ArticleScientific reports2017
Haralick texture features from apparent diffusion coefficient (ADC) MRI images depend on imaging and pre-processing parameters.
Article in Scientific reports, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 64 papers.
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Who cites it
64 citing papers in PubMed.
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- Assessment of Robustness of MRI Radiomic Features in the Abdomen: Impact of Deep Learning Reconstruction and Accelerated Acquisition.Journal of imaging informatics in medicine · 2026Article
- Brain texture alterations predict subtle visual perceptual dysfunctions in recent onset psychosis and clinical high-risk state.Translational psychiatry · 2026Article
- Multimodality and temporal analysis of cervical cancer treatment response.Quantitative imaging in medicine and surgery · 2026Article
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- Scaling of Haralick features with image bit depth and gray level co-occurrence matrix displacement vector for linear gradients.Computers in biology and medicine · 2025Article
- Hybrid Radiomics and Machine Learning for Brain Tumors Multi-Task Classification: An Exploratory Study on Integrating GLCM and Curvelet-Based Features for Enhanced Accuracy.Health science reports · 2025Article
- InterpolAI: deep learning-based optical flow interpolation and restoration of biomedical images for improved 3D tissue mapping.Nature methods · 2025Article
- Developing a Reproducible Radiomics Model for Diagnosis of Active Crohn's Disease on CT Enterography Across Annotation Variations and Acquisition Differences.Journal of imaging informatics in medicine · 2025Article
- Haralick Texture Analysis for Differentiating Suspicious Prostate Lesions from Normal Tissue in Low-Field MRI.Bioengineering (Basel, Switzerland) · 2025Article
- Scaling laws for Haralick texture features of linear gradients.PeerJ. Computer science · 2025Article
- Impact of Preprocessing Parameters in Medical Imaging-Based Radiomic Studies: A Systematic Review.Cancers · 2024Review
- Estimating the Workability of Concrete with a Stereovision Camera during Mixing.Sensors (Basel, Switzerland) · 2024Article
- Generative interpolation and restoration of images using deep learning for improved 3D tissue mapping.bioRxiv : the preprint server for biology · 2024Article
- Achieving imaging and computational reproducibility on multiparametric MRI radiomics features in brain tumor diagnosis: phantom and clinical validation.European radiology · 2024Article
- Radiomics from multisite MRI and clinical data to predict clinically significant prostate cancer.Acta radiologica (Stockholm, Sweden : 1987) · 2024Article
- Enhancing lung cancer detection through hybrid features and machine learning hyperparameters optimization techniques.Heliyon · 2024Article
- Value of diffusion kurtosis MR imaging and conventional diffusion weighed imaging for evaluating response to first-line chemotherapy in unresectable pancreatic cancer.Cancer imaging : the official publication of the International Cancer Imaging Society · 2024Article
4 more citing papers are in PubMed but not listed here.
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Authors and funding
8 authors.
Funding
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Abstract
In recent years, texture analysis of medical images has become increasingly popular in studies investigating diagnosis, classification and treatment response assessment of cancerous disease. Despite numerous applications in oncology and medical imaging in general, there is no consensus regarding texture analysis workflow, or reporting of parameter settings crucial for replication of results. The aim of this study was to assess how sensitive Haralick texture features of apparent diffusion coefficient (ADC) MR images are to changes in five parameters related to image acquisition and pre-processing: noise, resolution, how the ADC map is constructed, the choice of quantization method, and the number of gray levels in the quantized image. We found that noise, resolution, choice of quantization method and the number of gray levels in the quantized images had a significant influence on most texture features, and that the effect size varied between different features. Different methods for constructing the ADC maps did not have an impact on any texture feature. Based on our results, we recommend using images with similar resolutions and noise levels, using one quantization method, and the same number of gray levels in all quantized images, to make meaningful comparisons of texture feature results between different subjects.
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Registered trials
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