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Standardised convolutional filtering for radiomics

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arxiv 2006.05470 v9 pith:FGMBH226 submitted 2020-06-09 eess.IV cs.CV

classification eess.IVcs.CV
keywords imagereferencefiltersradiomicsconvolutionalfeaturesmanualstandard
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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.

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  1. GLoG-CSUnet: Enhancing Vision Transformers with Adaptable Radiomic Features for Medical Image Segmentation

    cs.CV 2025-01 conditional novelty 4.0 of 10

    Adding roughly 15 to 30 learnable Gabor and LoG filter parameters to a Swin-Unet patch embedding improves Dice scores by about one point on two medical segmentation benchmarks.

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