The field of medical imaging seen rapid development over the last two decades and has consequently revolutionized the way in which modern medicine is practiced. Diseases and their symptoms are constantly changing therefore continuous updating is necessary for the data to be relevant. Diseases fall into different categories, even a small difference in symptoms may result in categorising it in a different group altogether. Thus analysing data accurately is of critical importance. This book concentrates on diagnosing diseases like cancer or tumor from different modalities of images.
This book is divided into the following domains: Importance of big data in medical imaging, pre-processing, image registration, feature extraction, classification and retrieval. It is further supplemented by the medical analyst for a continuous treatment process. The book provides an automated system that could retrieve images based on user’s interest to a point of providing decision support. It will help medical analysts to take informed decisions before planning treatment and surgery. It will also be useful to researchers who are working in problems involved in medical imaging.
Big data in Medical Image Processing
An Introduction on big data, Medical Image Processing, Modality of medical images, Importance of medical images, Challenges in medical images, Hadoop & Map reduce technique.
Introduction, Importance of Speckles in medical images, Types of filter, Different methodologies, Metrics for speckle reduction.
Importance of medical image registration, Mono modal registration, Multi modal image registration, Intensity vs Feature based registration, Similarity measures – correlation coefficients, Mutual information, Geometric transformation, Optimization techniques, Different approaches and its implementation, Applications of medical image registration – case study.
Texture Feature Extraction
Introduction on Texture analysis – Importance of dimensionality reduction- Types of feature extraction – Haralick texture features – feature selection- metrics
Image Classification & Retrieval
Introduction on Machine learning techniques, Supervised vs unsupervised medical image classification, Relevance feedback classifier, Binary vs multiple SVM, Neural network, Fuzzy classifier, Image Retrieval – conclusion