TY - GEN
T1 - Automatic analysis method of protein expression images based on generalized data field
AU - Wang, Shuliang
AU - Li, Ying
AU - Tu, Wenchen
AU - Wang, Peng
PY - 2012
Y1 - 2012
N2 - 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.
AB - 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.
KW - clustering
KW - detection of protein expression
KW - generalized data field
UR - http://www.scopus.com/inward/record.url?scp=84872503759&partnerID=8YFLogxK
U2 - 10.1109/BIBM.2012.6392710
DO - 10.1109/BIBM.2012.6392710
M3 - Conference contribution
AN - SCOPUS:84872503759
SN - 9781467325585
T3 - Proceedings - 2012 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2012
SP - 407
EP - 410
BT - Proceedings - 2012 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2012
T2 - 2012 IEEE International Conference on Bioinformatics and Biomedicine, BIBM2012
Y2 - 4 October 2012 through 7 October 2012
ER -