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Overview

Credits: SWS : 2+2, Credits 4+2

Participants: Min: 1 Max: 12 Expected: 6

Course type : Seminar

Language: english

Materialien

Description

The availability of automated microscopy and fast imaging methods in combination with fluorescent protein labeling produce a huge amount of n-dimensional image data. A manual analysis of this image data is very time-consuming and hardly reproducible. Therefore, an increasing demand for algorithms to quantify biological image data can be observed. Reliable algorithms for (semi-)automatic quantification of large-scale movie or single image data are required. Computational methods such as standard image processing or machine learning approaches are therefore investigated in this seminar course.

The seminar course is divided into two parts, theory and practice:

Theory

In a 25min talk the algorithm is presented and advantages/disadvantages in the field of scientific image processing are discussed.

Practice

The algorithm has to be implemented in Java, with the help of ImageJ-Ops, the KNIME–Image Processing framework and ImgLib2.

Literature

KNIME Image Processing Framework:
tech.knime.org/community/image-processing

ImgLib2:
github.com/imglib/imglib2
imglib2.net

ImageJ-Ops:
github.com/imagej/imagej-ops

Important

Registration via StudIS required.

Preliminary discussion of the topics: Fr 04/24/2015, 1:00 - 2:00 pm

Participating in https://lsf.uni-konstanz.de/qisserver/rds?state=verpublish&status=init&vmfile=no&publishid=48049&moduleCall=webInfo&publishConfFile=webInfo&publishSubDir=veranstaltung is mandatory