An automated microscope for cytologic research a preliminary evaluation.

Brenner, John F.; Dew, Brock; Horton, John; King, Thomas C.; Neurath, Peter W.; Selles, W D

doi:10.1177/24.1.1254907

Cited by 224 publications

(139 citation statements)

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“…CDM and BGI were calculated using every second pixel scan line in x direction to increase processing speed. These features are similar to the focus algorithm described by Brenner et al (21) but differ in that they apply a threshold (T 5 20) for assessing large (CDM) and small (BGI) pixel intensity gradients. CDM was calculated as…”

Section: Image Processing Feature Extraction and Ann Designmentioning

confidence: 99%

“…Thus, most of the image pixels represent background ([98%) and the presence or absence of cells cannot be deduced by the MGV parameter. Therefore, the presence of cells was assessed by the CDM feature which is a custom modification of the Brenner Gradient algorithm (21). It quantifies gray level gradients by measuring the squared difference of the intensity of a pixel and its neighbor two pixel ahead on a scan line.…”

Section: Ann Input Datamentioning

confidence: 99%

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Automated quality assessment of autonomously acquired microscopic images of fluorescently stained bacteria

Zeder

Kohler

2009

Cytometry Pt A

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Quality assessment of autonomously acquired microscopic images is an important issue in high-throughput imaging systems. For example, the presence of low quality images (!10%) in a dataset significantly influences the counting precision of fluorescently stained bacterial cells. We present an approach based on an artificial neural network (ANN) to assess the quality of such images. Spatially invariant estimators were extracted as ANN input data from subdivided images by low level image processing. Different ANN designs were compared and [400 ANNs were trained and tested on a set of 25,000 manually classified images. The optimal ANN featured a correct identification rate of 94% (3% false positives, 3% false negatives) and could process about 10 images per second. We compared its performance with the image quality assessment by different humans and discuss the difficulties in assigning images to the correct quality class. The computer program and the documented source code (VB

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Section: Image Processing Feature Extraction and Ann Designmentioning

confidence: 99%

Section: Ann Input Datamentioning

confidence: 99%

Automated quality assessment of autonomously acquired microscopic images of fluorescently stained bacteria

Zeder

Kohler

2009

Cytometry Pt A

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“…An Abbott ADCdOO slide spinner and an Abbott ADC-500 slide stainer using a standard Giemsa stain were employed in slide presentation. Cells were digitized using the SCANIT automated microscope (2). Two sets of two interference filters each were used to obtain four digital images of every cell.…”

Section: Methodsmentioning

confidence: 99%

Spectral bandwidth in automated leukocyte classification

1986

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Investigators have repeatedly pointed out the importance of spectral information in the automated classification of white blood cells. In general, monochromatic images recorded through two or three color filters are used to extract this information. Although it has generally been thought that the use of narrow band filters provides "cleaner" color information than is obtainable through wide band filters, the choice has not been fully investigated and the question is far from being settled. The use of wide band filters has the clear practical advantage of increased light levels at the detector, resulting in higher signal-tonoise ratio with less demand on light source design.In order to investigate this issue, a series of 681 leukocytes of the most frequently occurring types were digitized by the use of both narrow (10 nm) and wide (90 nm) band filters. Parameters were extracted independently from both sets of images. These parameters were then used to develop a classifier for each set of images. The choice of features and classifier results indicate that there are no major performance differences between the two types of filters.

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“…Such methods are usually based on the calculation of a "sharpness function" (SF), which is a real-valued estimate of the image focus. Commonly used sharpness functions in literature have been based on image derivatives [45,6,32,12,47,40], statistics [13,20,46,35,39] and Fourier transforms [24,44].…”

Section: Review Of Passive Image Autofocusmentioning

confidence: 99%

The Weibull manifold in low-level image processing: An application to automatic image focusing

Zografos

Lenz

Felsberg

2013

Image and Vision Computing

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In this paper, we introduce a novel framework for low-level image processing and analysis. First, we process images with very simple, differencebased filter functions. Second, we fit the 2-parameter Weibull distribution to the filtered output. This maps each image to the 2D Weibull manifold. Third, we exploit the information geometry of this manifold and solve lowlevel image processing tasks as minimisation problems on point sets. For a proof-of-concept example, we examine the image autofocusing task. We propose appropriate cost functions together with a simple implicitly-constrained manifold optimisation algorithm and show that our framework compares very favourably against common autofocus methods from literature. In particular, our approach exhibits the best overall performance in terms of combined speed and accuracy.

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An automated microscope for cytologic research a preliminary evaluation.

Cited by 224 publications

References 1 publication

Automated quality assessment of autonomously acquired microscopic images of fluorescently stained bacteria

Automated quality assessment of autonomously acquired microscopic images of fluorescently stained bacteria

Spectral bandwidth in automated leukocyte classification

The Weibull manifold in low-level image processing: An application to automatic image focusing

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