A Practical Approach Based on Analytic Deformable Algorithm for Scenic Image Registration

Background

Image registration is to produce an entire scene by aligning all the acquired image sequences. A registration algorithm is necessary to tolerance as much as possible for intensity and geometric variation among images. However, captured image views of real scene usually produce unexpected distortions. They are generally derived from the optic characteristics of image sensors or caused by the specific scenes and objects.

Methods and Findings

An analytic registration algorithm considering the deformation is proposed for scenic image applications in this study. After extracting important features by the wavelet-based edge correlation method, an analytic registration approach is then proposed to achieve deformable and accurate matching of point sets. Finally, the registration accuracy is further refined to obtain subpixel precision by a feature-based Levenberg-Marquardt (FLM) method. It converges evidently faster than most other methods because of its feature-based characteristic.

Conclusions

We validate the performance of proposed method by testing with synthetic and real image sequences acquired by a hand-held digital still camera (DSC) and in comparison with an optical flow-based motion technique in terms of the squared sum of intensity differences (SSD) and correlation coefficient (CC). The results indicate that the proposed method is satisfactory in the registration accuracy and quality of DSC images.     

应用计算机图像技术识别间日疟原虫的初步研究

目的应用数字图像技术识别间日疟原虫。方法基于图像预处理、图像分割、特征提取、判决分类等步骤,分别对间日疟原虫薄血膜的裂殖体图像进行处理,观察其识别效率和准确度。结果边缘检测和图像灰度值检测两种方法均能够识别间日疟原虫裂殖体,而二者的联合方法可以得到较好的识别效果。结论初步探索采用边缘和图像灰度联合检测的方法能够识别间日疟原虫薄血膜的裂殖体。     

The effect of defocusing on the electron microscopic image of the extended particle of pyocin R1

The effect of defocusing on the electron microscopic image of the extended particle of pyocin R1 was examined with the aid of optical diffraction. The results show that the through-focus image of the cross-striated particle changed to a fishbone-like image with a little under-focusing, which had been considered to be a transitional state during the sheath contraction of phage G. In the optical diffraction pattern of the defocused image, a strong meridional reflection corresponding to the distance of cross-striations in the through-focus image almost disappeared, supporting the change to the fishbone-like image on defocusing.     

What Image Properties Regulate Eye Growth?

The growth of the eye, unlike other parts of the body, is not ballistic. It is guided by visual feedback with the eventual aim being optimal focus of the retinal image or emmetropization . It has been shown in animal models that interference with the quality of the retinal image leads to a disruption to the normal growth pattern, resulting in the development of refractive errors and defocused retinal images . While it is clear that retinal images rich in pattern information are needed to control eye growth, it is unclear what particular aspect of image structure is relevant. Retinal images comprise a range of spatial frequencies at different absolute and relative contrasts and in different degrees of spatial alignment. Here we show, by using synthetic images, that it is not the local edge structure produced by relative spatial frequency alignments within an image but rather the spatial frequency composition per se that is used to regulate the growth of the eye. Furthermore, it is the absolute energy at high spatial frequencies regardless of the spectral slope that is most effective. Neither result would be expected from currently accepted ideas of how human observers judge the degree of image "blur" in a scene where both phase alignments and the relative energy distribution across spatial frequency (i.e., spectral slope) are important.     

Microspectrofluorometry by digital image processing: measurement of cytoplasmic pH

An interface of our microspectrofluorometer with an image processing system performs microspectrofluorometric measurements in living cells by digital image processing. Fluorescence spectroscopic parameters can be measured by digital image processing directly from microscopic images of cells, and are automatically normalized for pathlength and accessible volume. Thus, an accurate cytoplasmic "map" of various spectroscopic parameters can be produced. The resting cytoplasmic pH of fibroblasts (3T3 cells) has been determined by measuring the ratio of fluorescein fluorescence exited by two successive wavelengths (489 and 452 nm). Fluorescein-labeled dextran microinjected into the cells is used as a pH indicator, since it is trapped in the cytoplasm but is excluded from the nucleus and other organelles. The average cytoplasmic pH is 6.83 (+/- 0.38). However, cytoplasmic pH exhibits a nonunimodal distribution, the lower mean pH being 6.74 (+/- 0.23). When 3T3 cells pinocytose medium containing fluorescein dextran, pinosomes peripheral to the nucleus exhibit a lower pH than those closer to the ruffling edge of the cell. The present image processing system is analyzed for linearity of detection, light scattering artifacts, signal to noise ratio, standard curves, and spatial resolution. The results obtained from digital image analysis are shown to be comparable to the results from standard microspectrofluorometry. We also discuss several other applications of this ratio imaging technique in cell biology.     

Registration accuracy and quality of real-life images

Background

A common registration problem for the application of consumer device is to align all the acquired image sequences into a complete scene. Image alignment requires a registration algorithm that will compensate as much as possible for geometric variability among images. However, images captured views from a real scene usually produce different distortions. Some are derived from the optic characteristics of image sensors, and others are caused by the specific scenes and objects.

Methodology/Principal Findings

An image registration algorithm considering the perspective projection is proposed for the application of consumer devices in this study. It exploits a multiresolution wavelet-based method to extract significant features. An analytic differential approach is then proposed to achieve fast convergence of point matching. Finally, the registration accuracy is further refined to obtain subpixel precision by a feature-based modified Levenberg-Marquardt method. Due to its feature-based and nonlinear characteristic, it converges considerably faster than most other methods. In addition, vignette compensation and color difference adjustment are also performed to further improve the quality of registration results.

Conclusions/Significance

The performance of the proposed method is evaluated by testing the synthetic and real images acquired by a hand-held digital still camera and in comparison with two registration techniques in terms of the squared sum of intensity differences (SSD) and correlation coefficient (CC). The results indicate that the proposed method is promising in registration accuracy and quality, which are statistically significantly better than other two approaches.     

基于多尺度植物种子图像边缘检测算法的设计与实现

小波变换,由于其具有时频局部化的特性及多尺度特性,能敏感地反映突变信号,是一种理想的边缘提取方法．本文系统地介绍了作者在图像边缘检测方面所做的理论探讨、算法及应用研究工作．目前的边缘提取方法有多种,本文将重点集中于基于小波变换的图像边缘检测方法的理论推导和算法实现．     

Image correlation method for DNA sequence alignment

The complexity of searches and the volume of genomic data make sequence alignment one of bioinformatics most active research areas. New alignment approaches have incorporated digital signal processing techniques. Among these, correlation methods are highly sensitive. This paper proposes a novel sequence alignment method based on 2-dimensional images, where each nucleic acid base is represented as a fixed gray intensity pixel. Query and known database sequences are coded to their pixel representation and sequence alignment is handled as object recognition in a scene problem. Query and database become object and scene, respectively. An image correlation process is carried out in order to search for the best match between them. Given that this procedure can be implemented in an optical correlator, the correlation could eventually be accomplished at light speed. This paper shows an initial research stage where results were "digitally" obtained by simulating an optical correlation of DNA sequences represented as images. A total of 303 queries (variable lengths from 50 to 4500 base pairs) and 100 scenes represented by 100 x 100 images each (in total, one million base pair database) were considered for the image correlation analysis. The results showed that correlations reached very high sensitivity (99.01%), specificity (98.99%) and outperformed BLAST when mutation numbers increased. However, digital correlation processes were hundred times slower than BLAST. We are currently starting an initiative to evaluate the correlation speed process of a real experimental optical correlator. By doing this, we expect to fully exploit optical correlation light properties. As the optical correlator works jointly with the computer, digital algorithms should also be optimized. The results presented in this paper are encouraging and support the study of image correlation methods on sequence alignment.     

toBeeView: a program for simulating the retinal image of visual scenes on nonhuman eyes

We present toBeeView, a program that produces from a digital photograph, or a set of photographs, an approximation of the image formed at the sampling station stage in the eye of an animal. toBeeView is freely available from https://github.com/EEZA-CSIC/compound-eye-simulator . toBeeView assumes that sampling stations in the retina are distributed on a hexagonal grid. Each sampling station computes the weighted average of the color of the part of the visual scene projecting on its photoreceptors, and the hexagon of the output image associated with the sampling station is filled in this average color. Users can specify the visual angle subtended by the scene and the basic parameters determining the spatial resolution of the eye: photoreceptor spatial distribution and optic quality of the eye. The photoreceptor distribution is characterized by the vertical and horizontal interommatidial angles—which can vary along the retina. The optic quality depends on the section of the visual scene projecting onto each sampling station, determined by the acceptance angle. The output of toBeeView provides a first approximation to the amount of visual information available at the retina for subsequent processing, summarizing in an intuitive way the interaction between eye optics and receptor density. This tool can be used whenever it is important to determine the visual acuity of a species and will be particularly useful to study processes where object detection and identification is important, such as visual displays, camouflage, and mimicry.     

Image processing software for enhanced visualization of faint or noisy autoradiographic images

A computer program for digital image processing is described which can be implemented using scanning densitometer hardware pre-existing in most biology departments plus computer video hardware which may either pre-exist in the biology department or would represent a moderate upgrade over an already planned computer purchase. The primary purpose of this computer program is to provide contrast enhancement of faint or low contrast autoradiograph images and to implement background subtraction and digital smoothing methods which permit visualization of blurry electrophoresis bands against noisy backgrounds. However, the program also has modest editing capabilities that allow its use in the routine preparation of images for publication. Finally, the program has facilities for deblurring, edge enhancement and multiple image averaging, which give it usefulness in other forms of photographic analysis.     

High-resolution,High-speed,Three-dimensional Video Imaging with Digital Fringe Projection Techniques

Digital fringe projection (DFP) techniques provide dense 3D measurements of dynamically changing surfaces. Like the human eyes and brain, DFP uses triangulation between matching points in two views of the same scene at different angles to compute depth. However, unlike a stereo-based method, DFP uses a digital video projector to replace one of the cameras¹. The projector rapidly projects a known sinusoidal pattern onto the subject, and the surface of the subject distorts these patterns in the camera’s field of view. Three distorted patterns (fringe images) from the camera can be used to compute the depth using triangulation.Unlike other 3D measurement methods, DFP techniques lead to systems that tend to be faster, lower in equipment cost, more flexible, and easier to develop. DFP systems can also achieve the same measurement resolution as the camera. For this reason, DFP and other digital structured light techniques have recently been the focus of intense research (as summarized in^1-5). Taking advantage of DFP, the graphics processing unit, and optimized algorithms, we have developed a system capable of 30 Hz 3D video data acquisition, reconstruction, and display for over 300,000 measurement points per frame^6,7. Binary defocusing DFP methods can achieve even greater speeds⁸.Diverse applications can benefit from DFP techniques. Our collaborators have used our systems for facial function analysis⁹, facial animation¹⁰, cardiac mechanics studies¹¹, and fluid surface measurements, but many other potential applications exist. This video will teach the fundamentals of DFP techniques and illustrate the design and operation of a binary defocusing DFP system.     

Non-destructive evaluation of chlorophyll content in quinoa and amaranth leaves by simple and multiple regression analysis of RGB image components

Leaf chlorophyll content provides valuable information about physiological status of plants; it is directly linked to photosynthetic potential and primary production. In vitro assessment by wet chemical extraction is the standard method for leaf chlorophyll determination. This measurement is expensive, laborious, and time consuming. Over the years alternative methods, rapid and non-destructive, have been explored. The aim of this work was to evaluate the applicability of a fast and non-invasive field method for estimation of chlorophyll content in quinoa and amaranth leaves based on RGB components analysis of digital images acquired with a standard SLR camera. Digital images of leaves from different genotypes of quinoa and amaranth were acquired directly in the field. Mean values of each RGB component were evaluated via image analysis software and correlated to leaf chlorophyll provided by standard laboratory procedure. Single and multiple regression models using RGB color components as independent variables have been tested and validated. The performance of the proposed method was compared to that of the widely used non-destructive SPAD method. Sensitivity of the best regression models for different genotypes of quinoa and amaranth was also checked. Color data acquisition of the leaves in the field with a digital camera was quick, more effective, and lower cost than SPAD. The proposed RGB models provided better correlation (highest R ²) and prediction (lowest RMSEP) of the true value of foliar chlorophyll content and had a lower amount of noise in the whole range of chlorophyll studied compared with SPAD and other leaf image processing based models when applied to quinoa and amaranth.     

基于局部调整动态轮廓模型提取超声图像乳腺肿瘤边缘

提出一种基于局部调整动态轮廓模型提取超声图像乳腺肿瘤边缘的算法。该算法在Chan—Vese（CV）模型基础上,定义了一个局部调整项,采用基于水平集的动态轮廓模型提取超声图像乳腺肿瘤边缘。将该算法应用于89例临床超声图像乳腺肿瘤的边缘提取实验,结果表明：该算法比CV模型更适用于具有区域非同质性的超声图像的分割,可有效实现超声图像乳腺肿瘤边缘的提取。     

基于PET医学图像边缘检测算法的研究

目的:边缘检测在图像处理中至关重要,可被广泛应用于目标区域识别、区域形状检测、图像分割等图像分析领域。边缘是图像中不平稳现象和不规则结构的重要表现,往往携带着图像中的大量信息,并给出图像轮廓。在医学图像三维显示技术中,为了更精确的临床判别需要得到单像素的清晰轮廓,因此我们提出一种新的边缘检测算法。方法:在传统的小波边缘检测的基础上,提出了一种新的边缘算法,即基于小波极大值边缘检测算法,应用模糊算法构造相应的隶属函数,再对得到的极大值进一步筛选。结果:将该算法应用到医学图像中,最终可以得到较清楚的单像素边缘轮廓,实验结果证明了该算法的可行性。结论:运用这种算法处理过的医学图像边缘锐化更好,更清晰,能够为肿瘤的早期识别提供依据,满足医学影像识别的需要。     

SpotEgg: an image‐processing tool for automatised analysis of colouration and spottiness

Colouration and patterning are widespread amongst organisms. Regarding avian eggs, colouration (reflectances) has been previously measured using spectrometers whereas spottiness has been determined using human‐based scoring methods or by applying global thresholding over the luminance channel on photographs. However, the availability of powerful computers and digital image‐processing algorithms and software offers new possibilities to develop systematised, automatable, and accurate methods to characterise visual information in eggs. Here, we provide a computing infrastructure (library of functions and a graphical user interface) for eggshell colouration and spottiness analysis called SpotEgg, which runs over MATLAB. Compared to previous methods, our method offers four novelties for eggshell visual analysis. First, we have developed a standardised non‐human biased method to determine spottiness. Spottiness determination is based on four parameters that allow direct comparisons between studies and may improve results when relating colouration and patterning to pigment extraction. Second, researcher time devoted to routine tasks is remarkably reduced thanks to the incorporation of image‐processing techniques that automatically detect the colour reference chart and egg‐like shapes in the scene. Third, SpotEgg reduces the errors in colour estimation through the eggshell that are created by the different angles of view subtended from different parts of the eggshell and the optical centre of the camera. Fourth, SpotEgg runs automatic Fractal Dimension analysis (a measure of how the details in a pattern change with the scale at which this pattern is measured) of the spots pattern in case researchers want to relate other measurements with this special spatial pattern. Finally, although initially conceived for eggshell analysis, SpotEgg can also be applied in images containing objects different from eggs as feathers, frogs, insects, etc., since it allows the user to manually draw any region to be analysed making this tool useful not only for oologist but also for other evolutionary biologists.     

Digital image analysis of Zostera marina leaf injury

Current methods for assessing leaf injury in Zostera marina (eelgrass) utilize subjective indexes for desiccation injury and wasting disease. Because of the subjective nature of these measures, they are inherently imprecise making them difficult to use in quantifying complex leaf injuries from multiple sources. We have developed a method using color digital photography of eelgrass leaves which are then manipulated using image processing programs and analyzed using geographic digital image analysis. The resulting false color images are then assigned by the user into uninjured and injured groupings which may then be reported as a percentage of leaf area affected. If images are rectified, leaf area (cm²) of injured and uninjured leaf segments may be determined. Although this method is time consuming and still requires some subjective judgments, it does allow for precise analysis of highly complex leaf injuries and has the potential to be a substantial improvement over existing leaf injury indexes.     

Using a multiscale image processing method to characterize the periodic growth patterns on scallop shells

The fine periodic growth patterns on shell surfaces have been widely used for studies in the ecology and evolution of scallops. Modern X‐ray CT scanners and digital cameras can provide high‐resolution image data that contain abundant information such as the shell formation rate, ontogenetic age, and life span of shellfish organisms. We introduced a novel multiscale image processing method based on matched filters with Gaussian kernels and partial differential equation (PDE) multiscale hierarchical decomposition to segment the small tubular and periodic structures in scallop shell images. The periodic patterns of structures (consisting of bifurcation points, crossover points of the rings and ribs, and the connected lines) could be found by our Space‐based Depth‐First Search (SDFS) algorithm. We created a MATLAB package to implement our method of periodic pattern extraction and pattern matching on the CT and digital scallop images available in this study. The results confirmed the hypothesis that the shell cyclic structure patterns encompass genetically specific information that can be used as an effective invariable biomarker for biological individual recognition. The package is available with a quick‐start guide and includes three examples: http://mgb.ouc.edu.cn/novegene/html/code.php .     

Correcting distortions in panoramic images using constructive neural networks

This paper presents a novel approach to the correction of panoramic (wide-angle) image distortions. Unlike traditional methods that separate the distortion of the panoramic image into radial and tangential components and then concentrate on the correction of one type of distortion at a time, the method presented in this paper uses an integrated approach that simultaneously corrects all non-linear distortions of the panoramic image. The system uses data obtained from carefully constructed calibration patterns to automatically build and train a constructive neural network of suitable complexity to approximate the characteristic distortion of the panoramic image. The trained neural network is then used to correct the distortions represented by the sample data. It is demonstrated that by applying the distortion correction method presented in this paper to panoramic images representing real world scenes, perspective-corrected views of the real world scene that are usable in a wide variety of applications can be generated.     

Automated pipette failure monitoring using image processing for point-of-care testing devices

Background

The accuracy and precision of liquid handling can be altered by several causes including wearing or failure of parts, and human error. The last cause is crucial since point-of-care testing (POCT) devices can be used by non-experienced users or patients themselves. Therefore it is important to improve the method of informing the users of POCT device malfunctions due to damage of parts or human error.

Methods

In this paper, image-based failure monitoring of the automated pipetting was introduced for POCT devices. An inexpensive, high-performance camera for smartphones was employed in our previous work to resolve various malfunctions such as incorrect insertion of the tip, false positioning of the tip and pump, and improper operation of the pump. The image acquired from the camera was analyzed to detect the malfunctions. In this paper, the reagent volume in the tip was estimated from the image processing to verify the pump operation. First, the color component corresponding to the reagent intrinsic color was extracted to identify the reagent area in the tip before applying the binary image processing. The extracted reagent area was projected horizontally and the support length of the projection image was calculated. As the support length was related to the reagent volume, it was referred to the volume length. The relationship between the measured volume length and the previously measured solution mass was investigated. If we can predict the mass of the solution by the volume length, we will be able to detect the pump malfunction.

Results

The cube of the volume length obtained by the proposed image processing method showed a very linear relationship with the reagent mass in the tip injected by the pumping operation (R²?=?0.996), indicating that the volume length could be utilized to estimate the reagent volume to monitor the accuracy and precision of the pumping operation.

Conclusions

An inexpensive smartphone camera was enough to detect various malfunctions of a POCT device with pumping operation. The proposed image processing could monitor the level of inaccuracy of pumping volume in limited range. The simple image processing such as a fixed threshold and projections was employed for the cost optimization and system robustness. However it delivered the promising results because the imaging condition was highly controllable in the devices.