Online citations, reference lists, and bibliographies.
Please confirm you are human
(Sign Up for free to never see this)
← Back to Search

Texture Analysis Of Medical Images.

G. Castellano, L. Bonilha, L. Li, F. Cendes
Published 2004 · Medicine

Save to my Library
Download PDF
Analyze on Scholarcy
The analysis of texture parameters is a useful way of increasing the information obtainable from medical images. It is an ongoing field of research, with applications ranging from the segmentation of specific anatomical structures and the detection of lesions, to differentiation between pathological and healthy tissue in different organs. Texture analysis uses radiological images obtained in routine diagnostic practice, but involves an ensemble of mathematical computations performed with the data contained within the images. In this article we clarify the principles of texture analysis and give examples of its applications, reviewing studies of the technique.
This paper references
The Fourier Transform and Its Applications
R. Bracewell (1965)
MRI texture analysis on texture test objects, normal brain and intracranial tumors.
S. Herlidou-Même (2003)
Texture analysis for classification of cervix lesions
Q. Ji (2000)
Discrete Gabor transform
S. Qian (1993)
Statistical and Structural Approaches to Texture
Cerebellum segmentation employing texture properties and knowledge based image processing: applied to normal adult controls and patients.
N. Saeed (2002)
An Introduction to Wavelet Analysis
D. Walnut (2004)
Multifeature analysis of Gd‐enhanced MR images of breast lesions
S. Sinha (1997)
Three dimensional texture analysis in MRI: a preliminary evaluation in gliomas.
Doaa Mahmoud-Ghoneim (2003)
Computational Models of MRI Characteristics of Focal Cortical Dysplasia Improve Lesion Detection
S. Antel (2002)
Texture analysis of spinal cord pathology in multiple sclerosis
J. M. Mathias (1999)
The Fourier Transform and its Applications
K. W. Cattermole (1965)
Comparison of automated and visual texture analysis in MRI: characterization of normal and diseased skeletal muscle.
S. Herlidou (1999)
Diagnosis of breast tumors with sonographic texture analysis using wavelet transform and neural networks.
D. Chen (2002)
Texture anisotropy in 3-D images
V. Kovalev (1999)
3D Texture Analysis of MRI Brain Datasets
V. Kovalev (2001)
Texture analysis and morphological processing of magnetic resonance imaging assist detection of focal cortical dysplasia in extra‐temporal partial epilepsy
A. Bernasconi (2001)
Texture Analysis of Hippocampal Sclerosis
L. Bonilha (2003)
Hands-on Morphological Image Processing
E. Dougherty (2003)
Texture Analysis Methods - A Review
A. Materka (1998)
Existence of contralateral abnormalities revealed by texture analysis in unilateral intractable hippocampal epilepsy.
O. Yu (2001)
Obstructive lung diseases: texture classification for differentiation at CT.
F. Chabat (2003)
Hippocampal texture analysis in patients with familial mesial temporal lobe epilepsy.
Gisele Resende Coelho Caselato (2003)
Automated detection of focal cortical dysplasia lesions using computational models of their MRI characteristics and texture analysis
S. Antel (2003)
Texture analysis of hippocampus for epilepsy
K. Jafari-Khouzani (2003)
Texture detection of simulated microcalcification susceptibility effects in magnetic resonance imaging of breasts
D. James (2001)
Computer-assisted enhanced volumetric segmentation magnetic resonance imaging data using a mixture of artificial neural networks.
Rigoberto Pérez de Alejo (2003)

This paper is referenced by
Learning to see the invisible: A data‐driven approach to finding the underlying patterns of abnormality in visually normal brain magnetic resonance images in patients with temporal lobe epilepsy
Oscar F Bennett (2019)
Measurement, modelling and potential clinical applications of spatial variations in magnetic resonance proton transverse relaxation rates in iron-loaded liver and heart tissue
B. Pontre (2006)
Characterization of image heterogeneity using 2D Minkowski functionals increases the sensitivity of detection of a targeted MRI contrast agent
Holly C. Canuto (2009)
Editorial: Artificial Intelligence for Medical Image Analysis of Neuroimaging Data
Nianyin Zeng (2020)
Correlation of texture feature analysis with bone marrow infiltration in initial staging of patients with lymphoma using 18F-fluorodeoxyglucose positron emission tomography combined with computed tomography
Mahmoud A. Kenawy (2020)
Three-dimensional solid texture analysis in biomedical imaging: Review and opportunities
A. Depeursinge (2014)
Evaluation of Texture Features for Analysis of Ovarian Follicular Development
Na Bian (2006)
Structured light imaging for breast-conserving surgery, part II: texture analysis and classification
Samuel S. Streeter (2019)
Texture features on T2-weighted magnetic resonance imaging: new potential biomarkers for prostate cancer aggressiveness.
A. Vignati (2015)
Differential Diagnostic Value of Texture Feature Analysis of Magnetic Resonance T2 Weighted Imaging between Glioblastoma and Primary Central Neural System Lymphoma.
Bo-Tao Wang (2019)
Electrical Stimulation During Gait Promotes Increase of Muscle Cross-sectional Area in Quadriplegics: A Preliminary Study
D. Abreu (2009)
Automated Preliminary Brain Tumor Segmentation Using
Shamla Mantri (2014)
Texture analysis as a tool for medical decision support. P. 1 Recent applications for cancer early detection
D. Duda (2014)
Clinical Applicability of MRI Texture Analysis
L. Harrison (2011)
Development of computer-based algorithms for unsupervised assessment of radiotherapy contouring
Huiqi Yang (2019)
Tissue characterization: Influence of ultrasound setting on texture features in vivo
M. A. Alqahtani (2010)
MRI texture analysis of GRMD dogs using orthogonal moments: A preliminary study
Guanyu Yang (2015)
Textural features and SUV-based variables assessed by dual time point 18F-FDG PET/CT in locally advanced breast cancer
A. García-Vicente (2017)
Towards personalized diagnosis of Glioblastoma in Fluid-attenuated inversion recovery (FLAIR) by topological interpretable machine learning.
M. Rucco (2019)
Harvesting, Detecting, and Characterizing Liver Lesions from Large-scale Multi-phase CT Data via Deep Dynamic Texture Learning
Yuankai Huo (2020)
Influence of gray level and space discretization on brain tumor heterogeneity measures obtained from magnetic resonance images
D. Molina-García (2016)
Relationship between Extension or Texture Features of Late Gadolinium Enhancement and Ventricular Tachyarrhythmias in Hypertrophic Cardiomyopathy
Y. Amano (2018)
ALTEA: A Software Tool for the Evaluation of New Biomarkers for Alzheimer’s Disease by Means of Textures Analysis on Magnetic Resonance Images
C. López-Gómez (2018)
A region-based segmentation of tumour from brain CT images using nonlinear support vector machine classifier
A. P. Nanthagopal (2012)
Iris Recognition - Selecting a Fuzzy Region of Interest in Standard Eye Images
E. T. Celik (2014)
The Use of texture analysis in the morpho-functional characterization of mast cell degranulation in rainbow trout (Onchorhynchus mykiss).
M. Manera (2013)
Texture-based quantification of lumbar intervertebral disc degeneration from conventional T2-weighted MRI
S. Michopoulou (2011)
Geostatistical Entropy for Texture Analysis: An Indicator Kriging Approach
T. Pham (2014)
The use of texture analysis in breast magnetic resonance imaging
S. Waugh (2014)
A CAD system for cerebral glioma based on texture features in DT-MR images
G. D. Nunzio (2011)
White Matter Lesions and Pattern Recognition in MRI of Neurodegenerative Dementia
K. Oppedal (2016)
The development of an integrated computer based system for knee joint related clinical diagnosis, pre-surgical planning and post-operative monitoring.
Jiahui. Ho (2010)
See more
Semantic Scholar Logo Some data provided by SemanticScholar