TY  - BOOK
AB  - In life science research bioimaging is often used to study two kinds of features in a sample simultaneously: morphology and co-location of molecular components. While
bioimaging technology is rapidly proposing and improving new multidimensional imaging platforms, bioimage informatics has to keep pace in order to develop algorithmic approaches to support biology experts in the complex task of data analysis. One particular problem is the availability and applicability of sophisticated image analysis algorithms via the web so different users can apply the same algorithms to their data (sometimes even to the same data to get the same results) and independently
from her/his whereabouts and from the technical features of
her/his computer. In this paper we describe TICAL, a visual
data mining approach to multivariate microscopy analysis which can be applied fully through the web.We describe the algorithmic approach, the software concept and present results obtained for different example images.
DA  - 2011
KW  - fluorescence microscopy
KW  - high-content screening
KW  - MALDI imaging
KW  - multi-tag imaging
KW  - MELC
KW  - TIS
KW  - visual datamining
KW  - visualization
KW  - machine learning
KW  - clustering
KW  - dimensional reduction
KW  - multivariate bioimage analysis
KW  - bioimage informatics
LA  - eng
PY  - 2011
TI  - TICAL - a web-tool for multivariate image clustering and data topology preserving visualization
UR  - https://nbn-resolving.org/urn:nbn:de:0070-pub-23237054
Y2  - 2024-11-22T01:20:14
ER  -