Simple digital control of cell mass in turbidostat

Summary The application of simple digital control method to the control of cell mass in a continuous stirred tank bioreactor (CSTBR) was experimentally examined. This algorithm based on the time-varying bilinear predictor model was as simple as proportional-integral-derivative(PID) control algorithm and showed better performance.     

Development of multicolour digital image analysis system to enumerate actively respiring bacteria in natural river water

AIMS: To develop a rapid and simple method for the assessment of metabolic activity of bacteria in natural environment. METHODS AND RESULTS: A rapid and simple multicolour digital image analysis system for enumerating viable bacteria based on active fluorescent staining has been developed. This system can accurately differentiate actively respiring bacteria and non-respiring bacteria by distinctive colour information in the digital image captured by an epifluorescence microscope equipped with low magnification objective lens. An algorithm to distinguish bacteria from considerable detritus, which produced bright fluorescence in different colours, by colour segmentation has also been developed. This system was applied to river water samples, and the total and respiratory active bacterial counts by digital image analysis were highly related to those by epifluorescence microscopy (r2 = 0.96 and 0.93, respectively). CONCLUSION: This system allowed the rapid and simple differentiation of bacteria from detritus and concurrent assessment of their metabolic activity, with results being available within 1 h. SIGNIFICANCE AND IMPACT OF THE STUDY: The low magnification image analysis allows more rapid and simple quantification of bacteria and could considerably decrease image data amount and acquisition time. This system could be easily applied to the rapid analysis of microbes in various environments.     

Quantitative immunohistochemistry by measuring cumulative signal strength using commercially available software photoshop and matlab.

Currently available techniques for performing quantitative immunohistochemistry (Q-IHC) rely upon pixel-counting algorithms and therefore cannot provide information as to the absolute amount of chromogen present. We describe a novel algorithm for true Q-IHC based on calculating the cumulative signal strength, or energy, of the digital file representing any portion of an image. This algorithm involves subtracting the energy of the digital file encoding the control image (i.e., not exposed to antibody) from that of the experimental image (i.e., antibody-treated). In this manner, the absolute amount of antibody-specific chromogen per pixel can be determined for any cellular region or structure. (J Histochem Cytochem 48:303-311, 2000)     

IHC Profiler: An Open Source Plugin for the Quantitative Evaluation and Automated Scoring of Immunohistochemistry Images of Human Tissue Samples

In anatomic pathology, immunohistochemistry (IHC) serves as a diagnostic and prognostic method for identification of disease markers in tissue samples that directly influences classification and grading the disease, influencing patient management. However, till today over most of the world, pathological analysis of tissue samples remained a time-consuming and subjective procedure, wherein the intensity of antibody staining is manually judged and thus scoring decision is directly influenced by visual bias. This instigated us to design a simple method of automated digital IHC image analysis algorithm for an unbiased, quantitative assessment of antibody staining intensity in tissue sections. As a first step, we adopted the spectral deconvolution method of DAB/hematoxylin color spectra by using optimized optical density vectors of the color deconvolution plugin for proper separation of the DAB color spectra. Then the DAB stained image is displayed in a new window wherein it undergoes pixel-by-pixel analysis, and displays the full profile along with its scoring decision. Based on the mathematical formula conceptualized, the algorithm is thoroughly tested by analyzing scores assigned to thousands (n = 1703) of DAB stained IHC images including sample images taken from human protein atlas web resource. The IHC Profiler plugin developed is compatible with the open resource digital image analysis software, ImageJ, which creates a pixel-by-pixel analysis profile of a digital IHC image and further assigns a score in a four tier system. A comparison study between manual pathological analysis and IHC Profiler resolved in a match of 88.6% (P<0.0001, CI = 95%). This new tool developed for clinical histopathological sample analysis can be adopted globally for scoring most protein targets where the marker protein expression is of cytoplasmic and/or nuclear type. We foresee that this method will minimize the problem of inter-observer variations across labs and further help in worldwide patient stratification potentially benefitting various multinational clinical trial initiatives.     

Quantitation of phosphotyrosine signals in human prostate cell adhesion sites

A simple method is described for the quantitation of phosphotyrosine signaling in human prostate cell cultures. The phosphotyrosine signals are observed by standard immunohistochemistry techniques, and the resulting digital images are analyzed using the Scion image software program. The signals within the cell adhesion sites are quantitated using the density slice and particle analysis features of the software. The immunohistochemistry results are compared with detection of phosphotyrosine signals using a standard Western blotting procedure with whole cell lysates. The resulting data is converted into graphs using the Sigma Plot Program. This method is illustrated using damage-induced signaling within cell adhesion sites after a low dose of ionizing radiation.     

Image analysis for monitoring the barley tempeh fermentation process

AIMS: To develop a fast, accurate, objective and nondestructive method for monitoring barley tempeh fermentation. METHODS AND RESULTS: Barley tempeh is a food made from pearled barley grains fermented with Rhizopus oligosporus. Rhizopus oligosporus growth is important for tempeh quality, but quantifying its growth is difficult and laborious. A system was developed for analysing digital images of fermentation stages using two image processing methods. The first employed statistical measures sensitive to image colour and surface structure, and these statistical measures were highly correlated (r=0.92, n=75, P<0.001) with ergosterol content of tempeh fermented with R. oligosporus and lactic acid bacteria (LAB). In the second method, an image-processing algorithm optimized to changes in images of final tempeh products was developed to measure number of visible barley grains. A threshold of 5 visible grains per Petri dish indicated complete tempeh fermentation. When images of tempeh cakes fermented with different inoculation levels of R. oligosporus were analysed the results from the two image processing methods were in good agreement. CONCLUSION: Image processing proved suitable for monitoring barley tempeh fermentation. The method avoids sampling, is nonintrusive, and only requires a digital camera with good resolution and image analysis software. SIGNIFICANCE AND IMPACT OF THE STUDY: The system provides a rapid visualization of tempeh product maturation and qualities during fermentation. Automated online monitoring of tempeh fermentation by coupling automated image acquisition with image processing software could be further developed for process control.     

Segmentation of complex cell clusters in microscopic images: application to bone marrow samples.

BACKGROUND: Morphologic examination of bone marrow and peripheral blood samples continues to be the cornerstone in diagnostic hematology. In recent years, interest in automatic leukocyte classification using image analysis has increased rapidly. Such systems collect a series of images in which each cell must be segmented accurately to be classified correctly. Although segmentation algorithms have been developed for sparse cells in peripheral blood, the problem of segmenting the complex cell clusters characterizing bone marrow images is harder and has not been addressed previously. METHODS: We present a novel algorithm for segmenting clusters of any number of densely packed cells. The algorithm first oversegments the image into cell subparts. These parts are then assembled into complete cells by solving a combinatorial optimization problem in an efficient way. RESULTS: Our experimental results show that the algorithm succeeds in correctly segmenting densely clustered leukocytes in bone marrow images. CONCLUSIONS: The presented algorithm enables image analysis-based analysis of bone marrow samples for the first time and may also be adopted for other digital cytometric applications where separation of complex cell clusters is required.     

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。     

Design and implementation of algorithms for focus automation in digital imaging time-lapse microscopy

BACKGROUND: Digital time-lapse microscopy using Nomarski-DIC requires that an autofocusing system adapt to changes in cell shape, size, and position while contending with drift, noise, and hysteresis in the microscope and imager. We have designed and implemented an autofocusing system that tracks subjects under dynamic conditions and maintains focus within a threshold of discriminability. METHODS: With the use of proven and novel algorithms for autofocusing in Nomarski, we performed "virtual" experiments on recorded image stacks to simulate drift and sudden displacements and test the search algorithm response. RESULTS: We found that combining a simple [1, -1] contrast function with an adaptive "warmer-colder" focusing algorithm yields a reasonable compromise between focusing precision and noise tolerance. This method was implemented to record growth kinetics of yeast cells in single and multiple fields of view over several hours. CONCLUSIONS: We have implemented a robust digital autofocus that maintains focus on optically complex samples imaged at high resolution. The tolerance of this system of drift and vibration suggests that it is a practical system for time-lapse imaging in many biological applications.     

Higuchi dimension of digital images

There exist several methods for calculating the fractal dimension of objects represented as 2D digital images. For example, Box counting, Minkowski dilation or Fourier analysis can be employed. However, there appear to be some limitations. It is not possible to calculate only the fractal dimension of an irregular region of interest in an image or to perform the calculations in a particular direction along a line on an arbitrary angle through the image. The calculations must be made for the whole image. In this paper, a new method to overcome these limitations is proposed. 2D images are appropriately prepared in order to apply 1D signal analyses, originally developed to investigate nonlinear time series. The Higuchi dimension of these 1D signals is calculated using Higuchi's algorithm, and it is shown that both regions of interests and directional dependencies can be evaluated independently of the whole picture. A thorough validation of the proposed technique and a comparison of the new method to the Fourier dimension, a common two dimensional method for digital images, are given. The main result is that Higuchi's algorithm allows a direction dependent as well as direction independent analysis. Actual values for the fractal dimensions are reliable and an effective treatment of regions of interests is possible. Moreover, the proposed method is not restricted to Higuchi's algorithm, as any 1D method of analysis, can be applied.     

Thin-section ratiometric Ca2+ images obtained by optical sectioning of fura-2 loaded mast cells

The availability of the ratiometric Ca2+ indicator dyes, fura-2, and indo-1, and advances in digital imaging and computer technology have made it possible to detect Ca2+ changes in single cells with high temporal and spatial resolution. However, the optical properties of the conventional epifluorescence microscope do not produce a perfect image of the specimen. Instead, the observed image is a spatial low pass filtered version of the object and is contaminated with out of focus information. As a result, the image has reduced contrast and an increased depth of field. This problem is especially important for measurements of localized Ca2+ concentrations. One solution to this problem is to use a scanning confocal microscope which only detects in focus information, but this approach has several disadvantages for low light fluorescence measurements in living cells. An alternative approach is to use digital image processing and a deblurring algorithm to remove the out of focus information by using a knowledge of the point spread function of the microscope. All of these algorithms require a stack of two-dimensional images taken at different focal planes, although the "nearest neighbor deblurring" algorithm only requires one image above and below the image plane. We have used a modification of this scheme to construct a simple inverse filter, which extracts optical sections comparable to those of the nearest neighbors scheme, but without the need for adjacent image sections. We have used this "no neighbors" processing scheme to deblur images of fura-2-loaded mast cells from beige mice and generate high resolution ratiometric Ca2+ images of thin sections through the cell. The shallow depth of field of these images is demonstrated by taking pairs of images at different focal planes, 0.5-microns apart. The secretory granules, which exclude the fura-2, appear in focus in all sections and distinct changes in their size and shape can be seen in adjacent sections. In addition, we show, with the aid of model objects, how the combination of inverse filtering and ratiometric imaging corrects for some of the inherent limitations of using an inverse filter and can be used for quantitative measurements of localized Ca2+ gradients. With this technique, we can observe Ca2+ transients in narrow regions of cytosol between the secretory granules and plasma membrane that can be less than 0.5-microns wide. Moreover, these Ca2+ increases can be seen to coincide with the swelling of the secretory granules that follows exocytotic fusion.     

Development of a digital image analysis method for real-time estimation of chlorophyll content in micropropagated potato plants

The present work describes a digital image analysis method based on leaf color analysis to estimate chlorophyll content of leaves of micropropagated potato plantlets. For estimation of chlorophyll content, a simple leaf digital analysis procedure using a simple digital still camera was applied in parallel to a SPAD chlorophyll content meter. RGB features were extracted from the image and correlated with the SPAD values. None of the mean brightness parameters (RGB) were correlated with the actual chlorophyll content following simple correlation studies. However, a correlation between the chromaticity co-ordinates ‘r’, ‘b’ and chlorophyll content was observed, while co-ordinate ‘g’ was not significantly correlated with chlorophyll content. Linear regression and artificial neural networks (ANN) were applied for correlating the mean brightness (RGB) and mean brightness ratio (rgb) features to chlorophyll content of plantlet leaves determined through a SPAD meter. The chlorophyll content as determined by the SPAD meter was significantly correlated (RMSE = 3.97 and 3.59, respectively, for linear and ANN models) to the rgb values of leaf image analysis. Both the models indicate successful prediction of chlorophyll content of leaves of micropropagated plants with high correlation. The developed RGB-based digital image analysis has the advantage over conventional subjective methods for being objective, fast, non-invasive, and inexpensive. The system could be utilized for real-time estimation of chlorophyll content and subsequent analysis of photosynthetic and hyperhydric status of the micropropagated plants for better ex vitro survival.     

温室白粉虱自动计数技术研究初报

应用计算机视觉技术对温室白粉虱自动计数技术进行了研究。采用胶卷照相机和家用摄像机对田间温室白粉虱等生的叶片进行拍摄,以获得其数字图象,对白粉虱图象的分割采用Ｊohannsen基于熵的分割算法,对分割后的二值图象利用区域标记算法得到白粉虱个体的数量。对叶片挨在一起的白粉虱个体采用数学形态学算法进行了分离。用１９个虫叶片样本的统计结果表明,直接利用分割图象进行白粉虱个体计数的累积准确率达９１．９９％,而分离处理的算法则需要改进,因此,这一技术具有进一步在生态研究和ＩＰＭ实践中推广的可能性,这将使田间微小昆虫的种群数量监测和测查的工作量大幅度降低,而铉得到显著提高。     

Three-Dimensional (3D) Tumor Spheroid Invasion Assay

Invasion of surrounding normal tissues is generally considered to be a key hallmark of malignant (as opposed to benign) tumors. For some cancers in particular (e.g., brain tumors such as glioblastoma multiforme and squamous cell carcinoma of the head and neck – SCCHN) it is a cause of severe morbidity and can be life-threatening even in the absence of distant metastases. In addition, cancers which have relapsed following treatment unfortunately often present with a more aggressive phenotype. Therefore, there is an opportunity to target the process of invasion to provide novel therapies that could be complementary to standard anti-proliferative agents. Until now, this strategy has been hampered by the lack of robust, reproducible assays suitable for a detailed analysis of invasion and for drug screening. Here we provide a simple micro-plate method (based on uniform, self-assembling 3D tumor spheroids) which has great potential for such studies. We exemplify the assay platform using a human glioblastoma cell line and also an SCCHN model where the development of resistance against targeted epidermal growth factor receptor (EGFR) inhibitors is associated with enhanced matrix-invasive potential. We also provide two alternative methods of semi-automated quantification: one using an imaging cytometer and a second which simply requires standard microscopy and image capture with digital image analysis.     

Enabling user-guided segmentation and tracking of surface-labeled cells in time-lapse image sets of living tissues

To study the process of morphogenesis, one often needs to collect and segment time-lapse images of living tissues to accurately track changing cellular morphology. This task typically involves segmenting and tracking tens to hundreds of individual cells over hundreds of image frames, a scale that would certainly benefit from automated routines; however, any automated routine would need to reliably handle a large number of sporadic, and yet typical problems (e.g., illumination inconsistency, photobleaching, rapid cell motions, and drift of focus or of cells moving through the imaging plane). Here, we present a segmentation and cell tracking approach based on the premise that users know their data best-interpreting and using image features that are not accounted for in any a priori algorithm design. We have developed a program, SeedWater Segmenter, that combines a parameter-less and fast automated watershed algorithm with a suite of manual intervention tools that enables users with little to no specialized knowledge of image processing to efficiently segment images with near-perfect accuracy based on simple user interactions.     

Cell and tissue deformation measurements: Texture correlation with third-order approximation of displacement gradients

Cells remarkably are capable of large deformations during motility and when subjected to mechanical force. Measurement of mechanical deformation (i.e. displacements, strain) is critical to understand functional changes in cells and biological tissues following disease, and to elucidate basic relationships between applied force and cellular biosynthesis. Microscopy-based imaging modalities provide the ability to noninvasively visualize small cell or tissue structures and track their motion over time, often using two-dimensional (2D) digital image (texture) correlation algorithms. For the measurement of complex and nonlinear motion in cells and tissues, implementation of texture correlation algorithms with high order approximations of displacement mapping terms are needed to minimize error. Here, we extend a texture correlation algorithm with up to third-order approximation of displacement mapping terms for the measurement of cell and tissue deformation. We additionally investigate relationships between measurement error and image texture, defined by subset entropy. Displacement measurement error is significantly reduced when the order of displacement mapping terms in the texture correlation algorithm matches or exceeds the order of the deformation observed. Displacement measurement error is also inversely proportional to subset entropy, with well-defined cell and tissue structures leading to high entropy and low error. For cell and tissue studies where complex or nonlinear displacements are expected, texture correlation algorithms with high order terms are required to best characterize the observed deformation.     

Digital image analysis of AgNORs in cervical smears of women with premalignant and malignant lesions of the uterine cervix

We performed a hospital-based, unmatched case-control study to investigate the association between progressive stages of cervical neoplasia and digital analysis of cell proliferation by silver stained nucleolus organizer region associated proteins (AgNORs). We measured cell proliferation levels in the cervical epithelial cells of 10 women with low grade squamous intraepithelial lesions (LG-SIL), eight with high grade squamous intraepithelial lesions (HG-SIL), 11 with cervical cancer (CC) and eight with no cervical lesions (controls) using the AgNORs technique. Cell proliferation was measured by digital image analysis (DIA). DIA revealed increased total areas of AgNORs in HG-SIL and CC compared to LG-SIL and control patients. AgNORs with a kidney or cluster shape exhibited greater areas than those with a spherical or long shape. We propose a cut-off of 118 pixels to differentiate benign (control and LG-SIL) from malignant (HG-SIL and CC) lesions. DIA of AgNORs is a simple and inexpensive method for studying proliferation. The increased total area of AgNORs in malignant lesions provides information regarding cell behavior and may be related to cervical carcinogenesis; however, further validation studies are required to establish its usefulness in cytological analysis.     

Movement correction of digital sequence angiographies of the retina]

Retinal hemodynamics can be quantified from videoangiographic image sequences by digital image processing. Intensity changes of dye dilution curves provide dynamics parameters of the local retinal blood flow. The measuring points of dye dilution curves have to be fixed on identical image contents in each image of a complete image sequence. To obtain measurements for every pixel on the retinal surface a motion-compensated image sequence is required. A new method adapted to the compensation of eye motion and movement artifacts in Scanning Laser Ophthalmoscopy in long image sequences (300-500 images) is presented in this paper. To inhibit error propagation of time sequential motion estimation, the eye movement is divided into two dynamic movements components. The method presented permits compensation for eye motion in retinal fluorescein angiographic sequences. Owing to the short calculation times, this algorithm can be used in clinical routine.     

A thresholding method for automatic cell image segmentation.

An algorithm for automatic segmentation of PAP-stained cell images and its digital implementation is described. First, the image is filtered in order to eliminate the granularily and small objects in the image which may upset the segmentation procedure. In a second step, information on gradient and compactness is extracted from the filtered image and stored in three histograms as functions of the extinction. From these histograms, two extinction thresholds are computed. These thresholds are suitable to separate the nucleus from the cytoplasm, and the cytoplasm from the background in the filtered image. Masks are determined in this way, and finally used to analyse the nucleus and the cytoplasm in the original image.     

Segmentation of the Clustered Cells with Optimized Boundary Detection in Negative Phase Contrast Images

Cell image segmentation plays a central role in numerous biology studies and clinical applications. As a result, the development of cell image segmentation algorithms with high robustness and accuracy is attracting more and more attention. In this study, an automated cell image segmentation algorithm is developed to get improved cell image segmentation with respect to cell boundary detection and segmentation of the clustered cells for all cells in the field of view in negative phase contrast images. A new method which combines the thresholding method and edge based active contour method was proposed to optimize cell boundary detection. In order to segment clustered cells, the geographic peaks of cell light intensity were utilized to detect numbers and locations of the clustered cells. In this paper, the working principles of the algorithms are described. The influence of parameters in cell boundary detection and the selection of the threshold value on the final segmentation results are investigated. At last, the proposed algorithm is applied to the negative phase contrast images from different experiments. The performance of the proposed method is evaluated. Results show that the proposed method can achieve optimized cell boundary detection and highly accurate segmentation for clustered cells.