Research Mission and Profile
Molecular Signal Processing
Bioorganic Chemistry
Biochemistry of Plant Interactions
Cell and Metabolic Biology
Independent Junior Research Groups
Program Center MetaCom
Publications
Good Scientific Practice
Research Funding
Networks and Collaborative Projects
Symposia and Colloquia
Alumni Research Groups
Books and chapters
For high-throughput screening of genetically modified plant cells, a system for the automatic analysis of huge collections of microscope images is needed to decide whether the cells are infected with fungi or not. To study the potential of feature based classification for this application, we compare different classifiers (kNN, SVM, MLP, LVQ) combined with several feature reduction techniques (PCA, LDA, Mutual Information, Fisher Discriminant Ratio, Recursive Feature Elimination). We achieve a significantly higher classification accuracy using a reduced feature vector instead of the full length feature vector.
This page was last modified on 27 Jan 2025 27 Jan 2025 .
