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Texture Analysis for Magnetic Resonance Imaging


It is the aim of this book to introduce and explain both the concepts of texture analysis in general and specifically in medical imaging. Magnetic resonance imaging is the particular focus and the range of established and possible clinical applications in that modality is dealt with in detail. CD-ROM with MaZda program included. The MaZda is a computer program for calculation of texture parameters ( features) in digitized images. Hard cover, 234 pages, ISBN : 80-903660-0-7 Publisher: Med4publishing s.r.o., 2006

Code P/01
Author/Authors Editors : M.Hajek, M.Dezortova, A.Materka, R.Lerski. List of all authors in detailed description of publication
Price € 90,00

Detailed description

List of authors in alphabetical order:

BENOIT-CATTIN,Hugues / BEZY-WENDLING, Johanne / COLLEWET Guylaine / de CERTAINES, Jacques / DEZORTOVA, Monika / ELIAT, Pierre-Antoine / HAJEK, Milan / JIRAK, Daniel / KARLSSON, Anders / KRETOWSKI M. / LERSKI, Richard / LUNDERVOLD, Arvid / MATERKA, Andrzej / SCHAD, Lothar / SIERRA, Raimundo / SPISNI, Alberto / STOEDKILDE-JOERGENSEN, Hans / STRZELECKI, Michal / SZCZYPINSKI, Piotr / SZEKELY, Gabor


The measurement of texture in image data has been of interest since the 1970s, primarily as a means of discriminating regions of an image for automatic segmentation. The meaning of texture in an image is essentially the patern of small-scale brightness, darkness and granularity in defined regions. Initially, the possibilities of this method were limited by the availability of high-quality digital image data but, in recent years, the wide availability of digital images has opened up a whole range of applications.

In medical imaging, the expansion of applications of texture analysis has been very marked rangingover almost all modalities from X-ray through ultrasound to nuclear medicine. The dramatic rise and development of magnetic resonance imaging (MRI) with its superb image quality has made this technique a prime candidate for texture analysis application. This book was written by the pioneers of texture analysis in MRI who have introduced and developed the methodology and applications in two COST projects supported by the European Community.



Pavel Taimr, M. (Erasmus MC, Rotterdam, NL)


… At the present time, in addition to the subjective evaluation of morphological changes observable on MR images, there is an effort to use objective mathematical-statistical methods to find even the tiniest changes commonly invisible to human eye. The presented book is the first comprehensive textbook ever dealing with texture analysis and together with the software MaZda enables better understanding of the changes in 2D and 3D digital images. The methodology can be used not only in biomedical applications but also in other fields of interest (industrial, agricultural, etc.). The editors successfully assembled group of specialists from various areas and the result is a unique one-volume treatise for all interested scientists and clinicians. Highly recommended!


Dr. Karoly Héberger (Chemical Research Center, Hungarian Academy of Sciences

Budapest, Hungary)


A new ‘stopgap’ work appeared! The book is a multi-author joint venture resulting from a European project termed COST B11: ”Quantitation of Magnetic Resonance Image Texture”. Analysis of magnetic resonance (MR) images (textures) is of crucial importance. The entire book is devoted to different aspects of texture analysis i.e. analysis of spatial arrangements of visual patterns in an image. Various chapters cover different aspects of MR analysis from theoretical foundations to chemometric data evaluation. The usefulness of the book extends to the clinical diagnosis, standardization tasks and covers a lot of interesting areas such as pattern recognition in food chemistry. Therefore, the book can be recommended to medical and analytical chemists, physicists, and statisticians just to name a few in the academic world and industry alike.




1. What is the texture?
1.1 Introduction
1.2 Texture analysis
1.3 Approaches to texture description
1.4 Feature extraction techniques
1.4.1 Statistical approach
1.4.2 Model-based approach
1.4.3 Image transform approach
1.5 Conclusions
2. Modelling: from living body to MRI
2.1 Introduction
2.2 The steps of a model designing
2.3 MRI and texture analysis
2.4 Modelling tissue morphology: tumour growth
2.4.1 Variable anatomical scene generation
2.4.2 Growth models
2.4.3 Cellular automaton
2.4.4 Particle system
2.4.5 Validation
2.5 Modelling vascularization: hepatic vascular tree
2.5.1 Vascular system modelling
2.5.2 Experimental results
2.6 Modelling pharmacokinetic processes: MR dynamic enhanced relaxometry
2.6.1 Basic DCE-MRI and dynamic relaxometry
2.6.2 Extraction of physiological parameters by modelling
2.6.3 Association of DCE-MRI and TA
2.7 From modelling to MR image simulation
2.7.1 Different approaches of MR Image simulation
2.7.2 Overview of the simulators of MR images
2.7.3 Time of computation
2.8 Virtual object definition
2.9 Conclusions
3. Statistical methods
3.1 Introduction
3.2 Random vectors
3.3 Estimation of parameters
3.4 Texture analysis and pattern recognition
3.5 Classifiers
3.6 Feature reduction and selection
3.7 Texture analysis example
3.8 Image segmentation
3.9 Conclusions
4. MaZda
5. Phantoms for texture analysis of MR images
5.1. Introduction - phantoms for MR imaging
5.2. Phantoms for texture analysis of MR images
5.2.1 Construction of phantom for TA
5.2.2 MR imaging protocol for phantom tests
5.2.3 Data evaluation
5.3. Summary of phantom tests
5.3.1 Construction and evaluation of PSAG phantoms
5.3.2 Texture parameters of PSAG phantoms
5.3.3 The influence of resolution on the separation of different structures
5.3.4 Test of the long-term stability of PSAG phantoms
5.3.5 Multicentre study
5.4 Conclusions
6. Influence of resolution and signal to noise ratio on MR image texture
6.1 Introduction
6.2 A short survey of measurement techniques and data collection strategies
6.2.1 The concept of sequence parameters Spin Echo (SE) technique Gradient Echo (GE) techniques
6.2.2 The concept of signal to noise ratio, SNR
6.2.3 Slice profile
6.2.4 k-space
6.2.5 RF excitation
6.3 The effect of SNR on image texture parameters
6.3.1 The effect of SNR on visual texture
6.3.2 The effect of SNR on quantitative texture analysis
6.4 The effect of spatial resolution on image texture parameters
6.4.1 Class-specific texture parameters as a function of voxel size
6.4.2 Quantification of structural anisotropy in trabecular bone using texture analysis
6.5 Conclusions
7. Clinical applications of texture analysis
7.1 Introduction
7.2 Brain
7.2.1 Brain symmetry and structure
7.2.2 Alzheimer’s disease
7.2.3 Epilepsy Hippocampal sclerosis Neocortical epilepsy
7.2.4 Multiple sclerosis
7.2.5 Brain tumours
7.3 Liver
7.4 Urogenital system
7.5 Bone
7.6 Muscle
7.6.1 Muscle diseases
7.6.2 Muscle tone
7.7 Breast
7.8 Head and neck
7.9 New techniques and automation
7.10 Conclusions
8. Textures in MR images of food products
8.1 Introduction
8.2 Texture analysis of MR images of food products
8.2.1 Examples of TA applications
8.2.2 Qualitative evaluation of textural characteristics of food products by MRI
8.2.3 Quantitative evaluation of structure and textural changes in food
8.3 Application of MR relaxometry to the study of food texture
9. The basics of magnetic resonance
9.1 Introduction
9.2 The principle of MR
9.3 The relaxation processes
9.4 MR image formation
9.5 MR hardware