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Haralick texture features from apparent diffusion coefficient (ADC) MRI images depend on imaging and pre-processing

Patrik Brynolfsson1, David Nilsson2, Turid Torheim3

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Texture analysis in medical imaging is sensitive to image acquisition and preprocessing. Haralick texture features of apparent diffusion coefficient (ADC) MR images are significantly influenced by noise, resolution, quantization, and gray levels, impacting cancer diagnosis.

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Area of Science:

  • Medical Imaging
  • Radiology
  • Oncology

Background:

  • Texture analysis is increasingly used in oncology for cancer diagnosis, classification, and treatment response assessment.
  • Lack of consensus on texture analysis workflows and parameter settings hinders result replication.

Purpose of the Study:

  • To evaluate the sensitivity of Haralick texture features from apparent diffusion coefficient (ADC) MR images to variations in image acquisition and preprocessing parameters.
  • To identify critical parameters affecting texture feature reliability for consistent clinical application.

Main Methods:

  • Assessed Haralick texture features from ADC MR images.
  • Varied five parameters: noise, resolution, ADC map construction, quantization method, and number of gray levels.
  • Analyzed the influence of each parameter on texture feature values.

Main Results:

  • Noise, image resolution, quantization method, and number of gray levels significantly impacted most Haralick texture features.
  • The magnitude of influence varied across different texture features.
  • Methods for constructing ADC maps did not affect any texture features.

Conclusions:

  • Standardization of image resolution, noise levels, quantization method, and gray levels is crucial for reliable texture analysis in medical imaging.
  • Consistent preprocessing and acquisition parameters are recommended for reproducible results in cancer studies using ADC texture features.