Abstract
Automatic tissue segmentation of hyperspectral images in liver and head neck surgeries using machine learning
Highlights
Identification of distinct types of tissues is a challenging task carried out visually by surgeons during intraoperative procedures
We present the most relevant experimental results obtained from the statistical comparison of the machine learning models for tissue segmentation in the two medical applications
The high dimensionality of Hyperspectral imaging (HSI) data is reduced using principal component analysis (PCA), the presence of noise is reduced with the Savitzky-Golay operator, and the spectra are normalized by the standard normal variate transform
Summary
Identification of distinct types of tissues is a challenging task carried out visually by surgeons during intraoperative procedures. Identification of image background can reduce the number of irrelevant regions falsely detected by a segmentation method as tissues of interest. This is achieved by defining a single set of boundaries that works as threshold for the reflectance spectra of relevant features. A pixel is identified by the mask M1 as image background if it presents a reflectance pattern outside the U’(w) and L’(w) boundaries for any wavelength unit as follows: (5). By analyzing the reflectance spectra of the HSI data of both applications, three simple rules were defined to identify specific objects in the scene.
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