Multivariate image analysis is an analytical technique used in many areas including remote sensing (satellites), medicine (MRIs) and the food and chemical industries. Though there are many good books on using this technique in remote sensing, this is one of the first that focuses on its use in chemistry, medicine and food analysis.

Taal / Language : English

Inhoudsopgave:

Preface		xi
1 Introduction		1	(22)
1.1 The image: pictures and imaging		1	(2)
1.2 Digital images		3	(4)
1.3 Spatial resolution		7	(3)
1.4 Intensity resolution		10	(2)
1.5 Color vision and color images		12	(1)
1.6 Operations on intensity images		13	(3)
1.7 Image-to-image operations		16	(1)
1.8 The relation of image analysis to data analysis		17	(2)
Appendix 1.1 The experiment		19	(4)
2 Images in the Natural Sciences and in Chemistry		23	(22)
2.1 Introduction		23	(4)
2.2 Physical and spectrometric principles of imaging		27	(4)
2.3 Imaging		31	(3)
2.4 Multivariate imaging		34	(7)
2.5 Multitemporal multivariate images		41	(1)
2.6 Spectral resolution versus spatial resolution		42	(3)
3 Medical Diagnostic Tools: Magnetic Resonance Imaging		45	(18)
3.1 General description of the magnetic resonance phenomenon		45	(2)
3.2 Historical background		47	(2)
3.3 The basic concept		49	(3)
3.4 Magnetic resonance		52	(1)
3.5 Relaxation		53	(2)
3.6 Imaging		55	(2)
3.7 Experimental fundamentals of magnetic resonance imaging		57	(2)
3.8 Interpretation of T(1)-, T(2)- and p-weighted images		59	(1)
3.9 Magnetic resonance imaging in preclinical research		60	(1)
3.10 Work in progress: multivariate magnetic resonance imaging		61	(1)
Appendix 3.1 In vivo magnetic resonance literature		62	(1)
4 Principal Component Analysis with Basic Matrix Algebra and Statistics		63	(36)
4.1 The data matrix and its elements		63	(5)
4.2 Some statistical definitions		68	(3)
4.3 Simple statistical operations on matrices		71	(7)
A simple demonstration in high-level computer language		74	(4)
4.4 Some simplifications of equations		78	(3)
Calculating Z, Z(cov) and Z(cor)		79	(2)
4.5 Eigenvalues and eigenvectors		81	(6)
Eigenvector and eigenvalue demonstration in high-level language		84	(3)
4.6 Principal component analysis		87	(8)
Singular value decomposition		90	(5)
A PCA example in high-level language		95	(1)
4.7 Geometrical interpretation: variable space		95	(4)
5 Preprocessing and Transformation of Images		99	(8)
5.1 The multivariate image as a three-way array: simple scalings		99	(3)
5.2 Non-linear scaling		102	(2)
5.3 Other three-way scalings		104	(3)
6 Principal Component Analysis on Multivariate Images		107	(30)
6.1 The multivariate image as a three-way array: three-way linear algebra		107	(3)
6.2 A simplification: reorganizing three-way arrays		110	(3)
6.3 Principal component analysis on three-way arrays: loadings		113	(10)
An example of a loadings calculation		118	(5)
6.4 Principal component analysis on three-way arrays: scores		123	(14)
Demonstration of a score calculation		132	(5)
7 Principal Component Analysis on Covariance and Correlation Matrices of Multivariate Images		137	(20)
7.1 Mean-centering and scaling of variables		137	(20)
PCA calculation using covariance and correlation matrices		145	(9)
Image calculations in high-level language		154	(3)
8 Visualization in Score Plots		157	(18)
8.1 Score images and score plots		157	(9)
8.2 Overview of the score plots: interpretation problems		166	(2)
Score plots in high-level language		167	(1)
8.3 Analysis of score plots		168	(7)
Historical overview		171	(4)
9 The Residual and Residual Images		175	(14)
9.1 The residual		175	(14)
Residual calculations		182	(7)
10 Local Models and Sampling		189	(26)
10.1 Local models		189	(2)
10.2 Background and regions of interest		191	(4)
10.3 Local models in principal component analysis		195	(3)
10.4 An example of the use of local models		198	(7)
11 Miscellaneous Examples		215	(20)
11.1 Polarization angle in microscopy		215	(6)
11.2 Wetlands and lakes in a satellite image		221	(8)
11.3 Microscopy of biological materials		229	(6)
12 Multivariate Image Analysis Applied to Magnetic Resonance Images		235	(34)
12.1 Visualization of multivariate magnetic resonance images: interpretation of medical images		235	(1)
12.2 Differently weighted magnetic resonance images		236	(2)
12.3 Experimental design in magnetic resonance imaging		238	(2)
12.4 Segmentation and classification		240	(2)
12.5 Segmentation by multivariate image analysis		242	(2)
12.6 An experimental magnetic resonance imaging study		244	(7)
12.7 Quality control: an experiment with apples		251	(4)
12.8 The multivariate analysis of the 16 x 256 x 256 apple image		255	(4)
12.9 A clinical magnetic resonance imaging study		259	(10)
13 Multivariate Image Regression		269	(26)
13.1 Regression between images		269	(4)
13.2 Univariate regression between images		273	(8)
A demonstration of univariate image regression in high-level language		279	(2)
13.3 Multivariate regression and discriminant regression		281	(5)
A demonstration of multivariate image regression in high-level language		284	(2)
13.4 Multivariate image regression in the literature		286	(1)
Appendix 13.1 Simple univariate regression		287	(3)
Appendix 13.2 Multiple regression		290	(3)
Appendix 13.3 The goals of regression analysis		293	(2)
14 Epilog		295	(4)
References		299	(14)
Index		313

Extra informatie: Gebonden: harde kaft, 330 pagina's Verschenen: november 1996 Gewicht: 672 gram Formaat: 238 x 164 x 24 mm Wiley-Blackwell

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