Real-time digital image enhancement

A programmable system for enhancing monocular and stereographic images at video rates is presented. The system provides both automatic and interactive enhancement modes based on histogram modification and intensity mapping techniques. Experimental results which illustrate enhancement capabilities under a variety of scene types and conditions are described.     

MATLAB在数字图像增强中的应用

基于数字图像增强对图像处理的重要性,将计算软件MATLAB应用于数字图像增强中,给出了用这一软件完成图像的对比度增强、直方图均衡化、平滑滤波、锐化等操作的示例,并给出了处理前后的对照图像。同时论述了MATLAB在进行图像处理试验时简洁、高效的特点。     

Data structures and computational organization in digital image enhancement

Two practical questions in developing an image enhancement computer program are considered: the data structure for the matrix of image samples, and computing the FFT of a large matrix. Experience in developing such programs is discussed, and characteristics of an enhancement program developed for the CDC 6600/7600 computers are described.     

Automatic image hanging protocol for chest radiographs in PACS.

Chest radiography is one of the most widely used techniques in diagnostic imaging. It comprises at least one-third of all diagnostic radiographic procedures in hospitals. However, in the picture archive and communication system, images are often stored with the projection and orientation unknown or mislabeled, which causes inefficiency for radiologists' interpretation. To address this problem, an automatic hanging protocol for chest radiographs is presented. The method targets the most effective region in a chest radiograph, and extracts a set of size-, rotation-, and translation-invariant features from it. Then, a well-trained classifier is used to recognize the projection. The orientation of the radiograph is later identified by locating the neck, heart, and abdomen positions in the radiographs. Initial experiments are performed on the radiographs collected from daily routine chest exams in hospitals and show promising results. Using the presented protocol, 98.2% of all cases could be hung correctly on projection view (without protocol, 62%), and 96.1% had correct orientation (without protocol, 75%). A workflow study on the protocol also demonstrates a significant improvement in efficiency for image display.     

一种数字图像增强系统的设计与实现

数字图像增强处理是根据特定的要求突出数字图像中感兴趣的部分,弱化和除去不感兴趣的内容,它是数字图像处理过程中的重要一环.利用MATLAB软件提供的图形用户界面开发环境和图像处理工具箱,开发了一个面向教学的可视化、开放性、交互性于一体的"数字图像增强系统".实际应用结果表明,该系统运行稳定、操作方便,可移植性和可扩展性强,具有一定的应用前景和较高的实用价值.     

高斯混合模型自适应微光图像增强

为了改善微光情况下可见光图像传感器输出图像的质量,提出了一种基于高斯混合模型的自适应微光图像增强算法。对图像的直方图进行混合高斯建模,应用改进的期望最大化算法对直方图拟合,从而获取高斯混合模型的最优参数,然后根据各个聚类的交点将直方图分区,最后确定输出图像所属聚类的映射关系,同时应用保持最大熵方法逼近人类视觉特性映射函数得到最终的增强图像。实验结果表明,此图像增强模型能自适应确定最佳聚类个数,提高直方图拟合的运算速度,一帧图像平均处理时间为0.37s,在相关信息熵和纹理信息等的客观评价中,增强结果明显优于传统方法,有效地提高了微光图像的对比度,同时保持了图像的细节。     

Computer-aided diagnosis in chest radiography: a survey

The traditional chest radiograph is still ubiquitous in clinical practice, and will likely remain so for quite some time. Yet, its interpretation is notoriously difficult. This explains the continued interest in computer-aided diagnosis for chest radiography. The purpose of this survey is to categorize and briefly review the literature on computer analysis of chest images, which comprises over 150 papers published in the last 30 years. Remaining challenges are indicated and some directions for future research are given.     

基于数据挖掘三维数字图像模糊增强系统设计

目前对于三维数字图像模糊增强使用的系统存在图像处理耗时过长的问题。为了解决此问题,设计基于数据挖掘三维数字图像模糊增强系统。设计系统整体框架包括电源模块、外围接口模块、核心处理模块以及存储模块。在整体框架基础上,使用ARM处理器,设计电源电路和外围接口电路,完成系统的框架搭建。利用数据挖掘技术对三维数字图像特征进行提取,并计算模糊特征值,通过模糊特征值和模糊隶属函数实现图像模糊增强,至此系统整体设计完成。测试结果表明,与传统的图像模糊增强系统相比,基于数据挖掘三维数字图像模糊增强系统的处理图像所耗时间缩短了25.86%,适合应用在三维数字图像增强中。     

Data hiding technologies for digital radiography

Research on data hiding is demonstrating every day that several applications can benefit from this technology; among these, medical data management. In particular, embedding patient information into a medical image through data hiding could improve the level of security and confidentiality that is essential for the diffusion of medical information systems. The design of a data hiding system for such an application has to take into account specific requirements, the most important are: high payload to identifying reliably a patient; quality preservation of the watermarked image; robustness to content modification. According to this analysis, a comparison between different data hiding approaches is presented, to evaluate the most suitable algorithms for embedding patient information into digital radiographs. In particular two algorithms based on statistical decision theory have been compared with schemes following the new approach of modelling data hiding as communication with side-information at the transmitter. These methods have been tested and compared in the framework of digital radiographies management in order to identify their benefits and drawbacks.     

Peer group image enhancement

Peer group image processing identifies a "peer group" for each pixel and then replaces the pixel intensity with the average over the peer group. Two parameters provide direct control over which image features are selectively enhanced: area (number of pixels in the feature) and window diameter (window size needed to enclose the feature). A discussion is given of how these parameters determine which features in the image are smoothed or preserved. We show that the Fisher discriminant can be used to automatically adjust the peer group averaging (PGA) parameters at each point in the image. This local parameter selection allows smoothing over uniform regions while preserving features like corners and edges. This adaptive procedure extends to multilevel and color forms of PGA. Comparisons are made with a variety of standard filtering techniques and an analysis is given of computational complexity and convergence issues.     

DICOM数字乳腺仪图像一致性的自动检测

提出了一种基于DICOM标准的数字乳腺仪图像一致性的自动检测的实现方式,该方法可减少检测时间并降低人为误差。将描述自动检测工具的具体实现,验证实验以及自动检测工具的性能。该工具已经在GE公司数字乳腺仪部门得到应用,也可以应用到其他基于DICOM标准的数字图像医疗设备。     

Wavelet domain image resolution enhancement

A wavelet-domain image resolution enhancement algorithm which is based on the estimation of detail wavelet coefficients at high resolution scales is proposed. The method exploits wavelet coefficient correlation in a local neighbourhood sense and employs linear least-squares regression to estimate the unknown detail coefficients. Results show that the proposed method is considerably superior to conventional image interpolation techniques, both in objective and subjective terms, while it also compares favourably with competing methods operating in the wavelet domain.     

Order statistics in digital image processing

A family of nonlinear filters based on order statistics is presented. A mathematical tool derived through robust estimation theory, order statistics has allowed engineers to develop nonlinear filters with excellent robustness properties. These filters are well suited to digital image processing because they preserve the edges and the fine details of an image much better than conventional linear filters. The probabilistic and deterministic properties of the best known and most widely used filter in this family, the median filter, are discussed. In addition, the authors consider filters that, while not based on order statistics, are related to them through robust estimation theory. A table that ranks nonlinear filters under a variety of performance criteria is included. Most of the topics treated are very active research areas, and the applications are varied, including HDTV, multichannel signal processing of geophysical and ECG/EEG data, and a variety of telecommunications applications     

数字图像中混合噪声的滤波

中值滤波法主要用来抑制椒盐噪声,均值滤波法主要用来抑制高斯噪声.当数字图像中即有椒盐噪声又有高斯噪声时,两种方法均无法达到令人满意的效果.提出了一种混合噪声的滤波方法.首先通过设定阈值,将椒盐噪声和高斯噪声加以区分.然后先对椒盐噪声使用改进了的中值滤波方法进行滤波,之后使用近邻域均值滤波法对高斯噪声进行滤波.仿真结果表明,文中提出的混合去噪声算法计算简单,对数字图像中存在的混合噪声有较好的滤波效果.     

Automatic image orientation detection

We present an algorithm for automatic image orientation estimation using a Bayesian learning framework. We demonstrate that a small codebook (the optimal size of codebook is selected using a modified MDL criterion) extracted from a learning vector quantizer (LVQ) can be used to estimate the class-conditional densities of the observed features needed for the Bayesian methodology. We further show how principal component analysis (PCA) and linear discriminant analysis (LDA) can be used as a feature extraction mechanism to remove redundancies in the high-dimensional feature vectors used for classification. The proposed method is compared with four different commonly used classifiers, namely k-nearest neighbor, support vector machine (SVM), a mixture of Gaussians, and hierarchical discriminating regression (HDR) tree. Experiments on a database of 16 344 images have shown that our proposed algorithm achieves an accuracy of approximately 98% on the training set and over 97% on an independent test set. A slight improvement in classification accuracy is achieved by employing classifier combination techniques.     

低信噪比图像的模糊增强算法

传统的机器视觉方法应用于低信噪比图像的目标增强,增强结果容易受噪声影响,鲁棒性较差.本文提出一种基于最大模糊熵的图象增强算法,对不同复杂背景下的图像具有增强函数强度自动可调和运算速度快的优点.其算法的实现关键是利用遗传算法自动搜寻满足最大熵条件的模糊增强函数的最佳带宽参数.进化过程中运用实数编码,在父代染色体构成的超平面内运用高斯交叉、变异算子创建后代,并采用欧式距离自适应调节交叉、变异概率.通过实验证明,该算法能够有效的增强复杂背景下的微弱目标信号.     

Automatic detection of rib borders in chest radiographs

An algorithm for detection of posterior rib borders in chest radiographs is presented. The algorithm first determines the thoracic cage boundary to restrict the area of search for the ribs. It then finds approximate rib borders using a knowledge-based Hough transform. Finally, the algorithm localizes the rib borders using an active contour model. Results of the proposed rib finding algorithm on 10 chest radiographs are presented.     

Automatic pupillometry from digital images

Determination of two-dimensional characteristics of the anterior surface of the eye is becoming increasingly important in modern optometry and ophthalmology practice. In particular, accurate estimation of the pupil size and centration is crucial in customized refractive surgery, corneal transplantation, and advanced contact lens fitting. The pupil parameters change under different lighting conditions so they often need to be related to some fixed reference such as the limbus outline. However, current commercial pupillometers do not estimate limbus position. We present a novel algorithm for automatic extraction of pupil parameters from digital images that takes the relative limbus information into account. The algorithm utilizes several customized image processing techniques that form a robust procedure which performs well for a wide range of clinical images. We apply the developed algorithm to images obtained by a standard digital camera, and specialized ophthalmic instruments such as a wavefront sensor and a high-speed imaging system.     

基于图像增强和复原的图像去雾方法研究

雾霭等天气下获得的图像存在对比度低、颜色退化、景物模糊等一系列图像退化的问题,直接影响了对图像信息的有效利用。因此,对雾天图像进行有效的去雾处理,有效改善降质图像的质量,具有一定的实际意义。分析讨论基于图像增强的多尺度Retinex算法和利用图像复原原理的基于暗原色先验理论的去雾算法,并对具有不同特点的单幅有雾图像进行去雾仿真。实验结果表明,不同理论基础的两种去雾算法各有特点,基于暗原色理论处理得到的图像去雾效果更显著,算法运行速度更快。