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2009 Symposium on Bio-inspired Learning and Intelligent Systems for Security
Histopathological Diagnostic Support Technology Using Higher-Order Local Autocorrelation Features
Edingburgh, United Kingdom
August 20-August 21
ISBN: 978-0-7695-3754-2
ASCII Text
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Hirokazu Nosato, Hidenori Sakanashi, Masahiro Murakawa, Tetsuya Higuchi, Nobuyuki Otsu, Kensuke Terai, Nobuyuki Hiruta, Noriaki Kameda, "Histopathological Diagnostic Support Technology Using Higher-Order Local Autocorrelation Features," 2009 Symposium on Bio-inspired Learning and Intelligent Systems for Security, pp. 61-65, 2009 Symposium on Bio-inspired Learning and Intelligent Systems for Security, 2009.
 
 
BibTex
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@article{ 10.1109/BLISS.2009.29,
author = {Hirokazu Nosato and Hidenori Sakanashi and Masahiro Murakawa and Tetsuya Higuchi and Nobuyuki Otsu and Kensuke Terai and Nobuyuki Hiruta and Noriaki Kameda},
title = {Histopathological Diagnostic Support Technology Using Higher-Order Local Autocorrelation Features},
journal ={2009 Symposium on Bio-inspired Learning and Intelligent Systems for Security},
volume = {0},
year = {2009},
isbn = {978-0-7695-3754-2},
pages = {61-65},
doi = {http://doi.ieeecomputersociety.org/10.1109/BLISS.2009.29},
publisher = {IEEE Computer Society},
address = {Los Alamitos, CA, USA},
}
 
 
RefWorks Procite/RefMan/Endnote
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TY - CONF
JO - 2009 Symposium on Bio-inspired Learning and Intelligent Systems for Security
TI - Histopathological Diagnostic Support Technology Using Higher-Order Local Autocorrelation Features
SN - 978-0-7695-3754-2
SP61
EP65
A1 - Hirokazu Nosato,
A1 - Hidenori Sakanashi,
A1 - Masahiro Murakawa,
A1 - Tetsuya Higuchi,
A1 - Nobuyuki Otsu,
A1 - Kensuke Terai,
A1 - Nobuyuki Hiruta,
A1 - Noriaki Kameda,
PY - 2009
KW - HLAC
KW - histopathological diagnosis
KW - cancer
KW - biopsy
KW - automatic screening
VL - 0
JA - 2009 Symposium on Bio-inspired Learning and Intelligent Systems for Security
ER -
 
This paper proposes a technology for histopathological diagnostic support that utilizes the correlation-based features of histopathological tissues. In histopathological diagnosis, a clinical pathologist conducts a diagnosis of normal tissues and cancerous tissues. However, recently, the shortage of clinical pathologists is posing increasing burdens to meet the demands for such diagnoses, and this is causing serious social problems. In order to overcome this problem, we propose a technology of histopathological diagnostic support that uses higher-order local autocorrelation (HLAC) features. The proposed method can automatically screen tissue that is believed to be normal tissue to detect cancerous tissue as well as tissue that is suspected of being cancerous to detect abnormalities. Consequently, we can reduce the burden on clinical pathologists, allowing them to concentrate on diagnosing cancer.
Index Terms:
HLAC, histopathological diagnosis, cancer, biopsy, automatic screening
Citation:
Hirokazu Nosato, Hidenori Sakanashi, Masahiro Murakawa, Tetsuya Higuchi, Nobuyuki Otsu, Kensuke Terai, Nobuyuki Hiruta, Noriaki Kameda, "Histopathological Diagnostic Support Technology Using Higher-Order Local Autocorrelation Features," bliss, pp.61-65, 2009 Symposium on Bio-inspired Learning and Intelligent Systems for Security, 2009
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