Title :
Automatic analysis method of protein expression images based on generalized data field
Author :
Wang, Shuliang ; Li, Ying ; Tu, Wenchen ; Wang, Peng
Author_Institution :
Int. Sch. of Software, Wuhan Univ., Wuhan, China
Abstract :
For detection of protein expression in biomedicai image, shape measurement of protein expression mostly depends on semi-automatic analysis of image analysis software which makes the results vulnerable to subjective factors, since the automatic analysis is too complicated to operate. Therefore, a novel algorithm based on generalized data field (GDF) is proposed to determine the region of protein expression. Instead of being directly divided into the measured object and background, all the data objects, namely pixels of an image, are naturally clustered into multiple classes based on potential distribution in generalized data field. Each class represents protein expression in different degree, which precisely describes the details of protein expression. Compared with image-pro plus software analysis, KM and EM, experiment results demonstrate that the protein expression can be extracted easily and objectively from an image by GDF. Furthermore, noises of background are eliminated by the smoothing procedure of GDF.
Keywords :
medical image processing; molecular biophysics; proteins; automatic analysis method; biomedicai image; generalized data field; image analysis software; image pixel; image-pro plus software analysis; protein expression image; semiautomatic analysis; shape measurement; Accuracy; Clustering algorithms; Educational institutions; Estimation; Protein engineering; Proteins; Software; clustering; detection of protein expression; generalized data field;
Conference_Titel :
Bioinformatics and Biomedicine (BIBM), 2012 IEEE International Conference on
Conference_Location :
Philadelphia, PA
Print_ISBN :
978-1-4673-2559-2
Electronic_ISBN :
978-1-4673-2558-5
DOI :
10.1109/BIBM.2012.6392710