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Cellular Image Classification

Background Clinical Problems: A Case Study on Autoimmune Diseases Cellular Imaging: A Case Study on Indirect Immunofluorescence Computer-Aided Diagnosis Experimental Datasets in the Book The ICPR2012 Dataset The ICIP2013 Training Dataset Structure of the Chapters References Fundamentals Optical Systems for Cellular Imaging Laser Scanning Confocal Microscope Fluorescent Probe and Laser Scanning Confocal Microscopy Imaging Techniques Fundamentals of Confocal Laser Scanning Microscopy Characteristics of Confocal Laser Scanning Microscope Application of Confocal Laser Scanning Microscope Multi-photon Fluorescence Imaging Total Internal Reflection Fluorescence Microscope The Principle of Total Internal Reflection Total Internal Reflection Fluorescence Microscopy (TIRFM)The Application of TIRFM Near-Field Scanning Optical Microscopy Imaging Technology Working Principle of the Near Field The Characteristics and Classification of Near-Field Scanning Optical Microscope The Application of Near-Field Scanning Optical Microscope Optical Coherence Tomography Technology Feature Extraction Low-Level Features Local Binary Patterns Scale Invariant Feature Transform Scale-Space Extrema Detection Keypoint Localization Orientation Assignment Keypoint Descriptor Mid-Level Features Classification Support Vector Machine Nearest Neighbor Classifier References Optical Systems for Cellular Imaging Introduction Optical Tweezer Introduction to Optical Tweezers Gradient and Scattering Force of Optical Tweezers Three-Dimensional Optical Trap Low-Order Fiber Mode LF2i Fiber Mode Coupling Theory Analysis of Field Distribution in Optical Fiber Solution to LP2i Mode Selective Excitation ofLP2l Mode The Twisting and Bending Characteristics ofLP2 Mode Why LP21 Mode?Optical Tweezer Using Focused LP2i Mode Fiber Axicons Cell Manipulation Modeling of Optical Trapping Force Force Analysis of Mie Particles in Optical Trap Gaussian Beam Simulation of Light Force on Mie Particle Summary References Image Representation with Bag-of-Words Introduction Coding Vector Quantization Soft Assignment Coding Locality-Constrained Linear Coding Pooling Summary References Image Coding Introduction Linear Local Distance Coding Method Distance Vector Local Distance Vector The Algorithm Framework Experiments and Analyses Experiment Setup Experimental Results on the ICPR2012 Dataset Experimental Results on the ICIP2013 Training Dataset Discussion Summary References Encoding Image Features Introduction Encoding Rotation Invariant Features of Images Pairwise LTPs with Spatial Rotation Invariant Encoding the SIFT Features with BoW Framework Experiments and Analyses Experiment Setup Experimental Results on the ICPR2012 Dataset Experimental Results on the ICIP2013 Training Dataset Discussion Summary References Defining Feature Space for Image Classification Introduction Adaptive Co-occurrence Differential Texton Space for Classification Co-occurrence Differential Texton Adaptive CoDT Feature Space HEp-2 Cell Image Representation in the Adaptive CoDT Feature Space Experiments and Analyses Experiment Setup Experimental Results on the ICPR2012 Dataset Experimental Results on the ICIP2013 Training Dataset Discussion Summary References Major Techniques Developed in the Book Directions and Future Work References

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Ultrasound Imaging: Classification Methods for Masses
Breast cancer is a common cancer among women all over the world. Combined with mammography, ultrasonography is used to suggest the degree of suspicion of malignancy to help determine whether follow-up or biopsy are necessary. Sonographic features that are assessed include shape, margin, echotexture,...
(Computational Anatomy Based on Whole Body Imaging: Basic Principles of Computer-Assisted Diagnosis and Therapy)
Cellular Imaging: A Case Study on Indirect Immunofluorescence
The antinuclear antibody (ANA), which refers to a unique group of autoantibodies targeting at the nuclear contents of the cell, has significant relation to specific AD, such as systemic lupus erythematosus (SLE), systemic sclerosis and rheumatoid arthritis. The identification of ANAs plays an important...
(Cellular Image Classification)
Optical Systems for Cellular Imaging
This section introduces application of optical technology in cell imaging, highlighting the basic principle and main features of a list of microscopic techniques, including laser scanning confocal microscope, multi-photon fluorescence imaging, total internal reflection fluorescence microscopy, near-field...
(Cellular Image Classification)
Optical Systems for Cellular Imaging
Abstract Optical imaging and manipulation of cells, associated with computer- aided cell recognition algorithm, are essential measures to execute cell identification and classification in modern biological and medical engineering. Generally, implementation of physically classification of cells/bio-particles...
(Cellular Image Classification)
Defining Feature Space for Image Classification
Abstract In this chapter, we design a Co-occurrence Differential Texton (CoDT) feature to represent the local image patches of HEp-2 cell image, and a generative model is built to adaptively characterize the CoDT feature space. We further exploit a more discriminant representation for the HEp-2...
(Cellular Image Classification)
Image Classification
The SVM is a type of ML method based upon the models of statistics. It can be a supervised learning method executed to identify designs in various fields. After the set of discriminate features has been selected with the earlier methods, the classification is executed to classify the various kinds of...
(Artificial Intelligence for the Internet of Health Things)
Ground Truth Data for Remote Sensing Image Classification
In machine learning, reference data samples are a genuine requirement for training as well as assessing the performance of learning algorithms. Based on reference data as seed data, machine learning algorithms can develop cognitive thinking like human beings. Supervised classifcation is a genuine...
(Fuzzy Machine Learning Algorithms for Remote Sensing Image Classification)
Convolutional Neural Networks Image Classification
A convolutional neural network (CNN) has different pre-processing layers in comparison to MLP neural networks as shown in Figure 4.4. Basically, CNN uses features called visual cortex (Hubei and Wiesel, 1968; Fukushima, 1980). The visual cortex in the brain is the main cortical region. Its main work...
(Fuzzy Machine Learning Algorithms for Remote Sensing Image Classification)
A Review of Handcrafted Feature Extraction Techniques for Content-Based Image Classification
Prelude An enormous quantity of valuable information is accessible from the image data archived in colossal databases. Assorted sources, including entertainment media, mainstream media, defense, sports events and social media platforms, contribute readily to form these image data archives [1]....
(Content-Based Image Classification: Efficient Machine Learning Using Robust Feature Extraction Techniques)
Feature Extraction Using Sorted Block Truncation Codingfor Content-Based Image Classification
The comparative results are given in Table 3.2.Image processing algorithms have considered noise extending adverse influence on image data by staining it to a large extent. The fact is evident inplenty of literature that has introduced varied noise elimination techniquesto decrease the effect of noise...
(Content-Based Image Classification: Efficient Machine Learning Using Robust Feature Extraction Techniques)