Texture analysis of imaging: what radiologists need to know

BA Varghese, SY Cen, DH Hwang… - American Journal of …, 2019 - Am Roentgen Ray Soc
OBJECTIVE. Radiologic texture is the variation in image intensities within an image and is
an important part of radiomics. The objective of this article is to discuss some parameters …

[HTML][HTML] A systematic review and quality of reporting checklist for repeatability and reproducibility of radiomic features

E Pfaehler, I Zhovannik, L Wei, R Boellaard… - Physics and imaging in …, 2021 - Elsevier
Abstract Background and Purpose Although quantitative image biomarkers (radiomics)
show promising value for cancer diagnosis, prognosis, and treatment assessment, these …

Clear cell renal cell carcinoma: machine learning-based quantitative computed tomography texture analysis for prediction of fuhrman nuclear grade

CT Bektas, B Kocak, AH Yardimci, MH Turkcanoglu… - European …, 2019 - Springer
Objective To evaluate the performance of quantitative computed tomography (CT) texture
analysis using different machine learning (ML) classifiers for discriminating low and high …

Brain tumor classification based on hybrid optimized multi-features analysis using magnetic resonance imaging dataset

SA Nawaz, DM Khan, S Qadri - Applied Artificial Intelligence, 2022 - Taylor & Francis
Brain tumors are deadly but become deadliest because of delayed and inefficient diagnosis
process. Large variations in tumor types also instigate additional complexity. Machine vision …

Imaging biomarker analysis of advanced multiparametric MRI for glioma grading

A Vamvakas, SC Williams, K Theodorou, E Kapsalaki… - Physica Medica, 2019 - Elsevier
Aims and objectives To investigate the value of advanced multiparametric MR imaging
biomarker analysis based on radiomic features and machine learning classification, in the …

Variation in algorithm implementation across radiomics software

JJ Foy, KR Robinson, H Li, ML Giger… - Journal of medical …, 2018 - spiedigitallibrary.org
Given the increased need for consistent quantitative image analysis, variations in radiomics
feature calculations due to differences in radiomics software were investigated. Two in …

Angiomyolipoma with minimal fat: differentiation from clear cell renal cell carcinoma and papillary renal cell carcinoma by texture analysis on CT images

L Yan, Z Liu, G Wang, Y Huang, Y Liu, Y Yu, C Liang - Academic Radiology, 2015 - Elsevier
Rationale and Objectives To retrospectively evaluate the diagnostic performance of texture
analysis (TA) for the discrimination of angiomyolipoma (AML) with minimal fat, clear cell …

Gray level Co-occurrence matrix, fractal and wavelet analyses of discrete changes in cell nuclear structure following osmotic stress: Focus on machine learning …

I Pantic, S Valjarevic, J Cumic, I Paunkovic… - Fractal and …, 2023 - mdpi.com
In this work, we demonstrate that it is possible to create supervised machine-learning
models using a support vector machine and random forest algorithms to separate yeast cells …

Machine learning based automated segmentation and hybrid feature analysis for diabetic retinopathy classification using fundus image

A Ali, S Qadri, W Khan Mashwani, W Kumam… - Entropy, 2020 - mdpi.com
The object of this study was to demonstrate the ability of machine learning (ML) methods for
the segmentation and classification of diabetic retinopathy (DR). Two-dimensional (2D) …

The diagnostic value of texture analysis in predicting WHO grades of meningiomas based on ADC maps: an attempt using decision tree and decision forest

Y Lu, L Liu, S Luan, J Xiong, D Geng, B Yin - European radiology, 2019 - Springer
Objectives The preoperative prediction of the WHO grade of a meningioma is important for
further treatment plans. This study aimed to assess whether texture analysis (TA) based on …