一种基于提升小波的快速图像融合算法

提出了一种基于提升小波变换的快速多聚焦图像融合方法。首先利用提升小波算法将原始图像分解为四个子带:LLLHHLHH后将代表三个方向高频细节子带LHHLHH采用提升小波反变换,以获得各方向子带的高频细节图像,采用高斯核权重算法计算所得到的高频细节图像的非均匀加权区域能量,再根据基于能量的图像融合规则得到最终融合图像。对比了几种多聚焦图像融合方案的性能,实验结果表明,在融合效果相当的情况下,文中方法比现有方法在处理速度上有明显的优势。     

Wavelet transform methods for object detection and recovery

We show that a biorthogonal spline wavelet closely approximates the prewhitening matched filter for detecting Gaussian objects in Markov noise. The filterbank implementation of the wavelet transform acts as a hierarchy of such detectors operating at discrete object scales. If the object to be detected is Gaussian and its scale happens to coincide with one of those computed by the wavelet transform, and if the background noise is truly Markov, then optimum detection is realized by thresholding the appropriate subband image. In reality, the Gaussian may be a rather coarse approximation of the object, and the background noise may deviate from the Markov assumption. In this case, we may view the wavelet decomposition as a means for computing an orthogonal feature set for input to a classifier. We use a supervised linear classifier applied to feature vectors comprised of samples taken from the subbands of an N-octave, undecimated wavelet transform. The resulting map of test statistic values indicates the presence and location of objects. The object itself is reconstructed by using the test statistic to emphasize wavelet subbands, followed by computing the inverse wavelet transform. We show two contrasting applications of the wavelets-based object recovery algorithm. For detecting microcalcifications in digitized mammograms, the object and noise models closely match the real image data, and the multiscale matched filter paradigm is highly appropriate. The second application, extracting ship outlines in noisy forward-looking infrared images, is presented as a case where good results are achieved despite the data models being less well matched to the assumptions of the algorithm.     

Detection of microcalcifications in digital mammograms usingwavelets

This paper presents an approach for detecting micro-calcifications in digital mammograms employing wavelet-based subband image decomposition. The microcalcifications appear in small clusters of few pixels with relatively high intensity compared with their neighboring pixels. These image features can be preserved by a detection system that employs a suitable image transform which can localize the signal characteristics in the original and the transform domain. Given that the microcalcifications correspond to high-frequency components of the image spectrum, detection of microcalcifications is achieved by decomposing the mammograms into different frequency subbands, suppressing the low-frequency subband, and, finally, reconstructing the mammogram from the subbands containing only high frequencies. Preliminary experiments indicate that further studies are needed to investigate the potential of wavelet-based subband image decomposition as a tool for detecting microcalcifications in digital mammograms     

Optimization of wavelet decomposition for image compression and feature preservation

A neural-network-based framework has been developed to search for an optimal wavelet kernel that can be used for a specific image processing task. In this paper, a linear convolution neural network was employed to seek a wavelet that minimizes errors and maximizes compression efficiency for an image or a defined image pattern such as microcalcifications in mammograms and bone in computed tomography (CT) head images. We have used this method to evaluate the performance of tap-4 wavelets on mammograms, CTs, magnetic resonance images, and Lena images. We found that the Daubechies wavelet or those wavelets with similar filtering characteristics can produce the highest compression efficiency with the smallest mean-square-error for many image patterns including general image textures as well as microcalcifications in digital mammograms. However, the Haar wavelet produces the best results on sharp edges and low-noise smooth areas. We also found that a special wavelet whose low-pass filter coefficients are 0.32252136, 0.85258927, 1.38458542, and -0.14548269) produces the best preservation outcomes in all tested microcalcification features including the peak signal-to-noise ratio, the contrast and the figure of merit in the wavelet lossy compression scheme. Having analyzed the spectrum of the wavelet filters, we can find the compression outcomes and feature preservation characteristics as a function of wavelets. This newly developed optimization approach can be generalized to other image analysis applications where a wavelet decomposition is employed.     

基于小波域的扩频水印算法

为了达到对数字产品版权保护的目的,采用一种基于小波域的扩频水印算法。该算法采用二值图像作为水印,首先将原始载体图像进行离散小波变换一级分解,然后用2个不相关的伪随机序列分别代表水印信息中的0和1,嵌入到小波分解后的LH,HL和HH三个子图上,最后进行逆向小波变换生成嵌入水印后的图像。通过在开发工具Matlab下的模拟实验,得出了该算法对滤波、噪声和剪切等攻击,显示出较强的鲁棒性,取得了隐蔽性与鲁棒性的良好折衷。     

A review of wavelets in biomedical applications

We present an overview of the various uses of the wavelet transform (WT) in medicine and biology. We start by describing the wavelet properties that are the most important for biomedical applications. In particular we provide an interpretation of the the continuous wavelet transform (CWT) as a prewhitening multiscale matched filter. We also briefly indicate the analogy between the WT and some of the the biological processing that occurs in the early components of the auditory and visual system. We then review the uses of the WT for the analysis of 1-D physiological signals obtained by phonocardiography, electrocardiography (ECG), mid electroencephalography (EEG), including evoked response potentials. Next, we provide a survey of wavelet developments in medical imaging. These include biomedical image processing algorithms (e.g., noise reduction, image enhancement, and detection of microcalcifications in mammograms), image reconstruction and acquisition schemes (tomography, and magnetic resonance imaging (MRI)), and multiresolution methods for the registration and statistical analysis of functional images of the brain (positron emission tomography (PET) and functional MRI (fMRI)). In each case, we provide the reader with same general background information and a brief explanation of how the methods work     

基于混沌与整数小波变换的脆弱盲水印

提出了一种新的脆弱盲水印生成算法用于图像完整性证明和内容篡改证明。首先将原始图像分块,对各分块进行一次整数小波变换;计算4个子带(LL,HL,LH,HH)小波系数绝对值的均值,分别映射为混沌初值,经过迭代、量化生成二值水印信息;最后将水印信息随机嵌入到4个子带中。实验结果表明,所提出的算法提高了篡改检测率,对图像的篡改具有高度敏感性和准确的篡改定位能力。水印提取为盲提取,篡改检测只用到了篡改后的水印图像。     

Integrated wavelets for enhancement of microcalcifications in digital mammography

This paper presents a new algorithm for enhancement of microcalcifications in mammograms. The main novelty is the application of techniques we have developed for construction of filterbanks derived from the continuous wavelet transform. These discrete wavelet decompositions, called integrated wavelets, are optimally designed for enhancement of multiscale structures in images. Furthermore, we use a model based approach to refine existing methods for general enhancement of mammograms resulting in a more specific enhancement of microcalcifications. We present results of our method and compare them with known algorithms. Finally, we want to indicate how these techniques can also be applied to the detection of microcalcifications. Our algorithm was positively evaluated in a clinical study. It has been implemented in a mammography workstation designed for soft-copy reading of digital mammograms developed by IMAGETOOL, Germany.     

A CAD system for the automatic detection of clustered microcalcifications in digitized mammogram films

Clusters of microcalcifications in mammograms are an important early sign of breast cancer. This paper presents a computer-aided diagnosis (CAD) system for the automatic detection of clustered microcalcifications in digitized mammograms. The proposed system consists of two main steps. First, potential microcalcification pixels in the mammograms are segmented out by using mixed features consisting of wavelet features and gray level statistical features, and labeled into potential individual microcalcification objects by their spatial connectivity. Second, individual microcalcifications are detected by using a set of 31 features extracted from the potential individual microcalcification objects. The discriminatory power of these features is analyzed using general regression neural networks via sequential forward and sequential backward selection methods. The classifiers used in these two steps are both multilayer feedforward neural networks. The method is applied to a database of 40 mammograms (Nijmegen database) containing 105 clusters of microcalcifications. A free-response operating characteristics (FROC) curve is used to evaluate the performance. Results show that the proposed system gives quite satisfactory detection performance. In particular, a 90% mean true positive detection rate is achieved at the cost of 0.5 false positive per image when mixed features are used in the first step and 15 features selected by the sequential backward selection method are used in the second step. However, we must be cautious when interpreting the results, since the 20 training samples are also used in the testing step.     

基于小波域扩频水印算法及实现

为了达到数字产品产权保护的目的,在此提出了一种基于小波域的扩频水印算法。该算法采用二值图像作为水印,首先对原始载体图像进行DWT一级分解,然后用2个不相关的伪随机序列分别代表水印信息中的0和1,嵌入到小波分解后的LH,HL和HH三个子图上,最后’进行逆向小波变换生成嵌入水印后的图像。通过在开发工具Matlab下的模拟实验,得出了该算法对滤波、噪声和剪切等攻击显示出较强的鲁棒性,取得了隐蔽性和鲁棒性的良好折衷。     

Segmentation of microcalcifications in mammograms

A systematic method for the detection and segmentation of microcalcifications in mammograms is presented. It is important to preserve size and shape of the individual calcifications as exactly as possible. A reliable diagnosis requires both rates of false positives as well as false negatives to be extremely low. The proposed approach uses a two-stage algorithm for spot detection and shape extraction. The first stage applies a weighted difference of Gaussians filter for the noise-invariant and size-specific detection of spots. A morphological filter reproduces the shape of the spots. The results of both filters are combined with a conditional thickening operation. The topology and the number of the spots are determined with the first filter, and the shape by means of the second. The algorithm is tested with a series of real mammograms, using identical parameter values for all images. The results are compared with the judgement of radiological experts, and they are very encouraging. The described approach opens up the possibility of a reproducible segmentation of microcalcifications, which is a necessary precondition for an efficient screening program.     

Multiresolution detection of spiculated lesions in digitalmammograms

We present a novel multiresolution scheme for the detection of spiculated lesions in digital mammograms. First, a multiresolution representation of the original mammogram is obtained using a linear phase nonseparable two-dimensional (2-D) wavelet transform. A set of features is then extracted at each resolution in the wavelet pyramid for every pixel. This approach addresses the difficulty of predetermining the neighborhood size for feature extraction to characterize objects that may appear in different sizes. Detection is performed from the coarsest resolution to the finest resolution using a binary tree classifier. This top-down approach requires less computation by starting with the least amount of data and propagating detection results to finer resolutions. Experimental results using the MIAS image database have shown that this algorithm is capable of detecting spiculated lesions of very different sizes at low false positive rates     

Analysis of asymmetry in mammograms via directional filtering with Gabor wavelets

This paper presents a procedure for the analysis of left-right (bilateral) asymmetry in mammograms. The procedure is based upon the detection of linear directional components by using a multiresolution representation based upon Gabor wavelets. A particular wavelet scheme with two-dimensional Gabor filters as elementary functions with varying tuning frequency and orientation, specifically designed in order to reduce the redundancy in the wavelet-based representation, is applied to the given image. The filter responses for different scales and orientation are analyzed by using the Karhunen-Loève (KL) transform and Otsu's method of thresholding. The KL transform is applied to select the principal components of the filter responses, preserving only the most relevant directional elements appearing at all scales. The selected principal components, thresholded by using Otsu's method, are used to obtain the magnitude and phase of the directional components of the image. Rose diagrams computed from the phase images and statistical measures computed thereof are used for quantitative and qualitative analysis of the oriented patterns. A total of 80 images from 20 normal cases, 14 asymmetric cases, and six architectural distortion cases from the Mini-MIAS (Mammographic Image Analysis Society, London, U.K.) database were used to evaluate the scheme using the leave-one-out methodology. Average classification accuracy rates of up to 74.4% were achieved.     

Dark satellite image enhancement using knee transfer function and gamma correction based on DWT–SVD

In this paper, an improved and simple approach for enhancement of dark and low contrast satellite image based on knee function and gamma correction using discrete wavelet transform with singular value decomposition (DWT–SVD) has been proposed for quality enhancement of feature. In addition, this method can also process the high resolution dark or very low contrast images, and offers best enhanced result using tuning parameter of Gamma. The technique decomposes the input image into four frequency subbands by using DWT and estimates the singular value matrix of the low–low subband image, and then compute the knee transfer function using gamma correction for further improvement of the LL component. Afterward, processed LL band image undergoes IDWT together with the unprocessed LH, HL, and HH subbands to generate an appropriate enhanced image. Although, various histogram equalization approaches has been proposed in the literature, they tend to degrade the overall image quality by exhibiting saturation artifacts in both low- and high-intensity regions. The proposed algorithm overcomes this problem using knee function and gamma correction. The experimental results show that the proposed algorithm enhances the overall contrast and visibility of local details better than the existing techniques.     

基于小波变换的双色红外图像融合检测方法

针对复杂背景中/长波红外图像的特点，提出了一种基于小波变换的中/长波红外图像融合检测方法。首先，对原始中/长波红外图像分别进行小波变换，提取出两幅图像的低频和高频信息；然后，分别对图像的低频和高频信息采用不同的融合准则进行融合，并用小波逆变换重建成融合图像，自适应选取阈值，获得最终的目标检测结果。在实验中，采用相同的方法分别用融合图像、单波段红外图像进行目标检测，实验结果证明，用文中提出的融合检测算法优于仅用长波或中波红外图像的目标检测。     

Scale-space signatures for the detection of clustered microcalculations in digital mammograms

A method is described for the automated detection of microcalcifications in digitized mammograms. The method is based on the Laplacian scale-space representation of the mammogram only. First, possible locations of microcalcifications are identified as local maxima in the filtered image on a range of scales. For each finding, the size and local contrast is estimated, based on the Laplacian response denoted as the scale-space signature. A finding is marked as a microcalcification if the estimated contrast is larger than a predefined threshold which depends on the size of the finding. It is shown that the signature has a characteristic peak, revealing the corresponding image features. This peak can be robustly determined. The basic method is significantly improved by consideration of the statistical variation of the estimated contrast, which is the result of the complex noise characteristic of the mammograms. The method is evaluated with the Nijmegen database and compared to other methods using these mammograms. Results are presented as the free-response receiver operating characteristic (FROC) performance. At a rate of one false positive cluster per image the method reaches a sensitivity of 0.84, which is comparable to the best results achieved so far.     

一种改进的基于小波域的图像盲水印算法

论文提出一种改进的基于小波域的图像盲水印算法。此算法根据人类视觉特性,提出在宿主图像小波域二级分解的子带LH2、HL2以及LL2的适当系数分别嵌入水印,以达到优势互补。水印的嵌入采用量化调制的方式,使水印实现盲提取。实验结果表明：此方案算法简单,量化间隔易调,具有良好的不可见性和鲁棒性。     

Detection of Clustered Microcalcifications on Mammograms Using Surrounding Region Dependence Method and Artificial Neural Network

Clustered microcalcifications on X-ray mammograms are an important sign in the detection of breast cancer. A statistical texture analysis method, called the surrounding region dependence method (SRDM), is proposed for the detection of clustered microcalcifications on digitized mammograms. The SRDM is based on the second-order histogram in two surrounding regions. This method defines four textural features to classify region of interests (ROIs) into positive ROIs containing clustered microcalcifications and negative ROIs of normal tissues. The database is composed of 64 positive and 76 negative ROI images, which are selected from digitized mammograms with a pixel size of 100 × 100 m² and 12 bits per pixel. An ROI is selected as an area of 128 × 128 pixels on the digitized mammograms. In order to classify ROIs into the two types, a three-layer backpropagation neural network is employed as a classifier. A segmentation of individual microcalcifications is also proposed to show their morphologies. The classification performance of the proposed method is evaluated by using the round-robin method and a free-response receiver operating-characteristics (FROC) analysis. A receiver operating-characteristics (ROC) analysis is employed to present the results of the round-robin testing for the case of several hidden neurons. The area under the ROC curve, A _z, is 0.997, which is achieved in the case of 4 hidden neurons. The FROC analysis is performed on 20 cropped images. A cropped image is selected as an area of 512 × 512 pixels on the digitized mammograms. In terms of the FROC, a sensitivity of more than 90% is obtained with a low false-positive (FP) detection rate of 0.67 per cropped image.     

基于Contourlet变换的红外弱小目标检测方法

介绍了存在背景干扰和噪声情况下的红外图像中弱小目标的检测问题,提出一种基于Contourlet变换的检测算法。首先对图像进行Contourlet变换,利用Contourlet分解后子图像的特性抑制背景和去噪声,最终实现对目标的检测。通过在含有随机目标的红外序列图像中的实验,并与小波变换进行比较,证明了算法的有效性。     

A tutorial on modern lossy wavelet image compression: foundationsof JPEG 2000

One of the purposes of this article is to give a general audience sufficient background into the details and techniques of wavelet coding to better understand the JPEG 2000 standard. The focus is on the fundamental principles of wavelet coding and not the actual standard itself. Some of the confusing design choices made in wavelet coders are explained. There are two types of filter choices: orthogonal and biorthogonal. Orthogonal filters have the property that there are energy or norm preserving. Nevertheless, modern wavelet coders use biorthogonal filters which do not preserve energy. Reasons for these specific design choices are explained. Another purpose of this article is to compare and contrast “early” wavelet coding with “modern” wavelet coding. This article compares the techniques of the modern wavelet coders to the subband coding techniques so that the reader can appreciate how different modern wavelet coding is from early wavelet coding. It discusses basic properties of the wavelet transform which are pertinent to image compression. It builds on the background material in generic transform coding given, shows that boundary effects motivate the use of biorthogonal wavelets, and introduces the symmetric wavelet transform. Subband coding or “early” wavelet coding method is discussed followed by an explanation of the EZW coding algorithm. Other modern wavelet coders that extend the ideas found in the EZW algorithm are also described