CR数字胸片图像的几种肋骨分割方法

在医学领域里，计算机X线摄影(computed radiography，CR)影像系统已经进入全新的发展阶段。图像分割在医学图像处理中占有很重要的位置，由于医学图像的一些特殊性，不同的分割方法会产生不同的效果。以CR数字胸片图像为研究对象，给出了概率松弛迭代法、K-均值聚类法和高斯曲面阈值法在胸片肋骨分割中的应用，并且对其结果给予了一定的评价。实验结果表明，几种分割方法中高斯曲面阈值法更为有效，它方便后继处理，可以得到比较完整的肋骨信息，为后期的计算机辅助诊断提供更为可靠的实验数据。     

Mutual spectral residual approach for multifocus image fusion

Multifocus image fusion is the process of obtaining a single image from multiple partially focused images such that the newly formed image consists of the well-defined information extracted from each source image. This paper proposes the use of saliency of the source images based on Mutual Spectral Residual. Spectral Residual brings out the unique/salient features of an image in frequency domain. The idea of proposed mutual spectral residual is to emphasize the relative unique features of a source image with respect to the other source images. The relative unique features are utilized to form saliency maps for each source image. These saliency maps can clearly indicate the focused and defocused parts of an image. Based on the saliency maps obtained, the image fusion takes place in spatial domain. Visual inspection and quantitative evaluation of the fused images obtained by the proposed method, using different evaluation metrics, demonstrate its effectiveness over several existing image fusion methods.     

Efficient Implementation of Image Warping on a Multimedia Processor

The spatial transformation of images, commonly known as image warping, is fundamental to many applications, e.g. remote sensing, medical imaging, computer vision, and computer graphics. Computational demands in image warping are high, requiring a geometric transformation, address and coefficient generation, and some form of interpolation. However, unlike most image processing algorithms, the data flow for image warping can be highly irregular, which makes any efficient implementation challenging.This paper describes an efficient algorithm which addresses these challenges by making use of the capabilities of a single-chip multiprocessing microprocessor, the Texas Instruments TMS320C80 MVP (multimedia video processor). the MVP's advanced digital signal processors (ADSPs) offer tremendous computational power through instruction-level parallelism and several key features designed for image processing. The MVP's intelligent input/output interface via the transfer controller (TC) permits efficient irregular memory accesses.Affine and perspective warps have been implemented for 8-bit, 16-bit and and RGB color data using bilinear interpolation.The affine warp can generate 512 × 512 warped output images faster than real-time video rates require. For 8-bit images, the performance is 14.1 ms. Although the amount of computation necessary is the same for 16-bit images, the execution time increases to 15.2 ms since twice as many bytes need to be transferred. For RGB color images, it takes 28.0 ms. The perspective warp requires 46.3 ms for 8-bit and 16-bit images, and 60.4 ms for RGB color images. This unprecedented performance for software-based image warping exceeds many hardware approaches reported in the literature.     

A low-cost system for digital image processing of moving images in real time: application to X-ray fluoroscopy.

A low-cost image processing system and two real-time digital image processing algorithms using parallel processing have been developed to provide automatic equalization of moving images at video frequencies. The algorithms differ in the processing time required and the characteristics of the final image. The system takes an entire image from the video sequence, digitizes it, automatically calculates its histogram and the lowest and highest useful grey levels, equalizes the histogram, and finally converts it to the analogue image. Methods were applied to X-ray fluoroscopy (angiography) images, which are usually saturated or of uniform intensity (low contrast), leading to information loss. An application of these methods is in interventional radiography, which requires images in real time.     

Noise- and compression-robust biological features for texture classification

Texture classification is an important aspect of many digital image processing applications such as surface inspection, content-based image retrieval, and biomedical image analysis. However, noise and compression artifacts in images cause problems for most texture analysis methods. This paper proposes the use of features based on the human visual system for texture classification using a semisupervised, hierarchical approach. The texture feature consists of responses of cells which are found in the visual cortex of higher primates. Classification experiments on different texture libraries indicate that the proposed features obtain a very high classification near 97%. In contrast to other well-established texture analysis methods, the experiments indicate that the proposed features are more robust to various levels of speckle and Gaussian noise. Furthermore, we show that the classification rate of the textures using the presented biologically inspired features is hardly affected by image compression techniques.     

TeraPro, a system for processing large biomedical images

Image analysis plays an important role both in medical diagnostics and in biology. The main reasons that prevent the creation of objective and reliable methods of analysis of biomedical images are the high variability and heterogeneity of the biological material, distortion introduced by the experimental procedures, and the large size of the images. This paper presents preliminary results on creating a system called Ter-aPro, which combines a platform for image processing (ProStack) and a raster data storage system (rasdaman). This integrated system can be used in a cloud environment, providing access to the methods of visualization, analysis, and processing of a large amount of images through the Internet. Such an approach increases the speed and quality of image understanding and softens the limitations imposed by other systems. The system allows us to view uploaded images in the browser without having to install additional software on any device connected to the Internet, such as tablet computers and smartphones. This paper presents the preliminary results of processing biomedical images.     

基于二维直方图和模糊熵准则的阈值化方法

图像分割是图像分析、识别和理解的基础。图像分割主要是指将图像分成各具特性的区域并提取出感兴趣目标的技术,其研究多年来一直受到人们的高度重视;阈值化法是图像分割的一种重要方法,在图像处理与识别中广为应用;针对图像分割中细节往往被忽略导致后续处理困难的问题,基于模糊关系和最大模糊熵原理提出了一种阈值化方法,对二维直方图进行模糊分割;为了获得图像分割中的细节,提出的方法根据最大熵原则自动确定模糊区域和门限,进而获得二维模糊熵和遗传算法最优解,最后获得图像细节;通过对不同灰度水平和颜色类型图像进行实验比较,实验结果表明提出的方法优于二维非模糊方法和一维模糊熵分割法,得到该方法在图像分割中获得细节的结论。     

Intelligent forms processing system

This paper describes an intelligent forms processing system (IFPS) which provides capabilities for automatically indexing form documents for storage/retrieval to/from a document library and for capturing information from scanned form images using intelligent character recognition (ICR). The system also provides capabilities for efficiently storing form images. IFPS consists of five major processing components: (1) An interactive document analysis stage that analyzes a blank form in order to define a model of each type of form to be accepted by the system; the parameters of each model are stored in a form library. (2) A form recognition module that collects features of an input form in order to match it against one represented in the form library; the primary features used in this step are the pattern of lines defining data areas on the form. (3) A data extraction component that registers the selected model to the input form, locates data added to the form in fields of interest, and removes the data image to a separate image area. A simple mask defining the center of the data region suffices to initiate the extraction process; search routines are invoked to track data that extends beyond the masks. Other special processing is called on to detect lines that intersect the data image and to delete the lines with minimum distortion to the rest of the image. (4) An ICR unit that converts the extracted image data to symbol code for input to data base or other conventional processing systems. Three types of ICR logic have been implemented in order to accommodate monospace typing, proportionally spaced machine text, and handprinted alphanumerics. (5) A forms dropout module that removes the fixed part of a form and retains only the data filled in for storage. The stored data can be later combined with the fixed form to reconstruct the original form. This provides for extremely efficient storage of form images, thus making possible the storage of very large number of forms in the system. IFPS is implemented as part of a larger image management system called Image and Records Management system (IRM). It is being applied in forms data management in several state government applications.     

Analysis of natural images processing for the extraction of agricultural elements

This work presents several developed computer-vision-based methods for the estimation of percentages of weed, crop and soil present in an image showing a region of interest of the crop field. The visual detection of weed, crop and soil is an arduous task due to physical similarities between weeds and crop and to the natural and therefore complex environments (with non-controlled illumination) encountered. The image processing was divided in three different stages at which each different agricultural element is extracted: (1) segmentation of vegetation against non-vegetation (soil), (2) crop row elimination (crop) and (3) weed extraction (weed). For each stage, different and interchangeable methods are proposed, each one using a series of input parameters which value can be changed for further refining the processing. A genetic algorithm was then used to find the best value of parameters and method combination for different sets of images. The whole system was tested on several images from different years and fields, resulting in an average correlation coefficient with real data (bio-mass) of 84%, with up to 96% correlation using the best methods on winter cereal images and of up to 84% on maize images. Moreover, the method’s low computational complexity leads to the possibility, as future work, of adapting them to real-time processing.     

远程实时运动目标识别技术的研究

根据远程实时性、识别精度等要求,通过分析,比较每一个子过程中所采用图像处理技术的优劣,吸取其中较优的方法加以改进、融合,提出一套合理的、高性能的、可靠的运动目标检测和识别算法和识别策略。     

Steady-state image processing

This paper presents a new approach to the application mode of image processing operators, the so-called steady-state image processing. The approach reminds a steady-state genetic processing of images by considering each pixel of the image as an individual. So, some pixels are selected, processed and copied back into the image. This differs from the standard approach, where all image pixels are processed at once. The proposed approach offers many choices for variation, and allows for the assignment of dynamic measures to images. This will serve new families of soft computing methods as, e.g. immune-based algorithms, which need images as non-static objects in order to fulfill reasonable tasks. This paper also introduces some basic steady-state operators and exemplifies the analysis of an image by means of a small example. Also, it is shown how steady-state image processing can be applied in the context of texture segmentation. Steady-state image processing can be considered a way of processing images, which is deeply inspired by genetic algorithms.     

基于图像差距度量的阈值选取方法

依靠阈值分割出目标与背景在图像处理与分析问题中经常使用,基于好阈值分割出的目标与背景之间的差距应议最大以及它们与原图像的差距应该都很大这一特点,在给出一些差距度量的基础上,一些新的阈值选取方法被提出,所有方法都有比Otsu方法类似的简单计算公式,特别地,根据这一原则,Otsu方法被多次导出,这也间接地说明了依据上面原则来构造阈值选取方法的合理性,所有方法均经实际图像验证证明是行之有效的。     

Central catadioptric image processing with geodesic metric

Because of the distortions produced by the insertion of a mirror, catadioptric images cannot be processed similarly to classical perspective images. Now, although the equivalence between such images and spherical images is well known, the use of spherical harmonic analysis often leads to image processing methods which are more difficult to implement. In this paper, we propose to define catadioptric image processing from the geodesic metric on the unitary sphere. We show that this definition allows to adapt very simply classical image processing methods. We focus more particularly on image gradient estimation, interest point detection, and matching. More generally, the proposed approach extends traditional image processing techniques based on Euclidean metric to central catadioptric images. We show in this paper the efficiency of the approach through different experimental results and quantitative evaluations.     

数字图像加密技术的研究

随着科学技术尤其是计算机网络技术的发展,数字图像信息越来越成为信息传输的重要形式,众多企业和个人信息的保存都要采用数字图像的形式。因此,对数字图像的保密技术成为信息保护的当务之急,也是当前国际上研究讨论的热门话题之一。本文综合阐述了当前数字图像加密技术的发展现状,深入分析了数字图像加密技术中使用的相关数据处理途径、原理、系统运行特点等,并且对其优点和缺点进行了仔细的对比分析,由此预测其发展方向。     

High-resolution image compression algorithms in remote sensing imaging

Digital image processing (DIP) has great application values in many fields, especially in remote sensing image processing, which represents the acquisition, enhancement, analysis, encoding, transmission, and storage of remote sensing images. With the development of chip technology and parallel computing technology, various digital image processing technologies have been successfully applied to satellite applications to help researchers exploit reliable information from remote-sensing images. However, the huge amount of images generated by ultra-high resolution optical remote sensing satellites put great pressure on existing transmission, storage, and processing technologies. Therefore, this paper proposes a spatio-temporal compression pipeline for remote sensing images based on lossy compression methods with ultra-high compression ratios to reduce the overhead required for the transmission and storage of remote sensing images while maintaining the quality of the compressed images. The experimental results show that the proposed method outperforms the classical image compression such as JPEG-2000.     

Distributed computing model for processing remotely sensed images based on grid computing

With advances in remote-sensing technology, the large volumes of data cannot be analyzed efficiently and rapidly, especially with arrival of high-resolution images. The development of image-processing technology is an urgent and complex problem for computer and geo-science experts. It involves, not only knowledge of remote sensing, but also of computing and networking. Remotely sensed images need to be processed rapidly and effectively in a distributed and parallel processing environment. Grid computing is a new form of distributed computing, providing an advanced computing and sharing model to solve large and computationally intensive problems. According to the basic principle of grid computing, we construct a distributed processing system for processing remotely sensed images. This paper focuses on the implementation of such a distributed computing and processing model based on the theory of grid computing. Firstly, problems in the field of remotely sensed image processing are analyzed. Then, the distributed (and parallel) computing model design, based on grid computing, is applied. Finally, implementation methods with middleware technology are discussed in detail. From a test analysis of our system, TARIES.NET, the whole image-processing system is evaluated, and the results show the feasibility of the model design and the efficiency of the remotely sensed image distributed and parallel processing system.     

图像增强对显著性目标检测的影响研究

目的 雾霾、雨雪天气和水下等非理想环境因素会引起图像退化,导致出现低质图像,从而影响人类主观视觉感受及机器视觉应用任务的性能,因此,低质图像被利用之前进行图像增强成为惯常的预处理过程。然而,图像增强能否提高图像机器视觉应用任务的性能及影响程度等问题鲜有系统性研究。针对上述问题,本文以图像显著性目标检测这一机器视觉应用为例,研究图像增强对显著性目标检测性能的影响。方法 首先利用包括5种传统方法、6种深度学习方法等共11种典型图像增强方法对图像进行增强处理,然后利用8种典型的显著性目标检测方法对增强前后的图像分别进行显著性目标检测实验,并对比分析其结果。结果 实验表明,图像增强对低质图像显著性目标检测方法性能的促进作用不明显,某些增强方法甚至表现出负面影响,也存在同一增强方法对不同的显著性目标检测方法作用不同的现象。结论 图像增强对于显著性目标检测及其他的机器视觉应用的实际效果值得进一步研究,如何根据图像机器视觉应用的需求来选择和设计有效的增强方法需进一步探讨。     

On edge preservation in multiresolution images

Multiresolution image processing and analysis has become popular in recent years. One of the most important factors for the success of such systems is the preservation of edges in the process of producing images with reduced resolutions. In this paper 10 image reduction methods are introduced and a comparative evaluation is presented by using a set of synthetic test images and several real images. The quantitative evaluation employs an error measure based on normalized mean-square errors and a set of well-defined image parameters. Edge separation parameter is found to have a strikingly decisive impact on the edge preservation in the context of image reduction. Noise and edge width also show their significant effects. A normalized local intensity variance is studied to bridge the gap between the simple synthetic images and the real images. Finally, suitable methods for producing multiresolution images are recommended.     

不同季相SPOT5影像镶嵌前色调处理方法研究

只有少数影像可供选择时,如何镶嵌季相差异大的影像,以取得较好的镶嵌效果,是一个很常见的技术问题。该问题的难点主要在于如何减小镶嵌影像间色调的差异。以厦门市两景不同年份、不同季相、色调差异很大的SPOT5卫星影像为例,采用数值调整,栅格编辑填充和特征信息提取、分类等图像处理方法,对影像上色调差异很大的斑块状地物和不规则零碎地物进行处理,达到了减小两景影像中同一地物特征色调差异的目的。文中提出的均色方法可以改善影像镶嵌效果,具有较强的实用性。