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Applying Hyperspectral Reflectance Imaging to Investigate the Palettes and the Techniques of Painters

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Evaluation of Preprocessing Methods on Independent Medical Hyperspectral Databases to Improve Analysis.

Beatriz Martinez-Vega¹, Mariia Tkachenko^2,3, Marianne Matkabi^2,4

¹Research Institute for Applied Microelectronics (IUMA), University of Las Palmas de Gran Canaria, 35017 Las Palmas de Gran Canaria, Spain.

Sensors (Basel, Switzerland)

|November 26, 2022

Summary

This summary is machine-generated.

Preprocessing hyperspectral imaging (HSI) data is crucial for accurate cancer detection. Min-Max scaling emerged as the most effective technique across colorectal, esophagogastric, and brain cancer datasets, outperforming other methods.

Keywords:

brain cancer colon cancer deep learning esophagogastric cancer hyperspectral imaging machine learning median filter min-max scaling standard normal variate normalization

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Area of Science:

Medical imaging
Computational pathology
Cancer diagnostics

Background:

Cancer remains a leading global cause of mortality, necessitating advancements in early and accurate detection.
Hyperspectral Imaging (HSI) combined with artificial intelligence (AI) shows promise for cancer detection.
Standardized preprocessing methods for medical HSI data are lacking, hindering algorithm performance.

Purpose of the Study:

To evaluate and compare various preprocessing techniques for medical HSI data.
To determine the optimal preprocessing strategy for enhancing tumor detection across different cancer types.
To assess the impact of preprocessing on AI-based pixel-wise classification of HSI data.

Main Methods:

Evaluated combinations of spatial/spectral smoothing, Min-Max scaling, Standard Normal Variate (SNV) normalization, and median spatial smoothing.
Utilized two machine learning and deep learning models for pixel-wise classification.
Tested preprocessing methods on HSI datasets for colorectal, esophagogastric, and brain cancers.

Main Results:

Preprocessing significantly impacts tumor identification performance.
Median Filter preprocessing yielded slightly better results for colorectal tumors (AUC 0.94).
Min-Max scaling preprocessing achieved higher accuracy for esophagogastric (AUC 0.93) and brain tumors (AUC 0.92).

Conclusions:

Min-Max scaling is identified as the most relevant and robust preprocessing technique for HSI-based cancer detection across diverse datasets and instrumentation.
Median Filter can smooth critical spectral features, leading to performance variability.
Standardized preprocessing is essential for reliable AI-driven HSI cancer diagnostics.