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Proceedings Paper

Five-dimensional analysis of multi-contrast Jones matrix tomography of posterior eye
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Paper Abstract

Pixel clustering algorithm tailored to multi-contrast Jones matrix based optical coherence tomography (MC-JMT) is demonstrated. This algorithm clusters multiple pixels of MC-JMT in a five-dimensional (5-D) feature space which comprises dimensions of lateral space, axial space, logarithmic scattering OCT intensity, squared power of Doppler shift and degree of polarization uniformity. This 5-D clustering provides clusters of pixels, so called as superpixels. The superpixels are utilized as local regions for pixels averaging. The averaging decreases the noise in the measurement as preserving structural details of the sample. A simple decision-tree algorithm is applied to classified superpixels into some tissue types. This classification process successfully segments tissues of a human posterior eye.

Paper Details

Date Published: 28 February 2014
PDF: 6 pages
Proc. SPIE 8930, Ophthalmic Technologies XXIV, 893008 (28 February 2014); doi: 10.1117/12.2036587
Show Author Affiliations
Udaya Bhaskar, Univ. of Tsukuba (Japan)
Indian Institute of Technology (India)
Young-Joo Hong, Univ. of Tsukuba (Japan)
Masahiro Miura, Univ. of Tsukuba (Japan)
Tokyo Medical Univ. Ibaraki Medical Ctr. (Japan)
Yoshiaki Yasuno, Univ. of Tsukuba (Japan)


Published in SPIE Proceedings Vol. 8930:
Ophthalmic Technologies XXIV
Fabrice Manns; Per G. Söderberg; Arthur Ho, Editor(s)

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