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Learning the Topological Properties of Brain Tumors
July-September 2005 (vol. 2 no. 3)
pp. 262-270
ASCII Text
x 
 
Cigdem Demir, S. Humayun Gultekin, B? Yener, "Learning the Topological Properties of Brain Tumors," IEEE/ACM Transactions on Computational Biology and Bioinformatics, vol. 2, no. 3, pp. 262-270, July-September, 2005.
 
 
BibTex
x
 
@article{ 10.1109/TCBB.2005.42,
author = {Cigdem Demir and S. Humayun Gultekin and B? Yener},
title = {Learning the Topological Properties of Brain Tumors},
journal ={IEEE/ACM Transactions on Computational Biology and Bioinformatics},
volume = {2},
number = {3},
issn = {1545-5963},
year = {2005},
pages = {262-270},
doi = {http://doi.ieeecomputersociety.org/10.1109/TCBB.2005.42},
publisher = {IEEE Computer Society},
address = {Los Alamitos, CA, USA},
}
 
 
RefWorks Procite/RefMan/Endnote
x 
 
TY - JOUR
JO - IEEE/ACM Transactions on Computational Biology and Bioinformatics
TI - Learning the Topological Properties of Brain Tumors
IS - 3
SN - 1545-5963
SP262
EP270
EPD - 262-270
A1 - Cigdem Demir,
A1 - S. Humayun Gultekin,
A1 - B? Yener,
PY - 2005
KW - Index Terms- Image representation
KW - machine learning
KW - model development
KW - graph theory
KW - medical information systems.
VL - 2
JA - IEEE/ACM Transactions on Computational Biology and Bioinformatics
ER -
 
This work presents a graph-based representation (a.k.a., cell-graph) of histopathological images for automated cancer diagnosis by probabilistically assigning a link between a pair of cells (or cell clusters). Since the node set of a cell-graph can include a cluster of cells as well as individual ones, it enables working with low-cost, low-magnification photomicrographs. The contributions of this work are twofold. First, it is shown that without establishing a pairwise spatial relation between the cells (i.e., the edges of a cell-graph), neither the spatial distribution of the cells nor the texture analysis of the images yields accurate results for tissue level diagnosis of brain cancer called malignant glioma. Second, this work defines a set of global metrics by processing the entire cell-graph to capture tissue level information coded into the histopathological images. In this work, the results are obtained on the photomicrographs of 646 archival brain biopsy samples of 60 different patients. It is shown that the global metrics of cell-graphs distinguish cancerous tissues from noncancerous ones with high accuracy (at least 99 percent accuracy for healthy tissues with lower cellular density level, and at least 92 percent accuracy for benign tissues with similar high cellular density level such as nonneoplastic reactive/inflammatory conditions).

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Index Terms:
Index Terms- Image representation, machine learning, model development, graph theory, medical information systems.
Citation:
Cigdem Demir, S. Humayun Gultekin, B? Yener, "Learning the Topological Properties of Brain Tumors," IEEE/ACM Transactions on Computational Biology and Bioinformatics, vol. 2, no. 3, pp. 262-270, July-Sept. 2005, doi:10.1109/TCBB.2005.42
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