Digital Image Processing

Jähne, Bernd

doi:10.1007/978-3-662-04781-1

Cited by 387 publications

(392 citation statements)

References 0 publications

Supporting

Mentioning

377

Contrasting

Unclassified

Order By: Relevance

“…where n is the number of pixels per image row, I i is pixel i of the image row I, and ı is the imaginary unit (see, e.g., Jähne 2002). As the distance measure we use the SSE applied to the Fourier amplitudes of corresponding rows j in the two images, a simple formulation would be…”

Section: Other Matched-filter Methodsmentioning

confidence: 99%

Local visual homing by matched-filter descent in image distances

Möller

Vardy

2006

Biol Cybern

View full text Add to dashboard Cite

In natural images, the distance measure between two images taken at different locations rises smoothly with increasing distance between the locations. This fact can be exploited for local visual homing where the task is to reach a goal location that is characterized by a snapshot image: descending in the image distance will lead the agent to the goal location. To compute an estimate of the spatial gradient in the distance measure, its value must be sampled at three noncollinear points. An animal or robot would have to insert exploratory movements into its home trajectory to collect these samples. Here we suggest a method based on the matched-filter concept that allows one to estimate the gradient without exploratory movements. Two matched filters -optical flow fields resulting from translatory movements in the horizontal plane -are used to predict two images in perpendicular directions from the current location. We investigate the relation to differential flow methods applied to the local homing problem and show that the matched-filter approach produces reliable homing behavior on image databases. Two alternative methods that only require a single matched filter are suggested. The matched-filter concept is also applied to derive a home-vector equation for a Fourier-based parameter method.

show abstract

Section: Other Matched-filter Methodsmentioning

confidence: 99%

Local visual homing by matched-filter descent in image distances

Möller

Vardy

2006

Biol Cybern

View full text Add to dashboard Cite

show abstract

“…These considerations made us consider image coherency as a possible quantitative measure for alterations in the actin cytoskeleton. Coherency is defined through the structure tensor, which evaluates the local orientation in a small region of an image (26). A strategy to extract the local orientationn is to assume that it deviates least from the gradient direction of the image gray values.…”

Section: Coherency As a Quantitative Measure For Structurementioning

confidence: 99%

“…In contrast to pattern recognition approaches like neural networks or vector-support machines based on motion-invariant image features like the Haralick texture features or Zernike moments (23)(24)(25), which are well-suited for classification of distinct subcellular phenotypes, the coherency measure appears to be particularly suited to characterize gradual changes in fibrous textures like the actin cytoskeleton. By its definition, coherency extracts the relative strength of the edges of structures compared to their surroundings (26). Thus, the parameter is a measure for the quality and amount of clear structures in an image.…”

mentioning

confidence: 99%

A quantitative measure for alterations in the actin cytoskeleton investigated with automated high‐throughput microscopy

et al. 2009

View full text Add to dashboard Cite

The actin cytoskeleton modulates a large variety of physiological and disease-related processes in the cell. For example, actin has been shown to be a crucial host factor for successful infection by HIV-1, but the underlying mechanistic details are still unknown. Automated approaches open up the perspective to clarify such an issue by processing many samples in a high-throughput manner. To analyze the alterations in the actin cytoskeleton within an automated setting, large-scale image acquisition and analysis were established for JC-53 cells stained for actin. As a quantitative measure in such an automated approach, we suggest a parameter called image coherency. We successfully benchmarked our analysis by calculating coherency for both a biophysical model of the actin cytoskeleton and for cells whose actin architecture had been disturbed pharmacologically by latrunculin B or cytochalasin D. We then tested the influence of HIV-1 infection on actin coherency, but observed no significant differences between uninfected and infected cells. ' 2009 International Society for Advancement of Cytometry

show abstract

“…Majority-set a pixel to 1 if five or more of its neighbors in a 3 3 3 area are 1. Edge detection-high-density areas are ''gated,'' using edge detection algorithms, such as sobel edge detection (12).…”

Section: Image Processingà àMorphological Operatorsmentioning

confidence: 99%

Analysis of flow cytometry data using an automatic processing tool

et al. 2008

View full text Add to dashboard Cite

In spite of recent advances in flow cytometry technology, most cytometry data is still analyzed manually which is labor-intensive for large datasets and prone to bias and inconsistency. We designed an automatic processing tool (APT) to rapidly and consistently define and describe cell populations across large datasets. Image processing, smoothing, and clustering algorithms were used to generate an expert system that automatically reproduces the functionality of commercial manual cytometry processing tools. The algorithms were developed using a dataset collected from CMV-infected infants and combined within a graphical user interface, to create the APT. The APT was used to identify regulatory T-cells in HIV-infected adults, based on expression of FOXP3. Results from the APT were compared directly with the manual analyses of five immunologists and showed close agreement, with a concordance correlation coefficient of 0.96 (95% CI 0.91-0.98). The APT was well accepted by users and able to process around 100 data files per hour. By applying consistent criteria to all data generated by a study, the APT can provide a level of objectivity that is difficult to match using conventional manual analysis. ' 2008 International Society for Advancement of Cytometry Key termsimmunophenotyping; clustering; population; T-cell SINCE the first experiments in identifying cell populations based on the fluorescence characteristics of individual cells were performed in the late 1960s (1), there has been a steady increase in both the scope and availability of this technology to the extent that the benchtop multiparametric flow cytometer is now a standard piece of equipment in an immunology laboratory. The essentials of immunophenotyping have been established for much of that time, as cells are stained with monoclonal antibodies conjugated to flurochromes with nonoverlapping emission peaks, and the intensity of fluorescence in each wavelength is detected (2). Recent years have seen a rapid expansion in the number of parameters that can be assessed on a single cell, and it is now possible to record the intensities of seventeen different flurochromes (3), although most laboratories are still using machines with considerably fewer detectors.While considerable advances have been made in the development of flurochromes and the means to detect them, developments in the tools for analysis of multiparametric data have not kept pace. After measuring fluorescence intensity, most systems output data to an attached computer where each parameter is recorded for each cell. Processing at this stage is based on a limited number of dedicated flow cytometry analysis packages. Nearly all analyses are dependent on the subjective assessment of the operator. An increasing number of studies involve the detection of many different populations and subpopulations in a large number of individuals who may be sampled over a considerable period of time. Under such circumstances, it is unlikely that an individual operator can consistently apply the same criter...

show abstract

Digital Image Processing

Cited by 387 publications

References 0 publications

Local visual homing by matched-filter descent in image distances

Local visual homing by matched-filter descent in image distances

A quantitative measure for alterations in the actin cytoskeleton investigated with automated high‐throughput microscopy

Analysis of flow cytometry data using an automatic processing tool

Contact Info

Product

Resources

About