TY - GEN
T1 - The local-threshold 2d-tophat cell segmentation for the two-photon confocal microscope image
AU - Yuan, Xiaoyang
AU - Gu, Lei
AU - Sun, Lei
AU - Ikenaga, Takeshi
N1 - Funding Information:
This work is supported by KAKENHI (23300018)
Publisher Copyright:
© 2013; MVA Organization. All rights reserved.
PY - 2013
Y1 - 2013
N2 - The Two-photon Confocal Microscopy (TCM) is a new technology which is useful in nondestructive analysis of tissue. However its cell experiment images are excessive and difficult for machine processing since the low resolution and lots of noise. Thus nowadays such a work is mainly done manually and costs a lot of time and effort. This paper proposes a cell segmentation algorithm for this situation by the local-threshold 2D-tophat which is based on the tophat combined with the OTSU and Mathematic Morphology. The proposed method uses the cell region gradient information to do the cell segmentation which uses a local-dynamic threshold instead of a static one and uses the 2D information only to minimize the influences caused by the background and also the algorithm complexity. This method has been applied to experiment and is proved that the true-positive ratio (TPR) can be kept above 85% and false-positive ratio (FPR)+miss-positive ratio(MPR) can be kept under 20% when compared with the original tophat method that the TPR is about 80% and FPR+MPR above 20%.
AB - The Two-photon Confocal Microscopy (TCM) is a new technology which is useful in nondestructive analysis of tissue. However its cell experiment images are excessive and difficult for machine processing since the low resolution and lots of noise. Thus nowadays such a work is mainly done manually and costs a lot of time and effort. This paper proposes a cell segmentation algorithm for this situation by the local-threshold 2D-tophat which is based on the tophat combined with the OTSU and Mathematic Morphology. The proposed method uses the cell region gradient information to do the cell segmentation which uses a local-dynamic threshold instead of a static one and uses the 2D information only to minimize the influences caused by the background and also the algorithm complexity. This method has been applied to experiment and is proved that the true-positive ratio (TPR) can be kept above 85% and false-positive ratio (FPR)+miss-positive ratio(MPR) can be kept under 20% when compared with the original tophat method that the TPR is about 80% and FPR+MPR above 20%.
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M3 - Conference contribution
AN - SCOPUS:85060860396
SN - 9784901122139
T3 - Proceedings of the 13th IAPR International Conference on Machine Vision Applications, MVA 2013
SP - 455
EP - 458
BT - Proceedings of the 13th IAPR International Conference on Machine Vision Applications, MVA 2013
PB - MVA Organization
T2 - 13th IAPR International Conference on Machine Vision Applications, MVA 2013
Y2 - 20 May 2013 through 23 May 2013
ER -