DocumentCode
3129751
Title
Counting in Intracellular Images Using Partial Least Squares Regression and Correlation between Features
Author
Kumagai, Shinya ; Hotta, Kazuhiro
Author_Institution
Meijo Univ., Nagoya, Japan
fYear
2013
fDate
4-6 Dec. 2013
Firstpage
275
Lastpage
280
Abstract
Light spot counting in intracellular images is important for investigating the cause diseases. However, light spots are manually counted by human observers now. This paper proposes an automatic light spot counting method by computer. To count light spots, we use partial least squares regression and correlation between 2 different types of features using higher-order local autocorrelation. The proposed method gives higher accuracy than counting by support vector regression and ImageJ. The effectiveness of our method is shown by experiments.
Keywords
biology computing; feature extraction; least squares approximations; regression analysis; ImageJ; automatic light spot counting method; higher-order local autocorrelation; intracellular images; partial least squares regression; support vector regression; Correlation; Feature extraction; Image edge detection; Information filters; Kernel; Lipidomics; Co-occurrence; Higher-order Local Auto Correlation; Intracellular Images; Light Spot Counting; Partial Least Squares Regression;
fLanguage
English
Publisher
ieee
Conference_Titel
Computing and Networking (CANDAR), 2013 First International Symposium on
Conference_Location
Matsuyama
Print_ISBN
978-1-4799-2795-1
Type
conf
DOI
10.1109/CANDAR.2013.48
Filename
6726910
Link To Document