A chaotic image cryptosystem based on Toeplitz and Hankel matrices

Abstract

Because of properties in chaos system such as the sensitive dependence on initial conditions, system parameters, pseudorandom property and ergodicity, chaotic image encryption algorithm can suggest a new and efficient way of encryption scheme, which has been studied more and more in recent years. A novel chaotic image encryption algorithm based on Toeplitz matrix and Hankel matrix is proposed in this paper. We shuffle totally the positions of image pixels to confuse the relationship between the plain image and cipher image combined with Toeplitz matrix, Hankel matrix and logistic chaotic system. Another hyper-chaos system of Chen's chaotic system is taken to change the grey values of image pixels to enhance the security further. Experimental results in Sections 3 and 4 demonstrate that the key space is large enough and the key is sensitive to initial conditions to resist the brute force attack in the proposed algorithm. Additionally, the distribution of grey values in encrypted image has a random-like behaviour to resist statistical analysis.     

The research of unsupervised change detection method based on the clustering characteristic of 3D histogram

Abstract

One of the main problems related to unsupervised change detection methods based on the ‘difference image’ lies in the lack of efficient automatic techniques for discriminating between changed and unchanged pixels in the difference image. Such discrimination is usually performed by using empirical strategies or manual trial-and-error procedures, which affect both the accuracy and the reliability of the change detection process. To overcome such drawbacks, in this paper, we propose an automatic techniques (based on the clustering characteristic of 3D histogram) for the analysis of the difference image. The 3D histogram is formed by pixel grey levels, contiguous average grey levels and local average grey levels of the difference image. First, the optimal plane threshold and plane direction are searched by using maximal entropy principle based on the clustering characteristic of 3D histogram. Then, by using the optimal plane threshold and plane direction, a plane is established to segment the 3D histogram into changed clustering and unchanged clustering. Finally, the changed pixels in the difference image are discriminated according to the segmentation of 3D histogram. The theoretical analysis and experiment results confirm the effectiveness of the proposed method.     

Mammographic Images Enhancement and Denoising for Breast Cancer Detection Using Dyadic Wavelet Processing

Mammography is the most effective method for the early detection of breast diseases. However, the typical diagnostic signs such as microcalcifications and masses are difficult to detect because mammograms are low-contrast and noisy images. In this paper, a novel algorithm for image denoising and enhancement based on dyadic wavelet processing is proposed. The denoising phase is based on a local iterative noise variance estimation. Moreover, in the case of microcalcifications, we propose an adaptive tuning of enhancement degree at different wavelet scales, whereas in the case of mass detection, we developed a new segmentation method combining dyadic wavelet information with mathematical morphology. The innovative approach consists of using the same algorithmic core for processing images to detect both microcalcifications and masses. The proposed algorithm has been tested on a large number of clinical images, comparing the results with those obtained by several other algorithms proposed in the literature through both analytical indexes and the opinions of radiologists. Through preliminary tests, the method seems to meaningfully improve the diagnosis in the early breast cancer detection with respect to other approaches.     

遗传算法在易拉罐罐盖喷码系统中的应用

目的为实现饮料易拉罐拉环背部激光打码的自动化,提出一种基于遗传算法的易拉罐罐盖图像识别新方法。方法首先搭建一套易拉罐盖激光自动打码机,基于所搭建的实验系统,利用CCD相机实时采集罐盖图像。对所采集到的图像进行中值滤波和灰度增强处理,在此基础上,研究基于遗传算法的罐盖图像阈值分割新方法,分析、确定算法的关键参数(个体数目、交叉率、变异率等),由此得到罐盖的二值化图像,并对算法处理结果进行误差分析。结果遗传算法经过约15代的迭代计算,能够收敛,获取到最优的图像阈值,整个算法的运行时间约30 ms,最终的图像精度约为7.9 pixel。结论基于遗传算法的图像阈值分割实时性好,分割后的图像精度高,与传统的Ostu阈值分割法相比,得到的信息更加丰厚,能抑制光线不均所造成的图像干扰。同时对遗传算法阈值分割后的图像进行了sobel边缘检测,得到了清晰的罐盖边缘,为激光打码的准确定位奠定了基础。     

Automatic segmentation system for liver tumors based on the multilevel thresholding and electromagnetism optimization algorithm

In this article, we propose an automated segmentation system for liver tumors using magnetic resonance imaging and computed tomography. The proposed system is based on the algorithm of multilevel thresholding with electromagnetism optimization (EMO). The system starts with visualizing a patient's digital communication in medicine (DICOM) abdominal data set in three views. Two-stage active contour segmentation methods that integrate region-based local and global techniques using the active geodesic contour technique are proposed to segment the liver. To increase the accuracy and speed of segmentation for liver images, we identify the optimal threshold of the image segmentation method based on EMO with Otsu and Kapur algorithms. EMO offers interesting search capabilities while keeping a low computational cost. The proposed system was tested using a set of five DICOM data sets. All images were of the same size and stored in JPEG format (512 × 512 pixels). Experimental results illustrate that the proposed system outperforms state-of-the-art methods such as the watershed algorithm. The average sensitivity, specificity, and accuracy of the segmented liver using the active contour model were 97.05%, 99.88%, and 98.47%, respectively. Moreover, the average sensitivity, specificity, and accuracy of the segmented liver tumor results were 94.15%, 99.57%, and 96.86%, respectively.     

Digital Image Processing Technique for Breast Cancer Detection

Breast cancer is the most common cause of death in women and the second leading cause of cancer deaths worldwide. Primary prevention in the early stages of the disease becomes complex as the causes remain almost unknown. However, some typical signatures of this disease, such as masses and microcalcifications appearing on mammograms, can be used to improve early diagnostic techniques, which is critical for women’s quality of life. X-ray mammography is the main test used for screening and early diagnosis, and its analysis and processing are the keys to improving breast cancer prognosis. As masses and benign glandular tissue typically appear with low contrast and often very blurred, several computer-aided diagnosis schemes have been developed to support radiologists and internists in their diagnosis. In this article, an approach is proposed to effectively analyze digital mammograms based on texture segmentation for the detection of early stage tumors. The proposed algorithm was tested over several images taken from the digital database for screening mammography for cancer research and diagnosis, and it was found to be absolutely suitable to distinguish masses and microcalcifications from the background tissue using morphological operators and then extract them through machine learning techniques and a clustering algorithm for intensity-based segmentation.     

A Method of Identifying Thunderstorm Clouds in Satellite Cloud Image Based on Clustering

In this paper, the clustering analysis is applied to the satellite image segmentation, and a cloud-based thunderstorm cloud recognition method is proposed in combination with the strong cloud computing power. The method firstly adopts the fuzzy C-means clustering (FCM) to obtain the satellite cloud image segmentation. Secondly, in the cloud image, we dispose the ‘high-density connected’ pixels in the same cloud clusters and the ‘low-density connected’ pixels in different cloud clusters. Therefore, we apply the DBSCAN algorithm to the cloud image obtained in the first step to realize cloud cluster knowledge. Finally, using the method of spectral threshold recognition and texture feature recognition in the steps of cloud clusters, thunderstorm cloud clusters are quickly and accurately identified. The experimental results show that cluster analysis has high research and application value in the segmentation processing of meteorological satellite cloud images.     

The foreground detection algorithm combined the temporal–spatial information and adaptive visual background extraction

Visual background extraction algorithm, which utilises a global threshold to complete the foreground segmentation, cannot adapt to illumination change well. It will easily choose the wrong pixels to initialise the background model, resulting in the emergence of the ghost in the beginning of detection. In order to address these problems, this article proposes an improved algorithm based on pixel’s temporal–spatial information to initialise the background model. First of all, the pixels in video image sequences and their neighbourhood pixels are used to complete background model initialisation in the first five frames. Second, the segmentation threshold is adaptively obtained by the complexity of background that uses the spatial neighbourhood pixels. Finally, the background model of the neighbourhood pixels is updated by a dynamic update rate which is gained by calculating the Euclidean distance between pixels. Experimental results and comparative study illustrate that the improved method can not only increase the accuracy of target detection by reducing the impact of illumination change effectively but also eliminate the ghost quickly.     

一种基于Graph Cuts的SAR图像分割方法

在最小化由马尔科夫随机场(MRF)图像分割模型建立的能量函数方面,基于Graph Cuts的alpha-expansion是一种比较有效的算法.但是,由此算法构建的s/t图中边的数目非常多,运算速度很慢.为了减少alpha-expansion算法的计算量,本文在标号为alpha的像素向其它像素膨胀的过程中,先隔离非alpha类间的联系,而只考虑alpha类与非alpha类之间的关系,从而避免了alpha-expansion算法需要构造辅助结点的问题,减少了s/t图中边的数目,提高了算法的计算效率.因放松了非alpha类间的关系对alpha膨胀的约束,使得算法可以更容易得跳出能量函数的局部极小点而获得更优的分割结果.实验中将改进的算法与传统的基于Graph Cuts的算法做了对比,显示了新算法在运算时间和最小化能量方面的有效性.     

印刷网点微观图像阈值分割算法研究

目的 通过阈值处理方法, 准确获取网点微观图像的特征参数, 将其与仪器测量值相结合, 综合评价印刷品复制质量。方法 提出一种基于高斯函数模型拟合网点图像灰度直方图数据的阈值分割算法, 寻找网点类图像最佳分割阈值, 对图像进行二值化处理, 得到准确的网点参数。结果 得到的印刷品网点面积率在全阶调范围内更接近于测量值, 分割效果明显优于传统的阈值分割算法。结论 提出的高斯拟合阈值分割算法更有利于提取网点类图像的微观参数, 精度高, 稳定性好,为获取准确的网点图像微观参数提供了理论与实践参考。     

基于不同空间结构Hadamard融合的图像边缘提取算法

目的针对图像边缘提取算法中噪声对边缘的影响,易导致边缘定位精度不高,出现虚假边缘与漏检等不足,设计一种不同空间结构Hadamard融合的图像边缘提取方案。方法首先,通过计算像素与相邻点之间的方差来分析像素的结构,得到边缘点的最大概率分布矩阵(MPDM),利用MPDM来表示候选边缘集。其次,通过分析邻域点之间的亮度,计算像素与其4个相邻像素之间的最大和最小差值,得到相应的差异矩阵,并引入Logistic回归分析对2种矩阵归一化处理,得到一个权重矩阵(WM)。然后,通过Hadamard乘积模型将MPDM与WM进行融合,从而设计边缘分割阈值函数。最后,通过比较WM和分割阈值,去掉非边缘点,检测出真实图像边缘。结果实验表明,与当前边缘提取方法对比,文中方法能够有效抑制噪声,得到的边缘清晰、完整,边缘细化度与平滑度良好,在客观评价FOM与ROC中具有更大的优势。结论所提算法具有良好的边缘提取精度,在图像处理与包装条码领域具有良好的应用价值。     

Image Segmentation of Brain MR Images Using Otsu’s Based Hybrid WCMFO Algorithm

In this study, a novel hybrid Water Cycle Moth-Flame Optimization (WCMFO) algorithm is proposed for multilevel thresholding brain image segmentation in Magnetic Resonance (MR) image slices. WCMFO constitutes a hybrid between the two techniques, comprising the water cycle and moth-flame optimization algorithms. The optimal thresholds are obtained by maximizing the between class variance (Otsu’s function) of the image. To test the performance of threshold searching process, the proposed algorithm has been evaluated on standard benchmark of ten axial T₂-weighted brain MR images for image segmentation. The experimental outcomes infer that it produces better optimal threshold values at a greater and quicker convergence rate. In contrast to other state-of-the-art methods, namely Adaptive Wind Driven Optimization (AWDO), Adaptive Bacterial Foraging (ABF) and Particle Swarm Optimization (PSO), the proposed algorithm has been found to be better at producing the best objective function, Peak Signal-to-Noise Ratio (PSNR), Standard Deviation (STD) and lower computational time values. Further, it was observed thatthe segmented image gives greater detail when the threshold level increases. Moreover, the statistical test result confirms that the best and mean values are almost zero and the average difference between best and mean value 1.86 is obtained through the 30 executions of the proposed algorithm.Thus, these images will lead to better segments of gray, white and cerebrospinal fluid that enable better clinical choices and diagnoses using a proposed algorithm.     

An algorithm for finger-vein segmentation based on modified repeated line tracking

Abstract

Image segmentation is an important step for finger-vein identification technique. However, it is difficult to extract precise details of the image because of the irregular noise and shades around the finger-vein. The repeated line tracking algorithm achieves good segmentation performance for low quality images of finger-vein, but it has some drawbacks such as low robustness and efficiency. In this paper, a modified repeated line tracking algorithm is proposed for image segmentation of finger-vein. Firstly, we propose a segmentation method called threshold image to execute rough segmentation and obtain binary and skeleton image of finger-vein. Secondly, the width of finger-vein is estimated based on the binary and skeleton image. The parameters are revised according to the width. Then, the modified repeated line tracking algorithm is executed to figure out the locus space of finger-vein based on the revised parameters. Finally, processing results are obtained by using Otsu algorithm which executes exact segmentation on the locus space. Experiments show that the proposed algorithm is more robust and efficient than traditional repeated line tracking algorithm.     

An Improved Jellyfish Algorithm for Multilevel Thresholding of Magnetic Resonance Brain Image Segmentations

Image segmentation is vital when analyzing medical images, especially magnetic resonance (MR) images of the brain. Recently, several image segmentation techniques based on multilevel thresholding have been proposed for medical image segmentation; however, the algorithms become trapped in local minima and have low convergence speeds, particularly as the number of threshold levels increases. Consequently, in this paper, we develop a new multilevel thresholding image segmentation technique based on the jellyfish search algorithm (JSA) (an optimizer). We modify the JSA to prevent descents into local minima, and we accelerate convergence toward optimal solutions. The improvement is achieved by applying two novel strategies: Ranking-based updating and an adaptive method. Ranking-based updating is used to replace undesirable solutions with other solutions generated by a novel updating scheme that improves the qualities of the removed solutions. We develop a new adaptive strategy to exploit the ability of the JSA to find a best-so-far solution; we allow a small amount of exploration to avoid descents into local minima. The two strategies are integrated with the JSA to produce an improved JSA (IJSA) that optimally thresholds brain MR images. To compare the performances of the IJSA and JSA, seven brain MR images were segmented at threshold levels of 3, 4, 5, 6, 7, 8, 10, 15, 20, 25, and 30. IJSA was compared with several other recent image segmentation algorithms, including the improved and standard marine predator algorithms, the modified salp and standard salp swarm algorithms, the equilibrium optimizer, and the standard JSA in terms of fitness, the Structured Similarity Index Metric (SSIM), the peak signal-to-noise ratio (PSNR), the standard deviation (SD), and the Features Similarity Index Metric (FSIM). The experimental outcomes and the Wilcoxon rank-sum test demonstrate the superiority of the proposed algorithm in terms of the FSIM, the PSNR, the objective values, and the SD; in terms of the SSIM, IJSA was competitive with the others.     

Noise deconvolution for area fraction measurements

Abstract

A novel approach to area fraction measurement is developed to deal with the limitations of the segmentation method. The new approach takes advantage of the statistical nature of the noise within an image to deconvolute the image histogram and calculate, by means of fitting, the area fractions. Both segmentation and noise deconvolution approaches are critically discussed, and their limitations and advantages tested using two extreme backscattered electron images of a metal matrix composite. The deconvolution approach is shown to produce accurate results in a situation in which the segmentation approach fails.     

Segmentation of liver in ultrasonic images applying local optimal threshold method

Abstract

Low brightness contrast and grey level discontinuities of the ultrasonic liver image make it difficult to segment the object and the background and to extract the edges of the object using the global optimal threshold method. In this paper, we investigate a local optimal threshold method for the segmentation of ultrasound liver image. First of all, the distributed energy of the ultrasound liver image is estimated in the proposed liver segmentation. Then, the polynomials are fitted from the distributed energy data and a peak zone is defined from the minimum of the fitted polynomials. Finally, a few blocked images are divided from the number of the peak zones. Furthermore, multiple local optimal thresholds are obtained from the blocked images using Otsu’s method, and the ultrasonic liver image is segmented according to all local optimal thresholds. Experimental results validate the segmentation and edge detection of liver in the ultrasound images.     

基于遗传算法的干涉图像样的最佳熵研究

将遗传算法用于计算云纹干涉图像的二值化阈值，提出基于改进遗传算法的图像分割方法，采用Otsu公式，找出分割图像最优阈值。通过算法实现表明，利用遗传算法所得到的最佳阚值进行二值化处理，效果非常好。     

一种有效的红外图像中人造目标分割方法

提出一种红外图像单阈值分割方法。为了减少计算量,结合先验信息选择包含待分割目标的感兴趣区域,利用Bezier曲线法平滑感兴趣区域直方图的噪声;对平滑后的感兴趣区域的直方图求解其曲率曲线,用曲率曲线的波峰所对应的灰度值作为初始分割阈值;基于先验信息从初始分割阈值中确定最佳分割阈值并进行初步分割。为了弥补单纯利用阈值法分割的缺陷,结合上述分割结果和目标的边缘信息得到封闭性良好的完整目标的二值图像。实验结果表明,提出的方法能快速有效地将红外目标从复杂的背景中分割出来,算法的计算复杂度为O(MN)。     

Brain MRI denoizing and segmentation based on improved adaptive nonlocal means

Denoizing of magnetic resonance (MR) brain images has been focus of numerous studies in the past. The performance of subsequent stages of image processing, in automated image analysis, is substantially improved by explicit consideration of noise. Nonlocal means (NLM) is a popular denoizing method which exploits usual redundancy present in an image to restore noise free image. It computes restored value of a pixel as weighted average of candidate pixels in a search window. In this article, we propose an improved version of the NLM algorithm which is modified in two ways. First, a robust threshold criterion is introduced, which helps selecting suitable pixels for participation in the restoration process. Second, the search window size is made adaptive using a window adaptation test based on the proposed threshold criterion. The modified NLM algorithm is named as improved adaptive nonlocal means (IANLM). An alternate implementation of IANLM is also proposed which exploits the image smoothness property to yield better denoizing performance. The computational burden is reduced significantly due to proposed modifications. Experiments are performed on simulated and real brain MR images at various noise levels. Results indicate that the proposed algorithm produces not only better denoizing results (quantitatively and qualitatively), but is also computationally more efficient. Moreover, the proposed technique is incorporated in an already proposed segmentation framework to check its validity in the practical scenario of segmentation. Improved segmentation results (quantitative and qualitative) verify the practical usefulness of the proposed algorithm in real world medical applications. © 2013 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 23, 235–248, 2013     

Reversible Data Hiding in Encrypted Images Based on Prediction and Adaptive Classification Scrambling

Reversible data hiding in encrypted images (RDH-EI) technology is widely used in cloud storage for image privacy protection. In order to improve the embedding capacity of the RDH-EI algorithm and the security of the encrypted images, we proposed a reversible data hiding algorithm for encrypted images based on prediction and adaptive classification scrambling. First, the prediction error image is obtained by a novel prediction method before encryption. Then, the image pixel values are divided into two categories by the threshold range, which is selected adaptively according to the image content. Multiple high-significant bits of pixels within the threshold range are used for embedding data and pixel values outside the threshold range remain unchanged. The optimal threshold selected adaptively ensures the maximum embedding capacity of the algorithm. Moreover, the security of encrypted images can be improved by the combination of XOR encryption and classification scrambling encryption since the embedded data is independent of the pixel position. Experiment results demonstrate that the proposed method has higher embedding capacity compared with the current state-ofthe-art methods for images with different texture complexity.