Extended information about some Huygens commands

This article gathers wiki pages related with different Tcl Huygens commands. This information is used by the Help Browser of the Huygens Software. A full

File and export operations

open save

• Supported File Formats
• ICSfile Format

create

• Huygens Data Types
• 3D image

preOpen

• ICSfile Format

Image restoration operations

tm

Quick Tikhonov-Miller (QTM) The QTM Restoration Method is not a Non Linear Iterative Method, but an Inverse Filtering method, and is intended for very special circumstances only. It can produce much noise amplification. For an iterative and more stable alternative the Iterative Constrained Tikhonov Miller (ITCM) is recommended.

ictm

Iterative Constrained Tikhonov-Miller (ICTM) algorithm

For fluorescence and from a theoretical viewpoint Maximum Likelihood Estimation (MLE) Restoration Methods are the best choices. However, for high Signal To Noise Ratios (SNR) as in widefield images this becomes rather academic. The ICTM method in the Huygens Professional is computationally more efficient than the Classic MLE (CMLE) algorithm, but in most cases the Quick-MLE (QMLE) method is found to be still faster. Since QMLE is also good at handling high SNR widefield images, QMLE has superceded ICTM. For this reason the ICTM menu entry is now located under menu 'Restoration->Legacy->Iterative Tikhonov-Miller' in the Operations window.

As to the 'quality' of the result, that is either a subjective criterion based on visual inspection (ICTM might win in low noise cases) or it is based on a mathematically sound criterion in which case MLE is proven to be best choice. For noisy images like most confocal ones, MLE is not only scientifically more correct, it also produces visually more pleasing images: far less background noise artifacts than ICTM.

For further information, see the FAQ MLE vs ICTM - Which method is more effective under certain circumstances?.

ictm tm

See also...

• Restoration Method
• Doing Deconvolution

cmle

Maximum Likelihood Estimation

The iterative Classic Maximum Likelihood Estimation (CMLE) algorithm is a Restoration Method avaliable in the Huygens Software based on the idea of optimizing the likelihood of an estimate of the object given the measured image and the Point Spread Function (PSF). The object estimate is in the form of a regular 3D image. The likelihood in this procedure is computed by a Quality Criterion under the assumption that the Photon Noise is governed by Poisson statistics. For this reason it is optimally suited for low-signal images. In addition, it is well suited for restoring images of point- line- or plane like objects.

It is a Non Linear Iterative Method that allows the recovery of some lost information.

The CMLE method uses a Quality Criterion directly derived from concept of Maximum Likelihood: the I-divergence. It efficiently optimizes the quality criterion, and has the possibility to escape local minima which would lead to a wrong solution.

There are however situations in which other Restoration Methods come to front, for example when deconvolving 3D-Time Series, which is very computationally intensive. In this case you may consider to use Quick Maximum Likelihood Estimation-time (QMLE) which is much faster than the CMLE-time and will give excellent results as well.

The time-enabled restoration tools are able to restore 4D Multi Channel images, provided the time option is included in the License String. If the time option is not present the tools will still handle multi channel images.

qmle

Quick Maximum Likelihood Estimation algorithm

There are very compute-intensive situations, for example when deconvolving 3D-Time Series, in which faster Restoration Methods can be used. In these cases you may consider to use Quick Maximum Likelihood Estimation (QMLE) which is much faster than the Classic Maximum Likelihood Estimation (CMLE) and will give excellent results as well.

The QMLE is roughly five times more efficient than the CMLE while it takes also slightly less time per iteration. So ten QMLE iterations are equivalent to fifty iterations in CMLE.

While CMLE is superior in handling low Signal To Noise Ratio (SNR) data, like low light level confocal images, it is slower that QMLE. In principle CMLE with a Signal To Noise Ratio > 60 converges to the same result as QMLE gives you, but after many more iterations.

In short for good quality widefield images QMLE is the best choice.

The QMLE is available in Huygens Professional, and in the Batch Processor of the Huygens Essential.

MLEreport

• cmle
• qmle

cmle qmle MLEreport

See also...

• Restoration Parameters
• Set The Signal To Noise Ratio

• Doing Deconvolution
• Step by step in the wizard: Essential Deconvolution Roadmap
• Multiple tasks: Batch Processor

recpsf recpsfEasy

• Point Spread Function
• Experimental Psf
• Recording Beads
• Psf Distiller

genpsf genpsfExpl

• Point Spread Function
• Theoretical Psf

avgspheres

• Psf Distiller
• Recording Beads to acquire an experimental Point Spread Function

zdrift

• Basics: Zdrift Correction

eqsampling

• Sampling Density

fft optrep

• Fourier Transform
• Optical Transfer Function
• Optical Representation

pnoise

• Photon Noise
• Poisson Distribution

Reporting & display operations

hist hist2Ch

• Image Histogram
• Two Channel Histogram

miniMIP multiMIP persMIP depthMIP

• Maximum Intensity Projection
• Fast Mip interactive renderer

sfp topSfp

• Sfp Renderer

nyq

• Sampling Density
• Nyquist Rate
• UnderSampling and OverSampling

setp

• Microscopic Parameters

stat

• Standard Deviation

debug

• Debug Mode

getSysId

• System Id

Image manipulation operations

convert

• Huygens Data Types

convertDim convert2d23d convert3d22d convertZ2T convertT2Z convert3d24d convert4d23d

• Convert The Data Set
• Related commands:
  • convertDim
  • 2D → 3D convert2d23d
  • 3D → 2D convert3d22d
  • Z → time convertZ2T
  • time → Z convertT2Z
  • 3D → 4D (3D + time) convert3d24d

autoCrop

• Why cropping?
• Interactive Intelligent Cropper

getframe

• Time Series

join split

• Multi Channel
• Joining Channels

mir

You may need to mirror the image in the presence of Spherical Aberration. See Mismatch Distorts Psf.

resample zoom isosampling

• Sample Size and Sampling Density
• Nyquist Rate
• Aliasing Artifacts

eqflux

• Equalize Flux

sthres

• Soft Threshold

sclip

• Clipped Images

Image analysis operations

analyze

• Interactive Object Analyzer
• Object Analyzer Tutorial

label

• Six Connected

cooccurence

• Cooccurence Theory
• Related command: coloc.

coloc

Colocalization

Colocalization__ refers to different data analysis methods to characterize the degree of overlap between Two Channels in an image (conventionally called R and G channels, or red and green channels, independently of the WaveLength they have actually registered). A typical application in fluorescence microscopy is to study the presence of two labeled targets in the same region of a cell.

For an accessible introduction see Colocalization Coefficients and Colocalization Basics.

Mind that Blur And Noise Affect Colocalization.

See Colocalization Analyzer and Colocalization Theory for more details.

Object colocalization

Notice that the Object Analyzer in the Huygens Software also provides colocalization measurements at the object level. The Colocalization Analyzer works more at the level of the whole image, despite some local statistics of the colocalizing regions can be easily retrieved.

Both analyzers work, in this sense, in complementary ways.

• The Object Analyzer allows you to define objects (see Object Segmentation) and see how much they overlap, in volume or intensity. Objects defined like this can overlap with other objects, or not. You can filter out objects that do not colocalize at all.
• The Colocalization Analyzer explores the whole image to search for colocalizing regions based on the usual colocalization coefficients. These regions are then segmented and treated as objects to analyze. These objects are therefore always volumes of intersection.
• Interactive Colocalization Analyzer and its tutorial
• Background: Colocalization Theory
• Definitions: Colocalization Map
• Related command: cooccurence.

estbg

• Estimate Average Background

ratio

• Ratiometric Images

Set & replace operations

Emtpy

Arithmetic operations

Empty

Undo operations

Emtpy

Miscellaneous operations

getGamma setGamma

• Gamma Correction

adopt

• Microscopic Parameters

version

• Current Versions of the Huygens Software
• What's new?

product

• Huygens Software

license getLicPath

• License String
• License String Details

Useful macros

help

The help procedure opens a built-in html browser to give information about an optionally provided Tcl Huygens command, as in

help cmle

It also provides general information, like in a apropos command, if you use it like in

help numerical aperture

huOpt

Most of the Tcl Huygens commands apply to existing images, and therefore the syntax usually starts with an image name. The huOpt (Huygens Option) keyword is used for commands that are not related with a particular image but with the system in general, like

huOpt version

Mind the letter case!!! The O in huOpt is an upper case o.

See more examples in Miscellaneous operations.

huopt

You probably mean huOpt. Mind the letter case!!!

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