Standardised convolutional filtering for radiomics
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Abstract (arXiv)
The Image Biomarker Standardisation Initiative (IBSI) aims to improve reproducibility of radiomics studies by standardising the computational process of extracting image biomarkers (features) from images. We have previously established reference values for 169 commonly used features, created a standard radiomics image processing scheme, and developed reporting guidelines for radiomic studies. However, several aspects are not standardised. Here we present a complete version of a reference manual on the use of convolutional filters in radiomics and quantitative image analysis. Filters, such as wavelets or Laplacian of Gaussian filters, play an important part in emphasising specific image characteristics such as edges and blobs. Features derived from filter response maps were found to be poorly reproducible. This reference manual provides definitions for convolutional filters, parameters that should be reported, reference feature values, and tests to verify software compliance with the reference standard.
Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). First posted on arXiv 9 Jun 2020.
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Citing papers per year: 2024: 2; 2025: 1.
3 papers.
- 2025GLoG-CSUnet: Enhancing Vision Transformers with Adaptable Radiomic Features for Medical Image Segmentationcited by 1
- 2024Instance-level quantitative saliency in multiple sclerosis lesion segmentationcited by 0
- 2024An updated overview of radiomics-based artificial intelligence (AI) methods in breast cancer screening and diagnosiscited by 0
- 2025GLoG-CSUnet: Enhancing Vision Transformers with Adaptable Radiomic Features for Medical Image Segmentation6 Jan 2025
- 2024An updated overview of radiomics-based artificial intelligence (AI) methods in breast cancer screening and diagnosis20 Jun 2024
- 2024Instance-level quantitative saliency in multiple sclerosis lesion segmentation13 Jun 2024