Reconstructing 3D light microscopic images using the EM algorithm

An iterative method to recover perfectly focused images from a set of light microscopic images is proposed. The method is based on the EM algorithm, and it assumes a prior knowledge about the Point Spread Function of the optical system, as well as about the optical parameter settings of the acquisition system. The method is applied to the visualization of integrated circuit samples through an optical microscope and to the recovery of their depth information.     

Automatic determination of cell division rate using microscope images

Estimating the dynamics of cell culture development using fluorescent microscopy images is of great interest in modern cellular and molecular biology. In large-scale studies of cell populations involving a great number of images taken within a certain interval, manual analysis has proved to be time consuming and inaccurate; therefore, various computer-aided tools are required. This paper presents an efficient algorithm for recognizing budding cells and determining their division rate using microscope images. Image binarization using the Laplacian of the Gaussian; classifications of objects on cells, buds, and noise; and the determination of the cell division rate based on the data obtained are considered.     

Reconstructing 2D images with natural neighbour interpolation

In this paper, we explore image reconstruction by natural neighbour interpolation from irregularly spaced samples. We sample the image irregularly with techniques based on the Laplacian or the derivative in the direction of the gradient. Local coordinates based on the Voronoi diagram are used in natural neighbour interpolation to quantify the “neighbourliness” of data sites. Then we use natural neighbour interpolation in order to reconstruct the image. The main result is that the image quality is always very good in the case of the sampling techniques based on the Laplacian.     

Segmentation of microscope images of living cells

This paper describes a segmentation method combining a texture based technique with a contour based method. The technique is designed to enable the study of cell behaviour over time by segmenting brightfield microscope image sequences. The technique was tested on artificial images, based on images of living cells and on real sequences acquired from microscope observations of neutrophils and lymphocytes as well as on a sequence of MRI images. The results of the segmentation are compared with the results of the watershed and snake segmentation methods. The results show that the method is both effective and practical.

基于微观图像的图像拼接算法研究

针对传统图像拼接算法不适用于局部特征点多的微观图像实时拼接问题,结合Harris角点、SURF算法和K-Means算法提出了一种改进的算法。具体的算法流程如下：通过Harris角点提取微观图像中的特征点,并在形成SURF描述子后利用最近近邻算法对这些特征点进行粗配准。通过K-Means算法对初次配准的特征点进行聚类分簇获取聚类中心,并提取有效聚类区域的特征点。对有效的特征点进行精确配准,并校验配准后特征点的斜率一致性和距离一致性,从而实现精确的特征点匹配。实验结果证明,该算法克服了特征点多造成图像拼接时间长和拼接误差大的问题,具有较强的鲁棒性和稳定性,可应用于微观图像实时拼接领域。     

Object segmentation within microscope images of palynofacies

Identification of fossil material under a microscope is the basis of micropalentology. Our task is to locate and count the pieces of inertinite and vitrinite in images of sieve sampled rock. The classical watershed algorithm oversegments the objects because of their irregular shapes. In this paper we propose a method for locating multiple objects in a black and white image while accounting for possible overlapping or touching. The method, called Centre Supported Segmentation (CSS), eliminates oversegmentation and is robust against differences in size and shape of the objects.     

The reconstruction of dynamic 3D structure of biological objects using stereo microscope images

In this paper, we address the analysis of 3D shape and shape change in non-rigid biological objects imaged via a stereo light microscope. We propose an integrated approach for the reconstruction of 3D structure and the motion analysis for images in which only a few informative features are available. The key components of this framework are: 1) image registration using a correlation-based approach, 2) region-of-interest extraction using motion-based segmentation, and 3) stereo and motion analysis using a cooperative spatial and temporal matching process. We describe these three stages of processing and illustrate the efficacy of the proposed approach using real images of a live frog's ventricle. The reconstructed dynamic 3D structure of the ventricle is demonstrated in our experimental results, and it agrees qualitatively with the observed images of the ventricle.     

Cell phenotype classification using multi threshold uniform local ternary patterns in fluorescence microscope images

Multimedia Tools and Applications - Identifying locations of protein expression in live cells plays an important role in several medical applications ranging from early disease diagnosis to...     

Reconstructing a 3-D depth map from one or more images

Several algorithms are suggested for recovering depth and orientation maps of a surface from its image intensities. They combine the advantages of stereo vision and shape-from-shading (SFS) methods. These algorithms generate accurate, unambiguous and dense surface depth and orientation maps. Most of the existing SFS algorithms cannot be directly extended to combine stereo images because the recovery of surface depth and that of orientation are separated in these formulations. We first present an SFS algorithm that couples the generation of depth and orientation maps. This formulation also ensures that the reconstructed surface depth and its orientation are consistent. The SFS algorithm for a single image is then extended to utilize stereo images. The correspondence over stereo images is established simultaneously with the generation of surface depth and orientation. An alternative approach is also suggested for combining stereo and SFS techniques. This approach can be used to combine needle maps which are directly available from other sources such as photometric stereo. Finally we present an algorithm to combine sparse depth measurements with an orientation map to reconstruct a surface. The same algorithm can be combined with the above algorithms for solving the SFS problem with sparse depth measurements. Thus various information sources can be used to accurately reconstruct a surface.     

A microprocessor-controlled axiomat microscope for acquisition of cell images

This paper describes a system's approach for matching the characteristics of the microscope optics, the light sensors and associated circuits to obtain improved cell images for the subsequent computer analysis. The reported improvements eliminate the need for low-pass filtering and repeated scanning. The advantages of the required modification to both the microscope optics and the electronic circuits are demonstrated using both a TV scanner and a 3-wavelength beam splitter for simultaneous measurement of red, green and blue components of the cell images.     

Using active learning to annotate microscope images of parasite eggs

Microscopic analysis forms an integral part of many scientific studies. It is a task which requires great expertise and care. However, it can often be an extremely repetitive and labourious task. In some cases many hundreds of slides may need to be analysed, a process that will require each slide to be meticulously examined. Machine vision tools could be used to help assist in just such repetitive and tedious tasks. However, many machine vision solutions involve a lengthy data acquisition phase and in many cases result in systems that are highly specialised and not easily adaptable. In this paper, we describe a framework that applies flexible machine vision techniques to microscope analysis and utilises active learning to help overcome the data acquisition and adaptability problems. In particular we investigate the potential of various aspects of our proposed framework on a particular real world microscopic task, the recognition of parasite eggs.     

Micromanipulation system using scanning electron microscope

In this study, we aimed to develop a micromechanism in which constituent parts of μm order are used and to establish the handling technology required to achieve this. On the basis of the results of previous studies, we fabricated end effectors driven by a small piece of shape memory alloy (SMA) and succeeded in grasping microobjects on the order of 10–40 μm. However, when the size of an object is 10 μm or smaller, because this size is equivalent to the resolution of optical microscopes, it is necessary to improve and change the design of observation technology to manipulate much smaller objects. In this study, an SEM was used as the observation system and the manipulation system was modified to be compatible with this observation system. A manipulation system for grasping much smaller objects was designed and developed.     

Reconstructing surfaces by volumetric regularization using radial basis functions

We present a new method of surface reconstruction that generates smooth and seamless models from sparse, noisy, nonuniform, and low resolution range data. Data acquisition techniques from computer vision, such as stereo range images and space carving, produce 3D point sets that are imprecise and nonuniform when compared to laser or optical range scanners. Traditional reconstruction algorithms designed for dense and precise data do not produce smooth reconstructions when applied to vision-based data sets. Our method constructs a 3D implicit surface, formulated as a sum of weighted radial basis functions. We achieve three primary advantages over existing algorithms: (1) the implicit functions we construct estimate the surface well in regions where there is little data, (2) the reconstructed surface is insensitive to noise in data acquisition because we can allow the surface to approximate, rather than exactly interpolate, the data, and (3) the reconstructed surface is locally detailed, yet globally smooth, because we use radial basis functions that achieve multiple orders of smoothness.     

Reconstructing social interactions using an unreliable wireless sensor network

In the very active field of complex networks, research advances have largely been stimulated by the availability of empirical data and the increase in computational power needed for their analysis. These works have led to the identification of similarities in the structures of such networks arising in very different fields, and to the development of a body of knowledge, tools and methods for their study.While many interesting questions remain open on the subject of static networks, challenging issues arise from the study of dynamic networks. In particular, the measurement, analysis and modeling of social interactions are first class concerns.In this article, we address the challenges of capturing physical proximity and social interaction by means of a wireless network. In particular, as a concrete case study, we exhibit the deployment of a wireless sensor network applied to the measurement of health care workers’ exposure to tuberculosis-infected patients in a service unit of the Bichat-Claude Bernard hospital in Paris, France. This network has continuously monitored the presence of all HCWs in all rooms of the service during a three month period.We both describe the measurement system that was deployed and some early analysis on the measured data. We highlight the bias introduced by the measurement system reliability and provide a reconstruction method which not only leads to a significantly more coherent and realistic dataset but also evidences phenomena a priori hidden in the raw data. By this analysis, we suggest that a processing step is required prior to any adequate exploitation of data gathered thanks to a non-fully reliable measurement architecture.     

Automated classification of cytological specimens based on features extracted from nuclei images

The paper describes the pattern recognition subsystem of an automated specimen analyser being developed for pre-screening applications with a high resolution system. The complete recognition system is divided into a hierarchy of two different recognition systems: the single cell classifier, and the specimen classifier. The single cell image classifier operates on the cell data of isolated cell images based on features which are extracted from the nucleus only. For each specimen a large number of single cells are processed by the single cell classifier. The collection of the resulting discriminant vectors-and not just the decisions only-are used as measurements for the subsequent specimen classifier, which has to produce a real-valued discriminant function indicating the degree of suspiciousness of the specimen analysed. By proper thresholding of this figure of malignancy, the final decision can be made. The approach offers the opportunity of detecting suspicious cell modifications in an early stage.     

Comparing images using joint histograms

Color histograms are widely used for content-based image retrieval due to their efficiency and robustness. However, a color histogram only records an image's overall color composition, so images with very different appearances can have similar color histograms. This problem is especially critical in large image databases, where many images have similar color histograms. In this paper, we propose an alternative to color histograms called a joint histogram, which incorporates additional information without sacrificing the robustness of color histograms. We create a joint histogram by selecting a set of local pixel features and constructing a multidimensional histogram. Each entry in a joint histogram contains the number of pixels in the image that are described by a particular combination of feature values. We describe a number of different joint histograms, and evaluate their performance for image retrieval on a database with over 210,000 images. On our benchmarks, joint histograms outperform color histograms by an order of magnitude.     

Geologic mapping using thermal images

In the past, remote sensing from aircraft and satellite for geologic mapping concentrated on the visible and reflective infrared parts of the spectrum, because of the availability of Landsat and aircraft multispectral scanners operating in this spectral range. With the launch of the Heat Capacity Mapping Mission (HCMM) satellite, regional thermal image data also became available. We have examined the HCMM data for geologic information over two desert areas in southern California, the Trona area and the Pisgah area. Three techniques were used for displaying and combining thermal data and visible and near infrared, including color additive composites, principal components, and calculation of thermal inertia images. Use of the color additive composite image was simplest and allowed for simultaneous display of both thermal and reflectance properties. Thermal data were found to provide additional geologic information, unavailable from Landsat data or from aircraft visible and near-infrared data alone. The addition of these data relating to thermal properties allowed separation of rock types with differing thermal properties but with similar reflectance characteristics.     

Modeling and analysis of experimental atomic force microscope images of hard colloidal particles

The fundamentals of atomic force microscopy (AFM) of hard colloidal particles are discussed. The idea of image modeling and deconvolution with the known AFM cantilever tip shape is described as well as a method for AFM tip calibration with the use of spherical colloidal particles. The algorithms and programs for AFM image modeling and experimental data analysis are presented together with exemplary analysis and possible applications. The difficulties of the method are discussed.     

Reconstructing cloudfree NDVI composites using Fourier analysis of time series

This letter describes the Harmonic ANalysis of Time Series (HANTS) algorithm. It performs two tasks: (i) screening and removal of cloud affected observations; and (ii) temporal interpolation of the remaining observations to reconstruct gapless images at a prescribed time. HANTS was applied to 36 AVHRR 10-days-maximum-NDVI composites covering most of Europe. The results show that cloud affected data are recognized successfully and replaced. Up to half the data points were rejected with no consequence for the successful reconstruction of seasonal NDVI profiles.