Knowledge Extraction for High-Throughput Biological Imaging

Author

Ahmed, Wamiq M. ; Ghafoor, Arif ; Robinson, J.Paul

Author_Institution

Purdue Univ., West Lafayette

Volume

14

Issue

4

fYear

2007

Firstpage

52

Lastpage

62

Abstract

We present a multilayered architecture and spatiotemporal models for searching, retrieving, and analyzing high-throughput biological imaging data. The analysis is divided into low-and high-level processing. At the lower level, we address issues like segmentation, tracking, and object recognition, and at the high level, we use finite state machine-and Petri-net-based models for spatiotemporal event recognition.

Keywords

Petri nets; biomedical imaging; finite state machines; image recognition; image segmentation; knowledge acquisition; Petri-net-based models; biological imaging; data analyzing; data retrieving; finite state machine; knowledge extraction; object recognition; spatiotemporal event recognition; spatiotemporal models; Biological system modeling; Clustering algorithms; Data analysis; Data mining; Drugs; Fluorescence; Image segmentation; Multimedia systems; Object recognition; Videos; Spatiotemporal modeling; and image understanding.; high-content screening; high-throughput imaging; semantic analysis;

fLanguage

English

Journal_Title

MultiMedia, IEEE

Publisher

ieee

ISSN

1070-986X

Type

jour

DOI

10.1109/MMUL.2007.77

Filename

4354157