The Segmentation of FMI Image Based on 2-D Dyadic Wavelet Transform

A key aspect in extracting quantitative information from FMI logs is to segment the FMI image to get images of pores, vugs and fractures. A segmentation method based on the dyadic wavelet transform in 2-D is introduced in this paper. The first step is to find all the edge pixels of the FMI image using the 2-D wavelet transform. The second step is to calculate a segmentation threshold based on the average value of the edge pixels. Field data processing examples show that sub-images of vugs and fractures can be correctly separated from original FMI data continuously and automatically along the depth axis. The image segmentation lays the foundation for in-situ parameter calculation.

Biomedical Image Processing Using FCM Algorithm Based on the Wavelet Transform

An effective processing method for biomedical images and the Fuzzy C-mean (FCM) algorithm based on the wavelet transform are investigated.By using hierarchical wavelet decomposition, an original image could be decomposed into one lower image and several detail images. The segmentation started at the lowest resolution with the FCM clustering algorithm and the texture feature extracted from various sub-bands. With the improvement of the FCM algorithm, FCM alternation frequency was decreased and the accuracy of segmentation was advanced.

RESEARCH OF PROBLEMS ON REALIZING DIRECT ALGORITHM OF WAVELET TRANSFORM

Direct algorithm of wavelet transform (WT) is the numerical algorithmobtained from the integral formula of WT by directly digitization. Some problems on realizing thealgorithm are studied. Some conclusions on the direct algorithm of discrete wavelet transform (DWT),such as discrete convolution operation formula of wavelet coefficients and wavelet components,sampling principle and technology to wavelets, deciding method for scale range of wavelets, measuresto solve edge effect problem, etc, are obtained. The realization of direct algorithm of continuouswavelet transform (CWT) is also studied. The computing cost of direct algorithm and Mallat algorithmof DWT are still studied, and the computing formulae are obtained. These works are beneficial todeeply understand WT and Mallat algorithm. Examples in the end show that direct algorithm can alsobe applied widely.

Wavelet Transform Approach to Segment Thermal Image

An efficient multi-threshold approach to segment thermal image is given based on wavelet transform. The gray-level histogram of original image is obtained. In order to reduce the effect of noise, the gray-level histogram is smoothed by Bezier curve and Bezier histogram is obtained. One dimension stationary wavelet transform is done to the curvature curve of Bezier histogram. Positions of peak values of curvature curve in wavelet domain are adjusted from 'fine-to-coarse' at all scales. The gray level values, which are located in adjusted peak values at all scales, are considered as segmentation thresholds. The gray level values of valley between peaks are considered as quantity gray levels. Optimal segmentation scale is obtained by a cost criterion. The results of experiment show that a target can be segmented effectively from complex background in thermal image by new approach.

Meaningful image encryption algorithm based on compressive sensing and integer wavelet transform

A new meaningful image encryption algorithm based on compressive sensing(CS)and integer wavelet transformation(IWT)is proposed in this study.First of all,the initial values of chaotic system are encrypted by RSA algorithm,and then they are open as public keys.To make the chaotic sequence more random,a mathematical model is constructed to improve the random performance.Then,the plain image is compressed and encrypted to obtain the secret image.Secondly,the secret image is inserted with numbers zero to extend its size same to the plain image.After applying IWT to the carrier image and discrete wavelet transformation(DWT)to the inserted image,the secret image is embedded into the carrier image.Finally,a meaningful carrier image embedded with secret plain image can be obtained by inverse IWT.Here,the measurement matrix is built by both chaotic system and Hadamard matrix,which not only retains the characteristics of Hadamard matrix,but also has the property of control and synchronization of chaotic system.Especially,information entropy of the plain image is employed to produce the initial conditions of chaotic system.As a result,the proposed algorithm can resist known-plaintext attack(KPA)and chosen-plaintext attack(CPA).By the help of asymmetric cipher algorithm RSA,no extra transmission is needed in the communication.Experimental simulations show that the normalized correlation(NC)values between the host image and the cipher image are high.That is to say,the proposed encryption algorithm is imperceptible and has good hiding effect.

MULTISCALE IMAGE SEGMENTATION USING FRACTAL AND NEURAL NETWORK

Clustering algorithms in feature space are important methods in image segmentation. The choice of the effective feature parameters and the construction of the clustering method are key problems encountered with clustering algorithms. In this paper, the multifractal dimensions are chosen as the segmentation feature parameters which are extracted from original image and wavelet-transformed image. SOM (Self-Organizing Map) network is applied to cluster the segmentation feature parameters. The experiment shows that the performance of the presented algorithm is very good.

Medical ultrasound image segmentation by modified local histogram range image method

Fast and satisfied medical ultrasound segmentation is known to be difficult due to speckle noises and other artificial effects. Since speckle noise is formed from random signals which are emitted by an ultrasound system, we can’t encounter the same way as other image noises. Lack of information in ultrasound images is another problem. Thus, segmentation results may not be accurate enough by means of customary image segmentation methods. Those methods that can specify undesirable effects and segment them by eliminating artificial effects, should be chosen. It seems to be a complicated work with high computational load. The current study presents a different approach to ultrasound image segmentation that relies mainly on local evaluation, named as local histogram range image method which is modified by means of discrete wavelet transform. Thus, a significant decrease in computational load is then achieved. The results show that it is possible for tissues to be segmented correctly.

Segmentation algorithm of complex ore images based on templates transformation and reconstruction

Lots of noises and heterogeneous objects with various sizes coexist in a complex image,such as an ore image;the classical image thresholding method cannot effectively distinguish between ores.To segment ore objects with various sizes simultaneously,two adaptive windows in the image were chosen for each pixel;the gray value of windows was calculated by Otsu＇s threshold method.To extract the object skeleton,the definition principle of distance transformation templates was proposed.The ores linked together in a binary image were separated by distance transformation and gray reconstruction.The seed region of each object was picked up from the local maximum gray region of the reconstruction image.Starting from these seed regions,the watershed method was used to segment ore object effectively.The proposed algorithm marks and segments most objects from complex images precisely.

Tropical Cyclone Cloud Image Segmentation by the B-Spline Histogram with Multi-Scale Transforms

An efficient tropical cyclone（TC） cloud image segmentation method is proposed by combining the curvelet transform,the cubic B-Spline curve,and the continuous wavelet transform.In order to enhance the global and local contrast of the original TC cloud image,a second-generation discrete curvelet transform is implemented for the original TC cloud image.Based on our prior work,the low frequency components are enhanced by using an incomplete Beta transform and the genetic algorithm in the curvelet domain. Then the enhanced TC cloud image is used to segment the main body of the TC from the TC cloud image. First,pre-processing is implemented by B-Spline curves to the original TC cloud image to remove unrelated small cloud masses.A region of interest（ROI） which includes the main body of TC can thus be obtained. Second,the gray-level histogram of ROI is obtained.In order to reduce oscillations of the histogram,the gray-level histogram is smoothed by cubic B-Spline curves and the B-Spline histogram is obtained.The one dimensional continuous wavelet transform is employed for the curvature curve of the B-Spline histogram. A new segmentation cost criterion is given by combining threshold,error,and structure similarity.The optimally segmented image can be obtained by the criterion in the continuous wavelet domain.The optimally segmented image is post-processed to obtain the final segmented TC image.The experimental results show that the main body of TC can be effectively segmented from the complex background in the TC cloud image by the proposed algorithm.

A Multiscale Approach to Automatic Medical Image Segmentation Using Self-Organizing Map

In this paper, a new medical image classification scheme is proposed using selforganizing map (SOM) combined with multiscale technique. It addresses the problem of the handling of edge pixels in the traditional multiscale SOM classifiers. First, to solve the difficulty in manual selection of edge pixels, a multiscale edge detection algorithm based on wavelet transform is proposed. Edge pixels detected are then selected into the training set as a new class and a mu1tiscale SoM classifier is trained using this training set. In this new scheme, the SoM classifier can perform both the classification on the entire image and the edge detection simultaneously. On the other hand, the misclassification of the traditional multiscale SoM classifier in regions near edges is greatly reduced and the correct classification is improved at the same time.

基于Swin-Transformer的颈动脉超声图像斑块分割

评估颈动脉超声图像斑块需要大量且经验丰富的临床医生,并且超声图像具有边界模糊、噪声干扰强等特性,使得评估斑块耗时费力,因此需要一种全自动的颈动脉斑块分割方法来解决人力稀缺问题。文中提出了一种基于Swin-Transformer(Shifted-Windows Transformer)模块的深度神经网络模型用于自动分割颈动脉斑块。在U-Net(U-Convolutional Network)架构基础上,编码部分使用3个用于图像下采样的卷积块以获得不同分辨率大小的特征图像,再添加6对连续Swin-Transformer模块用于更细化的特征提取。解码部分将Swin-Transformer模块输出的细化特征逐级上采样,分别与编码部分各级分辨率的特征图进行跳跃连接。基于同仁医院数据集进行对比实验,结果显示所提网络模型Dice指标达到0.8142,高于其他对比网络,证明了所提模型可以有效地提取颈动脉超声图像斑块特征,实现自动化、高精度的斑块分割。

Adaptive template filter method for image processing based on immune genetic algorithm

To preserve the original signal as much as possible and filter random noises as many as possible in image processing,a threshold optimization-based adaptive template filtering algorithm was proposed.Unlike conventional filters whose template shapes and coefficients were fixed,multi-templates were defined and the right template for each pixel could be matched adaptively based on local image characteristics in the proposed method.The superiority of this method was verified by former results concerning the matching experiment of actual image with the comparison of conventional filtering methods.The adaptive search ability of immune genetic algorithm with the elitist selection and elitist crossover(IGAE) was used to optimize threshold t of the transformation function,and then combined with wavelet transformation to estimate noise variance.Multi-experiments were performed to test the validity of IGAE.The results show that the filtered result of t obtained by IGAE is superior to that of t obtained by other methods,IGAE has a faster convergence speed and a higher computational efficiency compared with the canonical genetic algorithm with the elitism and the immune algorithm with the information entropy and elitism by multi-experiments.

An Improved Medical Image Fusion Algorithm for Anatomical and Functional Medical Images

In recent years,many medical image fusion methods had been exploited to derive useful information from multimodality medical image data,but,not an appropriate fusion algorithm for anatomical and functional medical images.In this paper,the traditional method of wavelet fusion is improved and a new fusion algorithm of anatomical and functional medical images,in which high-frequency and low-frequency coefficients are studied respectively.When choosing high-frequency coefficients,the global gradient of each sub-image is calculated to realize adaptive fusion,so that the fused image can reserve the functional information;while choosing the low coefficients is based on the analysis of the neighborbood region energy,so that the fused image can reserve the anatomical image's edge and texture feature.Experimental results and the quality evaluation parameters show that the improved fusion algorithm can enhance the edge and texture feature and retain the function information and anatomical information effectively.

An Adaptive Method of Image CompressionBased on Wavelet Analysis

Through research for image compression based on wavelet analysis in recent years, we put forward an adaptive wavelet decomposition strategy. Whether sub-images are to be decomposed or not are decided by their energy defined by certain criterion. Then we derive the adaptive wavelet decomposition tree (AWDT) and the way of adjustable compression ratio. According to the feature of AWDT, this paper also deals with the strategies which are used to handle different sub-images in the procedure of quantification and coding of the wavelet coefficients. Through experiments, not only the algorithm in the paper can adapt to various images, but also the quality of recovered image is improved though compression ratio is higher and adjustable. When their compression ratios are near, the quality of subjective vision and PSNR of the algorithm are better than those of JPEG algorithm.

New Optimal DWT Domain Image Watermarking Technique via Genetic Algorithm

A novel optimal image watermarking scheme is proposed in which the genetic algorithm (GA) is employed to obtain the improvement of algorithm performance. Arnold transform is utilized to obtain the scrambled watermark, and then the embedding and extraction of watermark are implemented in digital wavelet transform (DWT) domain. During the watermarking process, GA is employed to search optimal parameters of embedding strength and times of Arnold transform to gain the optimization of watermarking performance. Simulation results show that the proposed method can improve the quality of watermarked image and give almost the same robustness of the watermark.

基于边缘检测的数字媒体深度交互式图像分割算法

针对数字媒体深度交互式图像受噪声干扰,导致其边缘检测效果较差,影响其分割精度的问题,提出基于边缘检测的数字媒体深度交互式图像分割算法。首先,利用小波变换方法对数字媒体中的图像进行去噪处理,提高图像分割精度;其次,使用高斯函数与低通滤波增强去噪后的图像,提高图像的清晰度,为图像分割提供便利;最后,依据自适应阈值算法对数字媒体的图像进行边缘检测,像素合集中存在上下阈值。根据其上下阈值的计算求出像素合集中的高低两个阈值,并对其实施边缘连接,实现数字媒体图像分割。实验结果表明,所提方法对分割数字媒体图像去噪效果好,分割精度和效率高。

气象遥感图像去噪预处理方法研究

针对静止轨道遥感卫星上多通道扫描型载荷成像、传输与存储过程中,存在数据质量下降等问题,本文在经典三维块匹配算法(Block Matching 3D,BM3D)基础上,提出一种基于多层级小波分解的并行执行策略。首先,使用小波变换对原始气象遥感图像分解,得到4个图像分量;其次,将所得图像分量进一步进行3级分解,并选择其中的10个图像分量;最后,每个分量并行执行BM3D滤波器去噪,并重构10个分量的输出图像。与传统BM3D去噪算法相比,改进BM3D算法的计算量可有效降低20%以上。通过与中值滤波、均值滤波、NL-Bayes、BM3D四种降噪算法进行实验对比,所提算法的峰值信噪比平均增益在0.39~4.45 dB之间,特别是在高斯白噪声和脉冲噪声的混合噪声去噪方面要显著优于选取的四种对比算法。

Application of Image Fusion Methods to Cell Imaging Processing

Aim To fuse the fluorescence image and transmission image of a cell into a single image containing more information than any of the individual image. Methods Image fusion technology was applied to biological cell imaging processing. It could match the images and improve the confidence and spatial resolution of the images. Using two algorithms, double thresholds algorithm and denoising algorithm based on wavelet transform,the fluorescence image and transmission image of a Cell were merged into a composite image. Results and Conclusion The position of fluorescence and the structure of cell can be displyed in the composite image. The signal-to-noise ratio of the exultant image is improved to a large extent. The algorithms are not only useful to investigate the fluorescence and transmission images, but also suitable to observing two or more fluoascent label proes in a single cell.

灰度变换下多模态刚性医学图像分层增强仿真

为了提升医学图像增强效果,有效保留图像细节,提出基于灰度变换的多模态刚性医学图像增强算法。对多模态刚性医学图像去噪处理,采用免疫优化算法对自适应阈值优化处理,选择最佳阈值。根据去噪结果通过小波变换将医学图像分解为多个不同区间,分别对各个区间实行灰度变换处理,根据变换后的结果获取分层增强效果,实现多模态刚性医学图像的整体增强。经实验测试结果表明,所提算法可以获取更加满意的医学图像增强效果,提高了图像清晰度,图像视觉效果显著增强。

基于去降Mallat离散小波变换的彩色图像分割

该文针对Mallat快速离散小波变换,提出了一种利用变换平移不变性的离散小波变换的彩色图像分割方法。首先对原始图像进行平移不变性的小波变换,然后提取颜色和纹理特征,并采用k均值算法进行分割。实验表明该方法对纹理图像和彩色自然图像都具有较好的分割效果。