Big Data in Multimodal Medical Imaging

Ayman El-Baz, Jasjit S. Suri, Ayman El-Baz, Jasjit S. Suri

• Publisher: Chapman and Hall/CRC
• Year: 2019
• ISBN:
  9781351380720
• Edition: 1
• Topic: Computer Science
• Subtopic: Programming Games
• Index: Computer Science

There is an urgent need to develop and integrate new statistical, mathematical, visualization, and computational models with the ability to analyze Big Data in order to retrieve useful information to aid clinicians in accurately diagnosing and treating patients. The main focus of this book is to review and summarize state-of-the-art big data and deep learning approaches to analyze and integrate multiple data types for the creation of a decision matrix to aid clinicians in the early diagnosis and identification of high risk patients for human diseases and disorders. Leading researchers will contribute original research book chapters analyzing efforts to solve these important problems.

Chapter 1

Multimodal Imaging Radiomics and Machine Learning

Gengbo Liu, Youngho Seo, Debasis Mitra, and Benjamin L. Franc

1.1 Introduction

1.2 High-Quality Standardized Imaging Data Acquisition

1.3 Segmentation of the Region of Interest from the Image

1.4 High-Throughput Feature Extraction

1.4.1 First-Order Features with Image Statistics

1.4.2 Shape- and Size-Based Features

1.4.3 Second-Order Features

1.4.4 Wavelet-Based Features

1.4.5 Feature Extraction Software

1.5 Clinical Predictive Modeling with Machine Learning

1.5.1 Feature Selection Methods

1.5.2 Radiomic Features Classifying Outcomes

1.5.2.1 Clustering Algorithms

1.5.2.2 Regression Model

1.5.2.3 Support Vector Machine

1.6 Big Data and Machine Learning

1.7 Conclusions

Appendix

References

A. First-Order Statistics Features

B. Shape- and Size-Based Features

C. GLCM Features

D. GLRLM Features

E. Neighborhood Gray Tone Difference Matrix (NGTDM) Features

1.1 Introduction

According to Gerlinger et al. [1], single tumor biopsy samples can cause underestimation of intratumor heterogeneity. The heterogeneity in the gene, cell and tissue of the solid tumor limited the accuracy and representation of invasive detection results. In order to develop the concept of personalized medicine and attain a better understanding of tumor heterogeneity noninvasively, the concept of radiomics was proposed by Lambin et al. [2] in 2012. A suffix of “omics”, such as genomics, refers to the objects of study of a certain field and its relationships to biology and medicine. As genomics attempts to relate genes to phenotype, radiomics studies relate medical images to phenotypes of disease. Much of radiomics literature has focused on applications in oncologic medical imaging which presents a noninvasive, often quantitative observation of the overall shape of the tumor, the tumor development process and treatment response monitoring, providing a reliable mechanism to quantify tumor heterogeneity. Lambin et al. [2] hypothesized that microscopic-level gene or protein pattern changes can be expressed in macroscopic imaging features acquired during medical imaging. Therefore, Lambin et al. proposed that the high-throughput extraction of large amounts of image features from radiographic images would be able to capture intratumoral heterogeneity in a noninvasive way. Compared to traditional radiology practice, which is primarily based on visual interpretation and simple quantitative measurements, radiomics can dig deeper into data contained within medical images and potentially provide further objective support for clinical decisions. Due to the large number of image features generated from radiomics, many machine learning algorithms are...

Table of contents

• Cover
• Half-Title
• Title
• Copyright
• Dedication
• Contents
• Preface
• Editors
• Contributors
• Acknowledgements
• 1 Multimodal Imaging Radiomics and Machine Learning
• 2 Multimodal Medical Image Fusion in NSCT Domain
• 3 Computer Aided Diagnosis in Pre-Clinical Dementia: From Single-Modal Metrics to Multi-Modal Fused Methodologies
• 4 Automated Diagnosis and Prediction in Cardiovascular Diseases Using Tomographic Imaging
• 5 Big Data in Computational Health Informatics
• 6 Fast Dual Optimization for Medical Image Segmentation
• 7 Non-Parametric Bayesian Estimation of Rigid Registration for Multi-Contrast Data in Big Data Analysis
• 8 Multimodal Analysis in Biomedicine
• 9 Towards Big Data in Acute Renal Rejection
• 10 Overview of Deep Learning Algorithms Applied to Medical Images
• 11 Big Data in Prostate Cancer
• 12 Automatic Detection of Early Signs of Diabetic Retinopathy Based on Feature Fusion from OCT and OCTA Scans
• 13 Computer Aided Diagnosis System for Early Detection of Diabetic Retinopathy Using OCT Images
• Index