TY - GEN
T1 - SSTEM cell image segmentation based on top-down selective attention model
AU - Choi, Sangbok
AU - Paik, Sang Kyoo
AU - Bae, Yong Chul
AU - Lee, Minho
PY - 2009
Y1 - 2009
N2 - We propose an automatic method for segmenting neurons in the TEM cell images based on a top-down attention model, which is efficient to solve the discontinuity problems in TEM cell image caused by loss of section or branching of cell. At first, the proposed model enhances cell boundaries using a partial differential equation based on hessian matrix, which can improve the contrast and continuity of cell membranes in the TEM images. Then, a top-down attention model trains the shape characteristics of the desired target neurons through the reinforcement and inhibition learning process. The top-down attention model localizes a candidate neuronal region in subsequent TEM image, which was implemented by a growing fuzzy topology adaptation resonance theory network (GFTART) model. It is efficient to resolve the discontinuity problem of TEM cell image. The localized candidate target neurons are finally indicated whether they are correct ones by an active appearance model (AAM). Experimental results show that the proposed method is efficient to segment the TEM images.
AB - We propose an automatic method for segmenting neurons in the TEM cell images based on a top-down attention model, which is efficient to solve the discontinuity problems in TEM cell image caused by loss of section or branching of cell. At first, the proposed model enhances cell boundaries using a partial differential equation based on hessian matrix, which can improve the contrast and continuity of cell membranes in the TEM images. Then, a top-down attention model trains the shape characteristics of the desired target neurons through the reinforcement and inhibition learning process. The top-down attention model localizes a candidate neuronal region in subsequent TEM image, which was implemented by a growing fuzzy topology adaptation resonance theory network (GFTART) model. It is efficient to resolve the discontinuity problem of TEM cell image. The localized candidate target neurons are finally indicated whether they are correct ones by an active appearance model (AAM). Experimental results show that the proposed method is efficient to segment the TEM images.
KW - Cell image segmentation
KW - Serial-sectioning TEM (SSTEM)
KW - Top-down attention
UR - http://www.scopus.com/inward/record.url?scp=76649124259&partnerID=8YFLogxK
U2 - 10.1007/978-3-642-10677-4_87
DO - 10.1007/978-3-642-10677-4_87
M3 - Conference contribution
AN - SCOPUS:76649124259
SN - 3642106765
SN - 9783642106767
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 759
EP - 768
BT - Neural Information Processing - 16th International Conference, ICONIP 2009, Proceedings
T2 - 16th International Conference on Neural Information Processing, ICONIP 2009
Y2 - 1 December 2009 through 5 December 2009
ER -