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A Multi-layer Perceptron Approach to Automatically Detect Tissue via NIR Multispectral Imaging


We present a novel pixel-level spectra based multi-layer perceptron
(MLP) to discriminate regions of biomedical multispectral imaging
data into two categories: tissue and non-tissue. The spectra used
for this study are 740nm, 780nm, 850nm, and 945nm as these
wavelengths are on either side of the isosbestic point for oxyhemoglobin
and deoxyhemoglobin; absorbers that are common in all
healthy tissues. An MLP is trained using multispectral data from
12 human subjects and 12 non-tissue objects. The MLP is tested
on three multispectral challenge image sets, from which the accuracy,
sensitivity, and specificity of the model yield results of 91.3%
(+/-0.2%), 98.1% (+/-0.3%), and 88.5% (+/- 0.3%) respectively.