DocumentCode :
1849569
Title :
Cancer detection from pathological images using Higher-order Local Autocorrelation feature
Author :
Jia Qu ; Nosato, Hirokazu ; Sakanashi, Hidenori ; Terai, K. ; Hiruta, N.
Author_Institution :
Dept. of Intell. Interaction Technol., Univ. of Tsukuba, Tsukuba, Japan
Volume :
2
fYear :
2012
fDate :
21-25 Oct. 2012
Firstpage :
1198
Lastpage :
1201
Abstract :
Pathological diagnosis, plays a very crucial role in cancer examination. However, because of the shortage of pathologists, increasing working burden has been imposed on pathologists. Aiming to reduce pathologist´s burden and improve the reliability of cancer diagnosis, an innovative cancer detection method for pathological images and its extended approach for advanced ability is presented in this paper. The experiments demonstrate the effectiveness of the proposed method in cancer detection and cancer location estimation.
Keywords :
cancer; correlation methods; medical image processing; patient diagnosis; reliability; cancer detection; cancer diagnosis; cancer location estimation; higher-order local autocorrelation feature; pathological diagnosis; pathological images; reliability; HLAC; anomaly detection; pathological images;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Signal Processing (ICSP), 2012 IEEE 11th International Conference on
Conference_Location :
Beijing
ISSN :
2164-5221
Print_ISBN :
978-1-4673-2196-9
Type :
conf
DOI :
10.1109/ICoSP.2012.6491791
Filename :
6491791
Link To Document :
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