A microspectrophotometric study of Papanicolaou-stained cervical cells as an aid in computerized image processing.

As part of a study of cytologic automation, microspectrophotometric investigation of Papanicolaou-stained cervical cells was performed, using a Leitz MPV-II scanning photometer connected to a PDP 8/F minicomputer. It was shown that the selection of one single wavelength may result in difficulties in detecting boundries between background and cytoplasm and/or between cytoplasm nucleus. A set of two wavelengths, 530 nm and 570 nm, were found to be optimal for the image processing of these cells.     

An iterative region-growing process for cell image segmentation based on local color similarity and global shape criteria

An image segmentation process was derived from an image model that assumed that cell images represent objects having characteristic relationships, limited shape properties and definite local color features. These assumptions allowed the design of a region-growing process in which the color features were used to iteratively aggregate image points in alternation with a test of the convexity of the aggregate obtained. The combination of both local and global criteria allowed the self-adaptation of the algorithm to segmentation difficulties and led to a self-assessment of the adequacy of the final segmentation result. The quality of the segmentation was evaluated by visual control of the match between cell images and the corresponding segmentation masks proposed by the algorithm. A comparison between this region-growing process and the conventional gray-level thresholding is illustrated. A field test involving 700 bone marrow cells, randomly selected from May-Grünwald-Giemsa-stained smears, allowed the evaluation of the efficiency, effectiveness and confidence of the algorithm: 96% of the cells were evaluated as correctly segmented by the algorithm's self-assessment of adequacy, with a 98% confidence. The principles of the other major segmentation algorithms are also reviewed.     

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 stained blood cell images measured at high scanning density with high magnification and high numerical aperture optics

In hematological morphology, it is necessary to resolve and analyze the smallest possible cellular details appearing in the light microscope. A prerequisite for computer-aided analysis of subtle morphological features is measuring the cells at a high scanning density with high magnification and high numerical aperture optics. Contrary to visual observations, the information content in a measured picture can be increased by setting the condensor's numerical aperture (NA) greater than the objective's NA. The complexity and heterogeneity of such cell images necessitate a new segmentation method that conserves the morphological information required in the subsequent image analysis, feature extraction, and cell classification. In our segmentation strategy, characteristic color difference thresholds for each nucleus and cytoplasm are combined with geometric operations, probability functions, and a cell model. All thresholds are repeatedly recalculated during the successive improvements of the image masks. None of the thresholds are fixed. This strategy segments blood cell images containing touching cells and large variations in staining, texture, size, and shape. Biological inconsistencies in the calculated cell masks are eliminated by comparing each mask with the cell model criteria integrated into the entire segmentation process. All 20,000 leukocyte images from 120 smears in our leukemia project were segmented with this method.     

Segmentation improvement through denoising of PET images with 3D-context modelling in wavelet domain

Positron emission tomography (PET) images have been incorporated into the radiotherapy process as a powerful tool to assist in the contouring of lesions, leading to the emergence of a broad spectrum of automatic segmentation schemes for PET images (PET-AS). However, not all proposed PET-AS algorithms take into consideration the previous steps of image preparation. PET image noise has been shown to be one of the most relevant affecting factors in segmentation tasks. This study demonstrates a nonlinear filtering method based on spatially adaptive wavelet shrinkage using three-dimensional context modelling that considers the correlation of each voxel with its neighbours. Using this noise reduction method, excellent edge conservation properties are obtained. To evaluate the influence in the segmentation schemes of this filter, it was compared with a set of Gaussian filters (the most conventional) and with two previously optimised edge-preserving filters. Five segmentation schemes were used (most commonly implemented in commercial software): fixed thresholding, adaptive thresholding, watershed, adaptive region growing and affinity propagation clustering. Segmentation results were evaluated using the Dice similarity coefficient and classification error. A simple metric was also included to improve the characterisation of the filters used for induced blurring evaluation, based on the measurement of the average edge width. The proposed noise reduction procedure improves the results of segmentation throughout the performed settings and was shown to be more stable in low-contrast and high-noise conditions. Thus, the capacity of the segmentation method is reinforced by the denoising plan used.     

A selective mapping algorithm for computer analysis of voided urine cell images

One of the fundamental targets of the automated image analysis of cytologic preparations is the reduction of computer classification errors due to cells or other objects that do not lend themselves to image segmentation or that have morphologic features that may mislead the cell classification schemes. In prior work from this laboratory, the achievement of this goal was attempted by hierarchical analysis of sequential microscopic objects at high resolution. This paper reports on the successful development and implementation of an automated "selective mapping algorithm" that selects cells at low power for further analysis and eliminates a large proportion of unwanted "objects." The algorithm classifies the objects and extracts appropriate features from a 256 X 240 digital image obtained via a 10 X planachromatic objective. The five-node binary tree classifier used in this triage is described. The algorithm was trained and tested initially on 501 visually classified microscopic "objects," resulting in a correct acceptance rate of 61.3% and correct rejection rate of 81.3%. The selective mapping algorithm was subsequently integrated into the video-based image analysis system constructed at the Montefiore Medical Center for the diagnostic evaluation of sediments of voided urine. The algorithm was then tested on ten cytocentrifuge preparations for a preliminary evaluation of its performance. Up to 100 "objects" per case were selected by the algorithm for further classification by the computer at high power. Of the 810 "objects" selected by the selective mapping algorithm, 344 (42.5%) were classified by the computer at high resolution as cells of diagnostic value ("WELL" cells) and 466 were rejected.(ABSTRACT TRUNCATED AT 250 WORDS)     

New Automated Single-Cell Technique for Segmentation and Quantitation of Lipid Droplets

Lipid droplets are the major organelle for intracellular storage of triglycerides and cholesterol esters. Various methods have been attempted for automated quantitation of fluorescently stained lipid droplets using either thresholding or watershed methods. We find that thresholding methods deal poorly with clusters of lipid droplets, whereas watershed methods require a smoothing step that must be optimized to remove image noise. We describe here a novel three-stage hybrid method for automated segmentation and quantitation of lipid droplets. In this method, objects are initially identified by thresholding. They are then tested for circularity to distinguish single lipid droplets from clusters. Clusters are subjected to a secondary watershed segmentation. We provide a characterization of this method in simulated images. Additionally, we apply this method to images of fixed cells containing stained lipid droplets and GFP-tagged proteins to provide a proof-of-principle that this method can be used for colocalization studies. The circularity measure can additionally prove useful for the identification of inappropriate segmentation in an automated way; for example, of non-cellular material. We will make the programs and source code available to the community under the Gnu Public License. We believe this technique will be of interest to cell biologists for light microscopic studies of lipid droplet biology.     

A direction‐selective local‐thresholding method,DSLT, in combination with a dye‐based method for automated three‐dimensional segmentation of cells and airspaces in developing leaves

Quantifying the anatomical data acquired from three‐dimensional (3D) images has become increasingly important in recent years. Visualization and image segmentation are essential for acquiring accurate and detailed anatomical data from images; however, plant tissues such as leaves are difficult to image by confocal or multi‐photon laser scanning microscopy because their airspaces generate optical aberrations. To overcome this problem, we established a staining method based on Nile Red in silicone‐oil solution. Our staining method enables color differentiation between lipid bilayer membranes and airspaces, while minimizing any damage to leaf development. By repeated applications of our staining method we performed time‐lapse imaging of a leaf over 5 days. To counteract the drastic decline in signal‐to‐noise ratio at greater tissue depths, we also developed a local thresholding method (direction‐selective local thresholding, DSLT) and an automated iterative segmentation algorithm. The segmentation algorithm uses the DSLT to extract the anatomical structures. Using the proposed methods, we accurately segmented 3D images of intact leaves to single‐cell resolution, and measured the airspace volumes in intact leaves.     

Segmentation of Fluorescence Microscopy Images for Quantitative Analysis of Cell Nuclear Architecture

Considerable advances in microscopy, biophysics, and cell biology have provided a wealth of imaging data describing the functional organization of the cell nucleus. Until recently, cell nuclear architecture has largely been assessed by subjective visual inspection of fluorescently labeled components imaged by the optical microscope. This approach is inadequate to fully quantify spatial associations, especially when the patterns are indistinct, irregular, or highly punctate. Accurate image processing techniques as well as statistical and computational tools are thus necessary to interpret this data if meaningful spatial-function relationships are to be established. Here, we have developed a thresholding algorithm, stable count thresholding (SCT), to segment nuclear compartments in confocal laser scanning microscopy image stacks to facilitate objective and quantitative analysis of the three-dimensional organization of these objects using formal statistical methods. We validate the efficacy and performance of the SCT algorithm using real images of immunofluorescently stained nuclear compartments and fluorescent beads as well as simulated images. In all three cases, the SCT algorithm delivers a segmentation that is far better than standard thresholding methods, and more importantly, is comparable to manual thresholding results. By applying the SCT algorithm and statistical analysis, we quantify the spatial configuration of promyelocytic leukemia nuclear bodies with respect to irregular-shaped SC35 domains. We show that the compartments are closer than expected under a null model for their spatial point distribution, and furthermore that their spatial association varies according to cell state. The methods reported are general and can readily be applied to quantify the spatial interactions of other nuclear compartments.     

A new high‐resolution computed tomography (CT) segmentation method for trabecular bone architectural analysis

In the last decade, high‐resolution computed tomography (CT) and microcomputed tomography (micro‐CT) have been increasingly used in anthropological studies and as a complement to traditional histological techniques. This is due in large part to the ability of CT techniques to nondestructively extract three‐dimensional representations of bone structures. Despite prior studies employing CT techniques, no completely reliable method of bone segmentation has been established. Accurate preprocessing of digital data is crucial for measurement accuracy, especially when subtle structures such as trabecular bone are investigated. The research presented here is a new, reproducible, accurate, and fully automated computerized segmentation method for high‐resolution CT datasets of fossil and recent cancellous bone: the Ray Casting Algorithm (RCA). We compare this technique with commonly used methods of image thresholding (i.e., the half‐maximum height protocol and the automatic, adaptive iterative thresholding procedure). While the quality of the input images is crucial for conventional image segmentation, the RCA method is robust regarding the signal to noise ratio, beam hardening, ring artifacts, and blurriness. Tests with data of extant and fossil material demonstrate the superior quality of RCA compared with conventional thresholding procedures, and emphasize the need for careful consideration of optimal CT scanning parameters. Am J Phys Anthropol 2009. © 2009 Wiley‐Liss, Inc.     

Automated method for the rapid and precise estimation of adherent cell culture characteristics from phase contrast microscopy images

The quantitative determination of key adherent cell culture characteristics such as confluency, morphology, and cell density is necessary for the evaluation of experimental outcomes and to provide a suitable basis for the establishment of robust cell culture protocols. Automated processing of images acquired using phase contrast microscopy (PCM), an imaging modality widely used for the visual inspection of adherent cell cultures, could enable the non‐invasive determination of these characteristics. We present an image‐processing approach that accurately detects cellular objects in PCM images through a combination of local contrast thresholding and post hoc correction of halo artifacts. The method was thoroughly validated using a variety of cell lines, microscope models and imaging conditions, demonstrating consistently high segmentation performance in all cases and very short processing times (<1 s per 1,208 × 960 pixels image). Based on the high segmentation performance, it was possible to precisely determine culture confluency, cell density, and the morphology of cellular objects, demonstrating the wide applicability of our algorithm for typical microscopy image processing pipelines. Furthermore, PCM image segmentation was used to facilitate the interpretation and analysis of fluorescence microscopy data, enabling the determination of temporal and spatial expression patterns of a fluorescent reporter. We created a software toolbox (PHANTAST) that bundles all the algorithms and provides an easy to use graphical user interface. Source‐code for MATLAB and ImageJ is freely available under a permissive open‐source license. Biotechnol. Bioeng. 2014;111: 504–517. © 2013 Wiley Periodicals, Inc.     

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.     

Automated image detection and segmentation in blood smears.

A simple technique which automatically detects and then segments nucleated cells in Wright's giemsa-stained blood smears is presented. Our method differs from others in 1) the simplicity of our algorithms; 2) inclusion of touching (as well as nontouching) cells; and 3) use of these algorithms to segment as well as to detect nucleated cells employing conventionally prepared smears. Our method involves: 1) acquisition of spectral images; 2) preprocessing the acquired images; 3) detection of single and touching cells in the scene; 4) segmentation of the cells into nuclear and cytoplasmic regions; and 5) postprocessing of the segmented regions. The first two steps of this algorithm are employed to obtain high-quality images, to remove random noise, and to correct aberration and shading effects. Spectral information of the image is used in step 3 to segment the nucleated cells from the rest of the scene. Using the initial cell masks, nucleated cells which are just touching are detected and separated. Simple features are then extracted and conditions applied such that single nucleated cells are finally selected. In step 4, the intensity variations of the cells are then used to segment the nucleus from the cytoplasm. The success rate in segmenting the nucleated cells is between 81 and 93%. The major errors in segmentation of the nucleus and the cytoplasm in the recognized nucleated cells are 3.5% and 2.2%, respectively.     

The influence of Romanowsky-Giemsa type stains on nuclear and cytoplasmic features of cytological specimens

The aim of the present study was to compare the staining pattern of the standard azure B-eosin Y stain with commercial May-Grünwald-Giemsa (MGG) stains on cytological specimens by means of high resolution image analysis. Several cytological specimens (blood smears, abdominal serous effusions, bronchial scrape material) were air dried, methanol fixed and stained with the standard azure B-eosin Y stain and with commercial May-Grünwald-Giemsa stains. Integrated optical density (IOD) and colour intensities of cell nuclei and cytoplasm were measured with the IBAS 2000 image analyser. Commercial MGG stains gave much higher coefficients of variation for all parameters than the standard stain. Reproducibility of cell nuclei segmentation versus cytoplasm was significantly better for the standard stain. Contamination of the standard stain with methylene blue partly copied the staining pattern of commercial stains. The standard azure B-eosin Y stain is recommended for high resolution image analysis (HRIA) of cytological samples.     

Segmentation of microscopic cell scenes

Different methods for the automated segmentation of microscopic cell scenes are presented with examples. The techniques discussed include edge detection by thresholding, "blob" detection by split-and-merge algorithm, global thresholding using gray-level histograms, hierarchic thresholding using color information, global thresholding using two-dimensional histograms and segmentation by "blob" labeling. Methods are more robust against insignificant changes in the scene and perform more reliably as more a priori knowledge about the scene is incorporated in the segmentation algorithm. The inclusion of both photometric and geometric a priori knowledge can result in a high level of correct segmentations, the cost of which is increased computation time.     

N2分子激光泵浦染料激光对小鼠NK细胞活性和肿瘤生长的影响

N_2分子激光泵浦染料激光(简称N_2染料激光)照射正常小鼠脾区后检测脾指数及脾细胞NK活性(比色沉淀法)的变化。发现600nm波长的激光照射能积显著提高脾指数,增强NK细胞活性,570nm、530nm波长亦能增强NK活性,500nm时则起显著抑制作用,570nm波长对脾指数无影响,而530nm、500nm则极显著降低脾指数。接种艾氏腹水癌(EAG)的荷瘤小鼠经激光照射9、11、13天后瘤重显著减小,且荷瘤后增大的脾脏亦得以显著减小,NK活性显著增强。说明适当功率和适当照射时间的激光影响机体免疫功能是防治肿瘤的途径之一。     

Enhanced lateral resolution in continuous wave stimulated emission depletion microscopy using tightly focused annular radially polarized excitation beam

The lateral resolution of continuous wave (CW) stimulated emission depletion (STED) microscopy is enhanced about 12% by applying annular‐shaped amplitude modulation to the radially polarized excitation beam. A focused annularly filtered radially polarized excitation beam provides a more condensed point spread function (PSF), which contributes to enhance effective STED resolution of CW STED microscopy. Theoretical analysis shows that the FWHM of the effective PSF on the detection plane is smaller than for conventional CW STED. Simulation shows the donut‐shaped PSF of the depletion beam and confocal optics suppress undesired PSF sidelobes. Imaging experiments agree with the simulated resolution improvement.      

A Framework for White Blood Cell Segmentation in Microscopic Blood Images Using Digital Image Processing

Evaluation of blood smear is a commonly clinical test these days. Most of the time, the hematologists are interested on white blood cells (WBCs) only. Digital image processing techniques can help them in their analysis and diagnosis. For example, disease like acute leukemia is detected based on the amount and condition of the WBC. The main objective of this paper is to segment the WBC to its two dominant elements: nucleus and cytoplasm. The segmentation is conducted using a proposed segmentation framework that consists of an integration of several digital image processing algorithms. Twenty microscopic blood images were tested, and the proposed framework managed to obtain 92% accuracy for nucleus segmentation and 78% for cytoplasm segmentation. The results indicate that the proposed framework is able to extract the nucleus and cytoplasm region in a WBC image sample.     

3-D image analysis system and megakaryocyte quantitation

A quasi-automatic computer image analysis system has been developed for 3-D reconstruction of stained serial sections and implemented on an IBAS system. Some new automatic image analysis techniques have been designed and incorporated into the system. For image segmentation, a transition region determination based thresholding method is introduced. Neither histogram calculation nor empirical parameters are needed in the automatic threshold selection. A two step 3-D reconstruction procedure--symbolic and pictorial reconstructions--is designed to improve the flexibility and the computational capability of the system. The global level registration and local level registration are separated. The former consists of establishing the relationship among a large numbers of profile pairs dispersed in adjacent sections. A pattern matching method based on pattern recognition principles is devised to exploit the information about the statistical character of mismatch caused by deformation of sections and about the relationship of nearby objects. For the latter, an equivalent elliptical approximation method based on the physical theory of the rotation of rigid bodies is proposed. The system has been used for 3-D reconstruction and quantitation of megakaryocytes in human bone marrow tissue. Features about individual 3-D megakaryocyte cell and the spatial distribution of megakaryocytes are determined. The latter is a new contribution to megakaryocyte quantitation and is not possible by using conventional stereologic techniques. These experimental results have demonstrated the ability of the system to perform quantitative analysis.