Automated Image Analysis Algorithms Research Articles

Doubly stochastic Poisson distribution of platelet adhesion on material surfaces and its implication on fluorescence image analysis

An image based assay has been developed to quantify platelet adhesion on material surfaces. Briefly, citrated platelet rich plasma (PRP) is incubated with materials for 2 h to allow platelet adhesion on the surface, followed by fluorescence labeling of platelets with Celltracker Green. Multiple images are acquired by an automatic fluorescence microscope, IN Cell Analyzer 1000. Platelets are identified and counted by an automatic image analysis algorithm. We have observed that the variance of the counts is considerably greater than expected from simple distribution laws. Statistical analysis of that difference shows that these measurements will often follow a doubly stochastic Poisson process in which the variance is inherently very large. To overcome this, multiple images (n > or = 8 images/well, about 3% of total area) are necessary to achieve accurate counting. This method has been compared to the commonly used enzyme based platelet adhesion assay, lactate dehydrogenase (LDH) assay. It is concluded that the present method is only effective in quantifying adherent platelets when a large number of samples are used. However, this method does provide additional information on platelet morphology and spatial distribution, which is lacking in the LDH assay.

Nonlinear optical microscopy: use of second harmonic generation and two-photon microscopy for automated quantitative liver fibrosis studies

Liver fibrosis is associated with an abnormal increase in an extracellular matrix in chronic liver diseases. Quantitative characterization of fibrillar collagen in intact tissue is essential for both fibrosis studies and clinical applications. Commonly used methods, histological staining followed by either semiquantitative or computerized image analysis, have limited sensitivity, accuracy, and operator-dependent variations. The fibrillar collagen in sinusoids of normal livers could be observed through second-harmonic generation (SHG) microscopy. The two-photon excited fluorescence (TPEF) images, recorded simultaneously with SHG, clearly revealed the hepatocyte morphology. We have systematically optimized the parameters for the quantitative SHG/TPEF imaging of liver tissue and developed fully automated image analysis algorithms to extract the information of collagen changes and cell necrosis. Subtle changes in the distribution and amount of collagen and cell morphology are quantitatively characterized in SHG/TPEF images. By comparing to traditional staining, such as Masson's trichrome and Sirius red, SHG/TPEF is a sensitive quantitative tool for automated collagen characterization in liver tissue. Our system allows for enhanced detection and quantification of sinusoidal collagen fibers in fibrosis research and clinical diagnostics.

Automated image analysis of digital colposcopy for the detection of cervical neoplasia

Digital colposcopy is a promising technology for the detection of cervical intraepithelial neoplasia. Automated analysis of colposcopic images could provide an inexpensive alternative to existing screening tools. Our goal is to develop a diagnostic tool that can automatically identify neoplastic tissue from digital images. A multispectral digital colposcope (MDC) is used to acquire reflectance images of the cervix with white light before and after acetic-acid application in 29 patients. A diagnostic image analysis tool is developed to identify neoplasia in the digital images. The digital image analysis is performed in two steps. First, similar optical patterns are clustered together. Second, classification algorithms are used to determine the probability that these regions contain neoplastic tissue. The classification results of each patient's images are assessed relative to the gold standard of histopathology. Acetic acid induces changes in the intensity of reflected light as well as the ratio of green to red reflected light. These changes are used to differentiate high-grade squamous intraepithelial (HGSIL) and cancerous lesions from normal or low-grade squamous intraepithelial (LGSIL) tissue. We report diagnostic performance with a sensitivity of 79% and a specificity of 88%. We show that diagnostically useful digital images of the cervix can be obtained using a simple and inexpensive device, and that automated image analysis algorithms show a potential to identify histologically neoplastic tissue areas.

Bending Dynamics of Fluctuating Biopolymers Probed by Automated High-Resolution Filament Tracking

Microscope images of fluctuating biopolymers contain a wealth of information about their underlying mechanics and dynamics. However, successful extraction of this information requires precise localization of filament position and shape from thousands of noisy images. Here, we present careful measurements of the bending dynamics of filamentous (F-)actin and microtubules at thermal equilibrium with high spatial and temporal resolution using a new, simple but robust, automated image analysis algorithm with subpixel accuracy. We find that slender actin filaments have a persistence length of ∼17 μm, and display a q −4-dependent relaxation spectrum, as expected from viscous drag. Microtubules have a persistence length of several millimeters; interestingly, there is a small correlation between total microtubule length and rigidity, with shorter filaments appearing softer. However, we show that this correlation can arise, in principle, from intrinsic measurement noise that must be carefully considered. The dynamic behavior of the bending of microtubules also appears more complex than that of F-actin, reflecting their higher-order structure. These results emphasize both the power and limitations of light microscopy techniques for studying the mechanics and dynamics of biopolymers.

Measuring Size and Shape of the Hippocampus in MR Images Using a Deformable Shape Model

A method for segmentation and quantification of the shape and size of the hippocampus is proposed, based on an automated image analysis algorithm. The algorithm uses a deformable shape model to locate the hippocampus in magnetic resonance images and to determine a geometric representation of its boundary. The deformable model combines three types of information. First, it employs information about the geometric properties of the hippocampal boundary, from a local and relatively finer scale to a more global and relatively coarser scale. Second, the model includes a statistical characterization of normal shape variation across individuals, serving as prior knowledge to the algorithm. Third, the algorithm utilizes a number of manually defined boundary points, which can help guide the model deformation to the appropriate boundaries, wherever these boundaries are weak or not clearly defined in MR images. Excellent agreement is demonstrated between the algorithm and manual segmentations by well-trained raters, with a correlation coefficient equal to 0.97 and algorithm/rater differences statistically equivalent to interrater differences for manual definitions.

Segmentation of confocal microscope images of cell nuclei in thick tissue sections.

Segmentation of intact cell nuclei from three-dimensional (3D) images of thick tissue sections is an important basic capability necessary for many biological research studies. However, segmentation is often difficult because of the tight clustering of nuclei in many specimen types. We present a 3D segmentation approach that combines the recognition capabilities of the human visual system with the efficiency of automatic image analysis algorithms. The approach first uses automatic algorithms to separate the 3D image into regions of fluorescence-stained nuclei and unstained background. This includes a novel step, based on the Hough transform and an automatic focusing algorithm to estimate the size of nuclei. Then, using an interactive display, each nuclear region is shown to the analyst, who classifies it as either an individual nucleus, a cluster of multiple nuclei, partial nucleus or debris. Next, automatic image analysis based on morphological reconstruction and the watershed algorithm divides clusters into smaller objects, which are reclassified by the analyst. Once no more clusters remain, the analyst indicates which partial nuclei should be joined to form complete nuclei. The approach was assessed by calculating the fraction of correctly segmented nuclei for a variety of tissue types: Caenorhabditis elegans embryos (839 correct out of a total of 848), normal human skin (343/362), benign human breast tissue (492/525), a human breast cancer cell line grown as a xenograft in mice (425/479) and invasive human breast carcinoma (260/335). Furthermore, due to the analyst's involvement in the segmentation process, it is always known which nuclei in a population are correctly segmented and which not, assuming that the analyst's visual judgement is correct.

Differentiation of free and embedded porcine pancreatic islets using a novel automated image analysis algorithm

Differentiation of free and embedded porcine pancreatic islets using a novel automated image analysis algorithm

Differentiaiton of free and embedded porcine pancreatic islets using a novel automated image analysis algorithm

Differentiaiton of free and embedded porcine pancreatic islets using a novel automated image analysis algorithm