Image-based Metrics Research Articles

An Imaging Flow Cytometry-based approach to analyse the fission yeast cell cycle in fixed cells

Fission yeast (Schizosaccharomyces pombe) is an excellent model organism for studying eukaryotic cell division because many of the underlying principles and key regulators of cell cycle biology are conserved from yeast to humans. As such it can be employed as tool for understanding complex human diseases that arise from dis-regulation in cell cycle controls, including cancers. Conventional Flow Cytometry (CFC) is a high-throughput, multi-parameter, fluorescence-based single cell analysis technology. It is widely used for studying the mammalian cell cycle both in the context of the normal and disease states by measuring changes in DNA content during the transition through G1, S and G2/M using fluorescent DNA-binding dyes. Unfortunately analysis of the fission yeast cell cycle by CFC is not straightforward because, unlike mammalian cells, cytokinesis occurs after S-phase meaning that bi-nucleated G1 cells have the same DNA content as mono-nucleated G2 cells and cannot be distinguished using total integrated fluorescence (pulse area). It has been elegantly shown that the width of the DNA pulse can be used to distinguish G2 cells with a single 2C foci versus G1 cells with two 1C foci, however the accuracy of this measurement is dependent on the orientation of the cell as it traverses the laser beam. To this end we sought to improve the accuracy of the fission yeast cell cycle analysis and have developed an Imaging Flow Cytometry (IFC)-based method that is able to preserve the high throughput, objective analysis afforded by CFC in combination with the spatial and morphometric information provide by microscopy. We have been able to derive an analysis framework for subdividing the yeast cell cycle that is based on intensiometric and morphometric measurements and is thus robust against orientation-based miss-classification. In addition we can employ image-based metrics to define populations of septated/bi-nucleated cells and measure cellular dimensions. To our knowledge, this is the first use of IFC to study fission yeast and we are confident that this will provide a springboard for further IFC-based analysis across all aspects of fission yeast biology.

Uncertainty in Measurements of 18F Blood Concentration and Its Effect on Simplified Dynamic PET Analysis

The purpose of this study was to assess the accuracy and practicality of well counter- and thyroid probe-based methods, commonly available in nuclear medicine facilities, for measuring the concentration of (18)F-FDG in blood samples. The degree to which the accuracy of such methods influences quantitative analysis of dynamic PET scans was also assessed. Thirty-five patients with cancer of the head and neck underwent dynamic PET imaging as part of a study intended to evaluate the utility of quantitative, image-based metrics for assessment of early treatment response. The activity in blood samples from the patients, necessary to provide an estimate of the input function for quantitative analysis, was measured both using a thyroid probe and using a well counter. Three calibration techniques were compared: single-point calibration using a standard solution for the thyroid probe (ProbePoint technique), single-point calibration using a standard solution for the well counter (WellPoint technique), and multiple-point calibration over the full range of expected blood activities for the well counter (WellCurve technique). The WellCurve method was assumed to provide the most accurate estimate of blood activity. The precision of measuring blood volume using a micropipette was also evaluated by obtaining multiple blood samples. Simplified-kinetic-analysis multiple-time-point (SKA-M) uptake rates for the primary tumor were calculated for all 35 patients using PET images and each of the 3 methods for assessing blood concentration. Errors in blood activity measurements ranging from -9.5% to 7.6% were found using the ProbePoint method, whereas the error range was much less (from -1.3% to 0.9%) for the WellPoint method. The precision in blood volume measurements ranged from -6% to 12% in the 10 patients assessed. The errors in blood activity and volume measurements were reflected in the SKA-M measurements in the same range. The WellPoint method provides a compromise between accuracy and clinical practicality. Random errors in both blood activity and volume measurements accumulate and may compromise parameters--such as the SKA-M estimate of tumor uptake rate--that depend not only on images but also on blood concentration data.

Biomass density and filament length synergistically affect activated sludge settling: Systematic quantification and modeling

Settling of the biomass produced during biological treatment of wastewater is a critical and often problematic process. Filamentous bacteria content is the best-known factor affecting biomass settleability in activated sludge wastewater treatment systems, and varying biomass density has recently been shown to play an important role as well. The objective of this study was to systematically determine how filament content and biomass density combine to affect microbial biomass settling, with a focus on density variations over the range found in full-scale systems. A laboratory-scale bioreactor system was operated to produce biomass with a range of filamentous bacterium contents. Biomass density was systematically varied in samples from this system by addition of synthetic microspheres to allow separation of filament content and density effects on settleability. Fluorescent in-situ hybridization indicated that the culture was dominated by Sphaerotilus natans, a common contributor to poor settling in full-scale systems. A simple, image-based metric of filament content (filament length per floc area) was linearly correlated with the more commonly used filament length per dry biomass measurement. A non-linear, semi-empirical model of settleability as a function of filament content and density was developed and evaluated, providing a better understanding of how these two parameters combine to affect settleability. Filament content (length per dry biomass weight) was nearly linearly related to sludge volume index (SVI) values, with a slightly decreasing differential, and biomass density exhibited an asymptotic relationship with SVI. The filament content associated with bulking was shown to be a function of biomass density. The marginal effect of filament content on settleability increased with decreasing biomass density (low density biomass was more sensitive to changes in filament content than was high density biomass), indicating a synergistic relationship between these factors. Consideration of both biomass density and filament content, as by the methods and model described herein, should improve operation and troubleshooting of settling processes for biological solids.

A Comparative Study of HRCT Image Metrics and PFT Values for Characterization of ILD and COPD

The aim of this study was to compare the performance of various image-based metrics computed from thoracic high-resolution computed tomography (HRCT) with data from pulmonary function testing (PFT) in characterizing interstitial lung disease (ILD) and chronic obstructive pulmonary disease (COPD). Fourteen patients with ILD and 11 with COPD had undergone both PFT and HRCT within 3 days. For each patient, 93 image-based metrics were computed, and their relationships with the 21 clinically used PFT parameters were analyzed using a minimal-redundancy-maximal-relevance statistical framework. The first 20 features were selected among the total of 114 mixed image metrics and PFT values in the characterization of ILD and COPD. Among the best-performing 20 features, 14 were image metrics, derived from attenuation histograms and texture descriptions. The highest relevance value computed from PFT parameters was 0.47, and the highest from image metrics was 0.52, given the theoretical bound as [0, 0.69]. The ILD or COPD classifier using the first four features achieved a 1.92% error rate. Some image metrics are not only as good discriminators as PFT for the characterization of ILD and COPD but are also not redundant when PFT values are provided. Image metrics of attenuation histogram statistics and texture descriptions may be valuable for further investigation in computer-assisted diagnosis.

Using Image-Based Metrics to Model Pedestrian Detection Performance With Night-Vision Systems

The primary purpose of night-vision systems in civilian vehicles is to help drivers detect pedestrians. Pedestrian detection distance with night-vision systems has been modeled based on image metrics. However, the probability of pedestrian detection, in particular considering the factor of distance, has not been modeled based on image metrics. In this paper, we first describe a model of the probability of pedestrian detection, which compares several combinations of image-based clutter, contrast, and pedestrian size metrics using a simple mathematical equation. Next, we describe a model of the probability of pedestrian detection as a function of distance and image-based metrics by combining the model of pedestrian-detection probability and a model that represents the relationship between the distance to a pedestrian and an image-based pedestrian size metric. In the final model, image-based metrics are used to predict pedestrian-detection performance and can also be used to evaluate and support the development of night-vision systems in vehicles.

Spatial and Radiometric Fidelity of Airborne Multispectral Imagery in the Context of Land-Cover Change Analyses

The goal was to quantitatively estimate and compare the fidelity of images acquired with a digital imaging system (ADAR 5500) and generated through scanning of color infrared aerial photographs (SCIRAP) using image-based metrics. Images were collected nearly simultaneously in two repetitive flights to generate multi-temporal datasets. Spatial fidelity of ADAR was lower than that of SCIRAP images. Radiometric noise was higher for SCIRAP than for ADAR images, even though noise from misregistration effects was lower. These results suggest that with careful control of film scanning, the overall fidelity of SCIRAP imagery can be comparable to that of digital multispectral camera data. Therefore, SCIRAP images can likely be used in conjunction with digital metric camera imagery in long-term landcover change analyses.

Image-based contrast-to-clutter modeling of detection

Abstract : Using image-based metrics, contrast-to-clutter modeling is applied to the Search-2 visible image set and perception experiment data. To calculate the contrast metric, a new image is generated from the original image by replacing the target with an expected using the local background surrounding the target and the natural horizontal correlation present in most surface-to-surface scenes. The contrast metric is obtained from the difference of this new image and the original image. Via a simple mathematical formula, the ratio of the contrast measure to a clutter metric is used to predict performance.

Image metrics in the statistical analysis of DNA microarray data.

DNA microarrays represent an important new method for determining the complete expression profile of a cell. In "spotted" microarrays, slides carrying spots of target DNA are hybridized to fluorescently labeled cDNA from experimental and control cells and the arrays are imaged at two or more wavelengths. In this paper, we perform statistical analysis on images of microarrays and show that quantitating the amount of fluorescent DNA bound to microarrays is subject to considerable uncertainty because of large and small-scale intensity fluctuations within spots, nonadditive background, and fabrication artifacts. Pixel-by-pixel analysis of individual spots can be used to estimate these sources of error and establish the precision and accuracy with which gene expression ratios are determined. Simple weighting schemes based on these estimates are effective in improving significantly the quality of microarray data as it accumulates in a multiexperiment database. We propose that error estimates from image-based metrics should be one component in an explicitly probabilistic scheme for the analysis of DNA microarray data.