Abstract
We applied a learning network to a cell's boundary detection of human corneal endothelium photomicrographs measured by specular microscopy. Interconnections between units in our model are constrained to be locally space invariant to meet space-invariant processing. The neural network was trained to extract the cell's boundary by showing part of the photomicrograph and its subjective boundary image, which is an outline drawing made by hand. After training, the network showed good performance with the microphotograph that was not trained. Internal representations of the network were also studied.
© 1991 Optical Society of America
Full Article | PDF ArticleMore Like This
Juan S. Sierra, Jesus Pineda, Daniela Rueda, Alejandro Tello, Angélica M. Prada, Virgilio Galvis, Giovanni Volpe, Maria S. Millan, Lenny A. Romero, and Andres G. Marrugo
Biomed. Opt. Express 14(1) 335-351 (2023)
Xinwen Yao, Kavya Devarajan, René M. Werkmeister, Valentin Aranha dos Santos, Marcus Ang, Anthony Kuo, Damon W. K. Wong, Jacqueline Chua, Bingyao Tan, Veluchamy Amutha Barathi, and Leopold Schmetterer
Biomed. Opt. Express 10(11) 5675-5686 (2019)
Wei Zhang, Kazuyoshi Itoh, Jun Tanida, and Yoshiki Ichioka
Appl. Opt. 29(32) 4790-4797 (1990)