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
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;
Conference_Titel :
Computing and Networking (CANDAR), 2013 First International Symposium on
Conference_Location :
Matsuyama
Print_ISBN :
978-1-4799-2795-1
DOI :
10.1109/CANDAR.2013.48