基于量化表修改的F5隐写方法

传统的F5隐写方法采用标准的量化表量化后用矩阵编码的方法来嵌入信息,其安全性较高但隐写容量非常小。提出了一种改进的基于量化表修改的F5隐写方法（IQTM-F5）,采用给定的量化表量化和新的信息嵌入策略以避免传统的“退化”现象。实验结果表明：改进的隐写方法与传统F5隐写方法相比,不仅极大程度地增加了隐写容量,提高了嵌入效率,而且隐秘图像具有较高的图像质量和抗攻击性。     

一种基于离散Tchebichef正交多项式和傅里叶梅林矩的局部多特征图像检索算法

提出了一种基于离散Tchebichef正交多项式和傅里叶梅林矩的局部多特征图像检索算法。通过对图像进行正交变换和多分辨率重排序,在变换域中提取出纹理、颜色和形状特征,生成具有较强区分能力的图像特征。由于傅里叶梅林矩具有旋转不变性,因此在处理发生旋转变换和平移变换的图像时,检索效果较好。最后,对提出的算法用多个数据集进行了检索实验,并对实验结果进行了比较和分析。     

基于函数映射机制的鲁棒性二重数字水印算法

针对二重数字水印嵌入信息量相对较大及其水印之间相互干扰的特点,提出了一种基于函数映射机制的二重数字水印算法。将第一、二重水印建立某种函数映射关系,在嵌入水印时,只需嵌入一份水印样本,并结合小波变换、离散余弦变换和奇异值分解技术,将水印嵌入在寄主图像中。仿真实验结果表明：该算法不仅具有很好的透明性,而且对各种常规数字信号处理和几何攻击也有很强的鲁棒性。     

基于小波变换的最佳融合算法

目的：实现图像的最大融合,增强隐藏图像的抗攻击能力。方法：用最小二乘原理得到与图像有关的置乱变换,提高算法的融合度。将融合因子与置乱操作所用矩阵及迭代次数作为密钥,增加算法的安全性。结果：实验证明该算法使图像的融合因子和融合度得到较大提高。结论：可广泛应用于电子图像的版权保护和加密信息的网络安全传输。     

改进的区域生长法的医院调剂环境下药品图像分割

为了能够对医院调剂环境下的药品图像进行精准分割,进一步提高住院药房设备的自动化水平,本文提出了一种基于药品形状、颜色特性的改进区域生长算法。改进区域生长算法充分利用了传统区域生长算法实时性好和保持边界信息的优点,加入了药品尤其是对胶囊形状、颜色特性的考虑,弥补了传统区域生长算法对于胶囊药品同物双色特性没有很好适应的缺点,有效地解决了药品分割在胶囊药品以及粘连药品图像分割时药品不完整、边界不准确的问题,有助于医院调剂环境下药品图像中数量和种类的识别。     

一种基于可恢复性水印的医学图像认证方法

本文提出了一种适用于医学图像内容认证和保护的可恢复性水印箅法.选取图像的小波重要系数进行SPIHT编码生成水印:使用Arnold变换确定其嵌入块位置并将其加密嵌入剑图像数据低位;通过链式认证结构实现水印信号的检测和图像篡改数据的定位,由 SPIHT解码完成篡改数据的恢复.实验表明,该算法在保证含水印医学图像具有良好视觉透明性的基础上.还可以定位并恢复被篡改区域内容.     

一种基于改进SURF和K-Means聚类的布料图像匹配算法

计算机图像智能处理技术为服装设计师开展设计、启发灵感提供了方便和可能。通过提取布料图像的SURF特征可以实现布料图像形状分析,但由于SURF特征维数高、特征提取是基于灰度图进行,因此存在匹配速度慢、匹配结果不够符合人眼视觉特点的问题。本文提出了基于小波变换的自适应SURF特征提取算法和基于K-Means聚类的布料图像颜色分析方法。通过融合图像形状特征、颜色特征,加快了布料图像匹配速度,使布料图像的匹配结果更加符合人眼视觉感受。在8种不同类型布料图像上的实验验证了该算法的有效性。     

一种快速超分辨率图像重建算法

本文提出了一种新的自适应超分辨率重建算法,能够从序列低分辨率图像中重建出高分辨率图像.该算法充分考虑到低分辨率图像中的运动误差估计、点扩散函数以及加性高斯白噪声对重建算法的影响,通过构造新的非线性白适应正则化函数,并用实验方法分析函数的凸性以得到自适应步长因子.为验证算法的有效性,本文采用光学图像进行实验.实验结果表明,新算法能够有效地改善图像的空间分辨率和提高算法的收敛速度.     

一种新的边界提取算法及其在接触角测量中的应用

提出一种基于子图像邻域梯度信息度量的边界提取算法,通过构造邻域信息映射矩阵并利用矩阵梯度信息度量值提取图像目标的有效边界,详细阐述了应用本文算法实现劣质液滴图像的边界检测及接触角测量的方法与步骤。实验结果表明:本文算法能有效地提取劣质液滴图像的目标边界,完成液滴接触角的自动计算,接触角计算结果与人工计算结果基本一致。     

基于加权水平集演变算法的医学图像分割研究

目的本研究提出一种基于局部边缘特征的加权水平集演变算法,并应用于医学图像分割。方法首先,计算演变轮廓内外邻域的图像局部边缘特征;接着,计算演变轮廓邻近轮廓的平均边缘强度和图像梯度向量流场,构造加权函数项;然后,由此构建新的水平集算法能量函数的长度项和区域项,借助偏微分方程求得最小值获得图像目标理想边界。实验图像选用人工合成图像和临床实例图像,不同水平集算法分割性能采用Dice相似性系数。结果视觉分析显示,基于本研究算法获得的图像目标轮廓与真实图像目标区域边界吻合度最高。定量分析显示,基于本研究算法所得分割图像能获得更高的Dice相似性系数。此外,迭代次数较少时,本研究算法即可获得最佳目标轮廓,且增加迭代次数,本研究算法Dice相似性系数变化微弱不溢出。最后,初始轮廓位置不同,其他三种算法所得Dice相似性系数变化较大且低于本研究算法。结论本研究算法较其他水平集算法收敛速度快,对初始轮廓位置敏感度低,稳定性强,是一种可行的医学图像分割算法。     

A New Watermarking System Based on Discrete Cosine Transform (DCT) in Color Biometric Images

This paper recommend a biometric color images hiding approach An Watermarking System based on Discrete Cosine Transform (DCT), which is used to protect the security and integrity of transmitted biometric color images. Watermarking is a very important hiding information (audio, video, color image, gray image) technique. It is commonly used on digital objects together with the developing technology in the last few years. One of the common methods used for hiding information on image files is DCT method which used in the frequency domain. In this study, DCT methods in order to embed watermark data into face images, without corrupting their features.     

Impulse Noise Removal from Medical Images by Two Stage Quaternion Vector Median Filter

This paper presents a two-stage impulse noise removal filter from medical images. Quaternion is used to represent differences of two pixels. The pixels are sorted and assigned a rank based on the aggregated sum of pixel differences with other pixels inside the filtering window. The central pixel is considered as corrupted by an impulse if its rank is bigger than a predefined rank and the minimum difference between it and other pixels in the four edge direction inside the window is larger than a predefined threshold. The noisy pixel is replaced by output of vector median filter implemented using quaternion. For color images, both intensity and chromaticity components are used. Quaternion processes color images as single unit rather than as separated color channels. This preserves the correlation and three dimensional vector natures of the color channels. For grayscale medical images, the same algorithm is implemented by using the intensity difference between two pixels. Experimental results show improved performance of the proposed filter in suppressing the impulse noise while retaining the original image details comparing against other well-known filters.     

基于颜色空间变换的数字化人体切片图像背景移除算法

目的对数字化人体图片进行预处理,完整提取其中的人体数据。方法应用首例中国数字化可视人体数据集,提出一种基于颜色空间变换并引入标记矩阵进行移除背景的算法,并以头部切片为例进行详细阐述。结果将原始数据从RGB颜色空间变换到YIQ颜色空间后,对I灰度图取阈值为f=0进行标记,再结合标记矩阵对原始图像进行背景移除的操作,能够完好地提取出所需要的人体数据。结论本文所提出的算法简单易行而且运算效率高,对目标区域的数据保存较好,为图片的下一步处理奠定了基础。     

Diabetic Retinopathy: A Quadtree Based Blood Vessel Detection Algorithm Using RGB Components in Fundus Images

Blood vessel detection in retinal images is a fundamental step for feature extraction and interpretation of image content. This paper proposes a novel computational paradigm for detection of blood vessels in fundus images based on RGB components and quadtree decomposition. The proposed algorithm employs median filtering, quadtree decomposition, post filtration of detected edges, and morphological reconstruction on retinal images. The application of preprocessing algorithm helps in enhancing the image to make it better fit for the subsequent analysis and it is a vital phase before decomposing the image. Quadtree decomposition provides information on the different types of blocks and intensities of the pixels within the blocks. The post filtration and morphological reconstruction assist in filling the edges of the blood vessels and removing the false alarms and unwanted objects from the background, while restoring the original shape of the connected vessels. The proposed method which makes use of the three color components (RGB) is tested on various images of publicly available database. The results are compared with those obtained by other known methods as well as with the results obtained by using the proposed method with the green color component only. It is shown that the proposed method can yield true positive fraction values as high as 0.77, which are comparable to or somewhat higher than the results obtained by other known methods. It is also shown that the effect of noise can be reduced if the proposed method is implemented using only the green color component.     

美白剂对正畸后脱矿着色牙齿的硬度影响

目的 使用常用的美白剂治疗正畸后脱矿着色的牙齿，观察美白剂对牙齿硬度的影响。 方法 选取因正畸拔除的前磨牙120颗，建立早期脱矿着色模型，分为4组：Opalescence美白剂（A组）、Beyond美白剂（B组）、氟化钠（C组），空白对照组（D组）。观察色阶的变化和釉质显微硬度的变化。 结果 比色板比色：各组色阶值随着时间变化呈逐渐降低再升高的趋势；各组间·时间点比较色阶值差异有统计学意义（P＜0.05）。釉质表面显微硬度值（surface microhardness value，SMV）：A组SMV值随时间的变化呈现先升高再降低的趋势，其余3组SMV值随时间变化增加后趋于平稳；组间比较：除A组与C组差异无统计学意义（P＞0.05），其余各组间的差异有统计学意义（P＜0.05）。 结论 Opalescence美白剂和Beyond美白剂均可以使正畸脱矿牙发生再矿化，Opalescence Boost美白剂对釉质矿化程度略高于Beyond美白剂。     

Automatic Tracing of Optic Disc and Exudates from Color Fundus Images Using Fixed and Variable Thresholds

The detection of bright objects such as optic disc (OD) and exudates in color fundus images is an important step in the diagnosis of eye diseases such as diabetic retinopathy and glaucoma. In this paper, a novel approach to automatically segment the OD and exudates is proposed. The proposed algorithm makes use of the green component of the image and preprocessing steps such as average filtering, contrast adjustment, and thresholding. The other processing techniques used are morphological opening, extended maxima operator, minima imposition, and watershed transformation. The proposed algorithm is evaluated using the test images of STARE and DRIVE databases with fixed and variable thresholds. The images drawn by human expert are taken as the reference images. The proposed method yields sensitivity values as high as 96.7%, which are better than the results reported in the literature.     

Performance Evaluation of Color Models in the Fusion of Functional and Anatomical Images

Fusion of the functional image with an anatomical image provides additional diagnostic information. It is widely used in diagnosis, treatment planning, and follow-up of oncology. Functional image is a low-resolution pseudo color image representing the uptake of radioactive tracer that gives the important metabolic information. Whereas, anatomical image is a high-resolution gray scale image that gives structural details. Fused image should consist of all the anatomical details without any changes in the functional content. This is achieved through fusion in de-correlated color model and the choice of color model has greater impact on the fusion outcome. In the present work, suitability of different color models for functional and anatomical image fusion is studied. After converting the functional image into de-correlated color model, the achromatic component of functional image is fused with an anatomical image by using proposed nonsubsampled shearlet transform (NSST) based image fusion algorithm to get new achromatic component with all the anatomical details. This new achromatic and original chromatic channels of functional image are converted to RGB format to get fused functional and anatomical image. Fusion is performed in different color models. Different cases of SPECT-MRI images are used for this color model study. Based on visual and quantitative analysis of fused images, the best color model for the stated purpose is determined.     

Application of K- and Fuzzy c-Means for Color Segmentation of Thermal Infrared Breast Images

Color segmentation of infrared thermal images is an important factor in detecting the tumor region. The cancerous tissue with angiogenesis and inflammation emits temperature pattern different from the healthy one. In this paper, two color segmentation techniques, K-means and fuzzy c-means for color segmentation of infrared (IR) breast images are modeled and compared. Using the K-means algorithm in Matlab, some empty clusters may appear in the results. Fuzzy c-means is preferred because the fuzzy nature of IR breast images helps the fuzzy c-means segmentation to provide more accurate results with no empty cluster. Since breasts with malignant tumors have higher temperature than healthy breasts and even breasts with benign tumors, in this study, we look for detecting the hottest regions of abnormal breasts which are the suspected regions. The effect of IR camera sensitivity on the number of clusters in segmentation is also investigated. When the camera is ultra sensitive the number of clusters being considered may be increased.     

一种基于色差的彩色图像的边缘检测方法

传统的边缘检测方法大都是基于灰度图像的,这就不能充分利用彩色图像的全部信息。文章提出了一种基于色差的彩色图像的边缘检测方法,它能快速准确地检测到图像的边缘,具有低的错判率和较高的效率,优于传统基于灰度的边缘检测方法。