![](bg8.png)
Figure 10: A pipeline to be implemented for information
extraction from the KESM and other physical sectioning-
based microscope image stacks
REFERENCES
Al-Attas, R. and El-Zaart, A. (2007). Thresholding of med-
ical images using minimum cross entropy. In 3rd
Kuala Lumpur International Conference on Biomed-
ical Engineering 2006, pages 296–299. Springer.
Canny, J. (1986). A computational approach to edge detec-
tion. Pattern Analysis and Machine Intelligence, IEEE
Transactions on, (6):679–698.
Choe, Y., Mayerich, D., Kwon, J., Miller, D. E., Sung,
C., Chung, J. R., Huffman, T., Keyser, J., and Ab-
bott, L. C. (2011). Specimen preparation, imaging,
and analysis protocols for knife-edge scanning mi-
croscopy. (58):e3248.
Denk, W. and Horstmann, H. (2004). Serial block-
face scanning electron microscopy to reconstruct
three-dimensional tissue nanostructure. PLoS Biol,
2(11):e329.
Duda, R. O., Hart, P. E., et al. (1973). Pattern classification
and scene analysis. J. Wiley and Sons.
Galamhos, C., Matas, J., and Kittler, J. (1999). Progres-
sive probabilistic hough transform for line detection.
In Computer Vision and Pattern Recognition, 1999.
IEEE Computer Society Conference on., volume 1.
IEEE.
Hayworth, K., Kasthuri, N., Schalek, R., and Lichtman, J.
(2006). Automating the collection of ultrathin serial
sections for large volume tem reconstructions. Mi-
croscopy and Microanalysis, 12(S02):86–87.
Kwon, J., Mayerich, D., and Choe, Y. (2011). Automated
cropping and artifact removal for knife-edge scanning
microscopy. In Biomedical Imaging: From Nano
to Macro, 2011 IEEE International Symposium on,
pages 1366–1369. IEEE.
Li, C. H. and Lee, C. (1993). Minimum cross entropy
thresholding. Pattern Recognition, 26(4):617–625.
Maini, R. and Aggarwal, H. (2009). Study and comparison
of various image edge detection techniques. Interna-
tional journal of image processing (IJIP), 3(1):1–11.
Mayerich, D., Abbott, L., and McCormick, B. (2008).
Knife-edge scanning microscopy for imaging and re-
construction of three-dimensional anatomical struc-
tures of the mouse brain. Journal of microscopy,
231(1):134–143.
Peng, H., Ruan, Z., Long, F., Simpson, J. H., and Myers,
E. W. (2010). V3d enables real-time 3d visualization
and quantitative analysis of large-scale biological im-
age data sets. Nature biotechnology, 28(4):348–353.
Prewitt, J. M. (1970). Object enhancement and extraction.
Picture processing and Psychopictorics, 10(1):15–19.
Sarkar, S., Patra, G. R., and Das, S. (2011). A differ-
ential evolution based approach for multilevel image
segmentation using minimum cross entropy threshold-
ing. In Swarm, Evolutionary, and Memetic Comput-
ing, pages 51–58. Springer.
Shotton, D. M. (1989). Confocal scanning optical mi-
croscopy and its applications for biological speci-
mens. Journal of Cell Science, 94(2):175–206.
Tsai, P. S., Friedman, B., Ifarraguerri, A. I., Thompson,
B. D., Lev-Ram, V., Schaffer, C. B., Xiong, Q., Tsien,
R. Y., Squier, J. A., and Kleinfeld, D. (2003). All-
optical histology using ultrashort laser pulses. Neuron,
39(1):27–41.
Xiao, H. and Peng, H. (2013). App2: automatic tracing of
3d neuron morphology based on hierarchical pruning
of a gray-weighted image distance-tree. Bioinformat-
ics, 29(11):1448–1454.
Yi-de, M., Qing, L., and Zhi-Bai, Q. (2004). Automated im-
age segmentation using improved pcnn model based
on cross-entropy. In Intelligent Multimedia, Video and
Speech Processing, 2004. Proceedings of 2004 Inter-
national Symposium on, pages 743–746. IEEE.
BIOIMAGING 2016 - 3rd International Conference on Bioimaging
100