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E-BOOK
Title Biomedical texture analysis : fundamentals, tools and challenges / edited by Adrien Depeursinge, Omar S. Al-Kadi, J. Ross Mitchell.
Imprint London : Academic Press, ©2017.

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Description 1 online resource : illustrations
Series The Elsevier and MICCAI society book series
Elsevier and MICCAI Society book series.
Bibliog. Includes bibliographical references and index.
Note Available only to authorized UTEP users.
Subject Diagnostic imaging.
Image processing.
Image analysis.
Diagnostic Imaging.
Genre Electronic books.
Contents Front Cover; Biomedical Texture Analysis; Copyright; Contents; Preface; 1 Fundamentals of Texture Processing for Biomedical Image Analysis; 1.1 Introduction; 1.2 Biomedical texture processes; 1.2.1 Image intensity versus image texture; 1.2.2 Notation and sampling; 1.2.3 Texture functions as realizations of texture processes; 1.2.3.1 Texture stationarity; 1.2.4 Primitives and textons; 1.2.5 Biomedical image modalities; 1.3 Biomedical Texture Analysis (BTA); 1.3.1 Texture operators and aggregation functions; 1.3.2 Normalization; 1.3.3 Invariances.
1.3.3.1 Invariance and equivariance of operators1.3.3.2 Invariances of texture measurements; 1.3.3.3 Nongeometric invariances; 1.4 Conclusions; Acknowledgments; References; 2 Multiscale and Multidirectional Biomedical Texture Analysis; 2.1 Introduction; 2.2 Notation; 2.3 Multiscale image analysis; 2.3.1 Spatial versus spectral coverage of linear operators: the uncertainty principle; 2.3.2 Region of interest and response map aggregation; 2.4 Multidirectional image analysis; 2.4.1 The Local Organization of Image Directions (LOID); 2.4.2 Directional sensitivity of texture operators.
2.4.3 Locally rotation-invariant operators and moving frames representations2.4.4 Directionally insensitive, sensitive, and moving frames representations for texture classi cation: a quantitative performance comparison; 2.5 Discussions and conclusions; Acknowledgments; References; 3 Biomedical Texture Operators and Aggregation Functions; 3.1 Introduction; 3.2 Convolutional approaches; 3.2.1 Circularly/spherically symmetric lters; 3.2.2 Directional lters; 3.2.2.1 Gabor wavelets; 3.2.2.2 Maximum Response 8 (MR8); 3.2.2.3 Histogram of Oriented Gradients (HOG); 3.2.2.4 Riesz transform.
3.2.3 Learned lters3.2.3.1 Steerable Wavelet Machines (SWM); 3.2.3.2 Dictionary Learning (DL); 3.2.3.3 Deep Convolutional Neural Networks (CNN); 3.2.3.4 Data augmentation; 3.3 Gray-level matrices; 3.3.1 Gray-Level Cooccurrence Matrices (GLCM); 3.3.2 Gray-Level Run-Length Matrices (GLRLM); 3.3.3 Gray-Level Size Zone Matrices (GLSZM); 3.4 Local Binary Patterns (LBP); 3.5 Fractals; 3.6 Discussions and conclusions; Acknowledgments; References; 4 Deep Learning in Texture Analysis and Its Application to Tissue Image Classi cation; 4.1 Introduction.
4.2 Introduction to convolutional neural networks4.2.1 Neurons and nonlinearity; 4.2.2 Neural network; 4.2.3 Training; 4.2.3.1 Forward pass; 4.2.3.2 Error; 4.2.3.3 Backpropagation of the error; 4.2.3.4 Stochastic gradient descent; 4.2.3.5 Weights initialization; 4.2.3.6 Regularization; 4.2.4 CNN; 4.2.4.1 Main building blocks; 4.2.4.2 CNN architectures; 4.2.4.3 Visualization; 4.3 Deep learning for texture analysis: literature review; 4.3.1 Early work; 4.3.2 Texture speci c CNNs; 4.3.3 CNNs for biomedical texture classi cation; 4.4 End-to-end texture CNN: proposed solution; 4.4.1 Method.
Other Author Depeursinge, Adrien.
Al-kadi, Omar S.
Mitchell, J. Ross.
Other Title Print version: 9780128121337 0128121335