Kaufmann, Guillermo H

2013-03-20

We present a method to determine micro and nano in-plane displacements based on the phase singularities generated by application of directional wavelet transforms to speckle pattern images. The spatial distribution of the obtained phase singularities by the wavelet transform configures a network, which is characterized by two quasi-orthogonal directions. The displacement value is determined by identifying the intersection points of the network before and after the displacement produced by the tested object. The performance of this method is evaluated using simulated speckle patterns and experimental data. The proposed approach is compared with the optical vortex metrology and digital image correlation methods in terms of performance and noise robustness, and the advantages and limitations associated to each method are also discussed.

Wavelet-based higher-order neural networks for mine detection in thermal IR imagery

NASA Astrophysics Data System (ADS)

Baertlein, Brian A.; Liao, Wen-Jiao

2000-08-01

An image processing technique is described for the detection of miens in RI imagery. The proposed technique is based on a third-order neural network, which processes the output of a wavelet packet transform. The technique is inherently invariant to changes in signature position, rotation and scaling. The well-known memory limitations that arise with higher-order neural networks are addressed by (1) the data compression capabilities of wavelet packets, (2) protections of the image data into a space of similar triangles, and (3) quantization of that 'triangle space'. Using these techniques, image chips of size 28 by 28, which would require 0(109) neural net weights, are processed by a network having 0(102) weights. ROC curves are presented for mine detection in real and simulated imagery.

Pixel-based speckle adjustment for noise reduction in Fourier-domain OCT images.

PubMed

Zhang, Anqi; Xi, Jiefeng; Sun, Jitao; Li, Xingde

2017-03-01

Speckle resides in OCT signals and inevitably effects OCT image quality. In this work, we present a novel method for speckle noise reduction in Fourier-domain OCT images, which utilizes the phase information of complex OCT data. In this method, speckle area is pre-delineated pixelwise based on a phase-domain processing method and then adjusted by the results of wavelet shrinkage of the original image. Coefficient shrinkage method such as wavelet or contourlet is applied afterwards for further suppressing the speckle noise. Compared with conventional methods without speckle adjustment, the proposed method demonstrates significant improvement of image quality.

Wavelet-based polarimetry analysis

NASA Astrophysics Data System (ADS)

Ezekiel, Soundararajan; Harrity, Kyle; Farag, Waleed; Alford, Mark; Ferris, David; Blasch, Erik

2014-06-01

Wavelet transformation has become a cutting edge and promising approach in the field of image and signal processing. A wavelet is a waveform of effectively limited duration that has an average value of zero. Wavelet analysis is done by breaking up the signal into shifted and scaled versions of the original signal. The key advantage of a wavelet is that it is capable of revealing smaller changes, trends, and breakdown points that are not revealed by other techniques such as Fourier analysis. The phenomenon of polarization has been studied for quite some time and is a very useful tool for target detection and tracking. Long Wave Infrared (LWIR) polarization is beneficial for detecting camouflaged objects and is a useful approach when identifying and distinguishing manmade objects from natural clutter. In addition, the Stokes Polarization Parameters, which are calculated from 0°, 45°, 90°, 135° right circular, and left circular intensity measurements, provide spatial orientations of target features and suppress natural features. In this paper, we propose a wavelet-based polarimetry analysis (WPA) method to analyze Long Wave Infrared Polarimetry Imagery to discriminate targets such as dismounts and vehicles from background clutter. These parameters can be used for image thresholding and segmentation. Experimental results show the wavelet-based polarimetry analysis is efficient and can be used in a wide range of applications such as change detection, shape extraction, target recognition, and feature-aided tracking.

Content-based multiple bitstream image transmission over noisy channels.

PubMed

Cao, Lei; Chen, Chang Wen

2002-01-01

In this paper, we propose a novel combined source and channel coding scheme for image transmission over noisy channels. The main feature of the proposed scheme is a systematic decomposition of image sources so that unequal error protection can be applied according to not only bit error sensitivity but also visual content importance. The wavelet transform is adopted to hierarchically decompose the image. The association between the wavelet coefficients and what they represent spatially in the original image is fully exploited so that wavelet blocks are classified based on their corresponding image content. The classification produces wavelet blocks in each class with similar content and statistics, therefore enables high performance source compression using the set partitioning in hierarchical trees (SPIHT) algorithm. To combat the channel noise, an unequal error protection strategy with rate-compatible punctured convolutional/cyclic redundancy check (RCPC/CRC) codes is implemented based on the bit contribution to both peak signal-to-noise ratio (PSNR) and visual quality. At the receiving end, a postprocessing method making use of the SPIHT decoding structure and the classification map is developed to restore the degradation due to the residual error after channel decoding. Experimental results show that the proposed scheme is indeed able to provide protection both for the bits that are more sensitive to errors and for the more important visual content under a noisy transmission environment. In particular, the reconstructed images illustrate consistently better visual quality than using the single-bitstream-based schemes.

Edge detection based on adaptive threshold b-spline wavelet for optical sub-aperture measuring

NASA Astrophysics Data System (ADS)

Zhang, Shiqi; Hui, Mei; Liu, Ming; Zhao, Zhu; Dong, Liquan; Liu, Xiaohua; Zhao, Yuejin

2015-08-01

In the research of optical synthetic aperture imaging system, phase congruency is the main problem and it is necessary to detect sub-aperture phase. The edge of the sub-aperture system is more complex than that in the traditional optical imaging system. And with the existence of steep slope for large-aperture optical component, interference fringe may be quite dense when interference imaging. Deep phase gradient may cause a loss of phase information. Therefore, it's urgent to search for an efficient edge detection method. Wavelet analysis as a powerful tool is widely used in the fields of image processing. Based on its properties of multi-scale transform, edge region is detected with high precision in small scale. Longing with the increase of scale, noise is reduced in contrary. So it has a certain suppression effect on noise. Otherwise, adaptive threshold method which sets different thresholds in various regions can detect edge points from noise. Firstly, fringe pattern is obtained and cubic b-spline wavelet is adopted as the smoothing function. After the multi-scale wavelet decomposition of the whole image, we figure out the local modulus maxima in gradient directions. However, it also contains noise, and thus adaptive threshold method is used to select the modulus maxima. The point which greater than threshold value is boundary point. Finally, we use corrosion and expansion deal with the resulting image to get the consecutive boundary of image.

SPECT reconstruction using DCT-induced tight framelet regularization

NASA Astrophysics Data System (ADS)

Zhang, Jiahan; Li, Si; Xu, Yuesheng; Schmidtlein, C. R.; Lipson, Edward D.; Feiglin, David H.; Krol, Andrzej

2015-03-01

Wavelet transforms have been successfully applied in many fields of image processing. Yet, to our knowledge, they have never been directly incorporated to the objective function in Emission Computed Tomography (ECT) image reconstruction. Our aim has been to investigate if the ℓ1-norm of non-decimated discrete cosine transform (DCT) coefficients of the estimated radiotracer distribution could be effectively used as the regularization term for the penalized-likelihood (PL) reconstruction, where a regularizer is used to enforce the image smoothness in the reconstruction. In this study, the ℓ1-norm of 2D DCT wavelet decomposition was used as a regularization term. The Preconditioned Alternating Projection Algorithm (PAPA), which we proposed in earlier work to solve penalized likelihood (PL) reconstruction with non-differentiable regularizers, was used to solve this optimization problem. The DCT wavelet decompositions were performed on the transaxial reconstructed images. We reconstructed Monte Carlo simulated SPECT data obtained for a numerical phantom with Gaussian blobs as hot lesions and with a warm random lumpy background. Reconstructed images using the proposed method exhibited better noise suppression and improved lesion conspicuity, compared with images reconstructed using expectation maximization (EM) algorithm with Gaussian post filter (GPF). Also, the mean square error (MSE) was smaller, compared with EM-GPF. A critical and challenging aspect of this method was selection of optimal parameters. In summary, our numerical experiments demonstrated that the ℓ1-norm of discrete cosine transform (DCT) wavelet frame transform DCT regularizer shows promise for SPECT image reconstruction using PAPA method.

Texture segmentation of non-cooperative spacecrafts images based on wavelet and fractal dimension

NASA Astrophysics Data System (ADS)

Wu, Kanzhi; Yue, Xiaokui

2011-06-01

With the increase of on-orbit manipulations and space conflictions, missions such as tracking and capturing the target spacecrafts are aroused. Unlike cooperative spacecrafts, fixing beacons or any other marks on the targets is impossible. Due to the unknown shape and geometry features of non-cooperative spacecraft, in order to localize the target and obtain the latitude, we need to segment the target image and recognize the target from the background. The data and errors during the following procedures such as feature extraction and matching can also be reduced. Multi-resolution analysis of wavelet theory reflects human beings' recognition towards images from low resolution to high resolution. In addition, spacecraft is the only man-made object in the image compared to the natural background and the differences will be certainly observed between the fractal dimensions of target and background. Combined wavelet transform and fractal dimension, in this paper, we proposed a new segmentation algorithm for the images which contains complicated background such as the universe and planet surfaces. At first, Daubechies wavelet basis is applied to decompose the image in both x axis and y axis, thus obtain four sub-images. Then, calculate the fractal dimensions in four sub-images using different methods; after analyzed the results of fractal dimensions in sub-images, we choose Differential Box Counting in low resolution image as the principle to segment the texture which has the greatest divergences between different sub-images. This paper also presents the results of experiments by using the algorithm above. It is demonstrated that an accurate texture segmentation result can be obtained using the proposed technique.

Method and system for progressive mesh storage and reconstruction using wavelet-encoded height fields

NASA Technical Reports Server (NTRS)

Baxes, Gregory A. (Inventor); Linger, Timothy C. (Inventor)

2011-01-01

Systems and methods are provided for progressive mesh storage and reconstruction using wavelet-encoded height fields. A method for progressive mesh storage includes reading raster height field data, and processing the raster height field data with a discrete wavelet transform to generate wavelet-encoded height fields. In another embodiment, a method for progressive mesh storage includes reading texture map data, and processing the texture map data with a discrete wavelet transform to generate wavelet-encoded texture map fields. A method for reconstructing a progressive mesh from wavelet-encoded height field data includes determining terrain blocks, and a level of detail required for each terrain block, based upon a viewpoint. Triangle strip constructs are generated from vertices of the terrain blocks, and an image is rendered utilizing the triangle strip constructs. Software products that implement these methods are provided.

Method and system for progressive mesh storage and reconstruction using wavelet-encoded height fields

NASA Technical Reports Server (NTRS)

Baxes, Gregory A. (Inventor)

2010-01-01

Systems and methods are provided for progressive mesh storage and reconstruction using wavelet-encoded height fields. A method for progressive mesh storage includes reading raster height field data, and processing the raster height field data with a discrete wavelet transform to generate wavelet-encoded height fields. In another embodiment, a method for progressive mesh storage includes reading texture map data, and processing the texture map data with a discrete wavelet transform to generate wavelet-encoded texture map fields. A method for reconstructing a progressive mesh from wavelet-encoded height field data includes determining terrain blocks, and a level of detail required for each terrain block, based upon a viewpoint. Triangle strip constructs are generated from vertices of the terrain blocks, and an image is rendered utilizing the triangle strip constructs. Software products that implement these methods are provided.

Feature extraction using gray-level co-occurrence matrix of wavelet coefficients and texture matching for batik motif recognition

NASA Astrophysics Data System (ADS)

Suciati, Nanik; Herumurti, Darlis; Wijaya, Arya Yudhi

2017-02-01

Batik is one of Indonesian's traditional cloth. Motif or pattern drawn on a piece of batik fabric has a specific name and philosopy. Although batik cloths are widely used in everyday life, but only few people understand its motif and philosophy. This research is intended to develop a batik motif recognition system which can be used to identify motif of Batik image automatically. First, a batik image is decomposed into sub-images using wavelet transform. Six texture descriptors, i.e. max probability, correlation, contrast, uniformity, homogenity and entropy, are extracted from gray-level co-occurrence matrix of each sub-image. The texture features are then matched to the template features using canberra distance. The experiment is performed on Batik Dataset consisting of 1088 batik images grouped into seven motifs. The best recognition rate, that is 92,1%, is achieved using feature extraction process with 5 level wavelet decomposition and 4 directional gray-level co-occurrence matrix.

Wavelet compression of noisy tomographic images

NASA Astrophysics Data System (ADS)

Kappeler, Christian; Mueller, Stefan P.

1995-09-01

3D data acquisition is increasingly used in positron emission tomography (PET) to collect a larger fraction of the emitted radiation. A major practical difficulty with data storage and transmission in 3D-PET is the large size of the data sets. A typical dynamic study contains about 200 Mbyte of data. PET images inherently have a high level of photon noise and therefore usually are evaluated after being processed by a smoothing filter. In this work we examined lossy compression schemes under the postulate not induce image modifications exceeding those resulting from low pass filtering. The standard we will refer to is the Hanning filter. Resolution and inhomogeneity serve as figures of merit for quantification of image quality. The images to be compressed are transformed to a wavelet representation using Daubechies12 wavelets and compressed after filtering by thresholding. We do not include further compression by quantization and coding here. Achievable compression factors at this level of processing are thirty to fifty.

Image processing based automatic diagnosis of glaucoma using wavelet features of segmented optic disc from fundus image.

PubMed

Singh, Anushikha; Dutta, Malay Kishore; ParthaSarathi, M; Uher, Vaclav; Burget, Radim

2016-02-01

Glaucoma is a disease of the retina which is one of the most common causes of permanent blindness worldwide. This paper presents an automatic image processing based method for glaucoma diagnosis from the digital fundus image. In this paper wavelet feature extraction has been followed by optimized genetic feature selection combined with several learning algorithms and various parameter settings. Unlike the existing research works where the features are considered from the complete fundus or a sub image of the fundus, this work is based on feature extraction from the segmented and blood vessel removed optic disc to improve the accuracy of identification. The experimental results presented in this paper indicate that the wavelet features of the segmented optic disc image are clinically more significant in comparison to features of the whole or sub fundus image in the detection of glaucoma from fundus image. Accuracy of glaucoma identification achieved in this work is 94.7% and a comparison with existing methods of glaucoma detection from fundus image indicates that the proposed approach has improved accuracy of classification. Copyright © 2015 Elsevier Ireland Ltd. All rights reserved.

Analysis of dual tree M-band wavelet transform based features for brain image classification.

PubMed

Ayalapogu, Ratna Raju; Pabboju, Suresh; Ramisetty, Rajeswara Rao

2018-04-29

The most complex organ in the human body is the brain. The unrestrained growth of cells in the brain is called a brain tumor. The cause of a brain tumor is still unknown and the survival rate is lower than other types of cancers. Hence, early detection is very important for proper treatment. In this study, an efficient computer-aided diagnosis (CAD) system is presented for brain image classification by analyzing MRI of the brain. At first, the MRI brain images of normal and abnormal categories are modeled by using the statistical features of dual tree m-band wavelet transform (DTMBWT). A maximum margin classifier, support vector machine (SVM) is then used for the classification and validated with k-fold approach. Results show that the system provides promising results on a repository of molecular brain neoplasia data (REMBRANDT) with 97.5% accuracy using 4 th level statistical features of DTMBWT. Viewing the experimental results, we conclude that the system gives a satisfactory performance for the brain image classification. © 2018 International Society for Magnetic Resonance in Medicine.

Improved l1-SPIRiT using 3D walsh transform-based sparsity basis.

PubMed

Feng, Zhen; Liu, Feng; Jiang, Mingfeng; Crozier, Stuart; Guo, He; Wang, Yuxin

2014-09-01

l1-SPIRiT is a fast magnetic resonance imaging (MRI) method which combines parallel imaging (PI) with compressed sensing (CS) by performing a joint l1-norm and l2-norm optimization procedure. The original l1-SPIRiT method uses two-dimensional (2D) Wavelet transform to exploit the intra-coil data redundancies and a joint sparsity model to exploit the inter-coil data redundancies. In this work, we propose to stack all the coil images into a three-dimensional (3D) matrix, and then a novel 3D Walsh transform-based sparsity basis is applied to simultaneously reduce the intra-coil and inter-coil data redundancies. Both the 2D Wavelet transform-based and the proposed 3D Walsh transform-based sparsity bases were investigated in the l1-SPIRiT method. The experimental results show that the proposed 3D Walsh transform-based l1-SPIRiT method outperformed the original l1-SPIRiT in terms of image quality and computational efficiency. Copyright © 2014 Elsevier Inc. All rights reserved.

Research of second harmonic generation images based on texture analysis

NASA Astrophysics Data System (ADS)

Liu, Yao; Li, Yan; Gong, Haiming; Zhu, Xiaoqin; Huang, Zufang; Chen, Guannan

2014-09-01

Texture analysis plays a crucial role in identifying objects or regions of interest in an image. It has been applied to a variety of medical image processing, ranging from the detection of disease and the segmentation of specific anatomical structures, to differentiation between healthy and pathological tissues. Second harmonic generation (SHG) microscopy as a potential noninvasive tool for imaging biological tissues has been widely used in medicine, with reduced phototoxicity and photobleaching. In this paper, we clarified the principles of texture analysis including statistical, transform, structural and model-based methods and gave examples of its applications, reviewing studies of the technique. Moreover, we tried to apply texture analysis to the SHG images for the differentiation of human skin scar tissues. Texture analysis method based on local binary pattern (LBP) and wavelet transform was used to extract texture features of SHG images from collagen in normal and abnormal scars, and then the scar SHG images were classified into normal or abnormal ones. Compared with other texture analysis methods with respect to the receiver operating characteristic analysis, LBP combined with wavelet transform was demonstrated to achieve higher accuracy. It can provide a new way for clinical diagnosis of scar types. At last, future development of texture analysis in SHG images were discussed.

Classification of the Gabon SAR Mosaic Using a Wavelet Based Rule Classifier

NASA Technical Reports Server (NTRS)

Simard, Marc; Saatchi, Sasan; DeGrandi, Gianfranco

2000-01-01

A method is developed for semi-automated classification of SAR images of the tropical forest. Information is extracted using the wavelet transform (WT). The transform allows for extraction of structural information in the image as a function of scale. In order to classify the SAR image, a Desicion Tree Classifier is used. The method of pruning is used to optimize classification rate versus tree size. The results give explicit insight on the type of information useful for a given class.

Wavelet-Based Blind Superresolution from Video Sequence and in MRI

DTIC Science & Technology

2005-12-31

in Fig. 4(e) and (f), respectively. The PSNR- based optimal threshold gives better noise filtering but poor deblurring [see Fig. 4(c) and (e)] while...that ultimately produces the deblurred , noise filtered, superresolved image. Finite support linear shift invariant blurs are reasonable to assume... Deblurred and Noise Filtered HR Image Cameras with different PSFs Figure 1: Multichannel Blind Superresolution Model condition [11] on the zeros of the

Applications of wavelets in interferometry and artificial vision

NASA Astrophysics Data System (ADS)

Escalona Z., Rafael A.

2001-08-01

In this paper we present a different point of view of phase measurements performed in interferometry, image processing and intelligent vision using Wavelet Transform. In standard and white-light interferometry, the phase function is retrieved by using phase-shifting, Fourier-Transform, cosinus-inversion and other known algorithms. Our novel technique presented here is faster, robust and shows excellent accuracy in phase determinations. Finally, in our second application, fringes are no more generate by some light interaction but result from the observation of adapted strip set patterns directly printed on the target of interest. The moving target is simply observed by a conventional vision system and usual phase computation algorithms are adapted to an image processing by wavelet transform, in order to sense target position and displacements with a high accuracy. In general, we have determined that wavelet transform presents properties of robustness, relative speed of calculus and very high accuracy in phase computations.

Super-resolution reconstruction in frequency, image, and wavelet domains to reduce through-plane partial voluming in MRI.

PubMed

Gholipour, Ali; Afacan, Onur; Aganj, Iman; Scherrer, Benoit; Prabhu, Sanjay P; Sahin, Mustafa; Warfield, Simon K

2015-12-01

To compare and evaluate the use of super-resolution reconstruction (SRR), in frequency, image, and wavelet domains, to reduce through-plane partial voluming effects in magnetic resonance imaging. The reconstruction of an isotropic high-resolution image from multiple thick-slice scans has been investigated through techniques in frequency, image, and wavelet domains. Experiments were carried out with thick-slice T2-weighted fast spin echo sequence on the Academic College of Radiology MRI phantom, where the reconstructed images were compared to a reference high-resolution scan using peak signal-to-noise ratio (PSNR), structural similarity image metric (SSIM), mutual information (MI), and the mean absolute error (MAE) of image intensity profiles. The application of super-resolution reconstruction was then examined in retrospective processing of clinical neuroimages of ten pediatric patients with tuberous sclerosis complex (TSC) to reduce through-plane partial voluming for improved 3D delineation and visualization of thin radial bands of white matter abnormalities. Quantitative evaluation results show improvements in all evaluation metrics through super-resolution reconstruction in the frequency, image, and wavelet domains, with the highest values obtained from SRR in the image domain. The metric values for image-domain SRR versus the original axial, coronal, and sagittal images were PSNR = 32.26 vs 32.22, 32.16, 30.65; SSIM = 0.931 vs 0.922, 0.924, 0.918; MI = 0.871 vs 0.842, 0.844, 0.831; and MAE = 5.38 vs 7.34, 7.06, 6.19. All similarity metrics showed high correlations with expert ranking of image resolution with MI showing the highest correlation at 0.943. Qualitative assessment of the neuroimages of ten TSC patients through in-plane and out-of-plane visualization of structures showed the extent of partial voluming effect in a real clinical scenario and its reduction using SRR. Blinded expert evaluation of image resolution in resampled out-of-plane views consistently showed the superiority of SRR compared to original axial and coronal image acquisitions. Thick-slice 2D T2-weighted MRI scans are part of many routine clinical protocols due to their high signal-to-noise ratio, but are often severely affected by through-plane partial voluming effects. This study shows that while radiologic assessment is performed in 2D on thick-slice scans, super-resolution MRI reconstruction techniques can be used to fuse those scans to generate a high-resolution image with reduced partial voluming for improved postacquisition processing. Qualitative and quantitative evaluation showed the efficacy of all SRR techniques with the best results obtained from SRR in the image domain. The limitations of SRR techniques are uncertainties in modeling the slice profile, density compensation, quantization in resampling, and uncompensated motion between scans.

Gait recognition based on Gabor wavelets and modified gait energy image for human identification

NASA Astrophysics Data System (ADS)

Huang, Deng-Yuan; Lin, Ta-Wei; Hu, Wu-Chih; Cheng, Chih-Hsiang

2013-10-01

This paper proposes a method for recognizing human identity using gait features based on Gabor wavelets and modified gait energy images (GEIs). Identity recognition by gait generally involves gait representation, extraction, and classification. In this work, a modified GEI convolved with an ensemble of Gabor wavelets is proposed as a gait feature. Principal component analysis is then used to project the Gabor-wavelet-based gait features into a lower-dimension feature space for subsequent classification. Finally, support vector machine classifiers based on a radial basis function kernel are trained and utilized to recognize human identity. The major contributions of this paper are as follows: (1) the consideration of the shadow effect to yield a more complete segmentation of gait silhouettes; (2) the utilization of motion estimation to track people when walkers overlap; and (3) the derivation of modified GEIs to extract more useful gait information. Extensive performance evaluation shows a great improvement of recognition accuracy due to the use of shadow removal, motion estimation, and gait representation using the modified GEIs and Gabor wavelets.

The application of super wavelet finite element on temperature-pressure coupled field simulation of LPG tank under jet fire

NASA Astrophysics Data System (ADS)

Zhao, Bin

2015-02-01

Temperature-pressure coupled field analysis of liquefied petroleum gas (LPG) tank under jet fire can offer theoretical guidance for preventing the fire accidents of LPG tank, the application of super wavelet finite element on it is studied in depth. First, review of related researches on heat transfer analysis of LPG tank under fire and super wavelet are carried out. Second, basic theory of super wavelet transform is studied. Third, the temperature-pressure coupled model of gas phase and liquid LPG under jet fire is established based on the equation of state, the VOF model and the RNG k-ɛ model. Then the super wavelet finite element formulation is constructed using the super wavelet scale function as interpolating function. Finally, the simulation is carried out, and results show that the super wavelet finite element method has higher computing precision than wavelet finite element method.

Wavelet-based 3-D inversion for frequency-domain airborne EM data

NASA Astrophysics Data System (ADS)

Liu, Yunhe; Farquharson, Colin G.; Yin, Changchun; Baranwal, Vikas C.

2018-04-01

In this paper, we propose a new wavelet-based 3-D inversion method for frequency-domain airborne electromagnetic (FDAEM) data. Instead of inverting the model in the space domain using a smoothing constraint, this new method recovers the model in the wavelet domain based on a sparsity constraint. In the wavelet domain, the model is represented by two types of coefficients, which contain both large- and fine-scale informations of the model, meaning the wavelet-domain inversion has inherent multiresolution. In order to accomplish a sparsity constraint, we minimize an L1-norm measure in the wavelet domain that mostly gives a sparse solution. The final inversion system is solved by an iteratively reweighted least-squares method. We investigate different orders of Daubechies wavelets to accomplish our inversion algorithm, and test them on synthetic frequency-domain AEM data set. The results show that higher order wavelets having larger vanishing moments and regularity can deliver a more stable inversion process and give better local resolution, while the lower order wavelets are simpler and less smooth, and thus capable of recovering sharp discontinuities if the model is simple. At last, we test this new inversion algorithm on a frequency-domain helicopter EM (HEM) field data set acquired in Byneset, Norway. Wavelet-based 3-D inversion of HEM data is compared to L2-norm-based 3-D inversion's result to further investigate the features of the new method.

High resolution crustal image of South California Continental Borderland: Reverse time imaging including multiples

NASA Astrophysics Data System (ADS)

Bian, A.; Gantela, C.

2014-12-01

Strong multiples were observed in marine seismic data of Los Angeles Regional Seismic Experiment (LARSE).It is crucial to eliminate these multiples in conventional ray-based or one-way wave-equation based depth image methods. As long as multiples contain information of target zone along travelling path, it's possible to use them as signal, to improve the illumination coverage thus enhance the image quality of structural boundaries. Reverse time migration including multiples is a two-way wave-equation based prestack depth image method that uses both primaries and multiples to map structural boundaries. Several factors, including source wavelet, velocity model, back ground noise, data acquisition geometry and preprocessing workflow may influence the quality of image. The source wavelet is estimated from direct arrival of marine seismic data. Migration velocity model is derived from integrated model building workflow, and the sharp velocity interfaces near sea bottom needs to be preserved in order to generate multiples in the forward and backward propagation steps. The strong amplitude, low frequency marine back ground noise needs to be removed before the final imaging process. High resolution reverse time image sections of LARSE Lines 1 and Line 2 show five interfaces: depth of sea-bottom, base of sedimentary basins, top of Catalina Schist, a deep layer and a possible pluton boundary. Catalina Schist shows highs in the San Clemente ridge, Emery Knoll, Catalina Ridge, under Catalina Basin on both the lines, and a minor high under Avalon Knoll. The high of anticlinal fold in Line 1 is under the north edge of Emery Knoll and under the San Clemente fault zone. An area devoid of any reflection features are interpreted as sides of an igneous plume.

Distributed Compressive Sensing

DTIC Science & Technology

2009-01-01

example, smooth signals are sparse in the Fourier basis, and piecewise smooth signals are sparse in a wavelet basis [8]; the commercial coding standards MP3...including wavelets [8], Gabor bases [8], curvelets [35], etc., are widely used for representation and compression of natural signals, images, and...spikes and the sine waves of a Fourier basis, or the Fourier basis and wavelets . Signals that are sparsely represented in frames or unions of bases can

Speckle-reduction algorithm for ultrasound images in complex wavelet domain using genetic algorithm-based mixture model.

PubMed

Uddin, Muhammad Shahin; Tahtali, Murat; Lambert, Andrew J; Pickering, Mark R; Marchese, Margaret; Stuart, Iain

2016-05-20

Compared with other medical-imaging modalities, ultrasound (US) imaging is a valuable way to examine the body's internal organs, and two-dimensional (2D) imaging is currently the most common technique used in clinical diagnoses. Conventional 2D US imaging systems are highly flexible cost-effective imaging tools that permit operators to observe and record images of a large variety of thin anatomical sections in real time. Recently, 3D US imaging has also been gaining popularity due to its considerable advantages over 2D US imaging. It reduces dependency on the operator and provides better qualitative and quantitative information for an effective diagnosis. Furthermore, it provides a 3D view, which allows the observation of volume information. The major shortcoming of any type of US imaging is the presence of speckle noise. Hence, speckle reduction is vital in providing a better clinical diagnosis. The key objective of any speckle-reduction algorithm is to attain a speckle-free image while preserving the important anatomical features. In this paper we introduce a nonlinear multi-scale complex wavelet-diffusion based algorithm for speckle reduction and sharp-edge preservation of 2D and 3D US images. In the proposed method we use a Rayleigh and Maxwell-mixture model for 2D and 3D US images, respectively, where a genetic algorithm is used in combination with an expectation maximization method to estimate mixture parameters. Experimental results using both 2D and 3D synthetic, physical phantom, and clinical data demonstrate that our proposed algorithm significantly reduces speckle noise while preserving sharp edges without discernible distortions. The proposed approach performs better than the state-of-the-art approaches in both qualitative and quantitative measures.

Unified Heat Kernel Regression for Diffusion, Kernel Smoothing and Wavelets on Manifolds and Its Application to Mandible Growth Modeling in CT Images

PubMed Central

Chung, Moo K.; Qiu, Anqi; Seo, Seongho; Vorperian, Houri K.

2014-01-01

We present a novel kernel regression framework for smoothing scalar surface data using the Laplace-Beltrami eigenfunctions. Starting with the heat kernel constructed from the eigenfunctions, we formulate a new bivariate kernel regression framework as a weighted eigenfunction expansion with the heat kernel as the weights. The new kernel regression is mathematically equivalent to isotropic heat diffusion, kernel smoothing and recently popular diffusion wavelets. Unlike many previous partial differential equation based approaches involving diffusion, our approach represents the solution of diffusion analytically, reducing numerical inaccuracy and slow convergence. The numerical implementation is validated on a unit sphere using spherical harmonics. As an illustration, we have applied the method in characterizing the localized growth pattern of mandible surfaces obtained in CT images from subjects between ages 0 and 20 years by regressing the length of displacement vectors with respect to the template surface. PMID:25791435

Multispectral image sharpening using a shift-invariant wavelet transform and adaptive processing of multiresolution edges

USGS Publications Warehouse

Lemeshewsky, G.P.; Rahman, Z.-U.; Schowengerdt, R.A.; Reichenbach, S.E.

2002-01-01

Enhanced false color images from mid-IR, near-IR (NIR), and visible bands of the Landsat thematic mapper (TM) are commonly used for visually interpreting land cover type. Described here is a technique for sharpening or fusion of NIR with higher resolution panchromatic (Pan) that uses a shift-invariant implementation of the discrete wavelet transform (SIDWT) and a reported pixel-based selection rule to combine coefficients. There can be contrast reversals (e.g., at soil-vegetation boundaries between NIR and visible band images) and consequently degraded sharpening and edge artifacts. To improve performance for these conditions, I used a local area-based correlation technique originally reported for comparing image-pyramid-derived edges for the adaptive processing of wavelet-derived edge data. Also, using the redundant data of the SIDWT improves edge data generation. There is additional improvement because sharpened subband imagery is used with the edge-correlation process. A reported technique for sharpening three-band spectral imagery used forward and inverse intensity, hue, and saturation transforms and wavelet-based sharpening of intensity. This technique had limitations with opposite contrast data, and in this study sharpening was applied to single-band multispectral-Pan image pairs. Sharpening used simulated 30-m NIR imagery produced by degrading the spatial resolution of a higher resolution reference. Performance, evaluated by comparison between sharpened and reference image, was improved when sharpened subband data were used with the edge correlation.

Exploring the impact of wavelet-based denoising in the classification of remote sensing hyperspectral images

NASA Astrophysics Data System (ADS)

Quesada-Barriuso, Pablo; Heras, Dora B.; Argüello, Francisco

2016-10-01

The classification of remote sensing hyperspectral images for land cover applications is a very intensive topic. In the case of supervised classification, Support Vector Machines (SVMs) play a dominant role. Recently, the Extreme Learning Machine algorithm (ELM) has been extensively used. The classification scheme previously published by the authors, and called WT-EMP, introduces spatial information in the classification process by means of an Extended Morphological Profile (EMP) that is created from features extracted by wavelets. In addition, the hyperspectral image is denoised in the 2-D spatial domain, also using wavelets and it is joined to the EMP via a stacked vector. In this paper, the scheme is improved achieving two goals. The first one is to reduce the classification time while preserving the accuracy of the classification by using ELM instead of SVM. The second one is to improve the accuracy results by performing not only a 2-D denoising for every spectral band, but also a previous additional 1-D spectral signature denoising applied to each pixel vector of the image. For each denoising the image is transformed by applying a 1-D or 2-D wavelet transform, and then a NeighShrink thresholding is applied. Improvements in terms of classification accuracy are obtained, especially for images with close regions in the classification reference map, because in these cases the accuracy of the classification in the edges between classes is more relevant.

Twofold processing for denoising ultrasound medical images.

PubMed

Kishore, P V V; Kumar, K V V; Kumar, D Anil; Prasad, M V D; Goutham, E N D; Rahul, R; Krishna, C B S Vamsi; Sandeep, Y

2015-01-01

Ultrasound medical (US) imaging non-invasively pictures inside of a human body for disease diagnostics. Speckle noise attacks ultrasound images degrading their visual quality. A twofold processing algorithm is proposed in this work to reduce this multiplicative speckle noise. First fold used block based thresholding, both hard (BHT) and soft (BST), on pixels in wavelet domain with 8, 16, 32 and 64 non-overlapping block sizes. This first fold process is a better denoising method for reducing speckle and also inducing object of interest blurring. The second fold process initiates to restore object boundaries and texture with adaptive wavelet fusion. The degraded object restoration in block thresholded US image is carried through wavelet coefficient fusion of object in original US mage and block thresholded US image. Fusion rules and wavelet decomposition levels are made adaptive for each block using gradient histograms with normalized differential mean (NDF) to introduce highest level of contrast between the denoised pixels and the object pixels in the resultant image. Thus the proposed twofold methods are named as adaptive NDF block fusion with hard and soft thresholding (ANBF-HT and ANBF-ST). The results indicate visual quality improvement to an interesting level with the proposed twofold processing, where the first fold removes noise and second fold restores object properties. Peak signal to noise ratio (PSNR), normalized cross correlation coefficient (NCC), edge strength (ES), image quality Index (IQI) and structural similarity index (SSIM), measure the quantitative quality of the twofold processing technique. Validation of the proposed method is done by comparing with anisotropic diffusion (AD), total variational filtering (TVF) and empirical mode decomposition (EMD) for enhancement of US images. The US images are provided by AMMA hospital radiology labs at Vijayawada, India.

Automated wavelet denoising of photoacoustic signals for circulating melanoma cell detection and burn image reconstruction.

PubMed

Holan, Scott H; Viator, John A

2008-06-21

Photoacoustic image reconstruction may involve hundreds of point measurements, each of which contributes unique information about the subsurface absorbing structures under study. For backprojection imaging, two or more point measurements of photoacoustic waves induced by irradiating a biological sample with laser light are used to produce an image of the acoustic source. Each of these measurements must undergo some signal processing, such as denoising or system deconvolution. In order to process the numerous signals, we have developed an automated wavelet algorithm for denoising signals. We appeal to the discrete wavelet transform for denoising photoacoustic signals generated in a dilute melanoma cell suspension and in thermally coagulated blood. We used 5, 9, 45 and 270 melanoma cells in the laser beam path as test concentrations. For the burn phantom, we used coagulated blood in 1.6 mm silicon tube submerged in Intralipid. Although these two targets were chosen as typical applications for photoacoustic detection and imaging, they are of independent interest. The denoising employs level-independent universal thresholding. In order to accommodate nonradix-2 signals, we considered a maximal overlap discrete wavelet transform (MODWT). For the lower melanoma cell concentrations, as the signal-to-noise ratio approached 1, denoising allowed better peak finding. For coagulated blood, the signals were denoised to yield a clean photoacoustic resulting in an improvement of 22% in the reconstructed image. The entire signal processing technique was automated so that minimal user intervention was needed to reconstruct the images. Such an algorithm may be used for image reconstruction and signal extraction for applications such as burn depth imaging, depth profiling of vascular lesions in skin and the detection of single cancer cells in blood samples.

Efficient storage and management of radiographic images using a novel wavelet-based multiscale vector quantizer

NASA Astrophysics Data System (ADS)

Yang, Shuyu; Mitra, Sunanda

2002-05-01

Due to the huge volumes of radiographic images to be managed in hospitals, efficient compression techniques yielding no perceptual loss in the reconstructed images are becoming a requirement in the storage and management of such datasets. A wavelet-based multi-scale vector quantization scheme that generates a global codebook for efficient storage and transmission of medical images is presented in this paper. The results obtained show that even at low bit rates one is able to obtain reconstructed images with perceptual quality higher than that of the state-of-the-art scalar quantization method, the set partitioning in hierarchical trees.

Pixel-based speckle adjustment for noise reduction in Fourier-domain OCT images

PubMed Central

Zhang, Anqi; Xi, Jiefeng; Sun, Jitao; Li, Xingde

2017-01-01

Speckle resides in OCT signals and inevitably effects OCT image quality. In this work, we present a novel method for speckle noise reduction in Fourier-domain OCT images, which utilizes the phase information of complex OCT data. In this method, speckle area is pre-delineated pixelwise based on a phase-domain processing method and then adjusted by the results of wavelet shrinkage of the original image. Coefficient shrinkage method such as wavelet or contourlet is applied afterwards for further suppressing the speckle noise. Compared with conventional methods without speckle adjustment, the proposed method demonstrates significant improvement of image quality. PMID:28663860

Multiple-image encryption via lifting wavelet transform and XOR operation based on compressive ghost imaging scheme

NASA Astrophysics Data System (ADS)

Li, Xianye; Meng, Xiangfeng; Yang, Xiulun; Wang, Yurong; Yin, Yongkai; Peng, Xiang; He, Wenqi; Dong, Guoyan; Chen, Hongyi

2018-03-01

A multiple-image encryption method via lifting wavelet transform (LWT) and XOR operation is proposed, which is based on a row scanning compressive ghost imaging scheme. In the encryption process, the scrambling operation is implemented for the sparse images transformed by LWT, then the XOR operation is performed on the scrambled images, and the resulting XOR images are compressed in the row scanning compressive ghost imaging, through which the ciphertext images can be detected by bucket detector arrays. During decryption, the participant who possesses his/her correct key-group, can successfully reconstruct the corresponding plaintext image by measurement key regeneration, compression algorithm reconstruction, XOR operation, sparse images recovery, and inverse LWT (iLWT). Theoretical analysis and numerical simulations validate the feasibility of the proposed method.

Wavelet Analysis for RADARSAT Exploitation: Demonstration of Algorithms for Maritime Surveillance

DTIC Science & Technology

2007-02-01

this study , we demonstrate wavelet analysis for exploitation of RADARSAT ocean imagery, including wind direction estimation, oceanic and atmospheric ...of image striations that can arise as a texture pattern caused by turbulent coherent structures in the marine atmospheric boundary layer. The image...associated change in the pattern texture (i.e., the nature of the turbulent atmospheric structures) across the front. Due to the large spatial scale of

Study on Underwater Image Denoising Algorithm Based on Wavelet Transform

NASA Astrophysics Data System (ADS)

Jian, Sun; Wen, Wang

2017-02-01

This paper analyzes the application of MATLAB in underwater image processing, the transmission characteristics of the underwater laser light signal and the kinds of underwater noise has been described, the common noise suppression algorithm: Wiener filter, median filter, average filter algorithm is brought out. Then the advantages and disadvantages of each algorithm in image sharpness and edge protection areas have been compared. A hybrid filter algorithm based on wavelet transform has been proposed which can be used for Color Image Denoising. At last the PSNR and NMSE of each algorithm has been given out, which compares the ability to de-noising

The application of wavelet denoising in material discrimination system

NASA Astrophysics Data System (ADS)

Fu, Kenneth; Ranta, Dale; Guest, Clark; Das, Pankaj

2010-01-01

Recently, the need for cargo inspection imaging systems to provide a material discrimination function has become desirable. This is done by scanning the cargo container with x-rays at two different energy levels. The ratio of attenuations of the two energy scans can provide information on the composition of the material. However, with the statistical error from noise, the accuracy of such systems can be low. Because the moving source emits two energies of x-rays alternately, images from the two scans will not be identical. That means edges of objects in the two images are not perfectly aligned. Moreover, digitization creates blurry-edge artifacts. Different energy x-rays produce different edge spread functions. Those combined effects contribute to a source of false classification namely, the "edge effect." Other types of false classification are caused by noise, mainly Poisson noise associated with photons. The Poisson noise in xray images can be dealt with using either a Wiener filter or a wavelet shrinkage denoising approach. In this paper, we propose a method that uses the wavelet shrinkage denoising approach to enhance the performance of the material identification system. Test results show that this wavelet-based approach has improved performance in object detection and eliminating false positives due to the edge effects.

Evaluation of a 3D local multiresolution algorithm for the correction of partial volume effects in positron emission tomography.

PubMed

Le Pogam, Adrien; Hatt, Mathieu; Descourt, Patrice; Boussion, Nicolas; Tsoumpas, Charalampos; Turkheimer, Federico E; Prunier-Aesch, Caroline; Baulieu, Jean-Louis; Guilloteau, Denis; Visvikis, Dimitris

2011-09-01

Partial volume effects (PVEs) are consequences of the limited spatial resolution in emission tomography leading to underestimation of uptake in tissues of size similar to the point spread function (PSF) of the scanner as well as activity spillover between adjacent structures. Among PVE correction methodologies, a voxel-wise mutual multiresolution analysis (MMA) was recently introduced. MMA is based on the extraction and transformation of high resolution details from an anatomical image (MR/CT) and their subsequent incorporation into a low-resolution PET image using wavelet decompositions. Although this method allows creating PVE corrected images, it is based on a 2D global correlation model, which may introduce artifacts in regions where no significant correlation exists between anatomical and functional details. A new model was designed to overcome these two issues (2D only and global correlation) using a 3D wavelet decomposition process combined with a local analysis. The algorithm was evaluated on synthetic, simulated and patient images, and its performance was compared to the original approach as well as the geometric transfer matrix (GTM) method. Quantitative performance was similar to the 2D global model and GTM in correlated cases. In cases where mismatches between anatomical and functional information were present, the new model outperformed the 2D global approach, avoiding artifacts and significantly improving quality of the corrected images and their quantitative accuracy. A new 3D local model was proposed for a voxel-wise PVE correction based on the original mutual multiresolution analysis approach. Its evaluation demonstrated an improved and more robust qualitative and quantitative accuracy compared to the original MMA methodology, particularly in the absence of full correlation between anatomical and functional information.

Evaluation of a 3D local multiresolution algorithm for the correction of partial volume effects in positron emission tomography

PubMed Central

Le Pogam, Adrien; Hatt, Mathieu; Descourt, Patrice; Boussion, Nicolas; Tsoumpas, Charalampos; Turkheimer, Federico E.; Prunier-Aesch, Caroline; Baulieu, Jean-Louis; Guilloteau, Denis; Visvikis, Dimitris

2011-01-01

Purpose Partial volume effects (PVE) are consequences of the limited spatial resolution in emission tomography leading to under-estimation of uptake in tissues of size similar to the point spread function (PSF) of the scanner as well as activity spillover between adjacent structures. Among PVE correction methodologies, a voxel-wise mutual multi-resolution analysis (MMA) was recently introduced. MMA is based on the extraction and transformation of high resolution details from an anatomical image (MR/CT) and their subsequent incorporation into a low resolution PET image using wavelet decompositions. Although this method allows creating PVE corrected images, it is based on a 2D global correlation model which may introduce artefacts in regions where no significant correlation exists between anatomical and functional details. Methods A new model was designed to overcome these two issues (2D only and global correlation) using a 3D wavelet decomposition process combined with a local analysis. The algorithm was evaluated on synthetic, simulated and patient images, and its performance was compared to the original approach as well as the geometric transfer matrix (GTM) method. Results Quantitative performance was similar to the 2D global model and GTM in correlated cases. In cases where mismatches between anatomical and functional information were present the new model outperformed the 2D global approach, avoiding artefacts and significantly improving quality of the corrected images and their quantitative accuracy. Conclusions A new 3D local model was proposed for a voxel-wise PVE correction based on the original mutual multi-resolution analysis approach. Its evaluation demonstrated an improved and more robust qualitative and quantitative accuracy compared to the original MMA methodology, particularly in the absence of full correlation between anatomical and functional information. PMID:21978037

Imaging reconstruction based on improved wavelet denoising combined with parallel-beam filtered back-projection algorithm

NASA Astrophysics Data System (ADS)

Ren, Zhong; Liu, Guodong; Huang, Zhen

2012-11-01

The image reconstruction is a key step in medical imaging (MI) and its algorithm's performance determinates the quality and resolution of reconstructed image. Although some algorithms have been used, filter back-projection (FBP) algorithm is still the classical and commonly-used algorithm in clinical MI. In FBP algorithm, filtering of original projection data is a key step in order to overcome artifact of the reconstructed image. Since simple using of classical filters, such as Shepp-Logan (SL), Ram-Lak (RL) filter have some drawbacks and limitations in practice, especially for the projection data polluted by non-stationary random noises. So, an improved wavelet denoising combined with parallel-beam FBP algorithm is used to enhance the quality of reconstructed image in this paper. In the experiments, the reconstructed effects were compared between the improved wavelet denoising and others (directly FBP, mean filter combined FBP and median filter combined FBP method). To determine the optimum reconstruction effect, different algorithms, and different wavelet bases combined with three filters were respectively test. Experimental results show the reconstruction effect of improved FBP algorithm is better than that of others. Comparing the results of different algorithms based on two evaluation standards i.e. mean-square error (MSE), peak-to-peak signal-noise ratio (PSNR), it was found that the reconstructed effects of the improved FBP based on db2 and Hanning filter at decomposition scale 2 was best, its MSE value was less and the PSNR value was higher than others. Therefore, this improved FBP algorithm has potential value in the medical imaging.

Wake acoustic analysis and image decomposition via beamforming of microphone signal projections on wavelet subspaces

DOT National Transportation Integrated Search

2006-05-08

This paper describes the integration of wavelet analysis and time-domain beamforming : of microphone array output signals for analyzing the acoustic emissions from airplane : generated wake vortices. This integrated process provides visual and quanti...

Predicting Visual Semantic Descriptive Terms from Radiological Image Data: Preliminary Results with Liver Lesions in CT

PubMed Central

Depeursinge, Adrien; Kurtz, Camille; Beaulieu, Christopher F.; Napel, Sandy; Rubin, Daniel L.

2014-01-01

We describe a framework to model visual semantics of liver lesions in CT images in order to predict the visual semantic terms (VST) reported by radiologists in describing these lesions. Computational models of VST are learned from image data using high–order steerable Riesz wavelets and support vector machines (SVM). The organization of scales and directions that are specific to every VST are modeled as linear combinations of directional Riesz wavelets. The models obtained are steerable, which means that any orientation of the model can be synthesized from linear combinations of the basis filters. The latter property is leveraged to model VST independently from their local orientation. In a first step, these models are used to predict the presence of each semantic term that describes liver lesions. In a second step, the distances between all VST models are calculated to establish a non–hierarchical computationally–derived ontology of VST containing inter–term synonymy and complementarity. A preliminary evaluation of the proposed framework was carried out using 74 liver lesions annotated with a set of 18 VSTs from the RadLex ontology. A leave–one–patient–out cross–validation resulted in an average area under the ROC curve of 0.853 for predicting the presence of each VST when using SVMs in a feature space combining the magnitudes of the steered models with CT intensities. Likelihood maps are created for each VST, which enables high transparency of the information modeled. The computationally–derived ontology obtained from the VST models was found to be consistent with the underlying semantics of the visual terms. It was found to be complementary to the RadLex ontology, and constitutes a potential method to link the image content to visual semantics. The proposed framework is expected to foster human–computer synergies for the interpretation of radiological images while using rotation–covariant computational models of VSTs to (1) quantify their local likelihood and (2) explicitly link them with pixel–based image content in the context of a given imaging domain. PMID:24808406

Bone marrow cavity segmentation using graph-cuts with wavelet-based texture feature.

PubMed

Shigeta, Hironori; Mashita, Tomohiro; Kikuta, Junichi; Seno, Shigeto; Takemura, Haruo; Ishii, Masaru; Matsuda, Hideo

2017-10-01

Emerging bioimaging technologies enable us to capture various dynamic cellular activities [Formula: see text]. As large amounts of data are obtained these days and it is becoming unrealistic to manually process massive number of images, automatic analysis methods are required. One of the issues for automatic image segmentation is that image-taking conditions are variable. Thus, commonly, many manual inputs are required according to each image. In this paper, we propose a bone marrow cavity (BMC) segmentation method for bone images as BMC is considered to be related to the mechanism of bone remodeling, osteoporosis, and so on. To reduce manual inputs to segment BMC, we classified the texture pattern using wavelet transformation and support vector machine. We also integrated the result of texture pattern classification into the graph-cuts-based image segmentation method because texture analysis does not consider spatial continuity. Our method is applicable to a particular frame in an image sequence in which the condition of fluorescent material is variable. In the experiment, we evaluated our method with nine types of mother wavelets and several sets of scale parameters. The proposed method with graph-cuts and texture pattern classification performs well without manual inputs by a user.

Fast, large-scale hologram calculation in wavelet domain

NASA Astrophysics Data System (ADS)

Shimobaba, Tomoyoshi; Matsushima, Kyoji; Takahashi, Takayuki; Nagahama, Yuki; Hasegawa, Satoki; Sano, Marie; Hirayama, Ryuji; Kakue, Takashi; Ito, Tomoyoshi

2018-04-01

We propose a large-scale hologram calculation using WAvelet ShrinkAge-Based superpositIon (WASABI), a wavelet transform-based algorithm. An image-type hologram calculated using the WASABI method is printed on a glass substrate with the resolution of 65 , 536 × 65 , 536 pixels and a pixel pitch of 1 μm. The hologram calculation time amounts to approximately 354 s on a commercial CPU, which is approximately 30 times faster than conventional methods.

Standardized processing of MALDI imaging raw data for enhancement of weak analyte signals in mouse models of gastric cancer and Alzheimer's disease.

PubMed

Schwartz, Matthias; Meyer, Björn; Wirnitzer, Bernhard; Hopf, Carsten

2015-03-01

Conventional mass spectrometry image preprocessing methods used for denoising, such as the Savitzky-Golay smoothing or discrete wavelet transformation, typically do not only remove noise but also weak signals. Recently, memory-efficient principal component analysis (PCA) in conjunction with random projections (RP) has been proposed for reversible compression and analysis of large mass spectrometry imaging datasets. It considers single-pixel spectra in their local context and consequently offers the prospect of using information from the spectra of adjacent pixels for denoising or signal enhancement. However, little systematic analysis of key RP-PCA parameters has been reported so far, and the utility and validity of this method for context-dependent enhancement of known medically or pharmacologically relevant weak analyte signals in linear-mode matrix-assisted laser desorption/ionization (MALDI) mass spectra has not been explored yet. Here, we investigate MALDI imaging datasets from mouse models of Alzheimer's disease and gastric cancer to systematically assess the importance of selecting the right number of random projections k and of principal components (PCs) L for reconstructing reproducibly denoised images after compression. We provide detailed quantitative data for comparison of RP-PCA-denoising with the Savitzky-Golay and wavelet-based denoising in these mouse models as a resource for the mass spectrometry imaging community. Most importantly, we demonstrate that RP-PCA preprocessing can enhance signals of low-intensity amyloid-β peptide isoforms such as Aβ1-26 even in sparsely distributed Alzheimer's β-amyloid plaques and that it enables enhanced imaging of multiply acetylated histone H4 isoforms in response to pharmacological histone deacetylase inhibition in vivo. We conclude that RP-PCA denoising may be a useful preprocessing step in biomarker discovery workflows.

A deep convolutional neural network using directional wavelets for low-dose X-ray CT reconstruction.

PubMed

Kang, Eunhee; Min, Junhong; Ye, Jong Chul

2017-10-01

Due to the potential risk of inducing cancer, radiation exposure by X-ray CT devices should be reduced for routine patient scanning. However, in low-dose X-ray CT, severe artifacts typically occur due to photon starvation, beam hardening, and other causes, all of which decrease the reliability of the diagnosis. Thus, a high-quality reconstruction method from low-dose X-ray CT data has become a major research topic in the CT community. Conventional model-based de-noising approaches are, however, computationally very expensive, and image-domain de-noising approaches cannot readily remove CT-specific noise patterns. To tackle these problems, we want to develop a new low-dose X-ray CT algorithm based on a deep-learning approach. We propose an algorithm which uses a deep convolutional neural network (CNN) which is applied to the wavelet transform coefficients of low-dose CT images. More specifically, using a directional wavelet transform to extract the directional component of artifacts and exploit the intra- and inter- band correlations, our deep network can effectively suppress CT-specific noise. In addition, our CNN is designed with a residual learning architecture for faster network training and better performance. Experimental results confirm that the proposed algorithm effectively removes complex noise patterns from CT images derived from a reduced X-ray dose. In addition, we show that the wavelet-domain CNN is efficient when used to remove noise from low-dose CT compared to existing approaches. Our results were rigorously evaluated by several radiologists at the Mayo Clinic and won second place at the 2016 "Low-Dose CT Grand Challenge." To the best of our knowledge, this work is the first deep-learning architecture for low-dose CT reconstruction which has been rigorously evaluated and proven to be effective. In addition, the proposed algorithm, in contrast to existing model-based iterative reconstruction (MBIR) methods, has considerable potential to benefit from large data sets. Therefore, we believe that the proposed algorithm opens a new direction in the area of low-dose CT research. © 2017 American Association of Physicists in Medicine.

Denoising and 4D visualization of OCT images

PubMed Central

Gargesha, Madhusudhana; Jenkins, Michael W.; Rollins, Andrew M.; Wilson, David L.

2009-01-01

We are using Optical Coherence Tomography (OCT) to image structure and function of the developing embryonic heart in avian models. Fast OCT imaging produces very large 3D (2D + time) and 4D (3D volumes + time) data sets, which greatly challenge ones ability to visualize results. Noise in OCT images poses additional challenges. We created an algorithm with a quick, data set specific optimization for reduction of both shot and speckle noise and applied it to 3D visualization and image segmentation in OCT. When compared to baseline algorithms (median, Wiener, orthogonal wavelet, basic non-orthogonal wavelet), a panel of experts judged the new algorithm to give much improved volume renderings concerning both noise and 3D visualization. Specifically, the algorithm provided a better visualization of the myocardial and endocardial surfaces, and the interaction of the embryonic heart tube with surrounding tissue. Quantitative evaluation using an image quality figure of merit also indicated superiority of the new algorithm. Noise reduction aided semi-automatic 2D image segmentation, as quantitatively evaluated using a contour distance measure with respect to an expert segmented contour. In conclusion, the noise reduction algorithm should be quite useful for visualization and quantitative measurements (e.g., heart volume, stroke volume, contraction velocity, etc.) in OCT embryo images. With its semi-automatic, data set specific optimization, we believe that the algorithm can be applied to OCT images from other applications. PMID:18679509

Skeletonization of Gridded Potential-Field Images

NASA Astrophysics Data System (ADS)

Gao, L.; Morozov, I. B.

2012-12-01

A new approach to skeletonization was developed for gridded potential-field data. Generally, skeletonization is a pattern-recognition technique allowing automatic recognition of near-linear features in the images, measurement of their parameters, and analyzing them for similarities. Our approach decomposes the images into arbitrarily-oriented "wavelets" characterized by positive or negative amplitudes, orientation angles, spatial dimensions, polarities, and other attributes. Orientations of the wavelets are obtained by scanning the azimuths to detect the strike direction of each anomaly. The wavelets are connected according to the similarities of these attributes, which leads to a "skeleton" map of the potential-field data. In addition, 2-D filtering is conducted concurrently with the wavelet-identification process, which allows extracting parameters of background trends and reduces the adverse effects of low-frequency background (which is often strong in potential-field maps) on skeletonization.. By correlating the neighboring wavelets, linear anomalies are identified and characterized. The advantages of this algorithm are the generality and isotropy of feature detection, as well as being specifically designed for gridded data. With several options for background-trend extraction, the stability for identification of lineaments is improved and optimized. The algorithm is also integrated in a powerful processing system which allows combining it with numerous other tools, such as filtering, computation of analytical signal, empirical mode decomposition, and various types of plotting. The method is applied to potential-field data for the Western Canada Sedimentary Basin, in a study area which extends from southern Saskatchewan into southwestern Manitoba. The target is the structure of crystalline basement beneath Phanerozoic sediments. The examples illustrate that skeletonization aid in the interpretation of complex structures at different scale lengths. The results indicate that this method is useful for identifying structures in complex geophysical images and for automatic extraction of their attributes as well as for quantitative characterization and analysis of potential-field images. Skeletonized potential-field images should also be useful for inversion.

Four-dimensional wavelet compression of arbitrarily sized echocardiographic data.

PubMed

Zeng, Li; Jansen, Christian P; Marsch, Stephan; Unser, Michael; Hunziker, Patrick R

2002-09-01

Wavelet-based methods have become most popular for the compression of two-dimensional medical images and sequences. The standard implementations consider data sizes that are powers of two. There is also a large body of literature treating issues such as the choice of the "optimal" wavelets and the performance comparison of competing algorithms. With the advent of telemedicine, there is a strong incentive to extend these techniques to higher dimensional data such as dynamic three-dimensional (3-D) echocardiography [four-dimensional (4-D) datasets]. One of the practical difficulties is that the size of this data is often not a multiple of a power of two, which can lead to increased computational complexity and impaired compression power. Our contribution in this paper is to present a genuine 4-D extension of the well-known zerotree algorithm for arbitrarily sized data. The key component of our method is a one-dimensional wavelet algorithm that can handle arbitrarily sized input signals. The method uses a pair of symmetric/antisymmetric wavelets (10/6) together with some appropriate midpoint symmetry boundary conditions that reduce border artifacts. The zerotree structure is also adapted so that it can accommodate noneven data splitting. We have applied our method to the compression of real 3-D dynamic sequences from clinical cardiac ultrasound examinations. Our new algorithm compares very favorably with other more ad hoc adaptations (image extension and tiling) of the standard powers-of-two methods, in terms of both compression performance and computational cost. It is vastly superior to slice-by-slice wavelet encoding. This was seen not only in numerical image quality parameters but also in expert ratings, where significant improvement using the new approach could be documented. Our validation experiments show that one can safely compress 4-D data sets at ratios of 128:1 without compromising the diagnostic value of the images. We also display some more extreme compression results at ratios of 2000:1 where some key diagnostically relevant key features are preserved.

l0 regularization based on a prior image incorporated non-local means for limited-angle X-ray CT reconstruction.

PubMed

Zhang, Lingli; Zeng, Li; Guo, Yumeng

2018-01-01

Restricted by the scanning environment in some CT imaging modalities, the acquired projection data are usually incomplete, which may lead to a limited-angle reconstruction problem. Thus, image quality usually suffers from the slope artifacts. The objective of this study is to first investigate the distorted domains of the reconstructed images which encounter the slope artifacts and then present a new iterative reconstruction method to address the limited-angle X-ray CT reconstruction problem. The presented framework of new method exploits the structural similarity between the prior image and the reconstructed image aiming to compensate the distorted edges. Specifically, the new method utilizes l0 regularization and wavelet tight framelets to suppress the slope artifacts and pursue the sparsity. New method includes following 4 steps to (1) address the data fidelity using SART; (2) compensate for the slope artifacts due to the missed projection data using the prior image and modified nonlocal means (PNLM); (3) utilize l0 regularization to suppress the slope artifacts and pursue the sparsity of wavelet coefficients of the transformed image by using iterative hard thresholding (l0W); and (4) apply an inverse wavelet transform to reconstruct image. In summary, this method is referred to as "l0W-PNLM". Numerical implementations showed that the presented l0W-PNLM was superior to suppress the slope artifacts while preserving the edges of some features as compared to the commercial and other popular investigative algorithms. When the image to be reconstructed is inconsistent with the prior image, the new method can avoid or minimize the distorted edges in the reconstructed images. Quantitative assessments also showed that applying the new method obtained the highest image quality comparing to the existing algorithms. This study demonstrated that the presented l0W-PNLM yielded higher image quality due to a number of unique characteristics, which include that (1) it utilizes the structural similarity between the reconstructed image and prior image to modify the distorted edges by slope artifacts; (2) it adopts wavelet tight frames to obtain the first and high derivative in several directions and levels; and (3) it takes advantage of l0 regularization to promote the sparsity of wavelet coefficients, which is effective for the inhibition of the slope artifacts. Therefore, the new method can address the limited-angle CT reconstruction problem effectively and have practical significance.

Blind identification of image manipulation type using mixed statistical moments

NASA Astrophysics Data System (ADS)

Jeong, Bo Gyu; Moon, Yong Ho; Eom, Il Kyu

2015-01-01

We present a blind identification of image manipulation types such as blurring, scaling, sharpening, and histogram equalization. Motivated by the fact that image manipulations can change the frequency characteristics of an image, we introduce three types of feature vectors composed of statistical moments. The proposed statistical moments are generated from separated wavelet histograms, the characteristic functions of the wavelet variance, and the characteristic functions of the spatial image. Our method can solve the n-class classification problem. Through experimental simulations, we demonstrate that our proposed method can achieve high performance in manipulation type detection. The average rate of the correctly identified manipulation types is as high as 99.22%, using 10,800 test images and six manipulation types including the authentic image.

SU-C-304-05: Use of Local Noise Power Spectrum and Wavelets in Comprehensive EPID Quality Assurance

DOE Office of Scientific and Technical Information (OSTI.GOV)

Lee, S; Gopal, A; Yan, G

2015-06-15

Purpose: As EPIDs are increasingly used for IMRT QA and real-time treatment verification, comprehensive quality assurance (QA) of EPIDs becomes critical. Current QA with phantoms such as the Las Vegas and PIPSpro™ can fail in the early detection of EPID artifacts. Beyond image quality assessment, we propose a quantitative methodology using local noise power spectrum (NPS) to characterize image noise and wavelet transform to identify bad pixels and inter-subpanel flat-fielding artifacts. Methods: A total of 93 image sets including bar-pattern images and open exposure images were collected from four iViewGT a-Si EPID systems over three years. Quantitative metrics such asmore » modulation transform function (MTF), NPS and detective quantum efficiency (DQE) were computed for each image set. Local 2D NPS was calculated for each subpanel. A 1D NPS was obtained by radial averaging the 2D NPS and fitted to a power-law function. R-square and slope of the linear regression analysis were used for panel performance assessment. Haar wavelet transformation was employed to identify pixel defects and non-uniform gain correction across subpanels. Results: Overall image quality was assessed with DQE based on empirically derived area under curve (AUC) thresholds. Using linear regression analysis of 1D NPS, panels with acceptable flat fielding were indicated by r-square between 0.8 and 1, and slopes of −0.4 to −0.7. However, for panels requiring flat fielding recalibration, r-square values less than 0.8 and slopes from +0.2 to −0.4 were observed. The wavelet transform successfully identified pixel defects and inter-subpanel flat fielding artifacts. Standard QA with the Las Vegas and PIPSpro phantoms failed to detect these artifacts. Conclusion: The proposed QA methodology is promising for the early detection of imaging and dosimetric artifacts of EPIDs. Local NPS can accurately characterize the noise level within each subpanel, while the wavelet transforms can detect bad pixels and inter-subpanel flat fielding artifacts.« less

Simulation of groundwater level variations using wavelet combined with neural network, linear regression and support vector machine

NASA Astrophysics Data System (ADS)

Ebrahimi, Hadi; Rajaee, Taher

2017-01-01

Simulation of groundwater level (GWL) fluctuations is an important task in management of groundwater resources. In this study, the effect of wavelet analysis on the training of the artificial neural network (ANN), multi linear regression (MLR) and support vector regression (SVR) approaches was investigated, and the ANN, MLR and SVR along with the wavelet-ANN (WNN), wavelet-MLR (WLR) and wavelet-SVR (WSVR) models were compared in simulating one-month-ahead of GWL. The only variable used to develop the models was the monthly GWL data recorded over a period of 11 years from two wells in the Qom plain, Iran. The results showed that decomposing GWL time series into several sub-time series, extremely improved the training of the models. For both wells 1 and 2, the Meyer and Db5 wavelets produced better results compared to the other wavelets; which indicated wavelet types had similar behavior in similar case studies. The optimal number of delays was 6 months, which seems to be due to natural phenomena. The best WNN model, using Meyer mother wavelet with two decomposition levels, simulated one-month-ahead with RMSE values being equal to 0.069 m and 0.154 m for wells 1 and 2, respectively. The RMSE values for the WLR model were 0.058 m and 0.111 m, and for WSVR model were 0.136 m and 0.060 m for wells 1 and 2, respectively.

Perceptual security of encrypted images based on wavelet scaling analysis

NASA Astrophysics Data System (ADS)

Vargas-Olmos, C.; Murguía, J. S.; Ramírez-Torres, M. T.; Mejía Carlos, M.; Rosu, H. C.; González-Aguilar, H.

2016-08-01

The scaling behavior of the pixel fluctuations of encrypted images is evaluated by using the detrended fluctuation analysis based on wavelets, a modern technique that has been successfully used recently for a wide range of natural phenomena and technological processes. As encryption algorithms, we use the Advanced Encryption System (AES) in RBT mode and two versions of a cryptosystem based on cellular automata, with the encryption process applied both fully and partially by selecting different bitplanes. In all cases, the results show that the encrypted images in which no understandable information can be visually appreciated and whose pixels look totally random present a persistent scaling behavior with the scaling exponent α close to 0.5, implying no correlation between pixels when the DFA with wavelets is applied. This suggests that the scaling exponents of the encrypted images can be used as a perceptual security criterion in the sense that when their values are close to 0.5 (the white noise value) the encrypted images are more secure also from the perceptual point of view.

Wavelet denoising of multiframe optical coherence tomography data

PubMed Central

Mayer, Markus A.; Borsdorf, Anja; Wagner, Martin; Hornegger, Joachim; Mardin, Christian Y.; Tornow, Ralf P.

2012-01-01

We introduce a novel speckle noise reduction algorithm for OCT images. Contrary to present approaches, the algorithm does not rely on simple averaging of multiple image frames or denoising on the final averaged image. Instead it uses wavelet decompositions of the single frames for a local noise and structure estimation. Based on this analysis, the wavelet detail coefficients are weighted, averaged and reconstructed. At a signal-to-noise gain at about 100% we observe only a minor sharpness decrease, as measured by a full-width-half-maximum reduction of 10.5%. While a similar signal-to-noise gain would require averaging of 29 frames, we achieve this result using only 8 frames as input to the algorithm. A possible application of the proposed algorithm is preprocessing in retinal structure segmentation algorithms, to allow a better differentiation between real tissue information and unwanted speckle noise. PMID:22435103

Wavelet denoising of multiframe optical coherence tomography data.

PubMed

Mayer, Markus A; Borsdorf, Anja; Wagner, Martin; Hornegger, Joachim; Mardin, Christian Y; Tornow, Ralf P

2012-03-01

We introduce a novel speckle noise reduction algorithm for OCT images. Contrary to present approaches, the algorithm does not rely on simple averaging of multiple image frames or denoising on the final averaged image. Instead it uses wavelet decompositions of the single frames for a local noise and structure estimation. Based on this analysis, the wavelet detail coefficients are weighted, averaged and reconstructed. At a signal-to-noise gain at about 100% we observe only a minor sharpness decrease, as measured by a full-width-half-maximum reduction of 10.5%. While a similar signal-to-noise gain would require averaging of 29 frames, we achieve this result using only 8 frames as input to the algorithm. A possible application of the proposed algorithm is preprocessing in retinal structure segmentation algorithms, to allow a better differentiation between real tissue information and unwanted speckle noise.

Wavelet-based image analysis system for soil texture analysis

NASA Astrophysics Data System (ADS)

Sun, Yun; Long, Zhiling; Jang, Ping-Rey; Plodinec, M. John

2003-05-01

Soil texture is defined as the relative proportion of clay, silt and sand found in a given soil sample. It is an important physical property of soil that affects such phenomena as plant growth and agricultural fertility. Traditional methods used to determine soil texture are either time consuming (hydrometer), or subjective and experience-demanding (field tactile evaluation). Considering that textural patterns observed at soil surfaces are uniquely associated with soil textures, we propose an innovative approach to soil texture analysis, in which wavelet frames-based features representing texture contents of soil images are extracted and categorized by applying a maximum likelihood criterion. The soil texture analysis system has been tested successfully with an accuracy of 91% in classifying soil samples into one of three general categories of soil textures. In comparison with the common methods, this wavelet-based image analysis approach is convenient, efficient, fast, and objective.

Image Perception Wavelet Simulation and Enhancement for the Visually Impaired.

DTIC Science & Technology

1994-12-01

and Computational Harmonic Analysis, 1:54-81 (1993). 6. Cornsweet, Tom N. "The Staircase-Method in Psychophysics," The American Journal of Psychology ...of a Visual Model," Proceedings of the IEEE, 60(7):828-842 (July 1972). 33. Taylor, M. M. and C Douglas Creelman . "PEST: Efficient Estimates on

Wavelets, non-linearity and turbulence in fusion plasmas

NASA Astrophysics Data System (ADS)

van Milligen, B. Ph.

Introduction Linear spectral analysis tools Wavelet analysis Wavelet spectra and coherence Joint wavelet phase-frequency spectra Non-linear spectral analysis tools Wavelet bispectra and bicoherence Interpretation of the bicoherence Analysis of computer-generated data Coupled van der Pol oscillators A large eddy simulation model for two-fluid plasma turbulence A long wavelength plasma drift wave model Analysis of plasma edge turbulence from Langmuir probe data Radial coherence observed on the TJ-IU torsatron Bicoherence profile at the L/H transition on CCT Conclusions

Improving wavelet denoising based on an in-depth analysis of the camera color processing

NASA Astrophysics Data System (ADS)

Seybold, Tamara; Plichta, Mathias; Stechele, Walter

2015-02-01

While Denoising is an extensively studied task in signal processing research, most denoising methods are designed and evaluated using readily processed image data, e.g. the well-known Kodak data set. The noise model is usually additive white Gaussian noise (AWGN). This kind of test data does not correspond to nowadays real-world image data taken with a digital camera. Using such unrealistic data to test, optimize and compare denoising algorithms may lead to incorrect parameter tuning or suboptimal choices in research on real-time camera denoising algorithms. In this paper we derive a precise analysis of the noise characteristics for the different steps in the color processing. Based on real camera noise measurements and simulation of the processing steps, we obtain a good approximation for the noise characteristics. We further show how this approximation can be used in standard wavelet denoising methods. We improve the wavelet hard thresholding and bivariate thresholding based on our noise analysis results. Both the visual quality and objective quality metrics show the advantage of the proposed method. As the method is implemented using look-up-tables that are calculated before the denoising step, our method can be implemented with very low computational complexity and can process HD video sequences real-time in an FPGA.

A Method for Extracting Suspected Parotid Lesions in CT Images using Feature-based Segmentation and Active Contours based on Stationary Wavelet Transform

NASA Astrophysics Data System (ADS)

Wu, T. Y.; Lin, S. F.

2013-10-01

Automatic suspected lesion extraction is an important application in computer-aided diagnosis (CAD). In this paper, we propose a method to automatically extract the suspected parotid regions for clinical evaluation in head and neck CT images. The suspected lesion tissues in low contrast tissue regions can be localized with feature-based segmentation (FBS) based on local texture features, and can be delineated with accuracy by modified active contour models (ACM). At first, stationary wavelet transform (SWT) is introduced. The derived wavelet coefficients are applied to derive the local features for FBS, and to generate enhanced energy maps for ACM computation. Geometric shape features (GSFs) are proposed to analyze each soft tissue region segmented by FBS; the regions with higher similarity GSFs with the lesions are extracted and the information is also applied as the initial conditions for fine delineation computation. Consequently, the suspected lesions can be automatically localized and accurately delineated for aiding clinical diagnosis. The performance of the proposed method is evaluated by comparing with the results outlined by clinical experts. The experiments on 20 pathological CT data sets show that the true-positive (TP) rate on recognizing parotid lesions is about 94%, and the dimension accuracy of delineation results can also approach over 93%.

Multifractal modeling, segmentation, prediction, and statistical validation of posterior fossa tumors

NASA Astrophysics Data System (ADS)

Islam, Atiq; Iftekharuddin, Khan M.; Ogg, Robert J.; Laningham, Fred H.; Sivakumar, Bhuvaneswari

2008-03-01

In this paper, we characterize the tumor texture in pediatric brain magnetic resonance images (MRIs) and exploit these features for automatic segmentation of posterior fossa (PF) tumors. We focus on PF tumor because of the prevalence of such tumor in pediatric patients. Due to varying appearance in MRI, we propose to model the tumor texture with a multi-fractal process, such as a multi-fractional Brownian motion (mBm). In mBm, the time-varying Holder exponent provides flexibility in modeling irregular tumor texture. We develop a detailed mathematical framework for mBm in two-dimension and propose a novel algorithm to estimate the multi-fractal structure of tissue texture in brain MRI based on wavelet coefficients. This wavelet based multi-fractal feature along with MR image intensity and a regular fractal feature obtained using our existing piecewise-triangular-prism-surface-area (PTPSA) method, are fused in segmenting PF tumor and non-tumor regions in brain T1, T2, and FLAIR MR images respectively. We also demonstrate a non-patient-specific automated tumor prediction scheme based on these image features. We experimentally show the tumor discriminating power of our novel multi-fractal texture along with intensity and fractal features in automated tumor segmentation and statistical prediction. To evaluate the performance of our tumor prediction scheme, we obtain ROCs and demonstrate how sharply the curves reach the specificity of 1.0 sacrificing minimal sensitivity. Experimental results show the effectiveness of our proposed techniques in automatic detection of PF tumors in pediatric MRIs.

Hierarchical content-based image retrieval by dynamic indexing and guided search

NASA Astrophysics Data System (ADS)

You, Jane; Cheung, King H.; Liu, James; Guo, Linong

2003-12-01

This paper presents a new approach to content-based image retrieval by using dynamic indexing and guided search in a hierarchical structure, and extending data mining and data warehousing techniques. The proposed algorithms include: a wavelet-based scheme for multiple image feature extraction, the extension of a conventional data warehouse and an image database to an image data warehouse for dynamic image indexing, an image data schema for hierarchical image representation and dynamic image indexing, a statistically based feature selection scheme to achieve flexible similarity measures, and a feature component code to facilitate query processing and guide the search for the best matching. A series of case studies are reported, which include a wavelet-based image color hierarchy, classification of satellite images, tropical cyclone pattern recognition, and personal identification using multi-level palmprint and face features.

Dependence and risk assessment for oil prices and exchange rate portfolios: A wavelet based approach

NASA Astrophysics Data System (ADS)

Aloui, Chaker; Jammazi, Rania

2015-10-01

In this article, we propose a wavelet-based approach to accommodate the stylized facts and complex structure of financial data, caused by frequent and abrupt changes of markets and noises. Specifically, we show how the combination of both continuous and discrete wavelet transforms with traditional financial models helps improve portfolio's market risk assessment. In the empirical stage, three wavelet-based models (wavelet-EGARCH with dynamic conditional correlations, wavelet-copula, and wavelet-extreme value) are considered and applied to crude oil price and US dollar exchange rate data. Our findings show that the wavelet-based approach provides an effective and powerful tool for detecting extreme moments and improving the accuracy of VaR and Expected Shortfall estimates of oil-exchange rate portfolios after noise is removed from the original data.

PULSAR SIGNAL DENOISING METHOD BASED ON LAPLACE DISTRIBUTION IN NO-SUBSAMPLING WAVELET PACKET DOMAIN

DOE Office of Scientific and Technical Information (OSTI.GOV)

Wenbo, Wang; Yanchao, Zhao; Xiangli, Wang

2016-11-01

In order to improve the denoising effect of the pulsar signal, a new denoising method is proposed in the no-subsampling wavelet packet domain based on the local Laplace prior model. First, we count the true noise-free pulsar signal’s wavelet packet coefficient distribution characteristics and construct the true signal wavelet packet coefficients’ Laplace probability density function model. Then, we estimate the denosied wavelet packet coefficients by using the noisy pulsar wavelet coefficients based on maximum a posteriori criteria. Finally, we obtain the denoisied pulsar signal through no-subsampling wavelet packet reconstruction of the estimated coefficients. The experimental results show that the proposed method performs better when calculating the pulsar time of arrival than the translation-invariant wavelet denoising method.

A wavelet-based adaptive fusion algorithm of infrared polarization imaging

NASA Astrophysics Data System (ADS)

Yang, Wei; Gu, Guohua; Chen, Qian; Zeng, Haifang

2011-08-01

The purpose of infrared polarization image is to highlight man-made target from a complex natural background. For the infrared polarization images can significantly distinguish target from background with different features, this paper presents a wavelet-based infrared polarization image fusion algorithm. The method is mainly for image processing of high-frequency signal portion, as for the low frequency signal, the original weighted average method has been applied. High-frequency part is processed as follows: first, the source image of the high frequency information has been extracted by way of wavelet transform, then signal strength of 3*3 window area has been calculated, making the regional signal intensity ration of source image as a matching measurement. Extraction method and decision mode of the details are determined by the decision making module. Image fusion effect is closely related to the setting threshold of decision making module. Compared to the commonly used experiment way, quadratic interpolation optimization algorithm is proposed in this paper to obtain threshold. Set the endpoints and midpoint of the threshold searching interval as initial interpolation nodes, and compute the minimum quadratic interpolation function. The best threshold can be obtained by comparing the minimum quadratic interpolation function. A series of image quality evaluation results show this method has got improvement in fusion effect; moreover, it is not only effective for some individual image, but also for a large number of images.

Choice of word length in the design of a specialized hardware for lossless wavelet compression of medical images

NASA Astrophysics Data System (ADS)

Urriza, Isidro; Barragan, Luis A.; Artigas, Jose I.; Garcia, Jose I.; Navarro, Denis

1997-11-01

Image compression plays an important role in the archiving and transmission of medical images. Discrete cosine transform (DCT)-based compression methods are not suitable for medical images because of block-like image artifacts that could mask or be mistaken for pathology. Wavelet transforms (WTs) are used to overcome this problem. When implementing WTs in hardware, finite precision arithmetic introduces quantization errors. However, lossless compression is usually required in the medical image field. Thus, the hardware designer must look for the optimum register length that, while ensuring the lossless accuracy criteria, will also lead to a high-speed implementation with small chip area. In addition, wavelet choice is a critical issue that affects image quality as well as system design. We analyze the filters best suited to image compression that appear in the literature. For them, we obtain the maximum quantization errors produced in the calculation of the WT components. Thus, we deduce the minimum word length required for the reconstructed image to be numerically identical to the original image. The theoretical results are compared with experimental results obtained from algorithm simulations on random test images. These results enable us to compare the hardware implementation cost of the different filter banks. Moreover, to reduce the word length, we have analyzed the case of increasing the integer part of the numbers while maintaining constant the word length when the scale increases.

Wavelets for sign language translation

NASA Astrophysics Data System (ADS)

Wilson, Beth J.; Anspach, Gretel

1993-10-01

Wavelet techniques are applied to help extract the relevant parameters of sign language from video images of a person communicating in American Sign Language or Signed English. The compression and edge detection features of two-dimensional wavelet analysis are exploited to enhance the algorithms under development to classify the hand motion, hand location with respect to the body, and handshape. These three parameters have different processing requirements and complexity issues. The results are described for applying various quadrature mirror filter designs to a filterbank implementation of the desired wavelet transform. The overall project is to develop a system that will translate sign language to English to facilitate communication between deaf and hearing people.

The FBI wavelet/scalar quantization standard for gray-scale fingerprint image compression

DOE Office of Scientific and Technical Information (OSTI.GOV)

Bradley, J.N.; Brislawn, C.M.; Hopper, T.

1993-05-01

The FBI has recently adopted a standard for the compression of digitized 8-bit gray-scale fingerprint images. The standard is based on scalar quantization of a 64-subband discrete wavelet transform decomposition of the images, followed by Huffman coding. Novel features of the algorithm include the use of symmetric boundary conditions for transforming finite-length signals and a subband decomposition tailored for fingerprint images scanned at 500 dpi. The standard is intended for use in conjunction with ANSI/NBS-CLS 1-1993, American National Standard Data Format for the Interchange of Fingerprint Information, and the FBI`s Integrated Automated Fingerprint Identification System.

The FBI wavelet/scalar quantization standard for gray-scale fingerprint image compression

DOE Office of Scientific and Technical Information (OSTI.GOV)

Bradley, J.N.; Brislawn, C.M.; Hopper, T.

1993-01-01

The FBI has recently adopted a standard for the compression of digitized 8-bit gray-scale fingerprint images. The standard is based on scalar quantization of a 64-subband discrete wavelet transform decomposition of the images, followed by Huffman coding. Novel features of the algorithm include the use of symmetric boundary conditions for transforming finite-length signals and a subband decomposition tailored for fingerprint images scanned at 500 dpi. The standard is intended for use in conjunction with ANSI/NBS-CLS 1-1993, American National Standard Data Format for the Interchange of Fingerprint Information, and the FBI's Integrated Automated Fingerprint Identification System.

A wavelet domain adaptive image watermarking method based on chaotic encryption

NASA Astrophysics Data System (ADS)

Wei, Fang; Liu, Jian; Cao, Hanqiang; Yang, Jun

2009-10-01

A digital watermarking technique is a specific branch of steganography, which can be used in various applications, provides a novel way to solve security problems for multimedia information. In this paper, we proposed a kind of wavelet domain adaptive image digital watermarking method using chaotic stream encrypt and human eye visual property. The secret information that can be seen as a watermarking is hidden into a host image, which can be publicly accessed, so the transportation of the secret information will not attract the attention of illegal receiver. The experimental results show that the method is invisible and robust against some image processing.

Use of local noise power spectrum and wavelet analysis in quantitative image quality assurance for EPIDs

DOE Office of Scientific and Technical Information (OSTI.GOV)

Lee, Soyoung

Purpose: To investigate the use of local noise power spectrum (NPS) to characterize image noise and wavelet analysis to isolate defective pixels and inter-subpanel flat-fielding artifacts for quantitative quality assurance (QA) of electronic portal imaging devices (EPIDs). Methods: A total of 93 image sets including custom-made bar-pattern images and open exposure images were collected from four iViewGT a-Si EPID systems over three years. Global quantitative metrics such as modulation transform function (MTF), NPS, and detective quantum efficiency (DQE) were computed for each image set. Local NPS was also calculated for individual subpanels by sampling region of interests within each subpanelmore » of the EPID. The 1D NPS, obtained by radially averaging the 2D NPS, was fitted to a power-law function. The r-square value of the linear regression analysis was used as a singular metric to characterize the noise properties of individual subpanels of the EPID. The sensitivity of the local NPS was first compared with the global quantitative metrics using historical image sets. It was then compared with two commonly used commercial QA systems with images collected after applying two different EPID calibration methods (single-level gain and multilevel gain). To detect isolated defective pixels and inter-subpanel flat-fielding artifacts, Haar wavelet transform was applied on the images. Results: Global quantitative metrics including MTF, NPS, and DQE showed little change over the period of data collection. On the contrary, a strong correlation between the local NPS (r-square values) and the variation of the EPID noise condition was observed. The local NPS analysis indicated image quality improvement with the r-square values increased from 0.80 ± 0.03 (before calibration) to 0.85 ± 0.03 (after single-level gain calibration) and to 0.96 ± 0.03 (after multilevel gain calibration), while the commercial QA systems failed to distinguish the image quality improvement between the two calibration methods. With wavelet analysis, defective pixels and inter-subpanel flat-fielding artifacts were clearly identified as spikes after thresholding the inversely transformed images. Conclusions: The proposed local NPS (r-square values) showed superior sensitivity to the noise level variations of individual subpanels compared with global quantitative metrics such as MTF, NPS, and DQE. Wavelet analysis was effective in detecting isolated defective pixels and inter-subpanel flat-fielding artifacts. The proposed methods are promising for the early detection of imaging artifacts of EPIDs.« less

Segmentation of optical coherence tomography images for differentiation of the cavernous nerves from the prostate gland

NASA Astrophysics Data System (ADS)

Chitchian, Shahab; Weldon, Thomas P.; Fried, Nathaniel M.

2009-07-01

The cavernous nerves course along the surface of the prostate and are responsible for erectile function. Improvements in identification, imaging, and visualization of the cavernous nerves during prostate cancer surgery may improve nerve preservation and postoperative sexual potency. Two-dimensional (2-D) optical coherence tomography (OCT) images of the rat prostate were segmented to differentiate the cavernous nerves from the prostate gland. To detect these nerves, three image features were employed: Gabor filter, Daubechies wavelet, and Laws filter. The Gabor feature was applied with different standard deviations in the x and y directions. In the Daubechies wavelet feature, an 8-tap Daubechies orthonormal wavelet was implemented, and the low-pass sub-band was chosen as the filtered image. Last, Laws feature extraction was applied to the images. The features were segmented using a nearest-neighbor classifier. N-ary morphological postprocessing was used to remove small voids. The cavernous nerves were differentiated from the prostate gland with a segmentation error rate of only 0.058+/-0.019. This algorithm may be useful for implementation in clinical endoscopic OCT systems currently being studied for potential intraoperative diagnostic use in laparoscopic and robotic nerve-sparing prostate cancer surgery.

Wavelet-Based Visible and Infrared Image Fusion: A Comparative Study

PubMed Central

Sappa, Angel D.; Carvajal, Juan A.; Aguilera, Cristhian A.; Oliveira, Miguel; Romero, Dennis; Vintimilla, Boris X.

2016-01-01

This paper evaluates different wavelet-based cross-spectral image fusion strategies adopted to merge visible and infrared images. The objective is to find the best setup independently of the evaluation metric used to measure the performance. Quantitative performance results are obtained with state of the art approaches together with adaptations proposed in the current work. The options evaluated in the current work result from the combination of different setups in the wavelet image decomposition stage together with different fusion strategies for the final merging stage that generates the resulting representation. Most of the approaches evaluate results according to the application for which they are intended for. Sometimes a human observer is selected to judge the quality of the obtained results. In the current work, quantitative values are considered in order to find correlations between setups and performance of obtained results; these correlations can be used to define a criteria for selecting the best fusion strategy for a given pair of cross-spectral images. The whole procedure is evaluated with a large set of correctly registered visible and infrared image pairs, including both Near InfraRed (NIR) and Long Wave InfraRed (LWIR). PMID:27294938

Segmentation of optical coherence tomography images for differentiation of the cavernous nerves from the prostate gland.

PubMed

Chitchian, Shahab; Weldon, Thomas P; Fried, Nathaniel M

2009-01-01

The cavernous nerves course along the surface of the prostate and are responsible for erectile function. Improvements in identification, imaging, and visualization of the cavernous nerves during prostate cancer surgery may improve nerve preservation and postoperative sexual potency. Two-dimensional (2-D) optical coherence tomography (OCT) images of the rat prostate were segmented to differentiate the cavernous nerves from the prostate gland. To detect these nerves, three image features were employed: Gabor filter, Daubechies wavelet, and Laws filter. The Gabor feature was applied with different standard deviations in the x and y directions. In the Daubechies wavelet feature, an 8-tap Daubechies orthonormal wavelet was implemented, and the low-pass sub-band was chosen as the filtered image. Last, Laws feature extraction was applied to the images. The features were segmented using a nearest-neighbor classifier. N-ary morphological postprocessing was used to remove small voids. The cavernous nerves were differentiated from the prostate gland with a segmentation error rate of only 0.058+/-0.019. This algorithm may be useful for implementation in clinical endoscopic OCT systems currently being studied for potential intraoperative diagnostic use in laparoscopic and robotic nerve-sparing prostate cancer surgery.

Poster — Thur Eve — 09: Evaluation of electrical impedance and computed tomography fusion algorithms using an anthropomorphic phantom

DOE Office of Scientific and Technical Information (OSTI.GOV)

Chugh, Brige Paul; Krishnan, Kalpagam; Liu, Jeff

2014-08-15

Integration of biological conductivity information provided by Electrical Impedance Tomography (EIT) with anatomical information provided by Computed Tomography (CT) imaging could improve the ability to characterize tissues in clinical applications. In this paper, we report results of our study which compared the fusion of EIT with CT using three different image fusion algorithms, namely: weighted averaging, wavelet fusion, and ROI indexing. The ROI indexing method of fusion involves segmenting the regions of interest from the CT image and replacing the pixels with the pixels of the EIT image. The three algorithms were applied to a CT and EIT image ofmore » an anthropomorphic phantom, constructed out of five acrylic contrast targets with varying diameter embedded in a base of gelatin bolus. The imaging performance was assessed using Detectability and Structural Similarity Index Measure (SSIM). Wavelet fusion and ROI-indexing resulted in lower Detectability (by 35% and 47%, respectively) yet higher SSIM (by 66% and 73%, respectively) than weighted averaging. Our results suggest that wavelet fusion and ROI-indexing yielded more consistent and optimal fusion performance than weighted averaging.« less

Wavelet-Based Visible and Infrared Image Fusion: A Comparative Study.

PubMed

Sappa, Angel D; Carvajal, Juan A; Aguilera, Cristhian A; Oliveira, Miguel; Romero, Dennis; Vintimilla, Boris X

2016-06-10

This paper evaluates different wavelet-based cross-spectral image fusion strategies adopted to merge visible and infrared images. The objective is to find the best setup independently of the evaluation metric used to measure the performance. Quantitative performance results are obtained with state of the art approaches together with adaptations proposed in the current work. The options evaluated in the current work result from the combination of different setups in the wavelet image decomposition stage together with different fusion strategies for the final merging stage that generates the resulting representation. Most of the approaches evaluate results according to the application for which they are intended for. Sometimes a human observer is selected to judge the quality of the obtained results. In the current work, quantitative values are considered in order to find correlations between setups and performance of obtained results; these correlations can be used to define a criteria for selecting the best fusion strategy for a given pair of cross-spectral images. The whole procedure is evaluated with a large set of correctly registered visible and infrared image pairs, including both Near InfraRed (NIR) and Long Wave InfraRed (LWIR).

Image denoising via fundamental anisotropic diffusion and wavelet shrinkage: a comparative study

NASA Astrophysics Data System (ADS)

Bayraktar, Bulent; Analoui, Mostafa

2004-05-01

Noise removal faces a challenge: Keeping the image details. Resolving the dilemma of two purposes (smoothing and keeping image features in tact) working inadvertently of each other was an almost impossible task until anisotropic dif-fusion (AD) was formally introduced by Perona and Malik (PM). AD favors intra-region smoothing over inter-region in piecewise smooth images. Many authors regularized the original PM algorithm to overcome its drawbacks. We compared the performance of denoising using such 'fundamental' AD algorithms and one of the most powerful multiresolution tools available today, namely, wavelet shrinkage. The AD algorithms here are called 'fundamental' in the sense that the regularized versions center around the original PM algorithm with minor changes to the logic. The algorithms are tested with different noise types and levels. On top of the visual inspection, two mathematical metrics are used for performance comparison: Signal-to-noise ratio (SNR) and universal image quality index (UIQI). We conclude that some of the regu-larized versions of PM algorithm (AD) perform comparably with wavelet shrinkage denoising. This saves a lot of compu-tational power. With this conclusion, we applied the better-performing fundamental AD algorithms to a new imaging modality: Optical Coherence Tomography (OCT).

High-precision terahertz frequency modulated continuous wave imaging method using continuous wavelet transform

NASA Astrophysics Data System (ADS)

Zhou, Yu; Wang, Tianyi; Dai, Bing; Li, Wenjun; Wang, Wei; You, Chengwu; Wang, Kejia; Liu, Jinsong; Wang, Shenglie; Yang, Zhengang

2018-02-01

Inspired by the extensive application of terahertz (THz) imaging technologies in the field of aerospace, we exploit a THz frequency modulated continuous-wave imaging method with continuous wavelet transform (CWT) algorithm to detect a multilayer heat shield made of special materials. This method uses the frequency modulation continuous-wave system to catch the reflected THz signal and then process the image data by the CWT with different basis functions. By calculating the sizes of the defects area in the final images and then comparing the results with real samples, a practical high-precision THz imaging method is demonstrated. Our method can be an effective tool for the THz nondestructive testing of composites, drugs, and some cultural heritages.

Vessel extraction in retinal images using automatic thresholding and Gabor Wavelet.

PubMed

Ali, Aziah; Hussain, Aini; Wan Zaki, Wan Mimi Diyana

2017-07-01

Retinal image analysis has been widely used for early detection and diagnosis of multiple systemic diseases. Accurate vessel extraction in retinal image is a crucial step towards a fully automated diagnosis system. This work affords an efficient unsupervised method for extracting blood vessels from retinal images by combining existing Gabor Wavelet (GW) method with automatic thresholding. Green channel image is extracted from color retinal image and used to produce Gabor feature image using GW. Both green channel image and Gabor feature image undergo vessel-enhancement step in order to highlight blood vessels. Next, the two vessel-enhanced images are transformed to binary images using automatic thresholding before combined to produce the final vessel output. Combining the images results in significant improvement of blood vessel extraction performance compared to using individual image. Effectiveness of the proposed method was proven via comparative analysis with existing methods validated using publicly available database, DRIVE.

Super-resolution reconstruction in frequency, image, and wavelet domains to reduce through-plane partial voluming in MRI

DOE Office of Scientific and Technical Information (OSTI.GOV)

Gholipour, Ali, E-mail: ali.gholipour@childrens.harvard.edu; Afacan, Onur; Scherrer, Benoit

Purpose: To compare and evaluate the use of super-resolution reconstruction (SRR), in frequency, image, and wavelet domains, to reduce through-plane partial voluming effects in magnetic resonance imaging. Methods: The reconstruction of an isotropic high-resolution image from multiple thick-slice scans has been investigated through techniques in frequency, image, and wavelet domains. Experiments were carried out with thick-slice T2-weighted fast spin echo sequence on the Academic College of Radiology MRI phantom, where the reconstructed images were compared to a reference high-resolution scan using peak signal-to-noise ratio (PSNR), structural similarity image metric (SSIM), mutual information (MI), and the mean absolute error (MAE) ofmore » image intensity profiles. The application of super-resolution reconstruction was then examined in retrospective processing of clinical neuroimages of ten pediatric patients with tuberous sclerosis complex (TSC) to reduce through-plane partial voluming for improved 3D delineation and visualization of thin radial bands of white matter abnormalities. Results: Quantitative evaluation results show improvements in all evaluation metrics through super-resolution reconstruction in the frequency, image, and wavelet domains, with the highest values obtained from SRR in the image domain. The metric values for image-domain SRR versus the original axial, coronal, and sagittal images were PSNR = 32.26 vs 32.22, 32.16, 30.65; SSIM = 0.931 vs 0.922, 0.924, 0.918; MI = 0.871 vs 0.842, 0.844, 0.831; and MAE = 5.38 vs 7.34, 7.06, 6.19. All similarity metrics showed high correlations with expert ranking of image resolution with MI showing the highest correlation at 0.943. Qualitative assessment of the neuroimages of ten TSC patients through in-plane and out-of-plane visualization of structures showed the extent of partial voluming effect in a real clinical scenario and its reduction using SRR. Blinded expert evaluation of image resolution in resampled out-of-plane views consistently showed the superiority of SRR compared to original axial and coronal image acquisitions. Conclusions: Thick-slice 2D T2-weighted MRI scans are part of many routine clinical protocols due to their high signal-to-noise ratio, but are often severely affected by through-plane partial voluming effects. This study shows that while radiologic assessment is performed in 2D on thick-slice scans, super-resolution MRI reconstruction techniques can be used to fuse those scans to generate a high-resolution image with reduced partial voluming for improved postacquisition processing. Qualitative and quantitative evaluation showed the efficacy of all SRR techniques with the best results obtained from SRR in the image domain. The limitations of SRR techniques are uncertainties in modeling the slice profile, density compensation, quantization in resampling, and uncompensated motion between scans.« less

Super-resolution reconstruction in frequency, image, and wavelet domains to reduce through-plane partial voluming in MRI

PubMed Central

Gholipour, Ali; Afacan, Onur; Aganj, Iman; Scherrer, Benoit; Prabhu, Sanjay P.; Sahin, Mustafa; Warfield, Simon K.

2015-01-01

Purpose: To compare and evaluate the use of super-resolution reconstruction (SRR), in frequency, image, and wavelet domains, to reduce through-plane partial voluming effects in magnetic resonance imaging. Methods: The reconstruction of an isotropic high-resolution image from multiple thick-slice scans has been investigated through techniques in frequency, image, and wavelet domains. Experiments were carried out with thick-slice T2-weighted fast spin echo sequence on the Academic College of Radiology MRI phantom, where the reconstructed images were compared to a reference high-resolution scan using peak signal-to-noise ratio (PSNR), structural similarity image metric (SSIM), mutual information (MI), and the mean absolute error (MAE) of image intensity profiles. The application of super-resolution reconstruction was then examined in retrospective processing of clinical neuroimages of ten pediatric patients with tuberous sclerosis complex (TSC) to reduce through-plane partial voluming for improved 3D delineation and visualization of thin radial bands of white matter abnormalities. Results: Quantitative evaluation results show improvements in all evaluation metrics through super-resolution reconstruction in the frequency, image, and wavelet domains, with the highest values obtained from SRR in the image domain. The metric values for image-domain SRR versus the original axial, coronal, and sagittal images were PSNR = 32.26 vs 32.22, 32.16, 30.65; SSIM = 0.931 vs 0.922, 0.924, 0.918; MI = 0.871 vs 0.842, 0.844, 0.831; and MAE = 5.38 vs 7.34, 7.06, 6.19. All similarity metrics showed high correlations with expert ranking of image resolution with MI showing the highest correlation at 0.943. Qualitative assessment of the neuroimages of ten TSC patients through in-plane and out-of-plane visualization of structures showed the extent of partial voluming effect in a real clinical scenario and its reduction using SRR. Blinded expert evaluation of image resolution in resampled out-of-plane views consistently showed the superiority of SRR compared to original axial and coronal image acquisitions. Conclusions: Thick-slice 2D T2-weighted MRI scans are part of many routine clinical protocols due to their high signal-to-noise ratio, but are often severely affected by through-plane partial voluming effects. This study shows that while radiologic assessment is performed in 2D on thick-slice scans, super-resolution MRI reconstruction techniques can be used to fuse those scans to generate a high-resolution image with reduced partial voluming for improved postacquisition processing. Qualitative and quantitative evaluation showed the efficacy of all SRR techniques with the best results obtained from SRR in the image domain. The limitations of SRR techniques are uncertainties in modeling the slice profile, density compensation, quantization in resampling, and uncompensated motion between scans. PMID:26632048

Multiple description distributed image coding with side information for mobile wireless transmission

NASA Astrophysics Data System (ADS)

Wu, Min; Song, Daewon; Chen, Chang Wen

2005-03-01

Multiple description coding (MDC) is a source coding technique that involves coding the source information into multiple descriptions, and then transmitting them over different channels in packet network or error-prone wireless environment to achieve graceful degradation if parts of descriptions are lost at the receiver. In this paper, we proposed a multiple description distributed wavelet zero tree image coding system for mobile wireless transmission. We provide two innovations to achieve an excellent error resilient capability. First, when MDC is applied to wavelet subband based image coding, it is possible to introduce correlation between the descriptions in each subband. We consider using such a correlation as well as potentially error corrupted description as side information in the decoding to formulate the MDC decoding as a Wyner Ziv decoding problem. If only part of descriptions is lost, however, their correlation information is still available, the proposed Wyner Ziv decoder can recover the description by using the correlation information and the error corrupted description as side information. Secondly, in each description, single bitstream wavelet zero tree coding is very vulnerable to the channel errors. The first bit error may cause the decoder to discard all subsequent bits whether or not the subsequent bits are correctly received. Therefore, we integrate the multiple description scalar quantization (MDSQ) with the multiple wavelet tree image coding method to reduce error propagation. We first group wavelet coefficients into multiple trees according to parent-child relationship and then code them separately by SPIHT algorithm to form multiple bitstreams. Such decomposition is able to reduce error propagation and therefore improve the error correcting capability of Wyner Ziv decoder. Experimental results show that the proposed scheme not only exhibits an excellent error resilient performance but also demonstrates graceful degradation over the packet loss rate.

Assessing treatment response in triple-negative breast cancer from quantitative image analysis in perfusion magnetic resonance imaging.

PubMed

Banerjee, Imon; Malladi, Sadhika; Lee, Daniela; Depeursinge, Adrien; Telli, Melinda; Lipson, Jafi; Golden, Daniel; Rubin, Daniel L

2018-01-01

Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) is sensitive but not specific to determining treatment response in early stage triple-negative breast cancer (TNBC) patients. We propose an efficient computerized technique for assessing treatment response, specifically the residual tumor (RT) status and pathological complete response (pCR), in response to neoadjuvant chemotherapy. The proposed approach is based on Riesz wavelet analysis of pharmacokinetic maps derived from noninvasive DCE-MRI scans, obtained before and after treatment. We compared the performance of Riesz features with the traditional gray level co-occurrence matrices and a comprehensive characterization of the lesion that includes a wide range of quantitative features (e.g., shape and boundary). We investigated a set of predictive models ([Formula: see text]) incorporating distinct combinations of quantitative characterizations and statistical models at different time points of the treatment and some area under the receiver operating characteristic curve (AUC) values we reported are above 0.8. The most efficient models are based on first-order statistics and Riesz wavelets, which predicted RT with an AUC value of 0.85 and pCR with an AUC value of 0.83, improving results reported in a previous study by [Formula: see text]. Our findings suggest that Riesz texture analysis of TNBC lesions can be considered a potential framework for optimizing TNBC patient care.

Sparse Poisson noisy image deblurring.

PubMed

Carlavan, Mikael; Blanc-Féraud, Laure

2012-04-01

Deblurring noisy Poisson images has recently been a subject of an increasing amount of works in many areas such as astronomy and biological imaging. In this paper, we focus on confocal microscopy, which is a very popular technique for 3-D imaging of biological living specimens that gives images with a very good resolution (several hundreds of nanometers), although degraded by both blur and Poisson noise. Deconvolution methods have been proposed to reduce these degradations, and in this paper, we focus on techniques that promote the introduction of an explicit prior on the solution. One difficulty of these techniques is to set the value of the parameter, which weights the tradeoff between the data term and the regularizing term. Only few works have been devoted to the research of an automatic selection of this regularizing parameter when considering Poisson noise; therefore, it is often set manually such that it gives the best visual results. We present here two recent methods to estimate this regularizing parameter, and we first propose an improvement of these estimators, which takes advantage of confocal images. Following these estimators, we secondly propose to express the problem of the deconvolution of Poisson noisy images as the minimization of a new constrained problem. The proposed constrained formulation is well suited to this application domain since it is directly expressed using the antilog likelihood of the Poisson distribution and therefore does not require any approximation. We show how to solve the unconstrained and constrained problems using the recent alternating-direction technique, and we present results on synthetic and real data using well-known priors, such as total variation and wavelet transforms. Among these wavelet transforms, we specially focus on the dual-tree complex wavelet transform and on the dictionary composed of curvelets and an undecimated wavelet transform.

A Wavelet Neural Network Optimal Control Model for Traffic-Flow Prediction in Intelligent Transport Systems

NASA Astrophysics Data System (ADS)

Huang, Darong; Bai, Xing-Rong

Based on wavelet transform and neural network theory, a traffic-flow prediction model, which was used in optimal control of Intelligent Traffic system, is constructed. First of all, we have extracted the scale coefficient and wavelet coefficient from the online measured raw data of traffic flow via wavelet transform; Secondly, an Artificial Neural Network model of Traffic-flow Prediction was constructed and trained using the coefficient sequences as inputs and raw data as outputs; Simultaneous, we have designed the running principium of the optimal control system of traffic-flow Forecasting model, the network topological structure and the data transmitted model; Finally, a simulated example has shown that the technique is effectively and exactly. The theoretical results indicated that the wavelet neural network prediction model and algorithms have a broad prospect for practical application.

Detecting Buried Archaeological Remains by the Use of Geophysical Data Processing with 'Diffusion Maps' Methodology

NASA Astrophysics Data System (ADS)

Eppelbaum, Lev

2015-04-01

Geophysical methods are prompt, non-invasive and low-cost tool for quantitative delineation of buried archaeological targets. However, taking into account the complexity of geological-archaeological media, some unfavourable environments and known ambiguity of geophysical data analysis, a single geophysical method examination might be insufficient (Khesin and Eppelbaum, 1997). Besides this, it is well-known that the majority of inverse-problem solutions in geophysics are ill-posed (e.g., Zhdanov, 2002), which means, according to Hadamard (1902), that the solution does not exist, or is not unique, or is not a continuous function of observed geophysical data (when small perturbations in the observations will cause arbitrary mistakes in the solution). This fact has a wide application for informational, probabilistic and wavelet methodologies in archaeological geophysics (Eppelbaum, 2014a). The goal of the modern geophysical data examination is to detect the geophysical signatures of buried targets at noisy areas via the analysis of some physical parameters with a minimal number of false alarms and miss-detections (Eppelbaum et al., 2011; Eppelbaum, 2014b). The proposed wavelet approach to recognition of archaeological targets (AT) by the examination of geophysical method integration consists of advanced processing of each geophysical method and nonconventional integration of different geophysical methods between themselves. The recently developed technique of diffusion clustering combined with the abovementioned wavelet methods was utilized to integrate the geophysical data and detect existing irregularities. The approach is based on the wavelet packet techniques applied as to the geophysical images (or graphs) versus coordinates. For such an analysis may be utilized practically all geophysical methods (magnetic, gravity, seismic, GPR, ERT, self-potential, etc.). On the first stage of the proposed investigation a few tens of typical physical-archaeological models (PAM) (e.g., Eppelbaum et al., 2010; Eppelbaum, 2011) of the targets under study for the concrete area (region) are developed. These PAM are composed on the basis of the known archaeological and geological data, results of previous archaeogeophysical investigations and 3D modeling of geophysical data. It should be underlined that the PAMs must differ (by depth, size, shape and physical properties of AT as well as peculiarities of the host archaeological-geological media). The PAMs must include also noise components of different orders (corresponding to the archaeogeophysical conditions of the area under study). The same models are computed and without the AT. Introducing complex PAMs (for example, situated in the vicinity of electric power lines, some objects of infrastructure, etc. (Eppelbaum et al., 2001)) will reflect some real class of AT occurring in such unfavorable for geophysical searching conditions. Anomalous effects from such complex PAMs will significantly disturb the geophysical anomalies from AT and impede the wavelet methodology employment. At the same time, the 'self-learning' procedure laid in this methodology will help further to recognize the AT even in the cases of unfavorable S/N ratio. Modern developments in the wavelet theory and data mining are utilized for the analysis of the integrated data. Wavelet approach is applied for derivation of enhanced (e.g., coherence portraits) and combined images of geophysical fields. The modern methodologies based on the matching pursuit with wavelet packet dictionaries enables to extract desired signals even from strongly noised data (Averbuch et al., 2014). Researchers usually met the problem of extraction of essential features from available data contaminated by a random noise and by a non-relevant background (Averbuch et al., 2014). If the essential structure of a signal consists of several sine waves then we may represent it via trigonometric basis (Fourier analysis). In this case one can compare the signal with a set of sinusoids and extract consistent ones. An indicator of presence a wave in a signal f(t) is the Fourier coefficient ∫ f(t) sinwt dt. Wavelet analysis provides a rich library of waveforms available and fast, computationally efficient procedures of representation of signals and of selection of relevant waveforms. The basic assumption justifying an application of wavelet analysis is that the essential structure of a signal analyzed consists of not a large number of various waveforms. The best way to reveal this structure is representation of the signal by a set of basic elements containing waveforms coherent to the signal. For structures of the signal coherent to the basis, large coefficients are attributed to a few basic waveforms, whereas we expect small coefficients for the noise and structures incoherent to all basic waveforms. Wavelets are a family of functions ranging from functions of arbitrary smoothness to fractal ones. Wavelet procedure involves two aspects. The first one is a decomposition, i.e. breaking up a signal to obtain the wavelet coefficients and the 2nd one is a reconstruction, which consists of a reassembling the signal from coefficients There are many modifications of the WA. Note, first of all, so-called Continuous WA in whichsignal f(t) is tested for presence of waveforms ψ(t-b) a. Here, a is scaling parameter (dilation), bdetermines location of the wavelet ψ(t-b) a in a signal f(t). The integral ( ) ∫ t-b (W ψf) (b,a) = f (t) ψ a dt is the Continuous Wavelet Transform.When parameters a,b in ψ( ) t-ab take some discrete values, we have the Discrete Wavelet Transform. A general scheme of the Wavelet Decomposition Tree is shown, for instance, in (Averbuch et al., 2014; Eppelbaum et al., 2014). The signal is compared with the testing signal on each scale. It is estimated wavelet coefficients which enable to reconstruct the 1st approximation of the signal and details. On the next level, wavelet transform is applied to the approximation. Then, we can present A1 as A2 + D2, etc. So, if S - Signal, A - Approximation, D - Details, then S = A1 + D1 = A2 + D2 + D1 = A3 + D3 + D2 + D1. Wavelet packet transform is applied to both low pass results (approximations) and high pass results (Details). For analyzing the geophysical data, we used a technique based on the algorithm to characterize a geophysical image by a limited number of parameters (Eppelbaum et al., 2012). This set of parameters serves as a signature of the image and is utilized for discrimination of images (a) containing AT from the images (b) non-containing AT (let will designate these images as N). The constructed algorithm consists of the following main phases: (a) collection of the database, (b) characterization of geophysical images, (c) and dimensionality reduction. Then, each image is characterized by the histogram of the coherency directions (Alperovich et al., 2013). As a result of the previous steps we obtain two sets: containing AT and N of the signatures vectors for geophysical images. The obtained 3D set of the data representatives can be used as a reference set for the classification of newly arriving geophysical data. The obtained data sets are reduced by embedding features vectors into the 3D Euclidean space using the so-called diffusion map. This map enables to reveal the internal structure of the datasets AT and N and to distinctly separate them. For this, a matrix of the diffusion distances for the combined feature matrix F = FN ∴ FC of size 60 x C is constructed (Coifman and Lafon, 2006; Averbuch et al., 2010). Then, each row of the matrices FN and FC is projected onto three first eigenvectors of the matrix D(F ). As a result, each data curve is represented by a 3D point in the Euclidean space formed by eigenvectors of D(F ). The Euclidean distances between these 3D points reflect the similarity of the data curves. The scattered projections of the data curves onto the diffusion eigenvectors will be composed. Finally we observe that as a result of the above operations we embedded the original data into 3-dimensional space where data related to the AT subsurface are well separated from the N data. This 3D set of the data representatives can be used as a reference set for the classification of newly arriving data. Geophysically it means a reliable division of the studied areas for the AT-containing and not containing (N) these objects. Testing this methodology for delineation of archaeological cavities by magnetic and gravity data analysis displayed an effectiveness of this approach. References Alperovich, L., Eppelbaum, L., Zheludev, V., Dumoulin, J., Soldovieri, F., Proto, M., Bavusi, M. and Loperte, A., 2013. A new combined wavelet methodology applied to GPR and ERT data in the Montagnole experiment (French Alps). Journal of Geophysics and Engineering, 10, No. 2, 025017, 1-17. Averbuch, A., Hochman, K., Rabin, N., Schclar, A. and Zheludev, V., 2010. A diffusion frame-work for detection of moving vehicles. Digital Signal Processing, 20, No.1, 111-122. Averbuch A.Z., Neittaanmäki, P., and Zheludev, V.A., 2014. Spline and Spline Wavelet Methods with Applications to Signal and Image Processing. Volume I: Periodic Splines. Springer. Coifman, R.R. and Lafon, S., 2006. Diffusion maps, Applied and Computational Harmonic Analysis. Special issue on Diffusion Maps and Wavelets, 21, No. 7, 5-30. Eppelbaum, L.V., 2011. Study of magnetic anomalies over archaeological targets in urban conditions. Physics and Chemistry of the Earth, 36, No. 16, 1318-1330. Eppelbaum, L.V., 2014a. Geophysical observations at archaeological sites: Estimating informational content. Archaeological Prospection, 21, No. 2, 25-38. Eppelbaum, L.V. 2014b. Four Color Theorem and Applied Geophysics. Applied Mathematics, 5, 358-366. Eppelbaum, L.V., Alperovich, L., Zheludev, V. and Pechersky, A., 2011. Application of informational and wavelet approaches for integrated processing of geophysical data in complex environments. Proceed. of the 2011 SAGEEP Conference, Charleston, South Carolina, USA, 24, 24-60. Eppelbaum, L.V., Khesin, B.E. and Itkis, S.E., 2001. Prompt magnetic investigations of archaeological remains in areas of infrastructure development: Israeli experience. Archaeological Prospection, 8, No.3, 163-185. Eppelbaum, L.V., Khesin, B.E. and Itkis, S.E., 2010. Archaeological geophysics in arid environments: Examples from Israel. Journal of Arid Environments, 74, No. 7, 849-860. Eppelbaum, L.V., Zheludev, V. and Averbuch, A., 2014. Diffusion maps as a powerful tool for integrated geophysical field analysis to detecting hidden karst terranes. Izv. Acad. Sci. Azerb. Rep., Ser.: Earth Sciences, No. 1-2, 36-46. Hadamard, J., 1902. Sur les problèmes aux dérivées partielles et leur signification physique. Princeton University Bulletin, 13, 49-52. Khesin, B.E. and Eppelbaum, L.V., 1997. The number of geophysical methods required for target classification: quantitative estimation. Geoinformatics, 8, No.1, 31-39. Zhdanov, M.S., 2002. Geophysical Inverse Theory and Regularization Problems. Methods in Geochemistry and Geophysics, Vol. 36. Elsevier, Amsterdam.

Compression of multispectral fluorescence microscopic images based on a modified set partitioning in hierarchal trees

NASA Astrophysics Data System (ADS)

Mansoor, Awais; Robinson, J. Paul; Rajwa, Bartek

2009-02-01

Modern automated microscopic imaging techniques such as high-content screening (HCS), high-throughput screening, 4D imaging, and multispectral imaging are capable of producing hundreds to thousands of images per experiment. For quick retrieval, fast transmission, and storage economy, these images should be saved in a compressed format. A considerable number of techniques based on interband and intraband redundancies of multispectral images have been proposed in the literature for the compression of multispectral and 3D temporal data. However, these works have been carried out mostly in the elds of remote sensing and video processing. Compression for multispectral optical microscopy imaging, with its own set of specialized requirements, has remained under-investigated. Digital photography{oriented 2D compression techniques like JPEG (ISO/IEC IS 10918-1) and JPEG2000 (ISO/IEC 15444-1) are generally adopted for multispectral images which optimize visual quality but do not necessarily preserve the integrity of scientic data, not to mention the suboptimal performance of 2D compression techniques in compressing 3D images. Herein we report our work on a new low bit-rate wavelet-based compression scheme for multispectral fluorescence biological imaging. The sparsity of signicant coefficients in high-frequency subbands of multispectral microscopic images is found to be much greater than in natural images; therefore a quad-tree concept such as Said et al.'s SPIHT1 along with correlation of insignicant wavelet coefficients has been proposed to further exploit redundancy at high-frequency subbands. Our work propose a 3D extension to SPIHT, incorporating a new hierarchal inter- and intra-spectral relationship amongst the coefficients of 3D wavelet-decomposed image. The new relationship, apart from adopting the parent-child relationship of classical SPIHT, also brought forth the conditional "sibling" relationship by relating only the insignicant wavelet coefficients of subbands at the same level of decomposition. The insignicant quadtrees in dierent subbands in the high-frequency subband class are coded by a combined function to reduce redundancy. A number of experiments conducted on microscopic multispectral images have shown promising results for the proposed method over current state-of-the-art image-compression techniques.

Segmentation of blurred objects using wavelet transform: application to x-ray images

NASA Astrophysics Data System (ADS)

Barat, Cecile S.; Ducottet, Christophe; Bilgot, Anne; Desbat, Laurent

2004-02-01

First, we present a wavelet-based algorithm for edge detection and characterization, which is an adaptation of Mallat and Hwang"s method. This algorithm relies on a modelization of contours as smoothed singularities of three particular types (transitions, peaks and lines). On the one hand, it allows to detect and locate edges at an adapted scale. On the other hand, it is able to identify the type of each detected edge point and to measure its amplitude and smoothing size. The latter parameters represent respectively the contrast and the smoothness level of the edge point. Second, we explain that this method has been integrated in a 3D bone surface reconstruction algorithm designed for computer-assisted and minimal invasive orthopaedic surgery. In order to decrease the dose to the patient and to obtain rapidly a 3D image, we propose to identify a bone shape from few X-ray projections by using statistical shape models registered to segmented X-ray projections. We apply this approach to pedicle screw insertion (scoliosis, fractures...) where ten to forty percent of the screws are known to be misplaced. In this context, the proposed edge detection algorithm allows to overcome the major problem of vertebrae segmentation in the X-ray images.

An image adaptive, wavelet-based watermarking of digital images

NASA Astrophysics Data System (ADS)

Agreste, Santa; Andaloro, Guido; Prestipino, Daniela; Puccio, Luigia

2007-12-01

In digital management, multimedia content and data can easily be used in an illegal way--being copied, modified and distributed again. Copyright protection, intellectual and material rights protection for authors, owners, buyers, distributors and the authenticity of content are crucial factors in solving an urgent and real problem. In such scenario digital watermark techniques are emerging as a valid solution. In this paper, we describe an algorithm--called WM2.0--for an invisible watermark: private, strong, wavelet-based and developed for digital images protection and authenticity. Using discrete wavelet transform (DWT) is motivated by good time-frequency features and well-matching with human visual system directives. These two combined elements are important in building an invisible and robust watermark. WM2.0 works on a dual scheme: watermark embedding and watermark detection. The watermark is embedded into high frequency DWT components of a specific sub-image and it is calculated in correlation with the image features and statistic properties. Watermark detection applies a re-synchronization between the original and watermarked image. The correlation between the watermarked DWT coefficients and the watermark signal is calculated according to the Neyman-Pearson statistic criterion. Experimentation on a large set of different images has shown to be resistant against geometric, filtering and StirMark attacks with a low rate of false alarm.

Novel Algorithm for Classification of Medical Images

NASA Astrophysics Data System (ADS)

Bhushan, Bharat; Juneja, Monika

2010-11-01

Content-based image retrieval (CBIR) methods in medical image databases have been designed to support specific tasks, such as retrieval of medical images. These methods cannot be transferred to other medical applications since different imaging modalities require different types of processing. To enable content-based queries in diverse collections of medical images, the retrieval system must be familiar with the current Image class prior to the query processing. Further, almost all of them deal with the DICOM imaging format. In this paper a novel algorithm based on energy information obtained from wavelet transform for the classification of medical images according to their modalities is described. For this two types of wavelets have been used and have been shown that energy obtained in either case is quite distinct for each of the body part. This technique can be successfully applied to different image formats. The results are shown for JPEG imaging format.

Fusion of spectral and panchromatic images using false color mapping and wavelet integrated approach

NASA Astrophysics Data System (ADS)

Zhao, Yongqiang; Pan, Quan; Zhang, Hongcai

2006-01-01

With the development of sensory technology, new image sensors have been introduced that provide a greater range of information to users. But as the power limitation of radiation, there will always be some trade-off between spatial and spectral resolution in the image captured by specific sensors. Images with high spatial resolution can locate objects with high accuracy, whereas images with high spectral resolution can be used to identify the materials. Many applications in remote sensing require fusing low-resolution imaging spectral images with panchromatic images to identify materials at high resolution in clutter. A pixel-based false color mapping and wavelet transform integrated fusion algorithm is presented in this paper, the resulting images have a higher information content than each of the original images and retain sensor-specific image information. The simulation results show that this algorithm can enhance the visibility of certain details and preserve the difference of different materials.

Testing Pairwise Association between Spatially Autocorrelated Variables: A New Approach Using Surrogate Lattice Data

PubMed Central

Deblauwe, Vincent; Kennel, Pol; Couteron, Pierre

2012-01-01

Background Independence between observations is a standard prerequisite of traditional statistical tests of association. This condition is, however, violated when autocorrelation is present within the data. In the case of variables that are regularly sampled in space (i.e. lattice data or images), such as those provided by remote-sensing or geographical databases, this problem is particularly acute. Because analytic derivation of the null probability distribution of the test statistic (e.g. Pearson's r) is not always possible when autocorrelation is present, we propose instead the use of a Monte Carlo simulation with surrogate data. Methodology/Principal Findings The null hypothesis that two observed mapped variables are the result of independent pattern generating processes is tested here by generating sets of random image data while preserving the autocorrelation function of the original images. Surrogates are generated by matching the dual-tree complex wavelet spectra (and hence the autocorrelation functions) of white noise images with the spectra of the original images. The generated images can then be used to build the probability distribution function of any statistic of association under the null hypothesis. We demonstrate the validity of a statistical test of association based on these surrogates with both actual and synthetic data and compare it with a corrected parametric test and three existing methods that generate surrogates (randomization, random rotations and shifts, and iterative amplitude adjusted Fourier transform). Type I error control was excellent, even with strong and long-range autocorrelation, which is not the case for alternative methods. Conclusions/Significance The wavelet-based surrogates are particularly appropriate in cases where autocorrelation appears at all scales or is direction-dependent (anisotropy). We explore the potential of the method for association tests involving a lattice of binary data and discuss its potential for validation of species distribution models. An implementation of the method in Java for the generation of wavelet-based surrogates is available online as supporting material. PMID:23144961

Use of Multi-Resolution Wavelet Feature Pyramids for Automatic Registration of Multi-Sensor Imagery

NASA Technical Reports Server (NTRS)

Zavorin, Ilya; LeMoigne, Jacqueline

2003-01-01

The problem of image registration, or alignment of two or more images representing the same scene or object, has to be addressed in various disciplines that employ digital imaging. In the area of remote sensing, just like in medical imaging or computer vision, it is necessary to design robust, fast and widely applicable algorithms that would allow automatic registration of images generated by various imaging platforms at the same or different times, and that would provide sub-pixel accuracy. One of the main issues that needs to be addressed when developing a registration algorithm is what type of information should be extracted from the images being registered, to be used in the search for the geometric transformation that best aligns them. The main objective of this paper is to evaluate several wavelet pyramids that may be used both for invariant feature extraction and for representing images at multiple spatial resolutions to accelerate registration. We find that the band-pass wavelets obtained from the Steerable Pyramid due to Simoncelli perform better than two types of low-pass pyramids when the images being registered have relatively small amount of nonlinear radiometric variations between them. Based on these findings, we propose a modification of a gradient-based registration algorithm that has recently been developed for medical data. We test the modified algorithm on several sets of real and synthetic satellite imagery.

Filtering of the Radon transform to enhance linear signal features via wavelet pyramid decomposition

NASA Astrophysics Data System (ADS)

Meckley, John R.

1995-09-01

The information content in many signal processing applications can be reduced to a set of linear features in a 2D signal transform. Examples include the narrowband lines in a spectrogram, ship wakes in a synthetic aperture radar image, and blood vessels in a medical computer-aided tomography scan. The line integrals that generate the values of the projections of the Radon transform can be characterized as a bank of matched filters for linear features. This localization of energy in the Radon transform for linear features can be exploited to enhance these features and to reduce noise by filtering the Radon transform with a filter explicitly designed to pass only linear features, and then reconstructing a new 2D signal by inverting the new filtered Radon transform (i.e., via filtered backprojection). Previously used methods for filtering the Radon transform include Fourier based filtering (a 2D elliptical Gaussian linear filter) and a nonlinear filter ((Radon xfrm)**y with y >= 2.0). Both of these techniques suffer from the mismatch of the filter response to the true functional form of the Radon transform of a line. The Radon transform of a line is not a point but is a function of the Radon variables (rho, theta) and the total line energy. This mismatch leads to artifacts in the reconstructed image and a reduction in achievable processing gain. The Radon transform for a line is computed as a function of angle and offset (rho, theta) and the line length. The 2D wavelet coefficients are then compared for the Haar wavelets and the Daubechies wavelets. These filter responses are used as frequency filters for the Radon transform. The filtering is performed on the wavelet pyramid decomposition of the Radon transform by detecting the most likely positions of lines in the transform and then by convolving the local area with the appropriate response and zeroing the pyramid coefficients outside of the response area. The response area is defined to contain 95% of the total wavelet coefficient energy. The detection algorithm provides an estimate of the line offset, orientation, and length that is then used to index the appropriate filter shape. Additional wavelet pyramid decomposition is performed in areas of high energy to refine the line position estimate. After filtering, the new Radon transform is generated by inverting the wavelet pyramid. The Radon transform is then inverted by filtered backprojection to produce the final 2D signal estimate with the enhanced linear features. The wavelet-based method is compared to both the Fourier and the nonlinear filtering with examples of sparse and dense shapes in imaging, acoustics and medical tomography with test images of noisy concentric lines, a real spectrogram of a blow fish (a very nonstationary spectrum), and the Shepp Logan Computer Tomography phantom image. Both qualitative and derived quantitative measures demonstrate the improvement of wavelet-based filtering. Additional research is suggested based on these results. Open questions include what level(s) to use for detection and filtering because multiple-level representations exist. The lower levels are smoother at reduced spatial resolution, while the higher levels provide better response to edges. Several examples are discussed based on analytical and phenomenological arguments.

Multispectral image sharpening using wavelet transform techniques and spatial correlation of edges

USGS Publications Warehouse

Lemeshewsky, George P.; Schowengerdt, Robert A.

2000-01-01

Several reported image fusion or sharpening techniques are based on the discrete wavelet transform (DWT). The technique described here uses a pixel-based maximum selection rule to combine respective transform coefficients of lower spatial resolution near-infrared (NIR) and higher spatial resolution panchromatic (pan) imagery to produce a sharpened NIR image. Sharpening assumes a radiometric correlation between the spectral band images. However, there can be poor correlation, including edge contrast reversals (e.g., at soil-vegetation boundaries), between the fused images and, consequently, degraded performance. To improve sharpening, a local area-based correlation technique originally reported for edge comparison with image pyramid fusion is modified for application with the DWT process. Further improvements are obtained by using redundant, shift-invariant implementation of the DWT. Example images demonstrate the improvements in NIR image sharpening with higher resolution pan imagery.

Travel time tomography with local image regularization by sparsity constrained dictionary learning

NASA Astrophysics Data System (ADS)

Bianco, M.; Gerstoft, P.

2017-12-01

We propose a regularization approach for 2D seismic travel time tomography which models small rectangular groups of slowness pixels, within an overall or `global' slowness image, as sparse linear combinations of atoms from a dictionary. The groups of slowness pixels are referred to as patches and a dictionary corresponds to a collection of functions or `atoms' describing the slowness in each patch. These functions could for example be wavelets.The patch regularization is incorporated into the global slowness image. The global image models the broad features, while the local patch images incorporate prior information from the dictionary. Further, high resolution slowness within patches is permitted if the travel times from the global estimates support it. The proposed approach is formulated as an algorithm, which is repeated until convergence is achieved: 1) From travel times, find the global slowness image with a minimum energy constraint on the pixel variance relative to a reference. 2) Find the patch level solutions to fit the global estimate as a sparse linear combination of dictionary atoms.3) Update the reference as the weighted average of the patch level solutions.This approach relies on the redundancy of the patches in the seismic image. Redundancy means that the patches are repetitions of a finite number of patterns, which are described by the dictionary atoms. Redundancy in the earth's structure was demonstrated in previous works in seismics where dictionaries of wavelet functions regularized inversion. We further exploit redundancy of the patches by using dictionary learning algorithms, a form of unsupervised machine learning, to estimate optimal dictionaries from the data in parallel with the inversion. We demonstrate our approach on densely, but irregularly sampled synthetic seismic images.

Early Detection of Amyloid Plaque in Alzheimer’s Disease via X-Ray Phase CT

DTIC Science & Technology

2014-06-01

normal, pathologic and Alzheimer’s brains, in which the amyloid precursor protein (APP) will be included as a reference. Toward this goal, we have made...in x-ray flat panel imagers and the artifact removal using a wavelet -analysis-based algorithm” Med. Phys., 28(3): 812-25, 2001. 4. X Wu and H Liu...panel imagers and the artifact removal using a wavelet -analysis-based algorithm” Med. Phys., 28(3): 812-25, 2001 12. Tang X, Hsieh J, Nilsen RA

Early Detection of Amyloid Plaque in Alzheimer’s Disease via X-Ray Phase CT

DTIC Science & Technology

2013-06-01

fibrils in the x-ray phase contrast CT imaging, as a function over the molar concentrations corresponding to normal, pathologic and Alzheimer’s...panel imagers and the artifact removal using a wavelet -analysis-based algorithm” Med. Phys., 28(3): 812-25, 2001. 4. X Wu and H Liu, “Clinical...and the artifact removal using a wavelet -analysis-based algorithm” Med. Phys., 28(3): 812-25, 2001 12. Tang X, Hsieh J, Nilsen RA, Hagiwara A

Pixel-level multisensor image fusion based on matrix completion and robust principal component analysis

NASA Astrophysics Data System (ADS)

Wang, Zhuozheng; Deller, J. R.; Fleet, Blair D.

2016-01-01

Acquired digital images are often corrupted by a lack of camera focus, faulty illumination, or missing data. An algorithm is presented for fusion of multiple corrupted images of a scene using the lifting wavelet transform. The method employs adaptive fusion arithmetic based on matrix completion and self-adaptive regional variance estimation. Characteristics of the wavelet coefficients are used to adaptively select fusion rules. Robust principal component analysis is applied to low-frequency image components, and regional variance estimation is applied to high-frequency components. Experiments reveal that the method is effective for multifocus, visible-light, and infrared image fusion. Compared with traditional algorithms, the new algorithm not only increases the amount of preserved information and clarity but also improves robustness.

The FBI compression standard for digitized fingerprint images

DOE Office of Scientific and Technical Information (OSTI.GOV)

Brislawn, C.M.; Bradley, J.N.; Onyshczak, R.J.

1996-10-01

The FBI has formulated national standards for digitization and compression of gray-scale fingerprint images. The compression algorithm for the digitized images is based on adaptive uniform scalar quantization of a discrete wavelet transform subband decomposition, a technique referred to as the wavelet/scalar quantization method. The algorithm produces archival-quality images at compression ratios of around 15 to 1 and will allow the current database of paper fingerprint cards to be replaced by digital imagery. A compliance testing program is also being implemented to ensure high standards of image quality and interchangeability of data between different implementations. We will review the currentmore » status of the FBI standard, including the compliance testing process and the details of the first-generation encoder.« less

FBI compression standard for digitized fingerprint images

NASA Astrophysics Data System (ADS)

Brislawn, Christopher M.; Bradley, Jonathan N.; Onyshczak, Remigius J.; Hopper, Thomas

1996-11-01

The FBI has formulated national standards for digitization and compression of gray-scale fingerprint images. The compression algorithm for the digitized images is based on adaptive uniform scalar quantization of a discrete wavelet transform subband decomposition, a technique referred to as the wavelet/scalar quantization method. The algorithm produces archival-quality images at compression ratios of around 15 to 1 and will allow the current database of paper fingerprint cards to be replaced by digital imagery. A compliance testing program is also being implemented to ensure high standards of image quality and interchangeability of data between different implementations. We will review the current status of the FBI standard, including the compliance testing process and the details of the first-generation encoder.

A robust watermarking scheme using lifting wavelet transform and singular value decomposition

NASA Astrophysics Data System (ADS)

Bhardwaj, Anuj; Verma, Deval; Verma, Vivek Singh

2017-01-01

The present paper proposes a robust image watermarking scheme using lifting wavelet transform (LWT) and singular value decomposition (SVD). Second level LWT is applied on host/cover image to decompose into different subbands. SVD is used to obtain singular values of watermark image and then these singular values are updated with the singular values of LH2 subband. The algorithm is tested on a number of benchmark images and it is found that the present algorithm is robust against different geometric and image processing operations. A comparison of the proposed scheme is performed with other existing schemes and observed that the present scheme is better not only in terms of robustness but also in terms of imperceptibility.

Identification of large geomorphological anomalies based on 2D discrete wavelet transform

NASA Astrophysics Data System (ADS)

Doglioni, A.; Simeone, V.

2012-04-01

The identification and analysis based on quantitative evidences of large geomorphological anomalies is an important stage for the study of large landslides. Numerical geomorphic analyses represent an interesting approach to this kind of studies, allowing for a detailed and pretty accurate identification of hidden topographic anomalies that may be related to large landslides. Here a geomorphic numerical analyses of the Digital Terrain Model (DTM) is presented. The introduced approach is based on 2D discrete wavelet transform (Antoine et al., 2003; Bruun and Nilsen, 2003, Booth et al., 2009). The 2D wavelet decomposition of the DTM, and in particular the analysis of the detail coefficients of the wavelet transform can provide evidences of anomalies or singularities, i.e. discontinuities of the land surface. These discontinuities are not very evident from the DTM as it is, while 2D wavelet transform allows for grid-based analysis of DTM and for mapping the decomposition. In fact, the grid-based DTM can be assumed as a matrix, where a discrete wavelet transform (Daubechies, 1992) is performed columnwise and linewise, which basically represent horizontal and vertical directions. The outcomes of this analysis are low-frequency approximation coefficients and high-frequency detail coefficients. Detail coefficients are analyzed, since their variations are associated to discontinuities of the DTM. Detailed coefficients are estimated assuming to perform 2D wavelet transform both for the horizontal direction (east-west) and for the vertical direction (north-south). Detail coefficients are then mapped for both the cases, thus allowing to visualize and quantify potential anomalies of the land surface. Moreover, wavelet decomposition can be pushed to further levels, assuming a higher scale number of the transform. This may potentially return further interesting results, in terms of identification of the anomalies of land surface. In this kind of approach, the choice of a proper mother wavelet function is a tricky point, since it conditions the analysis and then their outcomes. Therefore multiple levels as well as multiple wavelet analyses are guessed. Here the introduced approach is applied to some interesting cases study of south Italy, in particular for the identification of large anomalies associated to large landslides at the transition between Apennine chain domain and the foredeep domain. In particular low Biferno valley and Fortore valley are here analyzed. Finally, the wavelet transforms are performed on multiple levels, thus trying to address the problem of which is the level extent for an accurate analysis fit to a specific problem. Antoine J.P., Carrette P., Murenzi R., and Piette B., (2003), Image analysis with two-dimensional continuous wavelet transform, Signal Processing, 31(3), pp. 241-272, doi:10.1016/0165-1684(93)90085-O. Booth A.M., Roering J.J., and Taylor Perron J., (2009), Automated landslide mapping using spectral analysis and high-resolution topographic data: Puget Sound lowlands, Washington, and Portland Hills, Oregon, Geomorphology, 109(3-4), pp. 132-147, doi:10.1016/j.geomorph.2009.02.027. Bruun B.T., and Nilsen S., (2003), Wavelet representation of large digital terrain models, Computers and Geoscience, 29(6), pp. 695-703, doi:10.1016/S0098-3004(03)00015-3. Daubechies, I. (1992), Ten lectures on wavelets, SIAM.

3D Texture Analysis in Renal Cell Carcinoma Tissue Image Grading

PubMed Central

Cho, Nam-Hoon; Choi, Heung-Kook

2014-01-01

One of the most significant processes in cancer cell and tissue image analysis is the efficient extraction of features for grading purposes. This research applied two types of three-dimensional texture analysis methods to the extraction of feature values from renal cell carcinoma tissue images, and then evaluated the validity of the methods statistically through grade classification. First, we used a confocal laser scanning microscope to obtain image slices of four grades of renal cell carcinoma, which were then reconstructed into 3D volumes. Next, we extracted quantitative values using a 3D gray level cooccurrence matrix (GLCM) and a 3D wavelet based on two types of basis functions. To evaluate their validity, we predefined 6 different statistical classifiers and applied these to the extracted feature sets. In the grade classification results, 3D Haar wavelet texture features combined with principal component analysis showed the best discrimination results. Classification using 3D wavelet texture features was significantly better than 3D GLCM, suggesting that the former has potential for use in a computer-based grading system. PMID:25371701

Comparative Study of Speckle Filtering Methods in PolSAR Radar Images

NASA Astrophysics Data System (ADS)

Boutarfa, S.; Bouchemakh, L.; Smara, Y.

2015-04-01

Images acquired by polarimetric SAR (PolSAR) radar systems are characterized by the presence of a noise called speckle. This noise has a multiplicative nature, corrupts both the amplitude and phase images, which complicates data interpretation, degrades segmentation performance and reduces the detectability of targets. Hence, the need to preprocess the images by adapted filtering methods before analysis.In this paper, we present a comparative study of implemented methods for reducing speckle in PolSAR images. These developed filters are: refined Lee filter based on the estimation of the minimum mean square error MMSE, improved Sigma filter with detection of strong scatterers based on the calculation of the coherency matrix to detect the different scatterers in order to preserve the polarization signature and maintain structures that are necessary for image interpretation, filtering by stationary wavelet transform SWT using multi-scale edge detection and the technique for improving the wavelet coefficients called SSC (sum of squared coefficients), and Turbo filter which is a combination between two complementary filters the refined Lee filter and the wavelet transform SWT. One filter can boost up the results of the other.The originality of our work is based on the application of these methods to several types of images: amplitude, intensity and complex, from a satellite or an airborne radar, and on the optimization of wavelet filtering by adding a parameter in the calculation of the threshold. This parameter will control the filtering effect and get a good compromise between smoothing homogeneous areas and preserving linear structures.The methods are applied to the fully polarimetric RADARSAT-2 images (HH, HV, VH, VV) acquired on Algiers, Algeria, in C-band and to the three polarimetric E-SAR images (HH, HV, VV) acquired on Oberpfaffenhofen area located in Munich, Germany, in P-band.To evaluate the performance of each filter, we used the following criteria: smoothing homogeneous areas, preserving edges and polarimetric information.Experimental results are included to illustrate the different implemented methods.

Research to improve the accuracy of determining the stroke volume of an artificial ventricle using the wavelet transform

NASA Astrophysics Data System (ADS)

Grad, Leszek; Murawski, Krzysztof; Sulej, Wojciech

2017-08-01

In the article we presented results obtained during research, which are the continuation of work on the use of artificial neural networks to determine the relationship between the view of the membrane and the stroke volume of the blood chamber of the mechanical prosthetic heart. The purpose of the research was to increase the accuracy of determining the blood chamber volume. Therefore, the study was focused on the technique of the features that the image extraction gives. During research we used the wavelet transform. The achieved results were compared to the results obtained by other previous methods. Tests were conducted on the same mechanical prosthetic heart model used in previous experiments.

Wavelet-based techniques for the gamma-ray sky

DOE Office of Scientific and Technical Information (OSTI.GOV)

McDermott, Samuel D.; Fox, Patrick J.; Cholis, Ilias

2016-07-01

Here, we demonstrate how the image analysis technique of wavelet decomposition can be applied to the gamma-ray sky to separate emission on different angular scales. New structures on scales that differ from the scales of the conventional astrophysical foreground and background uncertainties can be robustly extracted, allowing a model-independent characterization with no presumption of exact signal morphology. As a test case, we generate mock gamma-ray data to demonstrate our ability to extract extended signals without assuming a fixed spatial template. For some point source luminosity functions, our technique also allows us to differentiate a diffuse signal in gamma-rays from darkmore » matter annihilation and extended gamma-ray point source populations in a data-driven way.« less

[Recognition of landscape characteristic scale based on two-dimension wavelet analysis].

PubMed

Gao, Yan-Ni; Chen, Wei; He, Xing-Yuan; Li, Xiao-Yu

2010-06-01

Three wavelet bases, i. e., Haar, Daubechies, and Symlet, were chosen to analyze the validity of two-dimension wavelet analysis in recognizing the characteristic scales of the urban, peri-urban, and rural landscapes of Shenyang. Owing to the transform scale of two-dimension wavelet must be the integer power of 2, some characteristic scales cannot be accurately recognized. Therefore, the pixel resolution of images was resampled to 3, 3.5, 4, and 4.5 m to densify the scale in analysis. It was shown that two-dimension wavelet analysis worked effectively in checking characteristic scale. Haar, Daubechies, and Symle were the optimal wavelet bases to the peri-urban landscape, urban landscape, and rural landscape, respectively. Both Haar basis and Symlet basis played good roles in recognizing the fine characteristic scale of rural landscape and in detecting the boundary of peri-urban landscape. Daubechies basis and Symlet basis could be also used to detect the boundary of urban landscape and rural landscape, respectively.

Wavelet-enhanced convolutional neural network: a new idea in a deep learning paradigm.

PubMed

Savareh, Behrouz Alizadeh; Emami, Hassan; Hajiabadi, Mohamadreza; Azimi, Seyed Majid; Ghafoori, Mahyar

2018-05-29

Manual brain tumor segmentation is a challenging task that requires the use of machine learning techniques. One of the machine learning techniques that has been given much attention is the convolutional neural network (CNN). The performance of the CNN can be enhanced by combining other data analysis tools such as wavelet transform. In this study, one of the famous implementations of CNN, a fully convolutional network (FCN), was used in brain tumor segmentation and its architecture was enhanced by wavelet transform. In this combination, a wavelet transform was used as a complementary and enhancing tool for CNN in brain tumor segmentation. Comparing the performance of basic FCN architecture against the wavelet-enhanced form revealed a remarkable superiority of enhanced architecture in brain tumor segmentation tasks. Using mathematical functions and enhancing tools such as wavelet transform and other mathematical functions can improve the performance of CNN in any image processing task such as segmentation and classification.

Evaluating the Efficacy of Wavelet Configurations on Turbulent-Flow Data

DOE Office of Scientific and Technical Information (OSTI.GOV)

Li, Shaomeng; Gruchalla, Kenny; Potter, Kristin

2015-10-25

I/O is increasingly becoming a significant constraint for simulation codes and visualization tools on modern supercomputers. Data compression is an attractive workaround, and, in particular, wavelets provide a promising solution. However, wavelets can be applied in multiple configurations, and the variations in configuration impact accuracy, storage cost, and execution time. While the variation in these factors over wavelet configurations have been explored in image processing, they are not well understood for visualization and analysis of scientific data. To illuminate this issue, we evaluate multiple wavelet configurations on turbulent-flow data. Our approach is to repeat established analysis routines on uncompressed andmore » lossy-compressed versions of a data set, and then quantitatively compare their outcomes. Our findings show that accuracy varies greatly based on wavelet configuration, while storage cost and execution time vary less. Overall, our study provides new insights for simulation analysts and visualization experts, who need to make tradeoffs between accuracy, storage cost, and execution time.« less

Pre-processing, registration and selection of adaptive optics corrected retinal images.

PubMed

Ramaswamy, Gomathy; Devaney, Nicholas

2013-07-01

In this paper, the aim is to demonstrate enhanced processing of sequences of fundus images obtained using a commercial AO flood illumination system. The purpose of the work is to (1) correct for uneven illumination at the retina (2) automatically select the best quality images and (3) precisely register the best images. Adaptive optics corrected retinal images are pre-processed to correct uneven illumination using different methods; subtracting or dividing by the average filtered image, homomorphic filtering and a wavelet based approach. These images are evaluated to measure the image quality using various parameters, including sharpness, variance, power spectrum kurtosis and contrast. We have carried out the registration in two stages; a coarse stage using cross-correlation followed by fine registration using two approaches; parabolic interpolation on the peak of the cross-correlation and maximum-likelihood estimation. The angle of rotation of the images is measured using a combination of peak tracking and Procrustes transformation. We have found that a wavelet approach (Daubechies 4 wavelet at 6th level decomposition) provides good illumination correction with clear improvement in image sharpness and contrast. The assessment of image quality using a 'Designer metric' works well when compared to visual evaluation, although it is highly correlated with other metrics. In image registration, sub-pixel translation measured using parabolic interpolation on the peak of the cross-correlation function and maximum-likelihood estimation are found to give very similar results (RMS difference 0.047 pixels). We have confirmed that correcting rotation of the images provides a significant improvement, especially at the edges of the image. We observed that selecting the better quality frames (e.g. best 75% images) for image registration gives improved resolution, at the expense of poorer signal-to-noise. The sharpness map of the registered and de-rotated images shows increased sharpness over most of the field of view. Adaptive optics assisted images of the cone photoreceptors can be better pre-processed using a wavelet approach. These images can be assessed for image quality using a 'Designer Metric'. Two-stage image registration including correcting for rotation significantly improves the final image contrast and sharpness. © 2013 The Authors Ophthalmic & Physiological Optics © 2013 The College of Optometrists.

Hierarchical and successive approximate registration of the non-rigid medical image based on thin-plate splines

NASA Astrophysics Data System (ADS)

Hu, Jinyan; Li, Li; Yang, Yunfeng

2017-06-01

The hierarchical and successive approximate registration method of non-rigid medical image based on the thin-plate splines is proposed in the paper. There are two major novelties in the proposed method. First, the hierarchical registration based on Wavelet transform is used. The approximate image of Wavelet transform is selected as the registered object. Second, the successive approximation registration method is used to accomplish the non-rigid medical images registration, i.e. the local regions of the couple images are registered roughly based on the thin-plate splines, then, the current rough registration result is selected as the object to be registered in the following registration procedure. Experiments show that the proposed method is effective in the registration process of the non-rigid medical images.

Automated pathologies detection in retina digital images based on complex continuous wavelet transform phase angles.

PubMed

Lahmiri, Salim; Gargour, Christian S; Gabrea, Marcel

2014-10-01

An automated diagnosis system that uses complex continuous wavelet transform (CWT) to process retina digital images and support vector machines (SVMs) for classification purposes is presented. In particular, each retina image is transformed into two one-dimensional signals by concatenating image rows and columns separately. The mathematical norm of phase angles found in each one-dimensional signal at each level of CWT decomposition are relied on to characterise the texture of normal images against abnormal images affected by exudates, drusen and microaneurysms. The leave-one-out cross-validation method was adopted to conduct experiments and the results from the SVM show that the proposed approach gives better results than those obtained by other methods based on the correct classification rate, sensitivity and specificity.

Fusing Image Data for Calculating Position of an Object

NASA Technical Reports Server (NTRS)

Huntsberger, Terrance; Cheng, Yang; Liebersbach, Robert; Trebi-Ollenu, Ashitey

2007-01-01

A computer program has been written for use in maintaining the calibration, with respect to the positions of imaged objects, of a stereoscopic pair of cameras on each of the Mars Explorer Rovers Spirit and Opportunity. The program identifies and locates a known object in the images. The object in question is part of a Moessbauer spectrometer located at the tip of a robot arm, the kinematics of which are known. In the program, the images are processed through a module that extracts edges, combines the edges into line segments, and then derives ellipse centroids from the line segments. The images are also processed by a feature-extraction algorithm that performs a wavelet analysis, then performs a pattern-recognition operation in the wavelet-coefficient space to determine matches to a texture feature measure derived from the horizontal, vertical, and diagonal coefficients. The centroids from the ellipse finder and the wavelet feature matcher are then fused to determine co-location. In the event that a match is found, the centroid (or centroids if multiple matches are present) is reported. If no match is found, the process reports the results of the analyses for further examination by human experts.

Wavelet transform analysis of the small-scale X-ray structure of the cluster Abell 1367

NASA Technical Reports Server (NTRS)

Grebeney, S. A.; Forman, W.; Jones, C.; Murray, S.

1995-01-01

We have developed a new technique based on a wavelet transform analysis to quantify the small-scale (less than a few arcminutes) X-ray structure of clusters of galaxies. We apply this technique to the ROSAT position sensitive proportional counter (PSPC) and Einstein high-resolution imager (HRI) images of the central region of the cluster Abell 1367 to detect sources embedded within the diffuse intracluster medium. In addition to detecting sources and determining their fluxes and positions, we show that the wavelet analysis allows a characterization of the sources extents. In particular, the wavelet scale at which a given source achieves a maximum signal-to-noise ratio in the wavelet images provides an estimate of the angular extent of the source. To account for the widely varying point response of the ROSAT PSPC as a function of off-axis angle requires a quantitative measurement of the source size and a comparison to a calibration derived from the analysis of a Deep Survey image. Therefore, we assume that each source could be described as an isotropic two-dimensional Gaussian and used the wavelet amplitudes, at different scales, to determine the equivalent Gaussian Full Width Half-Maximum (FWHM) (and its uncertainty) appropriate for each source. In our analysis of the ROSAT PSPC image, we detect 31 X-ray sources above the diffuse cluster emission (within a radius of 24 min), 16 of which are apparently associated with cluster galaxies and two with serendipitous, background quasars. We find that the angular extents of 11 sources exceed the nominal width of the PSPC point-spread function. Four of these extended sources were previously detected by Bechtold et al. (1983) as 1 sec scale features using the Einstein HRI. The same wavelet analysis technique was applied to the Einstein HRI image. We detect 28 sources in the HRI image, of which nine are extended. Eight of the extended sources correspond to sources previously detected by Bechtold et al. Overall, using both the PSPC and the HRI observations, we detect 16 extended features, of which nine have galaxies coincided with the X-ray-measured positions (within the positional error circles). These extended sources have luminosities lying in the range (3 - 30) x 10(exp 40) ergs/s and gas masses of approximately (1 - 30) x 10(exp 9) solar mass, if the X-rays are of thermal origin. We confirm the presence of extended features in A1367 first reported by Bechtold et al. (1983). The nature of these systems remains uncertain. The luminosities are large if the emission is attributed to single galaxies, and several of the extended features have no associated galaxy counterparts. The extended features may be associated with galaxy groups, as suggested by Canizares, Fabbiano, & Trinchieri (1987), although the number required is large.

Wavelet library for constrained devices

NASA Astrophysics Data System (ADS)

Ehlers, Johan Hendrik; Jassim, Sabah A.

2007-04-01

The wavelet transform is a powerful tool for image and video processing, useful in a range of applications. This paper is concerned with the efficiency of a certain fast-wavelet-transform (FWT) implementation and several wavelet filters, more suitable for constrained devices. Such constraints are typically found on mobile (cell) phones or personal digital assistants (PDA). These constraints can be a combination of; limited memory, slow floating point operations (compared to integer operations, most often as a result of no hardware support) and limited local storage. Yet these devices are burdened with demanding tasks such as processing a live video or audio signal through on-board capturing sensors. In this paper we present a new wavelet software library, HeatWave, that can be used efficiently for image/video processing/analysis tasks on mobile phones and PDA's. We will demonstrate that HeatWave is suitable for realtime applications with fine control and range to suit transform demands. We shall present experimental results to substantiate these claims. Finally this library is intended to be of real use and applied, hence we considered several well known and common embedded operating system platform differences; such as a lack of common routines or functions, stack limitations, etc. This makes HeatWave suitable for a range of applications and research projects.

Joint sparse reconstruction of multi-contrast MRI images with graph based redundant wavelet transform.

PubMed

Lai, Zongying; Zhang, Xinlin; Guo, Di; Du, Xiaofeng; Yang, Yonggui; Guo, Gang; Chen, Zhong; Qu, Xiaobo

2018-05-03

Multi-contrast images in magnetic resonance imaging (MRI) provide abundant contrast information reflecting the characteristics of the internal tissues of human bodies, and thus have been widely utilized in clinical diagnosis. However, long acquisition time limits the application of multi-contrast MRI. One efficient way to accelerate data acquisition is to under-sample the k-space data and then reconstruct images with sparsity constraint. However, images are compromised at high acceleration factor if images are reconstructed individually. We aim to improve the images with a jointly sparse reconstruction and Graph-based redundant wavelet transform (GBRWT). First, a sparsifying transform, GBRWT, is trained to reflect the similarity of tissue structures in multi-contrast images. Second, joint multi-contrast image reconstruction is formulated as a ℓ 2, 1 norm optimization problem under GBRWT representations. Third, the optimization problem is numerically solved using a derived alternating direction method. Experimental results in synthetic and in vivo MRI data demonstrate that the proposed joint reconstruction method can achieve lower reconstruction errors and better preserve image structures than the compared joint reconstruction methods. Besides, the proposed method outperforms single image reconstruction with joint sparsity constraint of multi-contrast images. The proposed method explores the joint sparsity of multi-contrast MRI images under graph-based redundant wavelet transform and realizes joint sparse reconstruction of multi-contrast images. Experiment demonstrate that the proposed method outperforms the compared joint reconstruction methods as well as individual reconstructions. With this high quality image reconstruction method, it is possible to achieve the high acceleration factors by exploring the complementary information provided by multi-contrast MRI.

TWave: High-Order Analysis of Functional MRI

PubMed Central

Barnathan, Michael; Megalooikonomou, Vasileios; Faloutsos, Christos; Faro, Scott; Mohamed, Feroze B.

2011-01-01

The traditional approach to functional image analysis models images as matrices of raw voxel intensity values. Although such a representation is widely utilized and heavily entrenched both within neuroimaging and in the wider data mining community, the strong interactions among space, time, and categorical modes such as subject and experimental task inherent in functional imaging yield a dataset with “high-order” structure, which matrix models are incapable of exploiting. Reasoning across all of these modes of data concurrently requires a high-order model capable of representing relationships between all modes of the data in tandem. We thus propose to model functional MRI data using tensors, which are high-order generalizations of matrices equivalent to multidimensional arrays or data cubes. However, several unique challenges exist in the high-order analysis of functional medical data: naïve tensor models are incapable of exploiting spatiotemporal locality patterns, standard tensor analysis techniques exhibit poor efficiency, and mixtures of numeric and categorical modes of data are very often present in neuroimaging experiments. Formulating the problem of image clustering as a form of Latent Semantic Analysis and using the WaveCluster algorithm as a baseline, we propose a comprehensive hybrid tensor and wavelet framework for clustering, concept discovery, and compression of functional medical images which successfully addresses these challenges. Our approach reduced runtime and dataset size on a 9.3 GB finger opposition motor task fMRI dataset by up to 98% while exhibiting improved spatiotemporal coherence relative to standard tensor, wavelet, and voxel-based approaches. Our clustering technique was capable of automatically differentiating between the frontal areas of the brain responsible for task-related habituation and the motor regions responsible for executing the motor task, in contrast to a widely used fMRI analysis program, SPM, which only detected the latter region. Furthermore, our approach discovered latent concepts suggestive of subject handedness nearly 100x faster than standard approaches. These results suggest that a high-order model is an integral component to accurate scalable functional neuroimaging. PMID:21729758

A novel method for 3D measurement of RFID multi-tag network based on matching vision and wavelet

NASA Astrophysics Data System (ADS)

Zhuang, Xiao; Yu, Xiaolei; Zhao, Zhimin; Wang, Donghua; Zhang, Wenjie; Liu, Zhenlu; Lu, Dongsheng; Dong, Dingbang

2018-07-01

In the field of radio frequency identification (RFID), the three-dimensional (3D) distribution of RFID multi-tag networks has a significant impact on their reading performance. At the same time, in order to realize the anti-collision of RFID multi-tag networks in practical engineering applications, the 3D distribution of RFID multi-tag networks must be measured. In this paper, a novel method for the 3D measurement of RFID multi-tag networks is proposed. A dual-CCD system (vertical and horizontal cameras) is used to obtain images of RFID multi-tag networks from different angles. Then, the wavelet threshold denoising method is used to remove noise in the obtained images. The template matching method is used to determine the two-dimensional coordinates and vertical coordinate of each tag. The 3D coordinates of each tag are obtained subsequently. Finally, a model of the nonlinear relation between the 3D coordinate distribution of the RFID multi-tag network and the corresponding reading distance is established using the wavelet neural network. The experiment results show that the average prediction relative error is 0.71% and the time cost is 2.17 s. The values of the average prediction relative error and time cost are smaller than those of the particle swarm optimization neural network and genetic algorithm–back propagation neural network. The time cost of the wavelet neural network is about 1% of that of the other two methods. The method proposed in this paper has a smaller relative error. The proposed method can improve the real-time performance of RFID multi-tag networks and the overall dynamic performance of multi-tag networks.

Illumination-tolerant face verification of low-bit-rate JPEG2000 wavelet images with advanced correlation filters for handheld devices

NASA Astrophysics Data System (ADS)

Wijaya, Surya Li; Savvides, Marios; Vijaya Kumar, B. V. K.

2005-02-01

Face recognition on mobile devices, such as personal digital assistants and cell phones, is a big challenge owing to the limited computational resources available to run verifications on the devices themselves. One approach is to transmit the captured face images by use of the cell-phone connection and to run the verification on a remote station. However, owing to limitations in communication bandwidth, it may be necessary to transmit a compressed version of the image. We propose using the image compression standard JPEG2000, which is a wavelet-based compression engine used to compress the face images to low bit rates suitable for transmission over low-bandwidth communication channels. At the receiver end, the face images are reconstructed with a JPEG2000 decoder and are fed into the verification engine. We explore how advanced correlation filters, such as the minimum average correlation energy filter [Appl. Opt. 26, 3633 (1987)] and its variants, perform by using face images captured under different illumination conditions and encoded with different bit rates under the JPEG2000 wavelet-encoding standard. We evaluate the performance of these filters by using illumination variations from the Carnegie Mellon University's Pose, Illumination, and Expression (PIE) face database. We also demonstrate the tolerance of these filters to noisy versions of images with illumination variations.

Coherent multiscale image processing using dual-tree quaternion wavelets.

PubMed

Chan, Wai Lam; Choi, Hyeokho; Baraniuk, Richard G

2008-07-01

The dual-tree quaternion wavelet transform (QWT) is a new multiscale analysis tool for geometric image features. The QWT is a near shift-invariant tight frame representation whose coefficients sport a magnitude and three phases: two phases encode local image shifts while the third contains image texture information. The QWT is based on an alternative theory for the 2-D Hilbert transform and can be computed using a dual-tree filter bank with linear computational complexity. To demonstrate the properties of the QWT's coherent magnitude/phase representation, we develop an efficient and accurate procedure for estimating the local geometrical structure of an image. We also develop a new multiscale algorithm for estimating the disparity between a pair of images that is promising for image registration and flow estimation applications. The algorithm features multiscale phase unwrapping, linear complexity, and sub-pixel estimation accuracy.

A new set of wavelet- and fractals-based features for Gleason grading of prostate cancer histopathology images

NASA Astrophysics Data System (ADS)

Mosquera Lopez, Clara; Agaian, Sos

2013-02-01

Prostate cancer detection and staging is an important step towards patient treatment selection. Advancements in digital pathology allow the application of new quantitative image analysis algorithms for computer-assisted diagnosis (CAD) on digitized histopathology images. In this paper, we introduce a new set of features to automatically grade pathological images using the well-known Gleason grading system. The goal of this study is to classify biopsy images belonging to Gleason patterns 3, 4, and 5 by using a combination of wavelet and fractal features. For image classification we use pairwise coupling Support Vector Machine (SVM) classifiers. The accuracy of the system, which is close to 97%, is estimated through three different cross-validation schemes. The proposed system offers the potential for automating classification of histological images and supporting prostate cancer diagnosis.

Shift-invariant discrete wavelet transform analysis for retinal image classification.

PubMed

Khademi, April; Krishnan, Sridhar

2007-12-01

This work involves retinal image classification and a novel analysis system was developed. From the compressed domain, the proposed scheme extracts textural features from wavelet coefficients, which describe the relative homogeneity of localized areas of the retinal images. Since the discrete wavelet transform (DWT) is shift-variant, a shift-invariant DWT was explored to ensure that a robust feature set was extracted. To combat the small database size, linear discriminant analysis classification was used with the leave one out method. 38 normal and 48 abnormal (exudates, large drusens, fine drusens, choroidal neovascularization, central vein and artery occlusion, histoplasmosis, arteriosclerotic retinopathy, hemi-central retinal vein occlusion and more) were used and a specificity of 79% and sensitivity of 85.4% were achieved (the average classification rate is 82.2%). The success of the system can be accounted to the highly robust feature set which included translation, scale and semi-rotational, features. Additionally, this technique is database independent since the features were specifically tuned to the pathologies of the human eye.

The whole number axis integer linear transformation reversible information hiding algorithm on wavelet domain

NASA Astrophysics Data System (ADS)

Jiang, Zhuo; Xie, Chengjun

2013-12-01

This paper improved the algorithm of reversible integer linear transform on finite interval [0,255], which can realize reversible integer linear transform in whole number axis shielding data LSB (least significant bit). Firstly, this method use integer wavelet transformation based on lifting scheme to transform the original image, and select the transformed high frequency areas as information hiding area, meanwhile transform the high frequency coefficients blocks in integer linear way and embed the secret information in LSB of each coefficient, then information hiding by embedding the opposite steps. To extract data bits and recover the host image, a similar reverse procedure can be conducted, and the original host image can be lossless recovered. The simulation experimental results show that this method has good secrecy and concealment, after conducted the CDF (m, n) and DD (m, n) series of wavelet transformed. This method can be applied to information security domain, such as medicine, law and military.

Wavelet-linear genetic programming: A new approach for modeling monthly streamflow

NASA Astrophysics Data System (ADS)

Ravansalar, Masoud; Rajaee, Taher; Kisi, Ozgur

2017-06-01

The streamflows are important and effective factors in stream ecosystems and its accurate prediction is an essential and important issue in water resources and environmental engineering systems. A hybrid wavelet-linear genetic programming (WLGP) model, which includes a discrete wavelet transform (DWT) and a linear genetic programming (LGP) to predict the monthly streamflow (Q) in two gauging stations, Pataveh and Shahmokhtar, on the Beshar River at the Yasuj, Iran were used in this study. In the proposed WLGP model, the wavelet analysis was linked to the LGP model where the original time series of streamflow were decomposed into the sub-time series comprising wavelet coefficients. The results were compared with the single LGP, artificial neural network (ANN), a hybrid wavelet-ANN (WANN) and Multi Linear Regression (MLR) models. The comparisons were done by some of the commonly utilized relevant physical statistics. The Nash coefficients (E) were found as 0.877 and 0.817 for the WLGP model, for the Pataveh and Shahmokhtar stations, respectively. The comparison of the results showed that the WLGP model could significantly increase the streamflow prediction accuracy in both stations. Since, the results demonstrate a closer approximation of the peak streamflow values by the WLGP model, this model could be utilized for the simulation of cumulative streamflow data prediction in one month ahead.

Cryptosystem for Securing Image Encryption Using Structured Phase Masks in Fresnel Wavelet Transform Domain

NASA Astrophysics Data System (ADS)

Singh, Hukum

2016-12-01

A cryptosystem for securing image encryption is considered by using double random phase encoding in Fresnel wavelet transform (FWT) domain. Random phase masks (RPMs) and structured phase masks (SPMs) based on devil's vortex toroidal lens (DVTL) are used in spatial as well as in Fourier planes. The images to be encrypted are first Fresnel transformed and then single-level discrete wavelet transform (DWT) is apply to decompose LL,HL, LH and HH matrices. The resulting matrices from the DWT are multiplied by additional RPMs and the resultants are subjected to inverse DWT for the encrypted images. The scheme is more secure because of many parameters used in the construction of SPM. The original images are recovered by using the correct parameters of FWT and SPM. Phase mask SPM based on DVTL increases security that enlarges the key space for encryption and decryption. The proposed encryption scheme is a lens-less optical system and its digital implementation has been performed using MATLAB 7.6.0 (R2008a). The computed value of mean-squared-error between the retrieved and the input images shows the efficacy of scheme. The sensitivity to encryption parameters, robustness against occlusion, entropy and multiplicative Gaussian noise attacks have been analysed.

Roughness of the Mantle Transition Zone Discontinuities Revealed by High Resolution Wavefield Imaging with the Earthscope Transportable Array

NASA Astrophysics Data System (ADS)

Wang, Y.; Pavlis, G. L.

2015-12-01

We post-processed 141,080 pairs of high quality radial and transverse receiver functions from the Earthscope Automated Receiver Survey using a variant of what we have called generalized iterative deconvolution method and reshaped the spiking output into different scales of Ricker wavelets. We then used these data as input to our 3D plane wave migration method to produce an image volume of P to S scattering surfaces under all of the lower 48 states. The result is arguably the highest resolution image ever produce of the mantle transition zone. Due to the effect of migration impulse response, different scales of Ricker wavelets provide another important means of controlling the resolution of the image produced by 3D plane wave migration method. Model simulation shows that comparing to the widely used CCP stacking method with receiver functions shaped by Gaussian wavelet, the application of our methods is capable of resolving not only dipping discontinuities but also more subtle details of the discontinuities. Application to the latest USArray data reveals several previously unobserved features of the 410 and 660 discontinuities. Both discontinuities are resolved to a precision approaching 1 km under the stable interior, but degrading to the order of 10 km in the western US due to a probably combination of higher attenuation and velocity heterogeneity not resolved by current generation tomography models. Topography with many 10s of km is resolved at a range of scales. In addition, we observe large variation of relative amplitude on the radial component and large variations in the radial to transverse amplitude ratio that correlate with inferred variations in discontinuity topography. We argue this combination of observations can be explained by roughness at a range of scales. Roughness is consistent with the phase-change model for these discontinuities given there is little reason to think the mantle is homogeneous at these distance scales. Continental scale isopach of the transition zones shows the average thickness of the transition is approximately 15 km greater in the eastern US compared to the western US. This change occurs on a well define boundary roughly under the Mississippi River. The standard phase change model would thus predict higher transition zone temperatures on the western side of this boundary.

Rock classification based on resistivity patterns in electrical borehole wall images

NASA Astrophysics Data System (ADS)

Linek, Margarete; Jungmann, Matthias; Berlage, Thomas; Pechnig, Renate; Clauser, Christoph

2007-06-01

Electrical borehole wall images represent grey-level-coded micro-resistivity measurements at the borehole wall. Different scientific methods have been implemented to transform image data into quantitative log curves. We introduce a pattern recognition technique applying texture analysis, which uses second-order statistics based on studying the occurrence of pixel pairs. We calculate so-called Haralick texture features such as contrast, energy, entropy and homogeneity. The supervised classification method is used for assigning characteristic texture features to different rock classes and assessing the discriminative power of these image features. We use classifiers obtained from training intervals to characterize the entire image data set recovered in ODP hole 1203A. This yields a synthetic lithology profile based on computed texture data. We show that Haralick features accurately classify 89.9% of the training intervals. We obtained misclassification for vesicular basaltic rocks. Hence, further image analysis tools are used to improve the classification reliability. We decompose the 2D image signal by the application of wavelet transformation in order to enhance image objects horizontally, diagonally and vertically. The resulting filtered images are used for further texture analysis. This combined classification based on Haralick features and wavelet transformation improved our classification up to a level of 98%. The application of wavelet transformation increases the consistency between standard logging profiles and texture-derived lithology. Texture analysis of borehole wall images offers the potential to facilitate objective analysis of multiple boreholes with the same lithology.

Damage localization in aluminum plate with compact rectangular phased piezoelectric transducer array

NASA Astrophysics Data System (ADS)

Liu, Zenghua; Sun, Kunming; Song, Guorong; He, Cunfu; Wu, Bin

2016-03-01

In this work, a detection method for the damage in plate-like structure with a compact rectangular phased piezoelectric transducer array of 16 piezoelectric elements was presented. This compact array can not only detect and locate a single defect (through hole) in plate, but also identify multi-defects (through holes and surface defect simulated by an iron pillar glued to the plate). The experiments proved that the compact rectangular phased transducer array could detect the full range of plate structures and implement multiple-defect detection simultaneously. The processing algorithm proposed in this paper contains two parts: signal filtering and damage imaging. The former part was used to remove noise from signals. Continuous wavelet transform was applicable to signal filtering. Continuous wavelet transform can provide a plot of wavelet coefficients and the signal with narrow frequency band can be easily extracted from the plot. The latter part of processing algorithm was to implement damage detection and localization. In order to accurately locate defects and improve the imaging quality, two images were obtained from amplitude and phase information. One image was obtained with the Total Focusing Method (TFM) and another phase image was obtained with the Sign Coherence Factor (SCF). Furthermore, an image compounding technique for compact rectangular phased piezoelectric transducer array was proposed in this paper. With the proposed technique, the compounded image can be obtained by combining TFM image with SCF image, thus greatly improving the resolution and contrast of image.

Wavelet analysis techniques applied to removing varying spectroscopic background in calibration model for pear sugar content

NASA Astrophysics Data System (ADS)

Liu, Yande; Ying, Yibin; Lu, Huishan; Fu, Xiaping

2005-11-01

A new method is proposed to eliminate the varying background and noise simultaneously for multivariate calibration of Fourier transform near infrared (FT-NIR) spectral signals. An ideal spectrum signal prototype was constructed based on the FT-NIR spectrum of fruit sugar content measurement. The performances of wavelet based threshold de-noising approaches via different combinations of wavelet base functions were compared. Three families of wavelet base function (Daubechies, Symlets and Coiflets) were applied to estimate the performance of those wavelet bases and threshold selection rules by a series of experiments. The experimental results show that the best de-noising performance is reached via the combinations of Daubechies 4 or Symlet 4 wavelet base function. Based on the optimization parameter, wavelet regression models for sugar content of pear were also developed and result in a smaller prediction error than a traditional Partial Least Squares Regression (PLSR) mode.

Person Authentication Using Learned Parameters of Lifting Wavelet Filters

NASA Astrophysics Data System (ADS)

Niijima, Koichi

2006-10-01

This paper proposes a method for identifying persons by the use of the lifting wavelet parameters learned by kurtosis-minimization. Our learning method uses desirable properties of kurtosis and wavelet coefficients of a facial image. Exploiting these properties, the lifting parameters are trained so as to minimize the kurtosis of lifting wavelet coefficients computed for the facial image. Since this minimization problem is an ill-posed problem, it is solved by the aid of Tikhonov's regularization method. Our learning algorithm is applied to each of the faces to be identified to generate its feature vector whose components consist of the learned parameters. The constructed feature vectors are memorized together with the corresponding faces in a feature vectors database. Person authentication is performed by comparing the feature vector of a query face with those stored in the database. In numerical experiments, the lifting parameters are trained for each of the neutral faces of 132 persons (74 males and 58 females) in the AR face database. Person authentication is executed by using the smile and anger faces of the same persons in the database.

Prediction of troponin-T degradation using color image texture features in 10d aged beef longissimus steaks.

PubMed

Sun, X; Chen, K J; Berg, E P; Newman, D J; Schwartz, C A; Keller, W L; Maddock Carlin, K R

2014-02-01

The objective was to use digital color image texture features to predict troponin-T degradation in beef. Image texture features, including 88 gray level co-occurrence texture features, 81 two-dimension fast Fourier transformation texture features, and 48 Gabor wavelet filter texture features, were extracted from color images of beef strip steaks (longissimus dorsi, n = 102) aged for 10d obtained using a digital camera and additional lighting. Steaks were designated degraded or not-degraded based on troponin-T degradation determined on d 3 and d 10 postmortem by immunoblotting. Statistical analysis (STEPWISE regression model) and artificial neural network (support vector machine model, SVM) methods were designed to classify protein degradation. The d 3 and d 10 STEPWISE models were 94% and 86% accurate, respectively, while the d 3 and d 10 SVM models were 63% and 71%, respectively, in predicting protein degradation in aged meat. STEPWISE and SVM models based on image texture features show potential to predict troponin-T degradation in meat. © 2013.

Analysis of wavelet technology for NASA applications

NASA Technical Reports Server (NTRS)

Wells, R. O., Jr.

1994-01-01

The purpose of this grant was to introduce a broad group of NASA researchers and administrators to wavelet technology and to determine its future role in research and development at NASA JSC. The activities of several briefings held between NASA JSC scientists and Rice University researchers are discussed. An attached paper, 'Recent Advances in Wavelet Technology', summarizes some aspects of these briefings. Two proposals submitted to NASA reflect the primary areas of common interest. They are image analysis and numerical solutions of partial differential equations arising in computational fluid dynamics and structural mechanics.

Wigner functions from the two-dimensional wavelet group.

PubMed

Ali, S T; Krasowska, A E; Murenzi, R

2000-12-01

Following a general procedure developed previously [Ann. Henri Poincaré 1, 685 (2000)], here we construct Wigner functions on a phase space related to the similitude group in two dimensions. Since the group space in this case is topologically homeomorphic to the phase space in question, the Wigner functions so constructed may also be considered as being functions on the group space itself. Previously the similitude group was used to construct wavelets for two-dimensional image analysis; we discuss here the connection between the wavelet transform and the Wigner function.

Scalets, wavelets and (complex) turning point quantization

NASA Astrophysics Data System (ADS)

Handy, C. R.; Brooks, H. A.

2001-05-01

Despite the many successes of wavelet analysis in image and signal processing, the incorporation of continuous wavelet transform theory within quantum mechanics has lacked a compelling, first principles, motivating analytical framework, until now. For arbitrary one-dimensional rational fraction Hamiltonians, we develop a simple, unified formalism, which clearly underscores the complementary, and mutually interdependent, role played by moment quantization theory (i.e. via scalets, as defined herein) and wavelets. This analysis involves no approximation of the Hamiltonian within the (equivalent) wavelet space, and emphasizes the importance of (complex) multiple turning point contributions in the quantization process. We apply the method to three illustrative examples. These include the (double-well) quartic anharmonic oscillator potential problem, V(x) = Z2x2 + gx4, the quartic potential, V(x) = x4, and the very interesting and significant non-Hermitian potential V(x) = -(ix)3, recently studied by Bender and Boettcher.

Computer-aided diagnosis of melanoma using border and wavelet-based texture analysis.

PubMed

Garnavi, Rahil; Aldeen, Mohammad; Bailey, James

2012-11-01

This paper presents a novel computer-aided diagnosis system for melanoma. The novelty lies in the optimised selection and integration of features derived from textural, borderbased and geometrical properties of the melanoma lesion. The texture features are derived from using wavelet-decomposition, the border features are derived from constructing a boundaryseries model of the lesion border and analysing it in spatial and frequency domains, and the geometry features are derived from shape indexes. The optimised selection of features is achieved by using the Gain-Ratio method, which is shown to be computationally efficient for melanoma diagnosis application. Classification is done through the use of four classifiers; namely, Support Vector Machine, Random Forest, Logistic Model Tree and Hidden Naive Bayes. The proposed diagnostic system is applied on a set of 289 dermoscopy images (114 malignant, 175 benign) partitioned into train, validation and test image sets. The system achieves and accuracy of 91.26% and AUC value of 0.937, when 23 features are used. Other important findings include (i) the clear advantage gained in complementing texture with border and geometry features, compared to using texture information only, and (ii) higher contribution of texture features than border-based features in the optimised feature set.

Detection of Unilateral Hearing Loss by Stationary Wavelet Entropy.

PubMed

Zhang, Yudong; Nayak, Deepak Ranjan; Yang, Ming; Yuan, Ti-Fei; Liu, Bin; Lu, Huimin; Wang, Shuihua

2017-01-01

Sensorineural hearing loss is correlated to massive neurological or psychiatric disease. T1-weighted volumetric images were acquired from fourteen subjects with right-sided hearing loss (RHL), fifteen subjects with left-sided hearing loss (LHL), and twenty healthy controls (HC). We treated a three-class classification problem: HC, LHL, and RHL. Stationary wavelet entropy was employed to extract global features from magnetic resonance images of each subject. Those stationary wavelet entropy features were used as input to a single-hidden layer feedforward neuralnetwork classifier. The 10 repetition results of 10-fold cross validation show that the accuracies of HC, LHL, and RHL are 96.94%, 97.14%, and 97.35%, respectively. Our developed system is promising and effective in detecting hearing loss. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.org.

Forecasting of particulate matter time series using wavelet analysis and wavelet-ARMA/ARIMA model in Taiyuan, China.

PubMed

Zhang, Hong; Zhang, Sheng; Wang, Ping; Qin, Yuzhe; Wang, Huifeng

2017-07-01

Particulate matter with aerodynamic diameter below 10 μm (PM 10 ) forecasting is difficult because of the uncertainties in describing the emission and meteorological fields. This paper proposed a wavelet-ARMA/ARIMA model to forecast the short-term series of the PM 10 concentrations. It was evaluated by experiments using a 10-year data set of daily PM 10 concentrations from 4 stations located in Taiyuan, China. The results indicated the following: (1) PM 10 concentrations of Taiyuan had a decreasing trend during 2005 to 2012 but increased in 2013. PM 10 concentrations had an obvious seasonal fluctuation related to coal-fired heating in winter and early spring. (2) Spatial differences among the four stations showed that the PM 10 concentrations in industrial and heavily trafficked areas were higher than those in residential and suburb areas. (3) Wavelet analysis revealed that the trend variation and the changes of the PM 10 concentration of Taiyuan were complicated. (4) The proposed wavelet-ARIMA model could be efficiently and successfully applied to the PM 10 forecasting field. Compared with the traditional ARMA/ARIMA methods, this wavelet-ARMA/ARIMA method could effectively reduce the forecasting error, improve the prediction accuracy, and realize multiple-time-scale prediction. Wavelet analysis can filter noisy signals and identify the variation trend and the fluctuation of the PM 10 time-series data. Wavelet decomposition and reconstruction reduce the nonstationarity of the PM 10 time-series data, and thus improve the accuracy of the prediction. This paper proposed a wavelet-ARMA/ARIMA model to forecast the PM 10 time series. Compared with the traditional ARMA/ARIMA method, this wavelet-ARMA/ARIMA method could effectively reduce the forecasting error, improve the prediction accuracy, and realize multiple-time-scale prediction. The proposed model could be efficiently and successfully applied to the PM 10 forecasting field.

[Application of wavelet transform and neural network in the near-infrared spectrum analysis of oil shale].

PubMed

Li, Su-Yi; Ji, Yan-Ju; Liu, Wei-Yu; Wang, Zhi-Hong

2013-04-01

In the present study, an innovative method is proposed, employing both wavelet transform and neural network, to analyze the near-infrared spectrum data in oil shale survey. The method entails using db8 wavelet at 3 levels decomposition to process raw data, using the transformed data as the input matrix, and creating the model through neural network. To verify the validity of the method, this study analyzes 30 synthesized oil shale samples, in which 20 samples are randomly selected for network training, the other 10 for model prediction, and uses the full spectrum and the wavelet transformed spectrum to carry out 10 network models, respectively. Results show that the mean speed of the full spectrum neural network modeling is 570.33 seconds, and the predicted residual sum of squares (PRESS) and correlation coefficient of prediction are 0.006 012 and 0.843 75, respectively. In contrast, the mean speed of the wavelet network modeling method is 3.15 seconds, and the mean PRESS and correlation coefficient of prediction are 0.002 048 and 0.953 19, respectively. These results demonstrate that the wavelet neural network modeling method is significantly superior to the full spectrum neural network modeling method. This study not only provides a new method for more efficient and accurate detection of the oil content of oil shale, but also indicates the potential for applying wavelet transform and neutral network in broad near-infrared spectrum analysis.

Multiresolution motion planning for autonomous agents via wavelet-based cell decompositions.

PubMed

Cowlagi, Raghvendra V; Tsiotras, Panagiotis

2012-10-01

We present a path- and motion-planning scheme that is "multiresolution" both in the sense of representing the environment with high accuracy only locally and in the sense of addressing the vehicle kinematic and dynamic constraints only locally. The proposed scheme uses rectangular multiresolution cell decompositions, efficiently generated using the wavelet transform. The wavelet transform is widely used in signal and image processing, with emerging applications in autonomous sensing and perception systems. The proposed motion planner enables the simultaneous use of the wavelet transform in both the perception and in the motion-planning layers of vehicle autonomy, thus potentially reducing online computations. We rigorously prove the completeness of the proposed path-planning scheme, and we provide numerical simulation results to illustrate its efficacy.

Detection of spontaneous vesicle release at individual synapses using multiple wavelets in a CWT-based algorithm.

PubMed

Sokoll, Stefan; Tönnies, Klaus; Heine, Martin

2012-01-01

In this paper we present an algorithm for the detection of spontaneous activity at individual synapses in microscopy images. By employing the optical marker pHluorin, we are able to visualize synaptic vesicle release with a spatial resolution in the nm range in a non-invasive manner. We compute individual synaptic signals from automatically segmented regions of interest and detect peaks that represent synaptic activity using a continuous wavelet transform based algorithm. As opposed to standard peak detection algorithms, we employ multiple wavelets to match all relevant features of the peak. We evaluate our multiple wavelet algorithm (MWA) on real data and assess the performance on synthetic data over a wide range of signal-to-noise ratios.

Wavelets in Physics

NASA Astrophysics Data System (ADS)

van den Berg, J. C.

2004-03-01

A guided tour J. C. van den Berg; 1. Wavelet analysis, a new tool in physics J.-P. Antoine; 2. The 2-D wavelet transform, physical applications J.-P. Antoine; 3. Wavelets and astrophysical applications A. Bijaoui; 4. Turbulence analysis, modelling and computing using wavelets M. Farge, N. K.-R. Kevlahan, V. Perrier and K. Schneider; 5. Wavelets and detection of coherent structures in fluid turbulence L. Hudgins and J. H. Kaspersen; 6. Wavelets, non-linearity and turbulence in fusion plasmas B. Ph. van Milligen; 7. Transfers and fluxes of wind kinetic energy between orthogonal wavelet components during atmospheric blocking A. Fournier; 8. Wavelets in atomic physics and in solid state physics J.-P. Antoine, Ph. Antoine and B. Piraux; 9. The thermodynamics of fractals revisited with wavelets A. Arneodo, E. Bacry and J. F. Muzy; 10. Wavelets in medicine and physiology P. Ch. Ivanov, A. L. Goldberger, S. Havlin, C.-K. Peng, M. G. Rosenblum and H. E. Stanley; 11. Wavelet dimension and time evolution Ch.-A. Guérin and M. Holschneider.

Wavelets in Physics

NASA Astrophysics Data System (ADS)

van den Berg, J. C.

1999-08-01

A guided tour J. C. van den Berg; 1. Wavelet analysis, a new tool in physics J.-P. Antoine; 2. The 2-D wavelet transform, physical applications J.-P. Antoine; 3. Wavelets and astrophysical applications A. Bijaoui; 4. Turbulence analysis, modelling and computing using wavelets M. Farge, N. K.-R. Kevlahan, V. Perrier and K. Schneider; 5. Wavelets and detection of coherent structures in fluid turbulence L. Hudgins and J. H. Kaspersen; 6. Wavelets, non-linearity and turbulence in fusion plasmas B. Ph. van Milligen; 7. Transfers and fluxes of wind kinetic energy between orthogonal wavelet components during atmospheric blocking A. Fournier; 8. Wavelets in atomic physics and in solid state physics J.-P. Antoine, Ph. Antoine and B. Piraux; 9. The thermodynamics of fractals revisited with wavelets A. Arneodo, E. Bacry and J. F. Muzy; 10. Wavelets in medicine and physiology P. Ch. Ivanov, A. L. Goldberger, S. Havlin, C.-K. Peng, M. G. Rosenblum and H. E. Stanley; 11. Wavelet dimension and time evolution Ch.-A. Guérin and M. Holschneider.

Hybrid Discrete Wavelet Transform and Gabor Filter Banks Processing for Features Extraction from Biomedical Images

PubMed Central

Lahmiri, Salim; Boukadoum, Mounir

2013-01-01

A new methodology for automatic feature extraction from biomedical images and subsequent classification is presented. The approach exploits the spatial orientation of high-frequency textural features of the processed image as determined by a two-step process. First, the two-dimensional discrete wavelet transform (DWT) is applied to obtain the HH high-frequency subband image. Then, a Gabor filter bank is applied to the latter at different frequencies and spatial orientations to obtain new Gabor-filtered image whose entropy and uniformity are computed. Finally, the obtained statistics are fed to a support vector machine (SVM) binary classifier. The approach was validated on mammograms, retina, and brain magnetic resonance (MR) images. The obtained classification accuracies show better performance in comparison to common approaches that use only the DWT or Gabor filter banks for feature extraction. PMID:27006906

Improving the Performance of the Prony Method Using a Wavelet Domain Filter for MRI Denoising

PubMed Central

Lentini, Marianela; Paluszny, Marco

2014-01-01

The Prony methods are used for exponential fitting. We use a variant of the Prony method for abnormal brain tissue detection in sequences of T 2 weighted magnetic resonance images. Here, MR images are considered to be affected only by Rician noise, and a new wavelet domain bilateral filtering process is implemented to reduce the noise in the images. This filter is a modification of Kazubek's algorithm and we use synthetic images to show the ability of the new procedure to suppress noise and compare its performance with respect to the original filter, using quantitative and qualitative criteria. The tissue classification process is illustrated using a real sequence of T 2 MR images, and the filter is applied to each image before using the variant of the Prony method. PMID:24834108

Improving the performance of the prony method using a wavelet domain filter for MRI denoising.

PubMed

Jaramillo, Rodney; Lentini, Marianela; Paluszny, Marco

2014-01-01

The Prony methods are used for exponential fitting. We use a variant of the Prony method for abnormal brain tissue detection in sequences of T 2 weighted magnetic resonance images. Here, MR images are considered to be affected only by Rician noise, and a new wavelet domain bilateral filtering process is implemented to reduce the noise in the images. This filter is a modification of Kazubek's algorithm and we use synthetic images to show the ability of the new procedure to suppress noise and compare its performance with respect to the original filter, using quantitative and qualitative criteria. The tissue classification process is illustrated using a real sequence of T 2 MR images, and the filter is applied to each image before using the variant of the Prony method.

Multispectral image fusion based on fractal features

NASA Astrophysics Data System (ADS)

Tian, Jie; Chen, Jie; Zhang, Chunhua

2004-01-01

Imagery sensors have been one indispensable part of the detection and recognition systems. They are widely used to the field of surveillance, navigation, control and guide, et. However, different imagery sensors depend on diverse imaging mechanisms, and work within diverse range of spectrum. They also perform diverse functions and have diverse circumstance requires. So it is unpractical to accomplish the task of detection or recognition with a single imagery sensor under the conditions of different circumstances, different backgrounds and different targets. Fortunately, the multi-sensor image fusion technique emerged as important route to solve this problem. So image fusion has been one of the main technical routines used to detect and recognize objects from images. While, loss of information is unavoidable during fusion process, so it is always a very important content of image fusion how to preserve the useful information to the utmost. That is to say, it should be taken into account before designing the fusion schemes how to avoid the loss of useful information or how to preserve the features helpful to the detection. In consideration of these issues and the fact that most detection problems are actually to distinguish man-made objects from natural background, a fractal-based multi-spectral fusion algorithm has been proposed in this paper aiming at the recognition of battlefield targets in the complicated backgrounds. According to this algorithm, source images are firstly orthogonally decomposed according to wavelet transform theories, and then fractal-based detection is held to each decomposed image. At this step, natural background and man-made targets are distinguished by use of fractal models that can well imitate natural objects. Special fusion operators are employed during the fusion of area that contains man-made targets so that useful information could be preserved and features of targets could be extruded. The final fused image is reconstructed from the composition of source pyramid images. So this fusion scheme is a multi-resolution analysis. The wavelet decomposition of image can be actually considered as special pyramid decomposition. According to wavelet decomposition theories, the approximation of image (formula available in paper) at resolution 2j+1 equal to its orthogonal projection in space , that is, where Ajf is the low-frequency approximation of image f(x, y) at resolution 2j and , , represent the vertical, horizontal and diagonal wavelet coefficients respectively at resolution 2j. These coefficients describe the high-frequency information of image at direction of vertical, horizontal and diagonal respectively. Ajf, , and are independent and can be considered as images. In this paper J is set to be 1, so the source image is decomposed to produce the son-images Af, D1f, D2f and D3f. To solve the problem of detecting artifacts, the concepts of vertical fractal dimension FD1, horizontal fractal dimension FD2 and diagonal fractal dimension FD3 are proposed in this paper. The vertical fractal dimension FD1 corresponds to the vertical wavelet coefficients image after the wavelet decomposition of source image, the horizontal fractal dimension FD2 corresponds to the horizontal wavelet coefficients and the diagonal fractal dimension FD3 the diagonal one. These definitions enrich the illustration of source images. Therefore they are helpful to classify the targets. Then the detection of artifacts in the decomposed images is a problem of pattern recognition in 4-D space. The combination of FD0, FD1, FD2 and FD3 make a vector of (FD0, FD1, FD2, FD3), which can be considered as a united feature vector of the studied image. All the parts of the images are classified in the 4-D pattern space created by the vector of (FD0, FD1, FD2, FD3) so that the area that contains man-made objects could be detected. This detection can be considered as a coarse recognition, and then the significant areas in each son-images are signed so that they can be dealt with special rules. There has been various fusion rules developed with each one aiming at a special problem. These rules have different performance, so it is very important to select an appropriate rule during the design of an image fusion system. Recent research denotes that the rule should be adjustable so that it is always suitable to extrude the features of targets and to preserve the pixels of useful information. In this paper, owing to the consideration that fractal dimension is one of the main features to distinguish man-made targets from natural objects, the fusion rule was defined that if the studied region of image contains man-made target, the pixels of the source image whose fractal dimension is minimal are saved to be the pixels of the fused image, otherwise, a weighted average operator is adopted to avoid loss of information. The main idea of this rule is to store the pixels with low fractal dimensions, so it can be named Minimal Fractal dimensions (MFD) fusion rule. This fractal-based algorithm is compared with a common weighted average fusion algorithm. An objective assessment is taken to the two fusion results. The criteria of Entropy, Cross-Entropy, Peak Signal-to-Noise Ratio (PSNR) and Standard Gray Scale Difference are defined in this paper. Reversely to the idea of constructing an ideal image as the assessing reference, the source images are selected to be the reference in this paper. It can be deemed that this assessment is to calculate how much the image quality has been enhanced and the quantity of information has been increased when the fused image is compared with the source images. The experimental results imply that the fractal-based multi-spectral fusion algorithm can effectively preserve the information of man-made objects with a high contrast. It is proved that this algorithm could well preserve features of military targets because that battlefield targets are most man-made objects and in common their images differ from fractal models obviously. Furthermore, the fractal features are not sensitive to the imaging conditions and the movement of targets, so this fractal-based algorithm may be very practical.

Wavelet imaging cleaning method for atmospheric Cherenkov telescopes

NASA Astrophysics Data System (ADS)

Lessard, R. W.; Cayón, L.; Sembroski, G. H.; Gaidos, J. A.

2002-07-01

We present a new method of image cleaning for imaging atmospheric Cherenkov telescopes. The method is based on the utilization of wavelets to identify noise pixels in images of gamma-ray and hadronic induced air showers. This method selects more signal pixels with Cherenkov photons than traditional image processing techniques. In addition, the method is equally efficient at rejecting pixels with noise alone. The inclusion of more signal pixels in an image of an air shower allows for a more accurate reconstruction, especially at lower gamma-ray energies that produce low levels of light. We present the results of Monte Carlo simulations of gamma-ray and hadronic air showers which show improved angular resolution using this cleaning procedure. Data from the Whipple Observatory's 10-m telescope are utilized to show the efficacy of the method for extracting a gamma-ray signal from the background of hadronic generated images.

Remote-sensing image encryption in hybrid domains

NASA Astrophysics Data System (ADS)

Zhang, Xiaoqiang; Zhu, Guiliang; Ma, Shilong

2012-04-01

Remote-sensing technology plays an important role in military and industrial fields. Remote-sensing image is the main means of acquiring information from satellites, which always contain some confidential information. To securely transmit and store remote-sensing images, we propose a new image encryption algorithm in hybrid domains. This algorithm makes full use of the advantages of image encryption in both spatial domain and transform domain. First, the low-pass subband coefficients of image DWT (discrete wavelet transform) decomposition are sorted by a PWLCM system in transform domain. Second, the image after IDWT (inverse discrete wavelet transform) reconstruction is diffused with 2D (two-dimensional) Logistic map and XOR operation in spatial domain. The experiment results and algorithm analyses show that the new algorithm possesses a large key space and can resist brute-force, statistical and differential attacks. Meanwhile, the proposed algorithm has the desirable encryption efficiency to satisfy requirements in practice.

Infrared and visible image fusion with spectral graph wavelet transform.

PubMed

Yan, Xiang; Qin, Hanlin; Li, Jia; Zhou, Huixin; Zong, Jing-guo

2015-09-01

Infrared and visible image fusion technique is a popular topic in image analysis because it can integrate complementary information and obtain reliable and accurate description of scenes. Multiscale transform theory as a signal representation method is widely used in image fusion. In this paper, a novel infrared and visible image fusion method is proposed based on spectral graph wavelet transform (SGWT) and bilateral filter. The main novelty of this study is that SGWT is used for image fusion. On the one hand, source images are decomposed by SGWT in its transform domain. The proposed approach not only effectively preserves the details of different source images, but also excellently represents the irregular areas of the source images. On the other hand, a novel weighted average method based on bilateral filter is proposed to fuse low- and high-frequency subbands by taking advantage of spatial consistency of natural images. Experimental results demonstrate that the proposed method outperforms seven recently proposed image fusion methods in terms of both visual effect and objective evaluation metrics.

SHORT-TERM SOLAR FLARE PREDICTION USING MULTIRESOLUTION PREDICTORS

DOE Office of Scientific and Technical Information (OSTI.GOV)

Yu Daren; Huang Xin; Hu Qinghua

2010-01-20

Multiresolution predictors of solar flares are constructed by a wavelet transform and sequential feature extraction method. Three predictors-the maximum horizontal gradient, the length of neutral line, and the number of singular points-are extracted from Solar and Heliospheric Observatory/Michelson Doppler Imager longitudinal magnetograms. A maximal overlap discrete wavelet transform is used to decompose the sequence of predictors into four frequency bands. In each band, four sequential features-the maximum, the mean, the standard deviation, and the root mean square-are extracted. The multiresolution predictors in the low-frequency band reflect trends in the evolution of newly emerging fluxes. The multiresolution predictors in the high-frequencymore » band reflect the changing rates in emerging flux regions. The variation of emerging fluxes is decoupled by wavelet transform in different frequency bands. The information amount of these multiresolution predictors is evaluated by the information gain ratio. It is found that the multiresolution predictors in the lowest and highest frequency bands contain the most information. Based on these predictors, a C4.5 decision tree algorithm is used to build the short-term solar flare prediction model. It is found that the performance of the short-term solar flare prediction model based on the multiresolution predictors is greatly improved.« less

Wavelet detection of singularities in the presence of fractal noise

NASA Astrophysics Data System (ADS)

Noel, Steven E.; Gohel, Yogesh J.; Szu, Harold H.

1997-04-01

Here we detect singularities with generalized quadrature processing using the recently developed Hermitian Hat wavelet. Our intended application is radar target detection for the optimal fuzzing of ship self-defense munitions. We first develop a wavelet-based fractal noise model to represent sea clutter. We then investigate wavelet shrinkage as a way to reduce and smooth the noise before attempting wavelet detection. Finally, we use the complex phase of the Hermitian Hat wavelet to detect a simulated target singularity in the presence of our fractal noise.

Mixed raster content (MRC) model for compound image compression

NASA Astrophysics Data System (ADS)

de Queiroz, Ricardo L.; Buckley, Robert R.; Xu, Ming

1998-12-01

This paper will describe the Mixed Raster Content (MRC) method for compressing compound images, containing both binary test and continuous-tone images. A single compression algorithm that simultaneously meets the requirements for both text and image compression has been elusive. MRC takes a different approach. Rather than using a single algorithm, MRC uses a multi-layered imaging model for representing the results of multiple compression algorithms, including ones developed specifically for text and for images. As a result, MRC can combine the best of existing or new compression algorithms and offer different quality-compression ratio tradeoffs. The algorithms used by MRC set the lower bound on its compression performance. Compared to existing algorithms, MRC has some image-processing overhead to manage multiple algorithms and the imaging model. This paper will develop the rationale for the MRC approach by describing the multi-layered imaging model in light of a rate-distortion trade-off. Results will be presented comparing images compressed using MRC, JPEG and state-of-the-art wavelet algorithms such as SPIHT. MRC has been approved or proposed as an architectural model for several standards, including ITU Color Fax, IETF Internet Fax, and JPEG 2000.

Reducing the complexity of the CCSDS standard for image compression decreasing the DWT filter order

NASA Astrophysics Data System (ADS)

Ito, Leandro H.; Pinho, Marcelo S.

2014-10-01

The goal for this work is to evaluate the impact of utilizing shorter wavelet filters in the CCSDS standard for lossy and lossless image compression. Another constraint considered was the existence of symmetry in the filters. That approach was desired to maintain the symmetric extension compatibility of the filter banks. Even though this strategy works well for oat wavelets, it is not always the case for their integer approximations. The periodic extension was utilized whenever symmetric extension was not applicable. Even though the latter outperforms the former, for fair comparison the symmetric extension compatible integer-to-integer wavelet approximations were evaluated under both extensions. The evaluation methods adopted were bit rate (bpp), PSNR and the number of operations required by each wavelet transforms. All these results were compared against the ones obtained utilizing the standard CCSDS with 9/7 filter banks, for lossy and lossless compression. The tests were performed over tallies (512x512) of raw remote sensing images from CBERS-2B (China-Brazil Earth Resources Satellites) captured from its high resolution CCD camera. These images were cordially made available by INPE (National Institute for Space Research) in Brazil. For the CCSDS implementation, it was utilized the source code developed by Hongqiang Wang from the Electrical Department at Nebraska-Lincoln University, applying the appropriate changes on the wavelet transform. For lossy compression, the results have shown that the filter bank built from the Deslauriers-Dubuc scaling function, with respectively 2 and 4 vanishing moments on the synthesis and analysis banks, presented not only a reduction of 21% in the number of operations required, but also a performance on par with the 9/7 filter bank. In the lossless case, the biorthogonal Cohen-Daubechies-Feauveau with 2 vanishing moments presented a performance close to the 9/7 integer approximation of the CCSDS, with the number of operations reduced by 1/3.

Automatic detection of anomalies in screening mammograms

PubMed Central

2013-01-01

Background Diagnostic performance in breast screening programs may be influenced by the prior probability of disease. Since breast cancer incidence is roughly half a percent in the general population there is a large probability that the screening exam will be normal. That factor may contribute to false negatives. Screening programs typically exhibit about 83% sensitivity and 91% specificity. This investigation was undertaken to determine if a system could be developed to pre-sort screening-images into normal and suspicious bins based on their likelihood to contain disease. Wavelets were investigated as a method to parse the image data, potentially removing confounding information. The development of a classification system based on features extracted from wavelet transformed mammograms is reported. Methods In the multi-step procedure images were processed using 2D discrete wavelet transforms to create a set of maps at different size scales. Next, statistical features were computed from each map, and a subset of these features was the input for a concerted-effort set of naïve Bayesian classifiers. The classifier network was constructed to calculate the probability that the parent mammography image contained an abnormality. The abnormalities were not identified, nor were they regionalized. The algorithm was tested on two publicly available databases: the Digital Database for Screening Mammography (DDSM) and the Mammographic Images Analysis Society’s database (MIAS). These databases contain radiologist-verified images and feature common abnormalities including: spiculations, masses, geometric deformations and fibroid tissues. Results The classifier-network designs tested achieved sensitivities and specificities sufficient to be potentially useful in a clinical setting. This first series of tests identified networks with 100% sensitivity and up to 79% specificity for abnormalities. This performance significantly exceeds the mean sensitivity reported in literature for the unaided human expert. Conclusions Classifiers based on wavelet-derived features proved to be highly sensitive to a range of pathologies, as a result Type II errors were nearly eliminated. Pre-sorting the images changed the prior probability in the sorted database from 37% to 74%. PMID:24330643

Wavelet Compression of Satellite-Transmitted Digital Mammograms

NASA Technical Reports Server (NTRS)

Zheng, Yuan F.

2001-01-01

Breast cancer is one of the major causes of cancer death in women in the United States. The most effective way to treat breast cancer is to detect it at an early stage by screening patients periodically. Conventional film-screening mammography uses X-ray films which are effective in detecting early abnormalities of the breast. Direct digital mammography has the potential to improve the image quality and to take advantages of convenient storage, efficient transmission, and powerful computer-aided diagnosis, etc. One effective alternative to direct digital imaging is secondary digitization of X-ray films. This technique may not provide as high an image quality as the direct digital approach, but definitely have other advantages inherent to digital images. One of them is the usage of satellite-transmission technique for transferring digital mammograms between a remote image-acquisition site and a central image-reading site. This technique can benefit a large population of women who reside in remote areas where major screening and diagnosing facilities are not available. The NASA-Lewis Research Center (LeRC), in collaboration with the Cleveland Clinic Foundation (CCF), has begun a pilot study to investigate the application of the Advanced Communications Technology Satellite (ACTS) network to telemammography. The bandwidth of the T1 transmission is limited (1.544 Mbps) while the size of a mammographic image is huge. It takes a long time to transmit a single mammogram. For example, a mammogram of 4k by 4k pixels with 16 bits per pixel needs more than 4 minutes to transmit. Four images for a typical screening exam would take more than 16 minutes. This is too long a time period for a convenient screening. Consequently, compression is necessary for making satellite-transmission of mammographic images practically possible. The Wavelet Research Group of the Department of Electrical Engineering at The Ohio State University (OSU) participated in the LeRC-CCF collaboration by providing advanced compression technology using wavelet transform. OSU developed a time-efficient software package with various wavelets to compress a serious of mammographic images. This documents reports the result of the compression activities.

Identifying Degenerative Brain Disease Using Rough Set Classifier Based on Wavelet Packet Method.

PubMed

Cheng, Ching-Hsue; Liu, Wei-Xiang

2018-05-28

Population aging has become a worldwide phenomenon, which causes many serious problems. The medical issues related to degenerative brain disease have gradually become a concern. Magnetic Resonance Imaging is one of the most advanced methods for medical imaging and is especially suitable for brain scans. From the literature, although the automatic segmentation method is less laborious and time-consuming, it is restricted in several specific types of images. In addition, hybrid techniques segmentation improves the shortcomings of the single segmentation method. Therefore, this study proposed a hybrid segmentation combined with rough set classifier and wavelet packet method to identify degenerative brain disease. The proposed method is a three-stage image process method to enhance accuracy of brain disease classification. In the first stage, this study used the proposed hybrid segmentation algorithms to segment the brain ROI (region of interest). In the second stage, wavelet packet was used to conduct the image decomposition and calculate the feature values. In the final stage, the rough set classifier was utilized to identify the degenerative brain disease. In verification and comparison, two experiments were employed to verify the effectiveness of the proposed method and compare with the TV-seg (total variation segmentation) algorithm, Discrete Cosine Transform, and the listing classifiers. Overall, the results indicated that the proposed method outperforms the listing methods.

Line fitting based feature extraction for object recognition

NASA Astrophysics Data System (ADS)

Li, Bing

2014-06-01

Image feature extraction plays a significant role in image based pattern applications. In this paper, we propose a new approach to generate hierarchical features. This new approach applies line fitting to adaptively divide regions based upon the amount of information and creates line fitting features for each subsequent region. It overcomes the feature wasting drawback of the wavelet based approach and demonstrates high performance in real applications. For gray scale images, we propose a diffusion equation approach to map information-rich pixels (pixels near edges and ridge pixels) into high values, and pixels in homogeneous regions into small values near zero that form energy map images. After the energy map images are generated, we propose a line fitting approach to divide regions recursively and create features for each region simultaneously. This new feature extraction approach is similar to wavelet based hierarchical feature extraction in which high layer features represent global characteristics and low layer features represent local characteristics. However, the new approach uses line fitting to adaptively focus on information-rich regions so that we avoid the feature waste problems of the wavelet approach in homogeneous regions. Finally, the experiments for handwriting word recognition show that the new method provides higher performance than the regular handwriting word recognition approach.

Wavelet-based multicomponent denoising on GPU to improve the classification of hyperspectral images

NASA Astrophysics Data System (ADS)

Quesada-Barriuso, Pablo; Heras, Dora B.; Argüello, Francisco; Mouriño, J. C.

2017-10-01

Supervised classification allows handling a wide range of remote sensing hyperspectral applications. Enhancing the spatial organization of the pixels over the image has proven to be beneficial for the interpretation of the image content, thus increasing the classification accuracy. Denoising in the spatial domain of the image has been shown as a technique that enhances the structures in the image. This paper proposes a multi-component denoising approach in order to increase the classification accuracy when a classification method is applied. It is computed on multicore CPUs and NVIDIA GPUs. The method combines feature extraction based on a 1Ddiscrete wavelet transform (DWT) applied in the spectral dimension followed by an Extended Morphological Profile (EMP) and a classifier (SVM or ELM). The multi-component noise reduction is applied to the EMP just before the classification. The denoising recursively applies a separable 2D DWT after which the number of wavelet coefficients is reduced by using a threshold. Finally, inverse 2D-DWT filters are applied to reconstruct the noise free original component. The computational cost of the classifiers as well as the cost of the whole classification chain is high but it is reduced achieving real-time behavior for some applications through their computation on NVIDIA multi-GPU platforms.

High-resolution time-frequency representation of EEG data using multi-scale wavelets

NASA Astrophysics Data System (ADS)

Li, Yang; Cui, Wei-Gang; Luo, Mei-Lin; Li, Ke; Wang, Lina

2017-09-01

An efficient time-varying autoregressive (TVAR) modelling scheme that expands the time-varying parameters onto the multi-scale wavelet basis functions is presented for modelling nonstationary signals and with applications to time-frequency analysis (TFA) of electroencephalogram (EEG) signals. In the new parametric modelling framework, the time-dependent parameters of the TVAR model are locally represented by using a novel multi-scale wavelet decomposition scheme, which can allow the capability to capture the smooth trends as well as track the abrupt changes of time-varying parameters simultaneously. A forward orthogonal least square (FOLS) algorithm aided by mutual information criteria are then applied for sparse model term selection and parameter estimation. Two simulation examples illustrate that the performance of the proposed multi-scale wavelet basis functions outperforms the only single-scale wavelet basis functions or Kalman filter algorithm for many nonstationary processes. Furthermore, an application of the proposed method to a real EEG signal demonstrates the new approach can provide highly time-dependent spectral resolution capability.

Computer-aided diagnosis of breast microcalcifications based on dual-tree complex wavelet transform.

PubMed

Jian, Wushuai; Sun, Xueyan; Luo, Shuqian

2012-12-19

Digital mammography is the most reliable imaging modality for breast carcinoma diagnosis and breast micro-calcifications is regarded as one of the most important signs on imaging diagnosis. In this paper, a computer-aided diagnosis (CAD) system is presented for breast micro-calcifications based on dual-tree complex wavelet transform (DT-CWT) to facilitate radiologists like double reading. Firstly, 25 abnormal ROIs were extracted according to the center and diameter of the lesions manually and 25 normal ROIs were selected randomly. Then micro-calcifications were segmented by combining space and frequency domain techniques. We extracted three texture features based on wavelet (Haar, DB4, DT-CWT) transform. Totally 14 descriptors were introduced to define the characteristics of the suspicious micro-calcifications. Principal Component Analysis (PCA) was used to transform these descriptors to a compact and efficient vector expression. Support Vector Machine (SVM) classifier was used to classify potential micro-calcifications. Finally, we used the receiver operating characteristic (ROC) curve and free-response operating characteristic (FROC) curve to evaluate the performance of the CAD system. The results of SVM classifications based on different wavelets shows DT-CWT has a better performance. Compared with other results, DT-CWT method achieved an accuracy of 96% and 100% for the classification of normal and abnormal ROIs, and the classification of benign and malignant micro-calcifications respectively. In FROC analysis, our CAD system for clinical dataset detection achieved a sensitivity of 83.5% at a false positive per image of 1.85. Compared with general wavelets, DT-CWT could describe the features more effectively, and our CAD system had a competitive performance.

Computer-aided diagnosis of breast microcalcifications based on dual-tree complex wavelet transform

PubMed Central

2012-01-01

Background Digital mammography is the most reliable imaging modality for breast carcinoma diagnosis and breast micro-calcifications is regarded as one of the most important signs on imaging diagnosis. In this paper, a computer-aided diagnosis (CAD) system is presented for breast micro-calcifications based on dual-tree complex wavelet transform (DT-CWT) to facilitate radiologists like double reading. Methods Firstly, 25 abnormal ROIs were extracted according to the center and diameter of the lesions manually and 25 normal ROIs were selected randomly. Then micro-calcifications were segmented by combining space and frequency domain techniques. We extracted three texture features based on wavelet (Haar, DB4, DT-CWT) transform. Totally 14 descriptors were introduced to define the characteristics of the suspicious micro-calcifications. Principal Component Analysis (PCA) was used to transform these descriptors to a compact and efficient vector expression. Support Vector Machine (SVM) classifier was used to classify potential micro-calcifications. Finally, we used the receiver operating characteristic (ROC) curve and free-response operating characteristic (FROC) curve to evaluate the performance of the CAD system. Results The results of SVM classifications based on different wavelets shows DT-CWT has a better performance. Compared with other results, DT-CWT method achieved an accuracy of 96% and 100% for the classification of normal and abnormal ROIs, and the classification of benign and malignant micro-calcifications respectively. In FROC analysis, our CAD system for clinical dataset detection achieved a sensitivity of 83.5% at a false positive per image of 1.85. Conclusions Compared with general wavelets, DT-CWT could describe the features more effectively, and our CAD system had a competitive performance. PMID:23253202

Speckle noise reduction in ultrasound images using a discrete wavelet transform-based image fusion technique.

PubMed

Choi, Hyun Ho; Lee, Ju Hwan; Kim, Sung Min; Park, Sung Yun

2015-01-01

Here, the speckle noise in ultrasonic images is removed using an image fusion-based denoising method. To optimize the denoising performance, each discrete wavelet transform (DWT) and filtering technique was analyzed and compared. In addition, the performances were compared in order to derive the optimal input conditions. To evaluate the speckle noise removal performance, an image fusion algorithm was applied to the ultrasound images, and comparatively analyzed with the original image without the algorithm. As a result, applying DWT and filtering techniques caused information loss and noise characteristics, and did not represent the most significant noise reduction performance. Conversely, an image fusion method applying SRAD-original conditions preserved the key information in the original image, and the speckle noise was removed. Based on such characteristics, the input conditions of SRAD-original had the best denoising performance with the ultrasound images. From this study, the best denoising technique proposed based on the results was confirmed to have a high potential for clinical application.

Image Fusion Algorithms Using Human Visual System in Transform Domain

NASA Astrophysics Data System (ADS)

Vadhi, Radhika; Swamy Kilari, Veera; Samayamantula, Srinivas Kumar

2017-08-01

The endeavor of digital image fusion is to combine the important visual parts from various sources to advance the visibility eminence of the image. The fused image has a more visual quality than any source images. In this paper, the Human Visual System (HVS) weights are used in the transform domain to select appropriate information from various source images and then to attain a fused image. In this process, mainly two steps are involved. First, apply the DWT to the registered source images. Later, identify qualitative sub-bands using HVS weights. Hence, qualitative sub-bands are selected from different sources to form high quality HVS based fused image. The quality of the HVS based fused image is evaluated with general fusion metrics. The results show the superiority among the state-of-the art resolution Transforms (MRT) such as Discrete Wavelet Transform (DWT), Stationary Wavelet Transform (SWT), Contourlet Transform (CT), and Non Sub Sampled Contourlet Transform (NSCT) using maximum selection fusion rule.

Low-illumination image denoising method for wide-area search of nighttime sea surface

NASA Astrophysics Data System (ADS)

Song, Ming-zhu; Qu, Hong-song; Zhang, Gui-xiang; Tao, Shu-ping; Jin, Guang

2018-05-01

In order to suppress complex mixing noise in low-illumination images for wide-area search of nighttime sea surface, a model based on total variation (TV) and split Bregman is proposed in this paper. A fidelity term based on L1 norm and a fidelity term based on L2 norm are designed considering the difference between various noise types, and the regularization mixed first-order TV and second-order TV are designed to balance the influence of details information such as texture and edge for sea surface image. The final detection result is obtained by using the high-frequency component solved from L1 norm and the low-frequency component solved from L2 norm through wavelet transform. The experimental results show that the proposed denoising model has perfect denoising performance for artificially degraded and low-illumination images, and the result of image quality assessment index for the denoising image is superior to that of the contrastive models.

Wavelet-based multiscale performance analysis: An approach to assess and improve hydrological models

NASA Astrophysics Data System (ADS)

Rathinasamy, Maheswaran; Khosa, Rakesh; Adamowski, Jan; ch, Sudheer; Partheepan, G.; Anand, Jatin; Narsimlu, Boini

2014-12-01

The temporal dynamics of hydrological processes are spread across different time scales and, as such, the performance of hydrological models cannot be estimated reliably from global performance measures that assign a single number to the fit of a simulated time series to an observed reference series. Accordingly, it is important to analyze model performance at different time scales. Wavelets have been used extensively in the area of hydrological modeling for multiscale analysis, and have been shown to be very reliable and useful in understanding dynamics across time scales and as these evolve in time. In this paper, a wavelet-based multiscale performance measure for hydrological models is proposed and tested (i.e., Multiscale Nash-Sutcliffe Criteria and Multiscale Normalized Root Mean Square Error). The main advantage of this method is that it provides a quantitative measure of model performance across different time scales. In the proposed approach, model and observed time series are decomposed using the Discrete Wavelet Transform (known as the à trous wavelet transform), and performance measures of the model are obtained at each time scale. The applicability of the proposed method was explored using various case studies-both real as well as synthetic. The synthetic case studies included various kinds of errors (e.g., timing error, under and over prediction of high and low flows) in outputs from a hydrologic model. The real time case studies investigated in this study included simulation results of both the process-based Soil Water Assessment Tool (SWAT) model, as well as statistical models, namely the Coupled Wavelet-Volterra (WVC), Artificial Neural Network (ANN), and Auto Regressive Moving Average (ARMA) methods. For the SWAT model, data from Wainganga and Sind Basin (India) were used, while for the Wavelet Volterra, ANN and ARMA models, data from the Cauvery River Basin (India) and Fraser River (Canada) were used. The study also explored the effect of the choice of the wavelets in multiscale model evaluation. It was found that the proposed wavelet-based performance measures, namely the MNSC (Multiscale Nash-Sutcliffe Criteria) and MNRMSE (Multiscale Normalized Root Mean Square Error), are a more reliable measure than traditional performance measures such as the Nash-Sutcliffe Criteria (NSC), Root Mean Square Error (RMSE), and Normalized Root Mean Square Error (NRMSE). Further, the proposed methodology can be used to: i) compare different hydrological models (both physical and statistical models), and ii) help in model calibration.

A wavelet method for modeling and despiking motion artifacts from resting-state fMRI time series.

PubMed

Patel, Ameera X; Kundu, Prantik; Rubinov, Mikail; Jones, P Simon; Vértes, Petra E; Ersche, Karen D; Suckling, John; Bullmore, Edward T

2014-07-15

The impact of in-scanner head movement on functional magnetic resonance imaging (fMRI) signals has long been established as undesirable. These effects have been traditionally corrected by methods such as linear regression of head movement parameters. However, a number of recent independent studies have demonstrated that these techniques are insufficient to remove motion confounds, and that even small movements can spuriously bias estimates of functional connectivity. Here we propose a new data-driven, spatially-adaptive, wavelet-based method for identifying, modeling, and removing non-stationary events in fMRI time series, caused by head movement, without the need for data scrubbing. This method involves the addition of just one extra step, the Wavelet Despike, in standard pre-processing pipelines. With this method, we demonstrate robust removal of a range of different motion artifacts and motion-related biases including distance-dependent connectivity artifacts, at a group and single-subject level, using a range of previously published and new diagnostic measures. The Wavelet Despike is able to accommodate the substantial spatial and temporal heterogeneity of motion artifacts and can consequently remove a range of high and low frequency artifacts from fMRI time series, that may be linearly or non-linearly related to physical movements. Our methods are demonstrated by the analysis of three cohorts of resting-state fMRI data, including two high-motion datasets: a previously published dataset on children (N=22) and a new dataset on adults with stimulant drug dependence (N=40). We conclude that there is a real risk of motion-related bias in connectivity analysis of fMRI data, but that this risk is generally manageable, by effective time series denoising strategies designed to attenuate synchronized signal transients induced by abrupt head movements. The Wavelet Despiking software described in this article is freely available for download at www.brainwavelet.org. Copyright © 2014. Published by Elsevier Inc.

A wavelet method for modeling and despiking motion artifacts from resting-state fMRI time series

PubMed Central

Patel, Ameera X.; Kundu, Prantik; Rubinov, Mikail; Jones, P. Simon; Vértes, Petra E.; Ersche, Karen D.; Suckling, John; Bullmore, Edward T.

2014-01-01

The impact of in-scanner head movement on functional magnetic resonance imaging (fMRI) signals has long been established as undesirable. These effects have been traditionally corrected by methods such as linear regression of head movement parameters. However, a number of recent independent studies have demonstrated that these techniques are insufficient to remove motion confounds, and that even small movements can spuriously bias estimates of functional connectivity. Here we propose a new data-driven, spatially-adaptive, wavelet-based method for identifying, modeling, and removing non-stationary events in fMRI time series, caused by head movement, without the need for data scrubbing. This method involves the addition of just one extra step, the Wavelet Despike, in standard pre-processing pipelines. With this method, we demonstrate robust removal of a range of different motion artifacts and motion-related biases including distance-dependent connectivity artifacts, at a group and single-subject level, using a range of previously published and new diagnostic measures. The Wavelet Despike is able to accommodate the substantial spatial and temporal heterogeneity of motion artifacts and can consequently remove a range of high and low frequency artifacts from fMRI time series, that may be linearly or non-linearly related to physical movements. Our methods are demonstrated by the analysis of three cohorts of resting-state fMRI data, including two high-motion datasets: a previously published dataset on children (N = 22) and a new dataset on adults with stimulant drug dependence (N = 40). We conclude that there is a real risk of motion-related bias in connectivity analysis of fMRI data, but that this risk is generally manageable, by effective time series denoising strategies designed to attenuate synchronized signal transients induced by abrupt head movements. The Wavelet Despiking software described in this article is freely available for download at www.brainwavelet.org. PMID:24657353

Face recognition algorithm based on Gabor wavelet and locality preserving projections

NASA Astrophysics Data System (ADS)

Liu, Xiaojie; Shen, Lin; Fan, Honghui

2017-07-01

In order to solve the effects of illumination changes and differences of personal features on the face recognition rate, this paper presents a new face recognition algorithm based on Gabor wavelet and Locality Preserving Projections (LPP). The problem of the Gabor filter banks with high dimensions was solved effectively, and also the shortcoming of the LPP on the light illumination changes was overcome. Firstly, the features of global image information were achieved, which used the good spatial locality and orientation selectivity of Gabor wavelet filters. Then the dimensions were reduced by utilizing the LPP, which well-preserved the local information of the image. The experimental results shown that this algorithm can effectively extract the features relating to facial expressions, attitude and other information. Besides, it can reduce influence of the illumination changes and the differences in personal features effectively, which improves the face recognition rate to 99.2%.

Difference between healthy children and ADHD based on wavelet spectral analysis of nuclear magnetic resonance images

NASA Astrophysics Data System (ADS)

González Gómez, Dulce I.; Moreno Barbosa, E.; Martínez Hernández, Mario Iván; Ramos Méndez, José; Hidalgo Tobón, Silvia; Dies Suarez, Pilar; Barragán Pérez, Eduardo; De Celis Alonso, Benito

2014-11-01

The main goal of this project was to create a computer algorithm based on wavelet analysis of region of homogeneity images obtained during resting state studies. Ideally it would automatically diagnose ADHD. Because the cerebellum is an area known to be affected by ADHD, this study specifically analysed this region. Male right handed volunteers (infants with ages between 7 and 11 years old) were studied and compared with age matched controls. Statistical differences between the values of the absolute integrated wavelet spectrum were found and showed significant differences (p<0.0015) between groups. This difference might help in the future to distinguish healthy from ADHD patients and therefore diagnose ADHD. Even if results were statistically significant, the small size of the sample limits the applicability of this methods as it is presented here, and further work with larger samples and using freely available datasets must be done.

Tomographic reconstruction of tokamak plasma light emission from single image using wavelet-vaguelette decomposition

NASA Astrophysics Data System (ADS)

Nguyen van yen, R.; Fedorczak, N.; Brochard, F.; Bonhomme, G.; Schneider, K.; Farge, M.; Monier-Garbet, P.

2012-01-01

Images acquired by cameras installed in tokamaks are difficult to interpret because the three-dimensional structure of the plasma is flattened in a non-trivial way. Nevertheless, taking advantage of the slow variation of the fluctuations along magnetic field lines, the optical transformation may be approximated by a generalized Abel transform, for which we propose an inversion technique based on the wavelet-vaguelette decomposition. After validation of the new method using an academic test case and numerical data obtained with the Tokam 2D code, we present an application to an experimental movie obtained in the tokamak Tore Supra. A comparison with a classical regularization technique for ill-posed inverse problems, the singular value decomposition, allows us to assess the efficiency. The superiority of the wavelet-vaguelette technique is reflected in preserving local features, such as blobs and fronts, in the denoised emissivity map.

Coarse-to-fine wavelet-based airport detection

NASA Astrophysics Data System (ADS)

Li, Cheng; Wang, Shuigen; Pang, Zhaofeng; Zhao, Baojun

2015-10-01

Airport detection on optical remote sensing images has attracted great interest in the applications of military optics scout and traffic control. However, most of the popular techniques for airport detection from optical remote sensing images have three weaknesses: 1) Due to the characteristics of optical images, the detection results are often affected by imaging conditions, like weather situation and imaging distortion; and 2) optical images contain comprehensive information of targets, so that it is difficult for extracting robust features (e.g., intensity and textural information) to represent airport area; 3) the high resolution results in large data volume, which makes real-time processing limited. Most of the previous works mainly focus on solving one of those problems, and thus, the previous methods cannot achieve the balance of performance and complexity. In this paper, we propose a novel coarse-to-fine airport detection framework to solve aforementioned three issues using wavelet coefficients. The framework includes two stages: 1) an efficient wavelet-based feature extraction is adopted for multi-scale textural feature representation, and support vector machine(SVM) is exploited for classifying and coarsely deciding airport candidate region; and then 2) refined line segment detection is used to obtain runway and landing field of airport. Finally, airport recognition is achieved by applying the fine runway positioning to the candidate regions. Experimental results show that the proposed approach outperforms the existing algorithms in terms of detection accuracy and processing efficiency.

Wavelet analysis applied to the IRAS cirrus

NASA Technical Reports Server (NTRS)

Langer, William D.; Wilson, Robert W.; Anderson, Charles H.

1994-01-01

The structure of infrared cirrus clouds is analyzed with Laplacian pyramid transforms, a form of non-orthogonal wavelets. Pyramid and wavelet transforms provide a means to decompose images into their spatial frequency components such that all spatial scales are treated in an equivalent manner. The multiscale transform analysis is applied to IRAS 100 micrometer maps of cirrus emission in the north Galactic pole region to extract features on different scales. In the maps we identify filaments, fragments and clumps by separating all connected regions. These structures are analyzed with respect to their Hausdorff dimension for evidence of the scaling relationships in the cirrus clouds.

Compression of real time volumetric echocardiographic data using modified SPIHT based on the three-dimensional wavelet packet transform.

PubMed

Hang, X; Greenberg, N L; Shiota, T; Firstenberg, M S; Thomas, J D

2000-01-01

Real-time three-dimensional echocardiography has been introduced to provide improved quantification and description of cardiac function. Data compression is desired to allow efficient storage and improve data transmission. Previous work has suggested improved results utilizing wavelet transforms in the compression of medical data including 2D echocardiogram. Set partitioning in hierarchical trees (SPIHT) was extended to compress volumetric echocardiographic data by modifying the algorithm based on the three-dimensional wavelet packet transform. A compression ratio of at least 40:1 resulted in preserved image quality.

Local Histograms for Per-Pixel Classification

DTIC Science & Technology

2012-03-01

few axioms for such models are presented. These axioms are shown to be satisfied using the convergence of random wavelet expansions. The authors of...pathologists can accurately and consistently identify and delineate tissues and their pathologies , it is an expensive and time-consuming task, therefore...Automatic Identification and Delineation of Tissues and Pathologies in H&E Stained Images. PhD Thesis. Carnegie Mellon University, Pittsburgh, PA (September

Stationary wavelet transform for under-sampled MRI reconstruction.

PubMed

Kayvanrad, Mohammad H; McLeod, A Jonathan; Baxter, John S H; McKenzie, Charles A; Peters, Terry M

2014-12-01

In addition to coil sensitivity data (parallel imaging), sparsity constraints are often used as an additional lp-penalty for under-sampled MRI reconstruction (compressed sensing). Penalizing the traditional decimated wavelet transform (DWT) coefficients, however, results in visual pseudo-Gibbs artifacts, some of which are attributed to the lack of translation invariance of the wavelet basis. We show that these artifacts can be greatly reduced by penalizing the translation-invariant stationary wavelet transform (SWT) coefficients. This holds with various additional reconstruction constraints, including coil sensitivity profiles and total variation. Additionally, SWT reconstructions result in lower error values and faster convergence compared to DWT. These concepts are illustrated with extensive experiments on in vivo MRI data with particular emphasis on multiple-channel acquisitions. Copyright © 2014 Elsevier Inc. All rights reserved.

A Wavelet Support Vector Machine Combination Model for Singapore Tourist Arrival to Malaysia

NASA Astrophysics Data System (ADS)

Rafidah, A.; Shabri, Ani; Nurulhuda, A.; Suhaila, Y.

2017-08-01

In this study, wavelet support vector machine model (WSVM) is proposed and applied for monthly data Singapore tourist time series prediction. The WSVM model is combination between wavelet analysis and support vector machine (SVM). In this study, we have two parts, first part we compare between the kernel function and second part we compare between the developed models with single model, SVM. The result showed that kernel function linear better than RBF while WSVM outperform with single model SVM to forecast monthly Singapore tourist arrival to Malaysia.

An effective detection algorithm for region duplication forgery in digital images

NASA Astrophysics Data System (ADS)

Yavuz, Fatih; Bal, Abdullah; Cukur, Huseyin

2016-04-01

Powerful image editing tools are very common and easy to use these days. This situation may cause some forgeries by adding or removing some information on the digital images. In order to detect these types of forgeries such as region duplication, we present an effective algorithm based on fixed-size block computation and discrete wavelet transform (DWT). In this approach, the original image is divided into fixed-size blocks, and then wavelet transform is applied for dimension reduction. Each block is processed by Fourier Transform and represented by circle regions. Four features are extracted from each block. Finally, the feature vectors are lexicographically sorted, and duplicated image blocks are detected according to comparison metric results. The experimental results show that the proposed algorithm presents computational efficiency due to fixed-size circle block architecture.

Comparative performance evaluation of transform coding in image pre-processing

NASA Astrophysics Data System (ADS)

Menon, Vignesh V.; NB, Harikrishnan; Narayanan, Gayathri; CK, Niveditha

2017-07-01

We are in the midst of a communication transmute which drives the development as largely as dissemination of pioneering communication systems with ever-increasing fidelity and resolution. Distinguishable researches have been appreciative in image processing techniques crazed by a growing thirst for faster and easier encoding, storage and transmission of visual information. In this paper, the researchers intend to throw light on many techniques which could be worn at the transmitter-end in order to ease the transmission and reconstruction of the images. The researchers investigate the performance of different image transform coding schemes used in pre-processing, their comparison, and effectiveness, the necessary and sufficient conditions, properties and complexity in implementation. Whimsical by prior advancements in image processing techniques, the researchers compare various contemporary image pre-processing frameworks- Compressed Sensing, Singular Value Decomposition, Integer Wavelet Transform on performance. The paper exposes the potential of Integer Wavelet transform to be an efficient pre-processing scheme.

Unraveling Cell Processes: Interference Imaging Interwoven with Data Analysis

PubMed Central

Brazhe, A. R.; Pavlov, A. N.; Erokhova, L. A.; Yusipovich, A. I.; Maksimov, G. V.; Mosekilde, E.; Sosnovtseva, O. V.

2006-01-01

The paper presents results on the application of interference microscopy and wavelet-analysis for cell visualization and studies of cell dynamics. We demonstrate that interference imaging of erythrocytes can reveal reorganization of the cytoskeleton and inhomogenity in the distribution of hemoglobin, and that interference imaging of neurons can show intracellular compartmentalization and submembrane structures. We investigate temporal and spatial variations of the refractive index for different cell types: isolated neurons, mast cells and erythrocytes. We show that the refractive dynamical properties differ from cell type to cell type and depend on the cellular compartment. Our results suggest that low frequency variations (0.1–0.6 Hz) result from plasma membrane processes and that higher frequency variations (20–26 Hz) are related to the movement of vesicles. Using double-wavelet analysis, we study the modulation of the 1 Hz rhythm in neurons and reveal its changes under depolarization and hyperpolarization of the plasma membrane. We conclude that interference microscopy combined with wavelet analysis is a useful technique for non-invasive cell studies, cell visualization, and investigation of plasma membrane properties. PMID:19669463

High-Frequency Subband Compressed Sensing MRI Using Quadruplet Sampling

PubMed Central

Sung, Kyunghyun; Hargreaves, Brian A

2013-01-01

Purpose To presents and validates a new method that formalizes a direct link between k-space and wavelet domains to apply separate undersampling and reconstruction for high- and low-spatial-frequency k-space data. Theory and Methods High- and low-spatial-frequency regions are defined in k-space based on the separation of wavelet subbands, and the conventional compressed sensing (CS) problem is transformed into one of localized k-space estimation. To better exploit wavelet-domain sparsity, CS can be used for high-spatial-frequency regions while parallel imaging can be used for low-spatial-frequency regions. Fourier undersampling is also customized to better accommodate each reconstruction method: random undersampling for CS and regular undersampling for parallel imaging. Results Examples using the proposed method demonstrate successful reconstruction of both low-spatial-frequency content and fine structures in high-resolution 3D breast imaging with a net acceleration of 11 to 12. Conclusion The proposed method improves the reconstruction accuracy of high-spatial-frequency signal content and avoids incoherent artifacts in low-spatial-frequency regions. This new formulation also reduces the reconstruction time due to the smaller problem size. PMID:23280540

A novel partial volume effects correction technique integrating deconvolution associated with denoising within an iterative PET image reconstruction

DOE Office of Scientific and Technical Information (OSTI.GOV)

Merlin, Thibaut, E-mail: thibaut.merlin@telecom-bretagne.eu; Visvikis, Dimitris; Fernandez, Philippe

2015-02-15

Purpose: Partial volume effect (PVE) plays an important role in both qualitative and quantitative PET image accuracy, especially for small structures. A previously proposed voxelwise PVE correction method applied on PET reconstructed images involves the use of Lucy–Richardson deconvolution incorporating wavelet-based denoising to limit the associated propagation of noise. The aim of this study is to incorporate the deconvolution, coupled with the denoising step, directly inside the iterative reconstruction process to further improve PVE correction. Methods: The list-mode ordered subset expectation maximization (OSEM) algorithm has been modified accordingly with the application of the Lucy–Richardson deconvolution algorithm to the current estimationmore » of the image, at each reconstruction iteration. Acquisitions of the NEMA NU2-2001 IQ phantom were performed on a GE DRX PET/CT system to study the impact of incorporating the deconvolution inside the reconstruction [with and without the point spread function (PSF) model] in comparison to its application postreconstruction and to standard iterative reconstruction incorporating the PSF model. The impact of the denoising step was also evaluated. Images were semiquantitatively assessed by studying the trade-off between the intensity recovery and the noise level in the background estimated as relative standard deviation. Qualitative assessments of the developed methods were additionally performed on clinical cases. Results: Incorporating the deconvolution without denoising within the reconstruction achieved superior intensity recovery in comparison to both standard OSEM reconstruction integrating a PSF model and application of the deconvolution algorithm in a postreconstruction process. The addition of the denoising step permitted to limit the SNR degradation while preserving the intensity recovery. Conclusions: This study demonstrates the feasibility of incorporating the Lucy–Richardson deconvolution associated with a wavelet-based denoising in the reconstruction process to better correct for PVE. Future work includes further evaluations of the proposed method on clinical datasets and the use of improved PSF models.« less

High-resolution reconstruction for terahertz imaging.

PubMed

Xu, Li-Min; Fan, Wen-Hui; Liu, Jia

2014-11-20

We present a high-resolution (HR) reconstruction model and algorithms for terahertz imaging, taking advantage of super-resolution methodology and algorithms. The algorithms used include projection onto a convex sets approach, iterative backprojection approach, Lucy-Richardson iteration, and 2D wavelet decomposition reconstruction. Using the first two HR reconstruction methods, we successfully obtain HR terahertz images with improved definition and lower noise from four low-resolution (LR) 22×24 terahertz images taken from our homemade THz-TDS system at the same experimental conditions with 1.0 mm pixel. Using the last two HR reconstruction methods, we transform one relatively LR terahertz image to a HR terahertz image with decreased noise. This indicates potential application of HR reconstruction methods in terahertz imaging with pulsed and continuous wave terahertz sources.

Wavelet-based reversible watermarking for authentication

NASA Astrophysics Data System (ADS)

Tian, Jun

2002-04-01

In the digital information age, digital content (audio, image, and video) can be easily copied, manipulated, and distributed. Copyright protection and content authentication of digital content has become an urgent problem to content owners and distributors. Digital watermarking has provided a valuable solution to this problem. Based on its application scenario, most digital watermarking methods can be divided into two categories: robust watermarking and fragile watermarking. As a special subset of fragile watermark, reversible watermark (which is also called lossless watermark, invertible watermark, erasable watermark) enables the recovery of the original, unwatermarked content after the watermarked content has been detected to be authentic. Such reversibility to get back unwatermarked content is highly desired in sensitive imagery, such as military data and medical data. In this paper we present a reversible watermarking method based on an integer wavelet transform. We look into the binary representation of each wavelet coefficient and embed an extra bit to expandable wavelet coefficient. The location map of all expanded coefficients will be coded by JBIG2 compression and these coefficient values will be losslessly compressed by arithmetic coding. Besides these two compressed bit streams, an SHA-256 hash of the original image will also be embedded for authentication purpose.

Quantum computation and analysis of Wigner and Husimi functions: toward a quantum image treatment.

PubMed

Terraneo, M; Georgeot, B; Shepelyansky, D L

2005-06-01

We study the efficiency of quantum algorithms which aim at obtaining phase-space distribution functions of quantum systems. Wigner and Husimi functions are considered. Different quantum algorithms are envisioned to build these functions, and compared with the classical computation. Different procedures to extract more efficiently information from the final wave function of these algorithms are studied, including coarse-grained measurements, amplitude amplification, and measure of wavelet-transformed wave function. The algorithms are analyzed and numerically tested on a complex quantum system showing different behavior depending on parameters: namely, the kicked rotator. The results for the Wigner function show in particular that the use of the quantum wavelet transform gives a polynomial gain over classical computation. For the Husimi distribution, the gain is much larger than for the Wigner function and is larger with the help of amplitude amplification and wavelet transforms. We discuss the generalization of these results to the simulation of other quantum systems. We also apply the same set of techniques to the analysis of real images. The results show that the use of the quantum wavelet transform allows one to lower dramatically the number of measurements needed, but at the cost of a large loss of information.

Defect Detection in Textures through the Use of Entropy as a Means for Automatically Selecting the Wavelet Decomposition Level.

PubMed

Navarro, Pedro J; Fernández-Isla, Carlos; Alcover, Pedro María; Suardíaz, Juan

2016-07-27

This paper presents a robust method for defect detection in textures, entropy-based automatic selection of the wavelet decomposition level (EADL), based on a wavelet reconstruction scheme, for detecting defects in a wide variety of structural and statistical textures. Two main features are presented. One of the new features is an original use of the normalized absolute function value (NABS) calculated from the wavelet coefficients derived at various different decomposition levels in order to identify textures where the defect can be isolated by eliminating the texture pattern in the first decomposition level. The second is the use of Shannon's entropy, calculated over detail subimages, for automatic selection of the band for image reconstruction, which, unlike other techniques, such as those based on the co-occurrence matrix or on energy calculation, provides a lower decomposition level, thus avoiding excessive degradation of the image, allowing a more accurate defect segmentation. A metric analysis of the results of the proposed method with nine different thresholding algorithms determined that selecting the appropriate thresholding method is important to achieve optimum performance in defect detection. As a consequence, several different thresholding algorithms depending on the type of texture are proposed.

Video compression of coronary angiograms based on discrete wavelet transform with block classification.

PubMed

Ho, B T; Tsai, M J; Wei, J; Ma, M; Saipetch, P

1996-01-01

A new method of video compression for angiographic images has been developed to achieve high compression ratio (~20:1) while eliminating block artifacts which leads to loss of diagnostic accuracy. This method adopts motion picture experts group's (MPEGs) motion compensated prediction to takes advantage of frame to frame correlation. However, in contrast to MPEG, the error images arising from mismatches in the motion estimation are encoded by discrete wavelet transform (DWT) rather than block discrete cosine transform (DCT). Furthermore, the authors developed a classification scheme which label each block in an image as intra, error, or background type and encode it accordingly. This hybrid coding can significantly improve the compression efficiency in certain eases. This method can be generalized for any dynamic image sequences applications sensitive to block artifacts.

Evidence of Large Fluctuations of Stock Return and Financial Crises from Turkey: Using Wavelet Coherency and Varma Modeling to Forecast Stock Return

NASA Astrophysics Data System (ADS)

Oygur, Tunc; Unal, Gazanfer

Shocks, jumps, booms and busts are typical large fluctuation markers which appear in crisis. Models and leading indicators vary according to crisis type in spite of the fact that there are a lot of different models and leading indicators in literature to determine structure of crisis. In this paper, we investigate structure of dynamic correlation of stock return, interest rate, exchange rate and trade balance differences in crisis periods in Turkey over the period between October 1990 and March 2015 by applying wavelet coherency methodologies to determine nature of crises. The time period includes the Turkeys currency and banking crises; US sub-prime mortgage crisis and the European sovereign debt crisis occurred in 1994, 2001, 2008 and 2009, respectively. Empirical results showed that stock return, interest rate, exchange rate and trade balance differences are significantly linked during the financial crises in Turkey. The cross wavelet power, the wavelet coherency, the multiple wavelet coherency and the quadruple wavelet coherency methodologies have been used to examine structure of dynamic correlation. Moreover, in consequence of quadruple and multiple wavelet coherence, strongly correlated large scales indicate linear behavior and, hence VARMA (vector autoregressive moving average) gives better fitting and forecasting performance. In addition, increasing the dimensions of the model for strongly correlated scales leads to more accurate results compared to scalar counterparts.

Delineation of typhoon-induced shoreline changes in Taiwan

NASA Astrophysics Data System (ADS)

Lin, Yun-Bin; Chiang, Jie-Lun

2010-05-01

Taiwan, an island country located at the southwestern Pacific Ocean, has a coast line of 1,355 km long. And only 55% proportion of the coast line remains natural. The maximum daily accumulated rainfall over 1000 mm brought by the typhoon Mindulle in 2004 generated huge disaster, including a broad flood-prone area and a sick sedimentation, in the littoral zones of the low-latitude part of Taiwan. The event resulted in the official definition of the coastal area, which is a 9 km wide belt area surrounding Taiwan island and is composed of one third land area and two third sea area. And human constructions are restricted in the proposed coast area to prevent or reduce the possible disaster in the future. Not only the sea level rising induced by the global climate warming may seriously affect the littoral zones, but also the extreme climate accompanying with the global climate warming, such as typhoons and storms, can heavily disturb the coastal environment in Taiwan. In the storm area, the wave and the storm surge may induce the coast erosion. But even being outside the storm area, the coastal environment is still regularly influenced by the sediment transportation triggered by the storm in the Cainozoic zones in the central part of Taiwan. Therefore, the continuous and regular monitoring of shoreline changes is essential for the disaster management in Taiwan. The two dimensional Morlet wavelet analysis is used to detect edges on synthetic aperture radar (SAR) images. And a block tracing algorithm and an active contour model are integrated for the final shorelines auto-delineation in the study. The SAR image that is climate unaffected and is free of visible light can provide reliable information. The Morlet wavelet function has the smallest window size and is directional. Therefore, the Morlet wavelet function is more flexible and efficient in extracting specific information from image signals. The shoreline changes induced by the typhoon Mindulle were studied. The outcome that is well coincided with the result of a field survey can be obtained in a more efficient way. Keywords: shoreline, auto-delineation, wavelet analysis, SAR

An Automated Parallel Image Registration Technique Based on the Correlation of Wavelet Features

NASA Technical Reports Server (NTRS)

LeMoigne, Jacqueline; Campbell, William J.; Cromp, Robert F.; Zukor, Dorothy (Technical Monitor)

2001-01-01

With the increasing importance of multiple platform/multiple remote sensing missions, fast and automatic integration of digital data from disparate sources has become critical to the success of these endeavors. Our work utilizes maxima of wavelet coefficients to form the basic features of a correlation-based automatic registration algorithm. Our wavelet-based registration algorithm is tested successfully with data from the National Oceanic and Atmospheric Administration (NOAA) Advanced Very High Resolution Radiometer (AVHRR) and the Landsat/Thematic Mapper(TM), which differ by translation and/or rotation. By the choice of high-frequency wavelet features, this method is similar to an edge-based correlation method, but by exploiting the multi-resolution nature of a wavelet decomposition, our method achieves higher computational speeds for comparable accuracies. This algorithm has been implemented on a Single Instruction Multiple Data (SIMD) massively parallel computer, the MasPar MP-2, as well as on the CrayT3D, the Cray T3E and a Beowulf cluster of Pentium workstations.

Multiscale wavelet representations for mammographic feature analysis

NASA Astrophysics Data System (ADS)

Laine, Andrew F.; Song, Shuwu

1992-12-01

This paper introduces a novel approach for accomplishing mammographic feature analysis through multiresolution representations. We show that efficient (nonredundant) representations may be identified from digital mammography and used to enhance specific mammographic features within a continuum of scale space. The multiresolution decomposition of wavelet transforms provides a natural hierarchy in which to embed an interactive paradigm for accomplishing scale space feature analysis. Choosing wavelets (or analyzing functions) that are simultaneously localized in both space and frequency, results in a powerful methodology for image analysis. Multiresolution and orientation selectivity, known biological mechanisms in primate vision, are ingrained in wavelet representations and inspire the techniques presented in this paper. Our approach includes local analysis of complete multiscale representations. Mammograms are reconstructed from wavelet coefficients, enhanced by linear, exponential and constant weight functions localized in scale space. By improving the visualization of breast pathology we can improve the changes of early detection of breast cancers (improve quality) while requiring less time to evaluate mammograms for most patients (lower costs).

Graph wavelet alignment kernels for drug virtual screening.

PubMed

Smalter, Aaron; Huan, Jun; Lushington, Gerald

2009-06-01

In this paper, we introduce a novel statistical modeling technique for target property prediction, with applications to virtual screening and drug design. In our method, we use graphs to model chemical structures and apply a wavelet analysis of graphs to summarize features capturing graph local topology. We design a novel graph kernel function to utilize the topology features to build predictive models for chemicals via Support Vector Machine classifier. We call the new graph kernel a graph wavelet-alignment kernel. We have evaluated the efficacy of the wavelet-alignment kernel using a set of chemical structure-activity prediction benchmarks. Our results indicate that the use of the kernel function yields performance profiles comparable to, and sometimes exceeding that of the existing state-of-the-art chemical classification approaches. In addition, our results also show that the use of wavelet functions significantly decreases the computational costs for graph kernel computation with more than ten fold speedup.

Wavelet regression model in forecasting crude oil price

NASA Astrophysics Data System (ADS)

Hamid, Mohd Helmie; Shabri, Ani

2017-05-01

This study presents the performance of wavelet multiple linear regression (WMLR) technique in daily crude oil forecasting. WMLR model was developed by integrating the discrete wavelet transform (DWT) and multiple linear regression (MLR) model. The original time series was decomposed to sub-time series with different scales by wavelet theory. Correlation analysis was conducted to assist in the selection of optimal decomposed components as inputs for the WMLR model. The daily WTI crude oil price series has been used in this study to test the prediction capability of the proposed model. The forecasting performance of WMLR model were also compared with regular multiple linear regression (MLR), Autoregressive Moving Average (ARIMA) and Generalized Autoregressive Conditional Heteroscedasticity (GARCH) using root mean square errors (RMSE) and mean absolute errors (MAE). Based on the experimental results, it appears that the WMLR model performs better than the other forecasting technique tested in this study.

Bayesian denoising in digital radiography: a comparison in the dental field.

PubMed

Frosio, I; Olivieri, C; Lucchese, M; Borghese, N A; Boccacci, P

2013-01-01

We compared two Bayesian denoising algorithms for digital radiographs, based on Total Variation regularization and wavelet decomposition. The comparison was performed on simulated radiographs with different photon counts and frequency content and on real dental radiographs. Four different quality indices were considered to quantify the quality of the filtered radiographs. The experimental results suggested that Total Variation is more suited to preserve fine anatomical details, whereas wavelets produce images of higher quality at global scale; they also highlighted the need for more reliable image quality indices. Copyright © 2012 Elsevier Ltd. All rights reserved.

Anatomy assisted PET image reconstruction incorporating multi-resolution joint entropy

NASA Astrophysics Data System (ADS)

Tang, Jing; Rahmim, Arman

2015-01-01

A promising approach in PET image reconstruction is to incorporate high resolution anatomical information (measured from MR or CT) taking the anato-functional similarity measures such as mutual information or joint entropy (JE) as the prior. These similarity measures only classify voxels based on intensity values, while neglecting structural spatial information. In this work, we developed an anatomy-assisted maximum a posteriori (MAP) reconstruction algorithm wherein the JE measure is supplied by spatial information generated using wavelet multi-resolution analysis. The proposed wavelet-based JE (WJE) MAP algorithm involves calculation of derivatives of the subband JE measures with respect to individual PET image voxel intensities, which we have shown can be computed very similarly to how the inverse wavelet transform is implemented. We performed a simulation study with the BrainWeb phantom creating PET data corresponding to different noise levels. Realistically simulated T1-weighted MR images provided by BrainWeb modeling were applied in the anatomy-assisted reconstruction with the WJE-MAP algorithm and the intensity-only JE-MAP algorithm. Quantitative analysis showed that the WJE-MAP algorithm performed similarly to the JE-MAP algorithm at low noise level in the gray matter (GM) and white matter (WM) regions in terms of noise versus bias tradeoff. When noise increased to medium level in the simulated data, the WJE-MAP algorithm started to surpass the JE-MAP algorithm in the GM region, which is less uniform with smaller isolated structures compared to the WM region. In the high noise level simulation, the WJE-MAP algorithm presented clear improvement over the JE-MAP algorithm in both the GM and WM regions. In addition to the simulation study, we applied the reconstruction algorithms to real patient studies involving DPA-173 PET data and Florbetapir PET data with corresponding T1-MPRAGE MRI images. Compared to the intensity-only JE-MAP algorithm, the WJE-MAP algorithm resulted in comparable regional mean values to those from the maximum likelihood algorithm while reducing noise. Achieving robust performance in various noise-level simulation and patient studies, the WJE-MAP algorithm demonstrates its potential in clinical quantitative PET imaging.

Segmentation of deformable organs from medical images using particle swarm optimization and nonlinear shape priors

NASA Astrophysics Data System (ADS)

Afifi, Ahmed; Nakaguchi, Toshiya; Tsumura, Norimichi

2010-03-01

In many medical applications, the automatic segmentation of deformable organs from medical images is indispensable and its accuracy is of a special interest. However, the automatic segmentation of these organs is a challenging task according to its complex shape. Moreover, the medical images usually have noise, clutter, or occlusion and considering the image information only often leads to meager image segmentation. In this paper, we propose a fully automated technique for the segmentation of deformable organs from medical images. In this technique, the segmentation is performed by fitting a nonlinear shape model with pre-segmented images. The kernel principle component analysis (KPCA) is utilized to capture the complex organs deformation and to construct the nonlinear shape model. The presegmentation is carried out by labeling each pixel according to its high level texture features extracted using the overcomplete wavelet packet decomposition. Furthermore, to guarantee an accurate fitting between the nonlinear model and the pre-segmented images, the particle swarm optimization (PSO) algorithm is employed to adapt the model parameters for the novel images. In this paper, we demonstrate the competence of proposed technique by implementing it to the liver segmentation from computed tomography (CT) scans of different patients.

Wavelets and molecular structure

NASA Astrophysics Data System (ADS)

Carson, Mike

1996-08-01

The wavelet method offers possibilities for display, editing, and topological comparison of proteins at a user-specified level of detail. Wavelets are a mathematical tool that first found application in signal processing. The multiresolution analysis of a signal via wavelets provides a hierarchical series of `best' lower-resolution approximations. B-spline ribbons model the protein fold, with one control point per residue. Wavelet analysis sets limits on the information required to define the winding of the backbone through space, suggesting a recognizable fold is generated from a number of points equal to 1/4 or less the number of residues. Wavelets applied to surfaces and volumes show promise in structure-based drug design.

Three-Class Mammogram Classification Based on Descriptive CNN Features

PubMed Central

Zhang, Qianni; Jadoon, Adeel

2017-01-01

In this paper, a novel classification technique for large data set of mammograms using a deep learning method is proposed. The proposed model targets a three-class classification study (normal, malignant, and benign cases). In our model we have presented two methods, namely, convolutional neural network-discrete wavelet (CNN-DW) and convolutional neural network-curvelet transform (CNN-CT). An augmented data set is generated by using mammogram patches. To enhance the contrast of mammogram images, the data set is filtered by contrast limited adaptive histogram equalization (CLAHE). In the CNN-DW method, enhanced mammogram images are decomposed as its four subbands by means of two-dimensional discrete wavelet transform (2D-DWT), while in the second method discrete curvelet transform (DCT) is used. In both methods, dense scale invariant feature (DSIFT) for all subbands is extracted. Input data matrix containing these subband features of all the mammogram patches is created that is processed as input to convolutional neural network (CNN). Softmax layer and support vector machine (SVM) layer are used to train CNN for classification. Proposed methods have been compared with existing methods in terms of accuracy rate, error rate, and various validation assessment measures. CNN-DW and CNN-CT have achieved accuracy rate of 81.83% and 83.74%, respectively. Simulation results clearly validate the significance and impact of our proposed model as compared to other well-known existing techniques. PMID:28191461

Three-Class Mammogram Classification Based on Descriptive CNN Features.

PubMed

Jadoon, M Mohsin; Zhang, Qianni; Haq, Ihsan Ul; Butt, Sharjeel; Jadoon, Adeel

2017-01-01

In this paper, a novel classification technique for large data set of mammograms using a deep learning method is proposed. The proposed model targets a three-class classification study (normal, malignant, and benign cases). In our model we have presented two methods, namely, convolutional neural network-discrete wavelet (CNN-DW) and convolutional neural network-curvelet transform (CNN-CT). An augmented data set is generated by using mammogram patches. To enhance the contrast of mammogram images, the data set is filtered by contrast limited adaptive histogram equalization (CLAHE). In the CNN-DW method, enhanced mammogram images are decomposed as its four subbands by means of two-dimensional discrete wavelet transform (2D-DWT), while in the second method discrete curvelet transform (DCT) is used. In both methods, dense scale invariant feature (DSIFT) for all subbands is extracted. Input data matrix containing these subband features of all the mammogram patches is created that is processed as input to convolutional neural network (CNN). Softmax layer and support vector machine (SVM) layer are used to train CNN for classification. Proposed methods have been compared with existing methods in terms of accuracy rate, error rate, and various validation assessment measures. CNN-DW and CNN-CT have achieved accuracy rate of 81.83% and 83.74%, respectively. Simulation results clearly validate the significance and impact of our proposed model as compared to other well-known existing techniques.

Analysis of marine multi-channel seismic data using a 2D continuous wavelet transform

NASA Astrophysics Data System (ADS)

Vuong, A. K.; Zhang, J.; Gibson, R. L.; Sager, W. W.

2011-12-01

Marine multi-channel seismic (MCS) profiles provide important constraints on crustal structure beneath the sea floor. MCS data usually provide good images of the upper part of the oceanic crust, especially in sedimentary layers. In contrast, it is often difficult to interpret deeper layers, especially those within the igneous basement, which is often nearly seismically transparent. That difference in interpretability occurs because sediments typically have continuous, well-layered and easily-traced structural features, whereas volcanic materials are characterized by smaller features with poorer lateral continuity and often with weak impedance contrasts. Since the basement tends to create weaker reflections, the signal-to-noise ratio decreases, creating additional difficulties that can be exacerbated by the presence of multiples generated by the sea floor and other sources of noise. However, it is still important to characterize the basement accurately to better understand oceanic crust formation and associated basaltic volcanism. We analyzed marine MCS data collected by R/V Marcus G. Langseth across the TAMU Massif of Shatsky Rise in the Northwest Pacific. The seismic data from this experiment display the typical problems in imaging basement features. Therefore, we seek to facilitate interpretation by applying 2-D continuous wavelet transforms to the data. Conventional Fourier methods transform 2-D seismic data from space and time domains to wavenumber and frequency, but the results are global in that there is no knowledge of temporal or spatial variations in frequency or wavenumber content. In contrast, wavelet transforms provide estimates of the local frequency and wavenumber content of the seismic image. The transform achieves this result by utilizing a localized, 2D wavelet function instead of the infinite sines and cosines applied in Fourier transforms. We utilize an anisotropic Mexican hat wavelet, where the horizontal and vertical scales are related to wavelength and period of the data, respectively. When analyzing the Shatsky Rise data set, we find, for example, that much of the noise in the seismic image of the basement is at small wavelengths corresponding to several traces, about 25 m. Using the wavelet transforms, we can extract reflection events at longer wavelengths corresponding to expected features in the subsurface. Observing reflections at a certain wavelength provides an estimate of the size scale of the associated geologic structures. The results at a frequency of 31.25 Hz, near the dominant frequency of the data, provide images of reflectors in the deep part of oceanic crust with scales from 200 m to 2000 m that are much easier to interpret than in the original seismic image. In particular, at scales from 200 m to 1000 m, we can see many reflectors with consistent with sizes and locations for localized magma intrusions into the oceanic crust. However, for spatial scales of about 2000 m, only a few reflectors are observed, suggesting there are fewer intrusions of this dimension. These features can also be examined at a range of frequencies to provide additional insights, and the wavelet transform can also be generalized to estimate dips of reflectors.

Photometric Supernova Classification with Machine Learning

NASA Astrophysics Data System (ADS)

Lochner, Michelle; McEwen, Jason D.; Peiris, Hiranya V.; Lahav, Ofer; Winter, Max K.

2016-08-01

Automated photometric supernova classification has become an active area of research in recent years in light of current and upcoming imaging surveys such as the Dark Energy Survey (DES) and the Large Synoptic Survey Telescope, given that spectroscopic confirmation of type for all supernovae discovered will be impossible. Here, we develop a multi-faceted classification pipeline, combining existing and new approaches. Our pipeline consists of two stages: extracting descriptive features from the light curves and classification using a machine learning algorithm. Our feature extraction methods vary from model-dependent techniques, namely SALT2 fits, to more independent techniques that fit parametric models to curves, to a completely model-independent wavelet approach. We cover a range of representative machine learning algorithms, including naive Bayes, k-nearest neighbors, support vector machines, artificial neural networks, and boosted decision trees (BDTs). We test the pipeline on simulated multi-band DES light curves from the Supernova Photometric Classification Challenge. Using the commonly used area under the curve (AUC) of the Receiver Operating Characteristic as a metric, we find that the SALT2 fits and the wavelet approach, with the BDTs algorithm, each achieve an AUC of 0.98, where 1 represents perfect classification. We find that a representative training set is essential for good classification, whatever the feature set or algorithm, with implications for spectroscopic follow-up. Importantly, we find that by using either the SALT2 or the wavelet feature sets with a BDT algorithm, accurate classification is possible purely from light curve data, without the need for any redshift information.

Deep Convolutional Framelet Denosing for Low-Dose CT via Wavelet Residual Network.

PubMed

Kang, Eunhee; Chang, Won; Yoo, Jaejun; Ye, Jong Chul

2018-06-01

Model-based iterative reconstruction algorithms for low-dose X-ray computed tomography (CT) are computationally expensive. To address this problem, we recently proposed a deep convolutional neural network (CNN) for low-dose X-ray CT and won the second place in 2016 AAPM Low-Dose CT Grand Challenge. However, some of the textures were not fully recovered. To address this problem, here we propose a novel framelet-based denoising algorithm using wavelet residual network which synergistically combines the expressive power of deep learning and the performance guarantee from the framelet-based denoising algorithms. The new algorithms were inspired by the recent interpretation of the deep CNN as a cascaded convolution framelet signal representation. Extensive experimental results confirm that the proposed networks have significantly improved performance and preserve the detail texture of the original images.

Wavelet synthetic method for turbulent flow.

PubMed

Zhou, Long; Rauh, Cornelia; Delgado, Antonio

2015-07-01

Based on the idea of random cascades on wavelet dyadic trees and the energy cascade model known as the wavelet p model, a series of velocity increments in two-dimensional space are constructed in different levels of scale. The dynamics is imposed on the generated scales by solving the Euler equation in the Lagrangian framework. A dissipation model is used in order to cover the shortage of the p model, which only predicts in inertial range. Wavelet reconstruction as well as the multiresolution analysis are then performed on each scales. As a result, a type of isotropic velocity field is created. The statistical properties show that the constructed velocity fields share many important features with real turbulence. The pertinence of this approach in the prediction of flow intermittency is also discussed.

Hyperspectral image compressing using wavelet-based method

NASA Astrophysics Data System (ADS)

Yu, Hui; Zhang, Zhi-jie; Lei, Bo; Wang, Chen-sheng

2017-10-01

Hyperspectral imaging sensors can acquire images in hundreds of continuous narrow spectral bands. Therefore each object presented in the image can be identified from their spectral response. However, such kind of imaging brings a huge amount of data, which requires transmission, processing, and storage resources for both airborne and space borne imaging. Due to the high volume of hyperspectral image data, the exploration of compression strategies has received a lot of attention in recent years. Compression of hyperspectral data cubes is an effective solution for these problems. Lossless compression of the hyperspectral data usually results in low compression ratio, which may not meet the available resources; on the other hand, lossy compression may give the desired ratio, but with a significant degradation effect on object identification performance of the hyperspectral data. Moreover, most hyperspectral data compression techniques exploits the similarities in spectral dimensions; which requires bands reordering or regrouping, to make use of the spectral redundancy. In this paper, we explored the spectral cross correlation between different bands, and proposed an adaptive band selection method to obtain the spectral bands which contain most of the information of the acquired hyperspectral data cube. The proposed method mainly consist three steps: First, the algorithm decomposes the original hyperspectral imagery into a series of subspaces based on the hyper correlation matrix of the hyperspectral images between different bands. And then the Wavelet-based algorithm is applied to the each subspaces. At last the PCA method is applied to the wavelet coefficients to produce the chosen number of components. The performance of the proposed method was tested by using ISODATA classification method.

Measurement of the noise components in the medical x-ray intensity pattern due to overlaying nonrecognizable structures

NASA Astrophysics Data System (ADS)

Tischenko, Oleg; Hoeschen, Christoph; Effenberger, Olaf; Reissberg, Steffen; Buhr, Egbert; Doehring, Wilfried

2003-06-01

There are many aspects that influence and deteriorate the detection of pathologies in X-ray images. Some of those are due to effects taking place in the stage of forming the X-ray intensity pattern in front of the x-ray detector. These can be described as motion blurring, depth blurring, anatomical background, scatter noise and structural noise. Structural noise results from an overlapping of fine irrelevant anatomical structures. A method for measuring the combined effect of structural noise and scatter noise was developed and will be presented in this paper. This method is based on the consideration that within a pair of projections created after rotation of the object with a small angle (which is within the typical uncertainty in positioning the patient) both images would show the same relevant structures whereas the projection of the fine overlapping structures will appear quite differently in the two images. To demonstrate the method two X-ray radiographs of a lung phantom were produced. The second radiograph was achieved after rotating the lung by an angle of about 3. Dyadic wavelet representations of both images were regarded. For each value of the wavelet scale parameter the corresponding pair of approximations was matched using the cross correlation matching technique. The homologous regions of approximations were extracted. The image containing only those structures that appear in both images simultaneously was then reconstructed from the wavelet coefficients corresponding to the homologous regions. The difference between one of the original images and the noise-reduced image contains the structural noise and the scatter noise.

Advanced image fusion algorithms for Gamma Knife treatment planning. Evaluation and proposal for clinical use.

PubMed

Apostolou, N; Papazoglou, Th; Koutsouris, D

2006-01-01

Image fusion is a process of combining information from multiple sensors. It is a useful tool implemented in the treatment planning programme of Gamma Knife Radiosurgery. In this paper we evaluate advanced image fusion algorithms for Matlab platform and head images. We develop nine level grayscale image fusion methods: average, principal component analysis (PCA), discrete wavelet transform (DWT) and Laplacian, filter - subtract - decimate (FSD), contrast, gradient, morphological pyramid and a shift invariant discrete wavelet transform (SIDWT) method in Matlab platform. We test these methods qualitatively and quantitatively. The quantitative criteria we use are the Root Mean Square Error (RMSE), the Mutual Information (MI), the Standard Deviation (STD), the Entropy (H), the Difference Entropy (DH) and the Cross Entropy (CEN). The qualitative are: natural appearance, brilliance contrast, presence of complementary features and enhancement of common features. Finally we make clinically useful suggestions.

Improved image decompression for reduced transform coding artifacts

NASA Technical Reports Server (NTRS)

Orourke, Thomas P.; Stevenson, Robert L.

1994-01-01

The perceived quality of images reconstructed from low bit rate compression is severely degraded by the appearance of transform coding artifacts. This paper proposes a method for producing higher quality reconstructed images based on a stochastic model for the image data. Quantization (scalar or vector) partitions the transform coefficient space and maps all points in a partition cell to a representative reconstruction point, usually taken as the centroid of the cell. The proposed image estimation technique selects the reconstruction point within the quantization partition cell which results in a reconstructed image which best fits a non-Gaussian Markov random field (MRF) image model. This approach results in a convex constrained optimization problem which can be solved iteratively. At each iteration, the gradient projection method is used to update the estimate based on the image model. In the transform domain, the resulting coefficient reconstruction points are projected to the particular quantization partition cells defined by the compressed image. Experimental results will be shown for images compressed using scalar quantization of block DCT and using vector quantization of subband wavelet transform. The proposed image decompression provides a reconstructed image with reduced visibility of transform coding artifacts and superior perceived quality.

Compact Encoding of Robot-Generated 3D Maps for Efficient Wireless Transmission

DTIC Science & Technology

2003-01-01

Lempel - Ziv -Welch (LZW) and Ziv - Lempel (LZ77) respectively. Image based compression can also be based on dic- tionaries... compression of the data , without actually displaying a 3D model, printing statistical results for comparison of the different algorithms . 1http... compression algorithms , and wavelet algorithms tuned to the specific nature of the raw laser data . For most such applications, the usage of lossless

Application of wavelet based MFDFA on Mueller matrix images for cervical pre-cancer detection

NASA Astrophysics Data System (ADS)

Zaffar, Mohammad; Pradhan, Asima

2018-02-01

A systematic study has been conducted on application of wavelet based multifractal de-trended fluctuation analysis (MFDFA) on Mueller matrix (MM) images of cervical tissue sections for early cancer detection. Changes in multiple scattering and orientation of fibers are observed by utilizing a discrete wavelet transform (Daubechies) which identifies fluctuations over polynomial trends. Fluctuation profiles, after 9th level decomposition, for all elements of MM qualitatively establish a demarcation of different grades of cancer from normal tissue. Moreover, applying MFDFA on MM images, Hurst exponent profiles for images of MM qualitatively are seen to display differences. In addition, the values of Hurst exponent increase for the diagonal elements of MM with increasing grades of the cervical cancer, while the value for the elements which correspond to linear polarizance decrease. However, for circular polarizance the value increases with increasing grades. These fluctuation profiles reveal the trend of local variation of refractive -indices and along with Hurst exponent profile, may serve as a useful biological metric in the early detection of cervical cancer. The quantitative measurements of Hurst exponent for diagonal and first column (polarizance governing elements) elements which reflect changes in multiple scattering and structural anisotropy in stroma, may be sensitive indicators of pre-cancer.

Delineation of karst terranes in complex environments: Application of modern developments in the wavelet theory and data mining

NASA Astrophysics Data System (ADS)

Alperovich, Leonid; Averbuch, Amir; Eppelbaum, Lev; Zheludev, Valery

2013-04-01

Karst areas occupy about 14% of the world land. Karst terranes of different origin have caused difficult conditions for building, industrial activity and tourism, and are the source of heightened danger for environment. Mapping of karst (sinkhole) hazards, obviously, will be one of the most significant problems of engineering geophysics in the XXI century. Taking into account the complexity of geological media, some unfavourable environments and known ambiguity of geophysical data analysis, a single geophysical method examination might be insufficient. Wavelet methodology as whole has a significant impact on cardinal problems of geophysical signal processing such as: denoising of signals, enhancement of signals and distinguishing of signals with closely related characteristics and integrated analysis of different geophysical fields (satellite, airborne, earth surface or underground observed data). We developed a three-phase approach to the integrated geophysical localization of subsurface karsts (the same approach could be used for following monitoring of karst dynamics). The first phase consists of modeling devoted to compute various geophysical effects characterizing karst phenomena. The second phase determines development of the signal processing approaches to analyzing of profile or areal geophysical observations. Finally, at the third phase provides integration of these methods in order to create a new method of the combined interpretation of different geophysical data. In the base of our combine geophysical analysis we put modern developments in the wavelet technique of the signal and image processing. The development of the integrated methodology of geophysical field examination will enable to recognizing the karst terranes even by a small ratio of "useful signal - noise" in complex geological environments. For analyzing the geophysical data, we used a technique based on the algorithm to characterize a geophysical image by a limited number of parameters. This set of parameters serves as a signature of the image and is to be utilized for discrimination of images containing karst cavity (K) from the images non-containing karst (N). The constructed algorithm consists of the following main phases: (a) collection of the database, (b) characterization of geophysical images, (c) and dimensionality reduction. Then, each image is characterized by the histogram of the coherency directions. As a result of the previous steps we obtain two sets K and N of the signatures vectors for images from sections containing karst cavity and non-karst subsurface, respectively.

Histogram of gradient and binarized statistical image features of wavelet subband-based palmprint features extraction

NASA Astrophysics Data System (ADS)

Attallah, Bilal; Serir, Amina; Chahir, Youssef; Boudjelal, Abdelwahhab

2017-11-01

Palmprint recognition systems are dependent on feature extraction. A method of feature extraction using higher discrimination information was developed to characterize palmprint images. In this method, two individual feature extraction techniques are applied to a discrete wavelet transform of a palmprint image, and their outputs are fused. The two techniques used in the fusion are the histogram of gradient and the binarized statistical image features. They are then evaluated using an extreme learning machine classifier before selecting a feature based on principal component analysis. Three palmprint databases, the Hong Kong Polytechnic University (PolyU) Multispectral Palmprint Database, Hong Kong PolyU Palmprint Database II, and the Delhi Touchless (IIDT) Palmprint Database, are used in this study. The study shows that our method effectively identifies and verifies palmprints and outperforms other methods based on feature extraction.

A New Sparse Representation Framework for Reconstruction of an Isotropic High Spatial Resolution MR Volume From Orthogonal Anisotropic Resolution Scans.

PubMed

Jia, Yuanyuan; Gholipour, Ali; He, Zhongshi; Warfield, Simon K

2017-05-01

In magnetic resonance (MR), hardware limitations, scan time constraints, and patient movement often result in the acquisition of anisotropic 3-D MR images with limited spatial resolution in the out-of-plane views. Our goal is to construct an isotropic high-resolution (HR) 3-D MR image through upsampling and fusion of orthogonal anisotropic input scans. We propose a multiframe super-resolution (SR) reconstruction technique based on sparse representation of MR images. Our proposed algorithm exploits the correspondence between the HR slices and the low-resolution (LR) sections of the orthogonal input scans as well as the self-similarity of each input scan to train pairs of overcomplete dictionaries that are used in a sparse-land local model to upsample the input scans. The upsampled images are then combined using wavelet fusion and error backprojection to reconstruct an image. Features are learned from the data and no extra training set is needed. Qualitative and quantitative analyses were conducted to evaluate the proposed algorithm using simulated and clinical MR scans. Experimental results show that the proposed algorithm achieves promising results in terms of peak signal-to-noise ratio, structural similarity image index, intensity profiles, and visualization of small structures obscured in the LR imaging process due to partial volume effects. Our novel SR algorithm outperforms the nonlocal means (NLM) method using self-similarity, NLM method using self-similarity and image prior, self-training dictionary learning-based SR method, averaging of upsampled scans, and the wavelet fusion method. Our SR algorithm can reduce through-plane partial volume artifact by combining multiple orthogonal MR scans, and thus can potentially improve medical image analysis, research, and clinical diagnosis.

Characterization of palmprints by wavelet signatures via directional context modeling.

PubMed

Zhang, Lei; Zhang, David

2004-06-01

The palmprint is one of the most reliable physiological characteristics that can be used to distinguish between individuals. Current palmprint-based systems are more user friendly, more cost effective, and require fewer data signatures than traditional fingerprint-based identification systems. The principal lines and wrinkles captured in a low-resolution palmprint image provide more than enough information to uniquely identify an individual. This paper presents a palmprint identification scheme that characterizes a palmprint using a set of statistical signatures. The palmprint is first transformed into the wavelet domain, and the directional context of each wavelet subband is defined and computed in order to collect the predominant coefficients of its principal lines and wrinkles. A set of statistical signatures, which includes gravity center, density, spatial dispersivity and energy, is then defined to characterize the palmprint with the selected directional context values. A classification and identification scheme based on these signatures is subsequently developed. This scheme exploits the features of principal lines and prominent wrinkles sufficiently and achieves satisfactory results. Compared with the line-segments-matching or interesting-points-matching based palmprint verification schemes, the proposed scheme uses a much smaller amount of data signatures. It also provides a convenient classification strategy and more accurate identification.

Task-oriented lossy compression of magnetic resonance images

NASA Astrophysics Data System (ADS)

Anderson, Mark C.; Atkins, M. Stella; Vaisey, Jacques

1996-04-01

A new task-oriented image quality metric is used to quantify the effects of distortion introduced into magnetic resonance images by lossy compression. This metric measures the similarity between a radiologist's manual segmentation of pathological features in the original images and the automated segmentations performed on the original and compressed images. The images are compressed using a general wavelet-based lossy image compression technique, embedded zerotree coding, and segmented using a three-dimensional stochastic model-based tissue segmentation algorithm. The performance of the compression system is then enhanced by compressing different regions of the image volume at different bit rates, guided by prior knowledge about the location of important anatomical regions in the image. Application of the new system to magnetic resonance images is shown to produce compression results superior to the conventional methods, both subjectively and with respect to the segmentation similarity metric.

Wavelet transform approach for fitting financial time series data

NASA Astrophysics Data System (ADS)

Ahmed, Amel Abdoullah; Ismail, Mohd Tahir

2015-10-01

This study investigates a newly developed technique; a combined wavelet filtering and VEC model, to study the dynamic relationship among financial time series. Wavelet filter has been used to annihilate noise data in daily data set of NASDAQ stock market of US, and three stock markets of Middle East and North Africa (MENA) region, namely, Egypt, Jordan, and Istanbul. The data covered is from 6/29/2001 to 5/5/2009. After that, the returns of generated series by wavelet filter and original series are analyzed by cointegration test and VEC model. The results show that the cointegration test affirms the existence of cointegration between the studied series, and there is a long-term relationship between the US, stock markets and MENA stock markets. A comparison between the proposed model and traditional model demonstrates that, the proposed model (DWT with VEC model) outperforms traditional model (VEC model) to fit the financial stock markets series well, and shows real information about these relationships among the stock markets.

Research on artificial neural network intrusion detection photochemistry based on the improved wavelet analysis and transformation

NASA Astrophysics Data System (ADS)

Li, Hong; Ding, Xue

2017-03-01

This paper combines wavelet analysis and wavelet transform theory with artificial neural network, through the pretreatment on point feature attributes before in intrusion detection, to make them suitable for improvement of wavelet neural network. The whole intrusion classification model gets the better adaptability, self-learning ability, greatly enhances the wavelet neural network for solving the problem of field detection invasion, reduces storage space, contributes to improve the performance of the constructed neural network, and reduces the training time. Finally the results of the KDDCup99 data set simulation experiment shows that, this method reduces the complexity of constructing wavelet neural network, but also ensures the accuracy of the intrusion classification.

Automatic CT Brain Image Segmentation Using Two Level Multiresolution Mixture Model of EM

NASA Astrophysics Data System (ADS)

Jiji, G. Wiselin; Dehmeshki, Jamshid

2014-04-01

Tissue classification in computed tomography (CT) brain images is an important issue in the analysis of several brain dementias. A combination of different approaches for the segmentation of brain images is presented in this paper. A multi resolution algorithm is proposed along with scaled versions using Gaussian filter and wavelet analysis that extends expectation maximization (EM) algorithm. It is found that it is less sensitive to noise and got more accurate image segmentation than traditional EM. Moreover the algorithm has been applied on 20 sets of CT of the human brain and compared with other works. The segmentation results show the advantages of the proposed work have achieved more promising results and the results have been tested with Doctors.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Hudgins, L.H.

After a brief review of the elementary properties of Fourier Transforms, the Wavelet Transform is defined in Part I. Basic results are given for admissable wavelets. The Multiresolution Analysis, or MRA (a mathematical structure which unifies a large class of wavelets with Quadrature Mirror Filters) is then introduced. Some fundamental aspects of wavelet design are then explored. The Discrete Wavelet Transform is discussed and, in the context of an MRA, is seen to supply a Fast Wavelet Transform which competes with the Fast Fourier Transform for efficiency. In Part II, the Wavelet Transform is developed in terms of the scalemore » number variable s instead of the scale length variable a where a = 1/s. Basic results such as the admissibility condition, conservation of energy, and the reconstruction theorem are proven in this context. After reviewing some motivation for the usual Fourier power spectrum, a definition is given for the wavelet power spectrum. This `spectral density` is then intepreted in the context of spectral estimation theory. Parseval`s theorem for Wavelets then leads naturally to the Wavelet Cross Spectrum, Wavelet Cospectrum, and Wavelet Quadrature Spectrum. Wavelet Transforms are then applied in Part III to the analysis of atmospheric turbulence. Data collected over the ocean is examined in the wavelet transform domain for underlying structure. A brief overview of atmospheric turbulence is provided. Then the overall method of applying Wavelet Transform techniques to time series data is described. A trace study is included, showing some of the aspects of choosing the computational algorithm, and selection of a specific analyzing wavelet. A model for generating synthetic turbulence data is developed, and seen to yield useful results in comparing with real data for structural transitions. Results from the theory of Wavelet Spectral Estimation and Wavelength Cross-Transforms are applied to studying the momentum transport and the heat flux.« less

A new approach to global seismic tomography based on regularization by sparsity in a novel 3D spherical wavelet basis

NASA Astrophysics Data System (ADS)

Loris, Ignace; Simons, Frederik J.; Daubechies, Ingrid; Nolet, Guust; Fornasier, Massimo; Vetter, Philip; Judd, Stephen; Voronin, Sergey; Vonesch, Cédric; Charléty, Jean

2010-05-01

Global seismic wavespeed models are routinely parameterized in terms of spherical harmonics, networks of tetrahedral nodes, rectangular voxels, or spherical splines. Up to now, Earth model parametrizations by wavelets on the three-dimensional ball remain uncommon. Here we propose such a procedure with the following three goals in mind: (1) The multiresolution character of a wavelet basis allows for the models to be represented with an effective spatial resolution that varies as a function of position within the Earth. (2) This property can be used to great advantage in the regularization of seismic inversion schemes by seeking the most sparse solution vector, in wavelet space, through iterative minimization of a combination of the ℓ2 (to fit the data) and ℓ1 norms (to promote sparsity in wavelet space). (3) With the continuing increase in high-quality seismic data, our focus is also on numerical efficiency and the ability to use parallel computing in reconstructing the model. In this presentation we propose a new wavelet basis to take advantage of these three properties. To form the numerical grid we begin with a surface tesselation known as the 'cubed sphere', a construction popular in fluid dynamics and computational seismology, coupled with an semi-regular radial subdivison that honors the major seismic discontinuities between the core-mantle boundary and the surface. This mapping first divides the volume of the mantle into six portions. In each 'chunk' two angular and one radial variable are used for parametrization. In the new variables standard 'cartesian' algorithms can more easily be used to perform the wavelet transform (or other common transforms). Edges between chunks are handled by special boundary filters. We highlight the benefits of this construction and use it to analyze the information present in several published seismic compressional-wavespeed models of the mantle, paying special attention to the statistics of wavelet and scaling coefficients across scales. We also focus on the likely gains of future inversions of finite-frequency seismic data using a sparsity promoting penalty in combination with our new wavelet approach.

TU-AB-BRA-10: Prognostic Value of Intra-Radiation Treatment FDG-PET and CT Imaging Features in Locally Advanced Head and Neck Cancer

DOE Office of Scientific and Technical Information (OSTI.GOV)

Song, J; Pollom, E; Durkee, B

2015-06-15

Purpose: To predict response to radiation treatment using computational FDG-PET and CT images in locally advanced head and neck cancer (HNC). Methods: 68 patients with State III-IVB HNC treated with chemoradiation were included in this retrospective study. For each patient, we analyzed primary tumor and lymph nodes on PET and CT scans acquired both prior to and during radiation treatment, which led to 8 combinations of image datasets. From each image set, we extracted high-throughput, radiomic features of the following types: statistical, morphological, textural, histogram, and wavelet, resulting in a total of 437 features. We then performed unsupervised redundancy removalmore » and stability test on these features. To avoid over-fitting, we trained a logistic regression model with simultaneous feature selection based on least absolute shrinkage and selection operator (LASSO). To objectively evaluate the prediction ability, we performed 5-fold cross validation (CV) with 50 random repeats of stratified bootstrapping. Feature selection and model training was solely conducted on the training set and independently validated on the holdout test set. Receiver operating characteristic (ROC) curve of the pooled Result and the area under the ROC curve (AUC) was calculated as figure of merit. Results: For predicting local-regional recurrence, our model built on pre-treatment PET of lymph nodes achieved the best performance (AUC=0.762) on 5-fold CV, which compared favorably with node volume and SUVmax (AUC=0.704 and 0.449, p<0.001). Wavelet coefficients turned out to be the most predictive features. Prediction of distant recurrence showed a similar trend, in which pre-treatment PET features of lymph nodes had the highest AUC of 0.705. Conclusion: The radiomics approach identified novel imaging features that are predictive to radiation treatment response. If prospectively validated in larger cohorts, they could aid in risk-adaptive treatment of HNC.« less

Infrared and visual image fusion method based on discrete cosine transform and local spatial frequency in discrete stationary wavelet transform domain

NASA Astrophysics Data System (ADS)

Jin, Xin; Jiang, Qian; Yao, Shaowen; Zhou, Dongming; Nie, Rencan; Lee, Shin-Jye; He, Kangjian

2018-01-01

In order to promote the performance of infrared and visual image fusion and provide better visual effects, this paper proposes a hybrid fusion method for infrared and visual image by the combination of discrete stationary wavelet transform (DSWT), discrete cosine transform (DCT) and local spatial frequency (LSF). The proposed method has three key processing steps. Firstly, DSWT is employed to decompose the important features of the source image into a series of sub-images with different levels and spatial frequencies. Secondly, DCT is used to separate the significant details of the sub-images according to the energy of different frequencies. Thirdly, LSF is applied to enhance the regional features of DCT coefficients, and it can be helpful and useful for image feature extraction. Some frequently-used image fusion methods and evaluation metrics are employed to evaluate the validity of the proposed method. The experiments indicate that the proposed method can achieve good fusion effect, and it is more efficient than other conventional image fusion methods.

Push-Broom-Type Very High-Resolution Satellite Sensor Data Correction Using Combined Wavelet-Fourier and Multiscale Non-Local Means Filtering.

PubMed

Kang, Wonseok; Yu, Soohwan; Seo, Doochun; Jeong, Jaeheon; Paik, Joonki

2015-09-10

In very high-resolution (VHR) push-broom-type satellite sensor data, both destriping and denoising methods have become chronic problems and attracted major research advances in the remote sensing fields. Since the estimation of the original image from a noisy input is an ill-posed problem, a simple noise removal algorithm cannot preserve the radiometric integrity of satellite data. To solve these problems, we present a novel method to correct VHR data acquired by a push-broom-type sensor by combining wavelet-Fourier and multiscale non-local means (NLM) filters. After the wavelet-Fourier filter separates the stripe noise from the mixed noise in the wavelet low- and selected high-frequency sub-bands, random noise is removed using the multiscale NLM filter in both low- and high-frequency sub-bands without loss of image detail. The performance of the proposed method is compared to various existing methods on a set of push-broom-type sensor data acquired by Korean Multi-Purpose Satellite 3 (KOMPSAT-3) with severe stripe and random noise, and the results of the proposed method show significantly improved enhancement results over existing state-of-the-art methods in terms of both qualitative and quantitative assessments.

Unsupervised symmetrical trademark image retrieval in soccer telecast using wavelet energy and quadtree decomposition

NASA Astrophysics Data System (ADS)

Ong, Swee Khai; Lim, Wee Keong; Soo, Wooi King

2013-04-01

Trademark, a distinctive symbol, is used to distinguish products or services provided by a particular person, group or organization from other similar entries. As trademark represents the reputation and credit standing of the owner, it is important to differentiate one trademark from another. Many methods have been proposed to identify, classify and retrieve trademarks. However, most methods required features database and sample sets for training prior to recognition and retrieval process. In this paper, a new feature on wavelet coefficients, the localized wavelet energy, is introduced to extract features of trademarks. With this, unsupervised content-based symmetrical trademark image retrieval is proposed without the database and prior training set. The feature analysis is done by an integration of the proposed localized wavelet energy and quadtree decomposed regional symmetrical vector. The proposed framework eradicates the dependence on query database and human participation during the retrieval process. In this paper, trademarks for soccer games sponsors are the intended trademark category. Video frames from soccer telecast are extracted and processed for this study. Reasonably good localization and retrieval results on certain categories of trademarks are achieved. A distinctive symbol is used to distinguish products or services provided by a particular person, group or organization from other similar entries.

Functional magnetic resonance imaging activation detection: fuzzy cluster analysis in wavelet and multiwavelet domains.

PubMed

Jahanian, Hesamoddin; Soltanian-Zadeh, Hamid; Hossein-Zadeh, Gholam-Ali

2005-09-01

To present novel feature spaces, based on multiscale decompositions obtained by scalar wavelet and multiwavelet transforms, to remedy problems associated with high dimension of functional magnetic resonance imaging (fMRI) time series (when they are used directly in clustering algorithms) and their poor signal-to-noise ratio (SNR) that limits accurate classification of fMRI time series according to their activation contents. Using randomization, the proposed method finds wavelet/multiwavelet coefficients that represent the activation content of fMRI time series and combines them to define new feature spaces. Using simulated and experimental fMRI data sets, the proposed feature spaces are compared to the cross-correlation (CC) feature space and their performances are evaluated. In these studies, the false positive detection rate is controlled using randomization. To compare different methods, several points of the receiver operating characteristics (ROC) curves, using simulated data, are estimated and compared. The proposed features suppress the effects of confounding signals and improve activation detection sensitivity. Experimental results show improved sensitivity and robustness of the proposed method compared to the conventional CC analysis. More accurate and sensitive activation detection can be achieved using the proposed feature spaces compared to CC feature space. Multiwavelet features show superior detection sensitivity compared to the scalar wavelet features. (c) 2005 Wiley-Liss, Inc.

Push-Broom-Type Very High-Resolution Satellite Sensor Data Correction Using Combined Wavelet-Fourier and Multiscale Non-Local Means Filtering

PubMed Central

Kang, Wonseok; Yu, Soohwan; Seo, Doochun; Jeong, Jaeheon; Paik, Joonki

2015-01-01

In very high-resolution (VHR) push-broom-type satellite sensor data, both destriping and denoising methods have become chronic problems and attracted major research advances in the remote sensing fields. Since the estimation of the original image from a noisy input is an ill-posed problem, a simple noise removal algorithm cannot preserve the radiometric integrity of satellite data. To solve these problems, we present a novel method to correct VHR data acquired by a push-broom-type sensor by combining wavelet-Fourier and multiscale non-local means (NLM) filters. After the wavelet-Fourier filter separates the stripe noise from the mixed noise in the wavelet low- and selected high-frequency sub-bands, random noise is removed using the multiscale NLM filter in both low- and high-frequency sub-bands without loss of image detail. The performance of the proposed method is compared to various existing methods on a set of push-broom-type sensor data acquired by Korean Multi-Purpose Satellite 3 (KOMPSAT-3) with severe stripe and random noise, and the results of the proposed method show significantly improved enhancement results over existing state-of-the-art methods in terms of both qualitative and quantitative assessments. PMID:26378532

Directional filtering for block recovery using wavelet features

NASA Astrophysics Data System (ADS)

Hyun, Seung H.; Eom, Il K.; Kim, Yoo S.

2005-07-01

When images compressed with block-based compression techniques are transmitted over a noisy channel, unexpected block losses occur. Conventional methods that do not consider edge directions can cause blocked blurring artifacts. In this paper, we present a post-processing-based block recovery scheme using Haar wavelet features. The adaptive selection of neighboring blocks is performed based on the energy of wavelet subbands (EWS) and difference between DC values (DDC). The lost blocks are recovered by linear interpolation in the spatial domain using selected blocks. The method using only EWS performs well for horizontal and vertical edges, but not as well for diagonal edges. Conversely, only using DDC performs well for diagonal edges with the exception of line- or roof-type edge profiles. Therefore, we combine EWS and DDC for better results. The proposed directional recovery method is effective for the strong edge because exploit the varying neighboring blocks adaptively according to the edges and the directional information in the image. The proposed method outperforms the previous methods that used only fixed blocks.

Heterogeneity wavelet kinetics from DCE-MRI for classifying gene expression based breast cancer recurrence risk.

PubMed

Mahrooghy, Majid; Ashraf, Ahmed B; Daye, Dania; Mies, Carolyn; Feldman, Michael; Rosen, Mark; Kontos, Despina

2013-01-01

Breast tumors are heterogeneous lesions. Intra-tumor heterogeneity presents a major challenge for cancer diagnosis and treatment. Few studies have worked on capturing tumor heterogeneity from imaging. Most studies to date consider aggregate measures for tumor characterization. In this work we capture tumor heterogeneity by partitioning tumor pixels into subregions and extracting heterogeneity wavelet kinetic (HetWave) features from breast dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) to obtain the spatiotemporal patterns of the wavelet coefficients and contrast agent uptake from each partition. Using a genetic algorithm for feature selection, and a logistic regression classifier with leave one-out cross validation, we tested our proposed HetWave features for the task of classifying breast cancer recurrence risk. The classifier based on our features gave an ROC AUC of 0.78, outperforming previously proposed kinetic, texture, and spatial enhancement variance features which give AUCs of 0.69, 0.64, and 0.65, respectively.

A new approach of watermarking technique by means multichannel wavelet functions

NASA Astrophysics Data System (ADS)

Agreste, Santa; Puccio, Luigia

2012-12-01

The digital piracy involving images, music, movies, books, and so on, is a legal problem that has not found a solution. Therefore it becomes crucial to create and to develop methods and numerical algorithms in order to solve the copyright problems. In this paper we focus the attention on a new approach of watermarking technique applied to digital color images. Our aim is to describe the realized watermarking algorithm based on multichannel wavelet functions with multiplicity r = 3, called MCWM 1.0. We report a large experimentation and some important numerical results in order to show the robustness of the proposed algorithm to geometrical attacks.

A novel approach for detection and classification of mammographic microcalcifications using wavelet analysis and extreme learning machine.

PubMed

Malar, E; Kandaswamy, A; Chakravarthy, D; Giri Dharan, A

2012-09-01

The objective of this paper is to reveal the effectiveness of wavelet based tissue texture analysis for microcalcification detection in digitized mammograms using Extreme Learning Machine (ELM). Microcalcifications are tiny deposits of calcium in the breast tissue which are potential indicators for early detection of breast cancer. The dense nature of the breast tissue and the poor contrast of the mammogram image prohibit the effectiveness in identifying microcalcifications. Hence, a new approach to discriminate the microcalcifications from the normal tissue is done using wavelet features and is compared with different feature vectors extracted using Gray Level Spatial Dependence Matrix (GLSDM) and Gabor filter based techniques. A total of 120 Region of Interests (ROIs) extracted from 55 mammogram images of mini-Mias database, including normal and microcalcification images are used in the current research. The network is trained with the above mentioned features and the results denote that ELM produces relatively better classification accuracy (94%) with a significant reduction in training time than the other artificial neural networks like Bayesnet classifier, Naivebayes classifier, and Support Vector Machine. ELM also avoids problems like local minima, improper learning rate, and over fitting. Copyright © 2012 Elsevier Ltd. All rights reserved.

Single-image super-resolution based on Markov random field and contourlet transform

NASA Astrophysics Data System (ADS)

Wu, Wei; Liu, Zheng; Gueaieb, Wail; He, Xiaohai

2011-04-01

Learning-based methods are well adopted in image super-resolution. In this paper, we propose a new learning-based approach using contourlet transform and Markov random field. The proposed algorithm employs contourlet transform rather than the conventional wavelet to represent image features and takes into account the correlation between adjacent pixels or image patches through the Markov random field (MRF) model. The input low-resolution (LR) image is decomposed with the contourlet transform and fed to the MRF model together with the contourlet transform coefficients from the low- and high-resolution image pairs in the training set. The unknown high-frequency components/coefficients for the input low-resolution image are inferred by a belief propagation algorithm. Finally, the inverse contourlet transform converts the LR input and the inferred high-frequency coefficients into the super-resolved image. The effectiveness of the proposed method is demonstrated with the experiments on facial, vehicle plate, and real scene images. A better visual quality is achieved in terms of peak signal to noise ratio and the image structural similarity measurement.

Partial Deconvolution with Inaccurate Blur Kernel.

PubMed

Ren, Dongwei; Zuo, Wangmeng; Zhang, David; Xu, Jun; Zhang, Lei

2017-10-17

Most non-blind deconvolution methods are developed under the error-free kernel assumption, and are not robust to inaccurate blur kernel. Unfortunately, despite the great progress in blind deconvolution, estimation error remains inevitable during blur kernel estimation. Consequently, severe artifacts such as ringing effects and distortions are likely to be introduced in the non-blind deconvolution stage. In this paper, we tackle this issue by suggesting: (i) a partial map in the Fourier domain for modeling kernel estimation error, and (ii) a partial deconvolution model for robust deblurring with inaccurate blur kernel. The partial map is constructed by detecting the reliable Fourier entries of estimated blur kernel. And partial deconvolution is applied to wavelet-based and learning-based models to suppress the adverse effect of kernel estimation error. Furthermore, an E-M algorithm is developed for estimating the partial map and recovering the latent sharp image alternatively. Experimental results show that our partial deconvolution model is effective in relieving artifacts caused by inaccurate blur kernel, and can achieve favorable deblurring quality on synthetic and real blurry images.Most non-blind deconvolution methods are developed under the error-free kernel assumption, and are not robust to inaccurate blur kernel. Unfortunately, despite the great progress in blind deconvolution, estimation error remains inevitable during blur kernel estimation. Consequently, severe artifacts such as ringing effects and distortions are likely to be introduced in the non-blind deconvolution stage. In this paper, we tackle this issue by suggesting: (i) a partial map in the Fourier domain for modeling kernel estimation error, and (ii) a partial deconvolution model for robust deblurring with inaccurate blur kernel. The partial map is constructed by detecting the reliable Fourier entries of estimated blur kernel. And partial deconvolution is applied to wavelet-based and learning-based models to suppress the adverse effect of kernel estimation error. Furthermore, an E-M algorithm is developed for estimating the partial map and recovering the latent sharp image alternatively. Experimental results show that our partial deconvolution model is effective in relieving artifacts caused by inaccurate blur kernel, and can achieve favorable deblurring quality on synthetic and real blurry images.

Global spectral graph wavelet signature for surface analysis of carpal bones

NASA Astrophysics Data System (ADS)

Masoumi, Majid; Rezaei, Mahsa; Ben Hamza, A.

2018-02-01

Quantitative shape comparison is a fundamental problem in computer vision, geometry processing and medical imaging. In this paper, we present a spectral graph wavelet approach for shape analysis of carpal bones of the human wrist. We employ spectral graph wavelets to represent the cortical surface of a carpal bone via the spectral geometric analysis of the Laplace-Beltrami operator in the discrete domain. We propose global spectral graph wavelet (GSGW) descriptor that is isometric invariant, efficient to compute, and combines the advantages of both low-pass and band-pass filters. We perform experiments on shapes of the carpal bones of ten women and ten men from a publicly-available database of wrist bones. Using one-way multivariate analysis of variance (MANOVA) and permutation testing, we show through extensive experiments that the proposed GSGW framework gives a much better performance compared to the global point signature embedding approach for comparing shapes of the carpal bones across populations.

Global spectral graph wavelet signature for surface analysis of carpal bones.

PubMed

Masoumi, Majid; Rezaei, Mahsa; Ben Hamza, A

2018-02-05

Quantitative shape comparison is a fundamental problem in computer vision, geometry processing and medical imaging. In this paper, we present a spectral graph wavelet approach for shape analysis of carpal bones of the human wrist. We employ spectral graph wavelets to represent the cortical surface of a carpal bone via the spectral geometric analysis of the Laplace-Beltrami operator in the discrete domain. We propose global spectral graph wavelet (GSGW) descriptor that is isometric invariant, efficient to compute, and combines the advantages of both low-pass and band-pass filters. We perform experiments on shapes of the carpal bones of ten women and ten men from a publicly-available database of wrist bones. Using one-way multivariate analysis of variance (MANOVA) and permutation testing, we show through extensive experiments that the proposed GSGW framework gives a much better performance compared to the global point signature embedding approach for comparing shapes of the carpal bones across populations.

A Wavelet Model for Vocalic Speech Coarticulation

DTIC Science & Technology

1994-10-01

control vowel’s signal as the mother wavelet. A practical experiment is conducted to evaluate the coarticulation channel using samples 01 real speech...transformation from a control speech state (input) to an effected speech state (output). Specifically, a vowel produced in isolation is transformed into an...the wavelet transform of the effected vowel’s signal, using the control vowel’s signal as the mother wavelet. A practical experiment is conducted to

Application of Fourier-wavelet regularized deconvolution for improving image quality of free space propagation x-ray phase contrast imaging.

PubMed

Zhou, Zhongxing; Gao, Feng; Zhao, Huijuan; Zhang, Lixin

2012-11-21

New x-ray phase contrast imaging techniques without using synchrotron radiation confront a common problem from the negative effects of finite source size and limited spatial resolution. These negative effects swamp the fine phase contrast fringes and make them almost undetectable. In order to alleviate this problem, deconvolution procedures should be applied to the blurred x-ray phase contrast images. In this study, three different deconvolution techniques, including Wiener filtering, Tikhonov regularization and Fourier-wavelet regularized deconvolution (ForWaRD), were applied to the simulated and experimental free space propagation x-ray phase contrast images of simple geometric phantoms. These algorithms were evaluated in terms of phase contrast improvement and signal-to-noise ratio. The results demonstrate that the ForWaRD algorithm is most appropriate for phase contrast image restoration among above-mentioned methods; it can effectively restore the lost information of phase contrast fringes while reduce the amplified noise during Fourier regularization.

Automated Age-related Macular Degeneration screening system using fundus images.

PubMed

Kunumpol, P; Umpaipant, W; Kanchanaranya, N; Charoenpong, T; Vongkittirux, S; Kupakanjana, T; Tantibundhit, C

2017-07-01

This work proposed an automated screening system for Age-related Macular Degeneration (AMD), and distinguishing between wet or dry types of AMD using fundus images to assist ophthalmologists in eye disease screening and management. The algorithm employs contrast-limited adaptive histogram equalization (CLAHE) in image enhancement. Subsequently, discrete wavelet transform (DWT) and locality sensitivity discrimination analysis (LSDA) were used to extract features for a neural network model to classify the results. The results showed that the proposed algorithm was able to distinguish between normal eyes, dry AMD, or wet AMD with 98.63% sensitivity, 99.15% specificity, and 98.94% accuracy, suggesting promising potential as a medical support system for faster eye disease screening at lower costs.

Fusion and quality analysis for remote sensing images using contourlet transform

NASA Astrophysics Data System (ADS)

Choi, Yoonsuk; Sharifahmadian, Ershad; Latifi, Shahram

2013-05-01

Recent developments in remote sensing technologies have provided various images with high spatial and spectral resolutions. However, multispectral images have low spatial resolution and panchromatic images have low spectral resolution. Therefore, image fusion techniques are necessary to improve the spatial resolution of spectral images by injecting spatial details of high-resolution panchromatic images. The objective of image fusion is to provide useful information by improving the spatial resolution and the spectral information of the original images. The fusion results can be utilized in various applications, such as military, medical imaging, and remote sensing. This paper addresses two issues in image fusion: i) image fusion method and ii) quality analysis of fusion results. First, a new contourlet-based image fusion method is presented, which is an improvement over the wavelet-based fusion. This fusion method is then applied to a case study to demonstrate its fusion performance. Fusion framework and scheme used in the study are discussed in detail. Second, quality analysis for the fusion results is discussed. We employed various quality metrics in order to analyze the fusion results both spatially and spectrally. Our results indicate that the proposed contourlet-based fusion method performs better than the conventional wavelet-based fusion methods.

Wavelet analysis of polarization maps of the myocardium tissue microscopic images in the diagnosis of the causes of death

NASA Astrophysics Data System (ADS)

Ushenko, V. O.; Boichuk, T. M.; Bachinskiy, V. T.; Vanchuliak, O. Ya.; Minzer, O. P.; Dubolazov, O. V.; Marchuk, Yu. F.; Olar, O. I.

2015-08-01

The results of optical modeling of biological tissues polycrystalline multilayer networks have been presented. Algorithms of reconstruction of parameter distributions were determined that describe the linear and circular birefringence. For the separation of the manifestations of these mechanisms we propose a method of space-frequency filtering. Criteria for differentiation of benign and malignant tissues of the women reproductive sphere were found.

A new stationary gridline artifact suppression method based on the 2D discrete wavelet transform

DOE Office of Scientific and Technical Information (OSTI.GOV)

Tang, Hui, E-mail: corinna@seu.edu.cn; Key Laboratory of Computer Network and Information Integration; Centre de Recherche en Information Biomédicale sino-français, Laboratoire International Associé, Inserm, Université de Rennes 1, Rennes 35000

2015-04-15

Purpose: In digital x-ray radiography, an antiscatter grid is inserted between the patient and the image receptor to reduce scattered radiation. If the antiscatter grid is used in a stationary way, gridline artifacts will appear in the final image. In most of the gridline removal image processing methods, the useful information with spatial frequencies close to that of the gridline is usually lost or degraded. In this study, a new stationary gridline suppression method is designed to preserve more of the useful information. Methods: The method is as follows. The input image is first recursively decomposed into several smaller subimagesmore » using a multiscale 2D discrete wavelet transform. The decomposition process stops when the gridline signal is found to be greater than a threshold in one or several of these subimages using a gridline detection module. An automatic Gaussian band-stop filter is then applied to the detected subimages to remove the gridline signal. Finally, the restored image is achieved using the corresponding 2D inverse discrete wavelet transform. Results: The processed images show that the proposed method can remove the gridline signal efficiently while maintaining the image details. The spectra of a 1D Fourier transform of the processed images demonstrate that, compared with some existing gridline removal methods, the proposed method has better information preservation after the removal of the gridline artifacts. Additionally, the performance speed is relatively high. Conclusions: The experimental results demonstrate the efficiency of the proposed method. Compared with some existing gridline removal methods, the proposed method can preserve more information within an acceptable execution time.« less

Image-Data Compression Using Edge-Optimizing Algorithm for WFA Inference.

ERIC Educational Resources Information Center

Culik, Karel II; Kari, Jarkko

1994-01-01

Presents an inference algorithm that produces a weighted finite automata (WFA), in particular, the grayness functions of graytone images. Image-data compression results based on the new inference algorithm produces a WFA with a relatively small number of edges. Image-data compression results alone and in combination with wavelets are discussed.…

Speckle reduction in optical coherence tomography images based on wave atoms

PubMed Central

Du, Yongzhao; Liu, Gangjun; Feng, Guoying; Chen, Zhongping

2014-01-01

Abstract. Optical coherence tomography (OCT) is an emerging noninvasive imaging technique, which is based on low-coherence interferometry. OCT images suffer from speckle noise, which reduces image contrast. A shrinkage filter based on wave atoms transform is proposed for speckle reduction in OCT images. Wave atoms transform is a new multiscale geometric analysis tool that offers sparser expansion and better representation for images containing oscillatory patterns and textures than other traditional transforms, such as wavelet and curvelet transforms. Cycle spinning-based technology is introduced to avoid visual artifacts, such as Gibbs-like phenomenon, and to develop a translation invariant wave atoms denoising scheme. The speckle suppression degree in the denoised images is controlled by an adjustable parameter that determines the threshold in the wave atoms domain. The experimental results show that the proposed method can effectively remove the speckle noise and improve the OCT image quality. The signal-to-noise ratio, contrast-to-noise ratio, average equivalent number of looks, and cross-correlation (XCOR) values are obtained, and the results are also compared with the wavelet and curvelet thresholding techniques. PMID:24825507

Wavelet analysis enables system-independent texture analysis of optical coherence tomography images.

PubMed

Lingley-Papadopoulos, Colleen A; Loew, Murray H; Zara, Jason M

2009-01-01

Texture analysis for tissue characterization is a current area of optical coherence tomography (OCT) research. We discuss some of the differences between OCT systems and the effects those differences have on the resulting images and subsequent image analysis. In addition, as an example, two algorithms for the automatic recognition of bladder cancer are compared: one that was developed on a single system with no consideration for system differences, and one that was developed to address the issues associated with system differences. The first algorithm had a sensitivity of 73% and specificity of 69% when tested using leave-one-out cross-validation on data taken from a single system. When tested on images from another system with a different central wavelength, however, the method classified all images as cancerous regardless of the true pathology. By contrast, with the use of wavelet analysis and the removal of system-dependent features, the second algorithm reported sensitivity and specificity values of 87 and 58%, respectively, when trained on images taken with one imaging system and tested on images taken with another.

Wavelet analysis enables system-independent texture analysis of optical coherence tomography images

NASA Astrophysics Data System (ADS)

Lingley-Papadopoulos, Colleen A.; Loew, Murray H.; Zara, Jason M.

2009-07-01

Texture analysis for tissue characterization is a current area of optical coherence tomography (OCT) research. We discuss some of the differences between OCT systems and the effects those differences have on the resulting images and subsequent image analysis. In addition, as an example, two algorithms for the automatic recognition of bladder cancer are compared: one that was developed on a single system with no consideration for system differences, and one that was developed to address the issues associated with system differences. The first algorithm had a sensitivity of 73% and specificity of 69% when tested using leave-one-out cross-validation on data taken from a single system. When tested on images from another system with a different central wavelength, however, the method classified all images as cancerous regardless of the true pathology. By contrast, with the use of wavelet analysis and the removal of system-dependent features, the second algorithm reported sensitivity and specificity values of 87 and 58%, respectively, when trained on images taken with one imaging system and tested on images taken with another.

Change detection in synthetic aperture radar images based on image fusion and fuzzy clustering.

PubMed

Gong, Maoguo; Zhou, Zhiqiang; Ma, Jingjing

2012-04-01

This paper presents an unsupervised distribution-free change detection approach for synthetic aperture radar (SAR) images based on an image fusion strategy and a novel fuzzy clustering algorithm. The image fusion technique is introduced to generate a difference image by using complementary information from a mean-ratio image and a log-ratio image. In order to restrain the background information and enhance the information of changed regions in the fused difference image, wavelet fusion rules based on an average operator and minimum local area energy are chosen to fuse the wavelet coefficients for a low-frequency band and a high-frequency band, respectively. A reformulated fuzzy local-information C-means clustering algorithm is proposed for classifying changed and unchanged regions in the fused difference image. It incorporates the information about spatial context in a novel fuzzy way for the purpose of enhancing the changed information and of reducing the effect of speckle noise. Experiments on real SAR images show that the image fusion strategy integrates the advantages of the log-ratio operator and the mean-ratio operator and gains a better performance. The change detection results obtained by the improved fuzzy clustering algorithm exhibited lower error than its preexistences.

EnvironmentalWaveletTool: Continuous and discrete wavelet analysis and filtering for environmental time series

NASA Astrophysics Data System (ADS)

Galiana-Merino, J. J.; Pla, C.; Fernandez-Cortes, A.; Cuezva, S.; Ortiz, J.; Benavente, D.

2014-10-01

A MATLAB-based computer code has been developed for the simultaneous wavelet analysis and filtering of several environmental time series, particularly focused on the analyses of cave monitoring data. The continuous wavelet transform, the discrete wavelet transform and the discrete wavelet packet transform have been implemented to provide a fast and precise time-period examination of the time series at different period bands. Moreover, statistic methods to examine the relation between two signals have been included. Finally, the entropy of curves and splines based methods have also been developed for segmenting and modeling the analyzed time series. All these methods together provide a user-friendly and fast program for the environmental signal analysis, with useful, practical and understandable results.

Improved photo response non-uniformity (PRNU) based source camera identification.

PubMed

Cooper, Alan J

2013-03-10

The concept of using Photo Response Non-Uniformity (PRNU) as a reliable forensic tool to match an image to a source camera is now well established. Traditionally, the PRNU estimation methodologies have centred on a wavelet based de-noising approach. Resultant filtering artefacts in combination with image and JPEG contamination act to reduce the quality of PRNU estimation. In this paper, it is argued that the application calls for a simplified filtering strategy which at its base level may be realised using a combination of adaptive and median filtering applied in the spatial domain. The proposed filtering method is interlinked with a further two stage enhancement strategy where only pixels in the image having high probabilities of significant PRNU bias are retained. This methodology significantly improves the discrimination between matching and non-matching image data sets over that of the common wavelet filtering approach. Copyright © 2012 Elsevier Ireland Ltd. All rights reserved.

Lifting Scheme DWT Implementation in a Wireless Vision Sensor Network

NASA Astrophysics Data System (ADS)

Ong, Jia Jan; Ang, L.-M.; Seng, K. P.

This paper presents the practical implementation of a Wireless Visual Sensor Network (WVSN) with DWT processing on the visual nodes. WVSN consists of visual nodes that capture video and transmit to the base-station without processing. Limitation of network bandwidth restrains the implementation of real time video streaming from remote visual nodes through wireless communication. Three layers of DWT filters are implemented to process the captured image from the camera. With having all the wavelet coefficients produced, it is possible just to transmit the low frequency band coefficients and obtain an approximate image at the base-station. This will reduce the amount of power required in transmission. When necessary, transmitting all the wavelet coefficients will produce the full detail of image, which is similar to the image captured at the visual nodes. The visual node combines the CMOS camera, Xilinx Spartan-3L FPGA and wireless ZigBee® network that uses the Ember EM250 chip.

Automatic small bowel tumor diagnosis by using multi-scale wavelet-based analysis in wireless capsule endoscopy images.

PubMed

Barbosa, Daniel C; Roupar, Dalila B; Ramos, Jaime C; Tavares, Adriano C; Lima, Carlos S

2012-01-11

Wireless capsule endoscopy has been introduced as an innovative, non-invasive diagnostic technique for evaluation of the gastrointestinal tract, reaching places where conventional endoscopy is unable to. However, the output of this technique is an 8 hours video, whose analysis by the expert physician is very time consuming. Thus, a computer assisted diagnosis tool to help the physicians to evaluate CE exams faster and more accurately is an important technical challenge and an excellent economical opportunity. The set of features proposed in this paper to code textural information is based on statistical modeling of second order textural measures extracted from co-occurrence matrices. To cope with both joint and marginal non-Gaussianity of second order textural measures, higher order moments are used. These statistical moments are taken from the two-dimensional color-scale feature space, where two different scales are considered. Second and higher order moments of textural measures are computed from the co-occurrence matrices computed from images synthesized by the inverse wavelet transform of the wavelet transform containing only the selected scales for the three color channels. The dimensionality of the data is reduced by using Principal Component Analysis. The proposed textural features are then used as the input of a classifier based on artificial neural networks. Classification performances of 93.1% specificity and 93.9% sensitivity are achieved on real data. These promising results open the path towards a deeper study regarding the applicability of this algorithm in computer aided diagnosis systems to assist physicians in their clinical practice.

Wavelet processing techniques for digital mammography

NASA Astrophysics Data System (ADS)

Laine, Andrew F.; Song, Shuwu

1992-09-01

This paper introduces a novel approach for accomplishing mammographic feature analysis through multiresolution representations. We show that efficient (nonredundant) representations may be identified from digital mammography and used to enhance specific mammographic features within a continuum of scale space. The multiresolution decomposition of wavelet transforms provides a natural hierarchy in which to embed an interactive paradigm for accomplishing scale space feature analysis. Similar to traditional coarse to fine matching strategies, the radiologist may first choose to look for coarse features (e.g., dominant mass) within low frequency levels of a wavelet transform and later examine finer features (e.g., microcalcifications) at higher frequency levels. In addition, features may be extracted by applying geometric constraints within each level of the transform. Choosing wavelets (or analyzing functions) that are simultaneously localized in both space and frequency, results in a powerful methodology for image analysis. Multiresolution and orientation selectivity, known biological mechanisms in primate vision, are ingrained in wavelet representations and inspire the techniques presented in this paper. Our approach includes local analysis of complete multiscale representations. Mammograms are reconstructed from wavelet representations, enhanced by linear, exponential and constant weight functions through scale space. By improving the visualization of breast pathology we can improve the chances of early detection of breast cancers (improve quality) while requiring less time to evaluate mammograms for most patients (lower costs).

Comparison between deterministic and statistical wavelet estimation methods through predictive deconvolution: Seismic to well tie example from the North Sea

NASA Astrophysics Data System (ADS)

de Macedo, Isadora A. S.; da Silva, Carolina B.; de Figueiredo, J. J. S.; Omoboya, Bode

2017-01-01

Wavelet estimation as well as seismic-to-well tie procedures are at the core of every seismic interpretation workflow. In this paper we perform a comparative study of wavelet estimation methods for seismic-to-well tie. Two approaches to wavelet estimation are discussed: a deterministic estimation, based on both seismic and well log data, and a statistical estimation, based on predictive deconvolution and the classical assumptions of the convolutional model, which provides a minimum-phase wavelet. Our algorithms, for both wavelet estimation methods introduce a semi-automatic approach to determine the optimum parameters of deterministic wavelet estimation and statistical wavelet estimation and, further, to estimate the optimum seismic wavelets by searching for the highest correlation coefficient between the recorded trace and the synthetic trace, when the time-depth relationship is accurate. Tests with numerical data show some qualitative conclusions, which are probably useful for seismic inversion and interpretation of field data, by comparing deterministic wavelet estimation and statistical wavelet estimation in detail, especially for field data example. The feasibility of this approach is verified on real seismic and well data from Viking Graben field, North Sea, Norway. Our results also show the influence of the washout zones on well log data on the quality of the well to seismic tie.

Wavelet Decomposition for Discrete Probability Maps

DTIC Science & Technology

2007-08-01

using other wavelet basis functions, such as those mentioned in Section 7 15 DSTO–TN–0760 References 1. P. M. Bentley and J . T . E. McDonnell. Wavelet...84, 1995. 0272-1716. 18. E. J . Stollnitz, T . D. DeRose, and D. H. Salesin. Wavelets for computer graphics: a primer. 2. Computer Graphics and...and Computer Modelling in 2006 from the University of South Australia, Mawson Lakes. Part of this de- gree was undertaken at the University of Twente