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Biomedical texture analysis : fundamentals, tools and challenges /
| Clasificación: | Libro Electrónico |
|---|---|
| Otros Autores: | , , |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
London :
Academic Press,
�2017.
|
| Colección: | Elsevier and MICCAI Society book series.
|
| Temas: | |
| Acceso en línea: | Texto completo |
Tabla de Contenidos:
- 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.