Cytometry Development Workshop
Carnegie Mellon University
Computational Biology Department
Center for Bioimage Informatics
Biological Sciences Department
Biomedical Engineering Department
Machine Learning Department
Murphy Lab -
Extracting and Classifying Fluorescence Microscope Images of Cells from Online Journals
Jie Yao, graduate student at the
Center for Automated Learning and Discovery
Meel Velliste, graduate student in
We are interested in creating a self-populating knowledge base that
can extract and store assertions about protein subcellular location
from published literature in an
automated manner. Such kind of knowledge base can serve not only as
a resource for biologists but also as a test bed for knowledge
reasoning systems that can generate new hypotheses under uncertainty.
As a starting point, we have developed an automated system to find
fluorescence microscope images from on-line journal articles.
Our system includes:
- web robot to download articles from PubMed matching a keyword
- tool to extract figures and captions from PDF files,
- algorithm for splitting figures into individual panels,
- program for distinguishing fluorescence microscope images from
other types of images,
- program to find scale information from the images and
- tool to remove annotations (such as characters and arrows) from
the fluorescence microscope images,
- segmentation program to isolate individual cells from images
containing multiple cells,
- classifier that can rank the returned images by their "likelyhood"
of belonging to a particular location class
Evaluation of each of the parts of this system revealed good precision (number
of correct results out of all results returned) and reasonable recall (number of
correct results out of all possible correct results). To demonstrate the usefulness
of the system a search was performed using the keyword "Tubulin". 8 out of the top
10 images returned were actually images of tubulin.
When combined with utilities that extract assertions from figure captions and body
text, this fully automated online image extractor will provide a truly useful tool
for harnessing the vast amounts of information about protein subcellular location
available in online journal articles.
R. F. Murphy, M. Velliste, J. Yao, and G. Porreca (2001). Searching Online Journals
for Fluorescence Microscope Images Depicting Protein Subcellular Location Patterns.
Proc IEEE Int Symp Bio-Informat Biomed Eng (BIBE 2001) 2; pp. 119-128.
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