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040 |a YDX  |b eng  |e pn  |c YDX  |d N$T  |d IDEBK  |d EBLCP  |d NLE  |d OPELS  |d N$T  |d OCLCF  |d MERER  |d OCLCQ  |d UPM  |d OCLCO  |d D6H  |d OCLCO  |d OCLCQ  |d OCLCO  |d U3W  |d OCLCQ  |d CHVBK  |d OCLCQ  |d AU@  |d WYU  |d OCLCQ  |d S2H  |d OCLCO  |d VT2  |d OCLCQ  |d OCLCO  |d OCLCQ  |d OCLCO  |d K6U  |d OCLCQ  |d SFB  |d OCLCQ  |d OCLCO 
019 |a 1002613511  |a 1066557816  |a 1235843534 
020 |a 9780128123218  |q (electronic bk.) 
020 |a 0128123214  |q (electronic bk.) 
020 |z 9780128121337 
020 |z 0128121335 
035 |a (OCoLC)1002195690  |z (OCoLC)1002613511  |z (OCoLC)1066557816  |z (OCoLC)1235843534 
050 4 |a RC78.7.D53 
072 7 |a HEA  |x 039000  |2 bisacsh 
072 7 |a MED  |x 014000  |2 bisacsh 
072 7 |a MED  |x 022000  |2 bisacsh 
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072 7 |a MED  |x 045000  |2 bisacsh 
082 0 4 |a 616.07/54  |2 23 
082 0 4 |a 610.3  |2 23 
245 0 0 |a Biomedical texture analysis :  |b fundamentals, tools and challenges /  |c edited by Adrien Depeursinge, Omar S. Al-Kadi, J. Ross Mitchell. 
260 |a London :  |b Academic Press,  |c �2017. 
300 |a 1 online resource :  |b illustrations 
336 |a text  |b txt  |2 rdacontent 
337 |a computer  |b c  |2 rdamedia 
338 |a online resource  |b cr  |2 rdacarrier 
490 1 |a The Elsevier and MICCAI society book series 
504 |a Includes bibliographical references and index. 
505 0 |a 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. 
505 8 |a 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. 
505 8 |a 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. 
505 8 |a 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. 
505 8 |a 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. 
650 0 |a Diagnostic imaging. 
650 0 |a Image processing. 
650 0 |a Image analysis. 
650 1 2 |a Diagnostic Imaging  |0 (DNLM)D003952 
650 6 |a Imagerie pour le diagnostic.  |0 (CaQQLa)201-0146124 
650 6 |a Traitement d'images.  |0 (CaQQLa)201-0029952 
650 6 |a Analyse d'images.  |0 (CaQQLa)201-0313660 
650 7 |a image processing.  |2 aat  |0 (CStmoGRI)aat300237864 
650 7 |a HEALTH & FITNESS  |x Diseases  |x General.  |2 bisacsh 
650 7 |a MEDICAL  |x Clinical Medicine.  |2 bisacsh 
650 7 |a MEDICAL  |x Diseases.  |2 bisacsh 
650 7 |a MEDICAL  |x Evidence-Based Medicine.  |2 bisacsh 
650 7 |a MEDICAL  |x Internal Medicine.  |2 bisacsh 
650 7 |a Diagnostic imaging  |2 fast  |0 (OCoLC)fst00892354 
650 7 |a Image analysis  |2 fast  |0 (OCoLC)fst00967482 
650 7 |a Image processing  |2 fast  |0 (OCoLC)fst00967501 
700 1 |a Depeursinge, Adrien. 
700 1 |a Al-kadi, Omar S. 
700 1 |a Mitchell, J. Ross. 
776 0 8 |i Print version:  |z 9780128121337  |z 0128121335  |w (OCoLC)979562345 
830 0 |a Elsevier and MICCAI Society book series. 
856 4 0 |u https://sciencedirect.uam.elogim.com/science/book/9780128121337  |z Texto completo