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Related Experiment Video

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Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns
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Classification of THz pulse signals using two-dimensional cross-correlation feature extraction and non-linear

Siuly1, Xiaoxia Yin1, Sillas Hadjiloucas2

  • 1Centre for Applied Informatics, College of Engineering & Science, Victoria University, Melbourne, Australia.

Computer Methods and Programs in Biomedicine
|March 23, 2016
PubMed
Summary

Multinomial logistic regression (MLR) and k-nearest neighbours (KNN) classifiers showed superior accuracy in identifying powder samples using terahertz (THz) imaging. Two-dimensional cross-correlations enhanced classification performance.

Keywords:
2-D cross-correlationMultinomial logistic regression classifierNaïve BayesSupport vector machineTerahertz spectroscopyk-Nearest neighbours

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Area of Science:

  • Spectroscopy and Spectroscopic Imaging
  • Machine Learning Applications
  • Terahertz (THz) Science and Technology

Background:

  • Terahertz (THz) spectroscopy is sensitive to material properties like extinction coefficient, refractive index, and scattering.
  • Similar optical properties of substances can make classification challenging based solely on complex insertion loss.
  • Scattering effects in spectroscopic experiments can be difficult to quantify, limiting direct classification accuracy.

Purpose of the Study:

  • To compare the performance of four machine learning classifiers (MLR, KNN, SVM, NB) for analyzing THz transient time domain sequences.
  • To evaluate the effectiveness of 2-D cross-correlations in noise suppression and feature extraction for hyperspectral datasets.
  • To establish a general methodology for assessing hyperspectral dataset classifiers using 2-D cross-correlations.

Main Methods:

  • Utilized terahertz (THz) transient time domain spectroscopy on pixelated images of powder samples with varying thicknesses (2mm, 3mm, 4mm) and mixtures.
  • Applied two-dimensional (2-D) cross-correlations between background and sample interferograms to generate statistical features.
  • Performed cross-validation by classifying samples and mixtures using multinomial logistic regression (MLR), k-nearest neighbours (KNN), support vector machine (SVM), and naïve Bayes (NB) classifiers.

Main Results:

  • MLR (least accuracy 88.24%) and KNN (least accuracy 90.19%) consistently outperformed SVM (least accuracy 74.51%) and NB (least accuracy 56.86%) in classification accuracy and robustness.
  • The use of 2-D cross-correlations provided effective noise suppression and generated robust features for classification.
  • Classification accuracy was assessed across different sample thicknesses and complex mixtures, confirming classifier consistency.

Conclusions:

  • MLR and KNN are highly effective and robust classifiers for hyperspectral THz imaging data, particularly when enhanced by 2-D cross-correlation analysis.
  • The developed methodology using 2-D cross-correlations offers a reliable approach for evaluating other hyperspectral dataset classifiers.
  • This work supports the advancement of automated THz imaging systems for applications in biomedical imaging, industrial processing, and quality control.