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A Novel Algorithm for Hyperspectral Image Denoising in Medical Application.

Kirubanandasarathy Nageswaran1, Karthikeyan Nagarajan2, Ramasubramanian Bandiya3

  • 1Department of Electronics & Communication Engineering, Syed Ammal Engineering College, Ramanathapuram, 623 502, India. dr_nksarathy@syedengg.ac.in.

Journal of Medical Systems
|July 24, 2019
PubMed
Summary

This study introduces a novel tensor-based filtering method using PARAFAC decomposition for hyperspectral image denoising. The approach effectively reduces both signal-dependent and signal-independent noise, outperforming existing methods.

Keywords:
Hyperspectral imageMedical applicationNovel denoising techniquePARAFAC

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Area of Science:

  • Image Processing
  • Signal Processing
  • Medical Imaging

Background:

  • Hyperspectral images (HSI) require preprocessing, including noise reduction, due to degradation from atmospheric conditions and device-specific noise.
  • Existing denoising methods often fail to address both stationary and non-stationary noise, such as gauge boson and thermal noise, present in HSI.
  • Effective noise reduction is crucial for accurate analysis and application of HSI data.

Purpose of the Study:

  • To propose a novel denoising framework for hyperspectral images (HSI) that addresses both signal-dependent (PN) and signal-independent (TN) noise.
  • To introduce a tensor-based filtering method utilizing Parallel Factor (PARAFAC) tensor decomposition for enhanced noise reduction in HSI.
  • To evaluate the performance of the proposed method against conventional techniques like Multiple Linear Regression (MLR) and Multidimensional Wavelet Transforms with Multiway Wiener Filter (MWPT-MWF).

Main Methods:

  • A novel denoising framework based on tensor-based filtering is proposed.
  • The framework employs Parallel Factor (PARAFAC) tensor decomposition to decompose the HSI data.
  • The denoising performance is evaluated by comparing it with Multiple Linear Regression (MLR) and Multidimensional Wavelet Transforms with Multiway Wiener Filter (MWPT-MWF) techniques.

Main Results:

  • The proposed tensor-based filtering method demonstrates superior performance in noise reduction compared to MLR and MWPT-MWF algorithms.
  • The technique is highly effective in reducing both signal-dependent (PN) and signal-independent (TN) noise in hyperspectral images.
  • Performance analysis confirms the efficiency of the novel denoising framework.

Conclusions:

  • The developed tensor-based filtering framework using PARAFAC decomposition offers a significant advancement in hyperspectral image denoising.
  • This novel approach provides a more effective solution for reducing complex noise patterns in HSI.
  • The proposed algorithm shows promise for various medical applications, including skin allergy detection, retinal exudate identification, and diagnosis of diabetes mellitus and retinopathy.