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Table of Contents
Preface; Contents; 1 Introduction; 1.1 Background; 1.1.1 Clinical Problems: A Case Study on Autoimmune Diseases; 1.1.2 Cellular Imaging: A Case Study on Indirect Immunofluorescence; 1.2 Computer-Aided Diagnosis; 1.3 Experimental Datasets in the Book; 1.3.1 The ICPR2012 Dataset; 1.3.2 The ICIP2013 Training Dataset; 1.4 Structure of the Chapters; References; 2 Fundamentals; 2.1 Optical Systems for Cellular Imaging; 2.1.1 Laser Scanning Confocal Microscope; 2.1.2 Multi-photon Fluorescence Imaging; 2.1.3 Total Internal Reflection Fluorescence Microscope
2.1.4 Near-Field Scanning Optical Microscopy Imaging Technology2.1.5 Optical Coherence Tomography Technology; 2.2 Feature Extraction; 2.2.1 Low-Level Features; 2.2.2 Mid-Level Features; 2.3 Classification; 2.3.1 Support Vector Machine; 2.3.2 Nearest Neighbor Classifier; References; 3 Optical Systems for Cellular Imaging; 3.1 Introduction; 3.2 Optical Tweezer; 3.2.1 Introduction to Optical Tweezers; 3.2.2 Gradient and Scattering Force of Optical Tweezers; 3.2.3 Three-Dimensional Optical Trap; 3.3 Low-Order Fiber Mode LP21; 3.3.1 Fiber Mode Coupling Theory
3.3.2 Analysis of Field Distribution in Optical Fiber 3.3.3 Solution to LP21 Mode; 3.3.4 Selective Excitation of LP21 Mode; 3.3.5 The Twisting and Bending Characteristics of LP21 Mode; 3.3.6 Why LP21 Mode?; 3.4 Optical Tweezer Using Focused LP21 Mode; 3.4.1 Fiber Axicons; 3.4.2 Cell Manipulation; 3.5 Modeling of Optical Trapping Force; 3.5.1 Force Analysis of Mie Particles in Optical Trap; 3.5.2 Gaussian Beam; 3.5.3 Simulation of Light Force on Mie Particle; 3.6 Summary; References; 4 Image Representation with Bag-of-Words; 4.1 Introduction; 4.2 Coding; 4.2.1 Vector Quantization
4.2.2 Soft Assignment Coding4.2.3 Locality-Constrained Linear Coding; 4.3 Pooling; 4.4 Summary; References; 5 Image Coding; 5.1 Introduction; 5.2 Linear Local Distance Coding Method; 5.2.1 Distance Vector; 5.2.2 Local Distance Vector; 5.2.3 The Algorithm Framework; 5.3 Experiments and Analyses; 5.3.1 Experiment Setup; 5.3.2 Experimental Results on the ICPR2012 Dataset; 5.3.3 Experimental Results on the ICIP2013 Training Dataset; 5.3.4 Discussion; 5.4 Summary; References; 6 Encoding Image Features; 6.1 Introduction; 6.2 Encoding Rotation Invariant Features of Images
6.2.1 Pairwise LTPs with Spatial Rotation Invariant6.2.2 Encoding the SIFT Features with BoW Framework; 6.3 Experiments and Analyses; 6.3.1 Experiment Setup; 6.3.2 Experimental Results on the ICPR2012 Dataset; 6.3.3 Experimental Results on the ICIP2013 Training Dataset; 6.3.4 Discussion; 6.4 Summary; References; 7 Defining Feature Space for Image Classification; 7.1 Introduction; 7.2 Adaptive Co-occurrence Differential Texton Space for Classification; 7.2.1 Co-occurrence Differential Texton; 7.2.2 Adaptive CoDT Feature Space
2.1.4 Near-Field Scanning Optical Microscopy Imaging Technology2.1.5 Optical Coherence Tomography Technology; 2.2 Feature Extraction; 2.2.1 Low-Level Features; 2.2.2 Mid-Level Features; 2.3 Classification; 2.3.1 Support Vector Machine; 2.3.2 Nearest Neighbor Classifier; References; 3 Optical Systems for Cellular Imaging; 3.1 Introduction; 3.2 Optical Tweezer; 3.2.1 Introduction to Optical Tweezers; 3.2.2 Gradient and Scattering Force of Optical Tweezers; 3.2.3 Three-Dimensional Optical Trap; 3.3 Low-Order Fiber Mode LP21; 3.3.1 Fiber Mode Coupling Theory
3.3.2 Analysis of Field Distribution in Optical Fiber 3.3.3 Solution to LP21 Mode; 3.3.4 Selective Excitation of LP21 Mode; 3.3.5 The Twisting and Bending Characteristics of LP21 Mode; 3.3.6 Why LP21 Mode?; 3.4 Optical Tweezer Using Focused LP21 Mode; 3.4.1 Fiber Axicons; 3.4.2 Cell Manipulation; 3.5 Modeling of Optical Trapping Force; 3.5.1 Force Analysis of Mie Particles in Optical Trap; 3.5.2 Gaussian Beam; 3.5.3 Simulation of Light Force on Mie Particle; 3.6 Summary; References; 4 Image Representation with Bag-of-Words; 4.1 Introduction; 4.2 Coding; 4.2.1 Vector Quantization
4.2.2 Soft Assignment Coding4.2.3 Locality-Constrained Linear Coding; 4.3 Pooling; 4.4 Summary; References; 5 Image Coding; 5.1 Introduction; 5.2 Linear Local Distance Coding Method; 5.2.1 Distance Vector; 5.2.2 Local Distance Vector; 5.2.3 The Algorithm Framework; 5.3 Experiments and Analyses; 5.3.1 Experiment Setup; 5.3.2 Experimental Results on the ICPR2012 Dataset; 5.3.3 Experimental Results on the ICIP2013 Training Dataset; 5.3.4 Discussion; 5.4 Summary; References; 6 Encoding Image Features; 6.1 Introduction; 6.2 Encoding Rotation Invariant Features of Images
6.2.1 Pairwise LTPs with Spatial Rotation Invariant6.2.2 Encoding the SIFT Features with BoW Framework; 6.3 Experiments and Analyses; 6.3.1 Experiment Setup; 6.3.2 Experimental Results on the ICPR2012 Dataset; 6.3.3 Experimental Results on the ICIP2013 Training Dataset; 6.3.4 Discussion; 6.4 Summary; References; 7 Defining Feature Space for Image Classification; 7.1 Introduction; 7.2 Adaptive Co-occurrence Differential Texton Space for Classification; 7.2.1 Co-occurrence Differential Texton; 7.2.2 Adaptive CoDT Feature Space