Book: Medical Image Recognition, Segmentation and Parsing.

Official Weblink

Table of contents

Chapter 1 – Introduction to Medical Image Recognition, Segmentation, and Parsing

Part 1: Automatic Recognition and Detection Algorithms

Chapter 2 – A Survey of Anatomy Detection
Chapter 3 – Robust Multi-Landmark Detection Based on Information Theoretic Scheduling
Chapter 4 – Landmark Detection Using Submodular Functions
Chapter 5 – Random Forests for Localization of Spinal Anatomy
Chapter 6 – Integrated Detection Network for Multiple Object Recognition
Chapter 7 – Organ Detection Using Deep Learning

Part 2: Automatic Segmentation and Parsing Algorithms

Chapter 8 – A Probabilistic Framework for Multiple Organ Segmentation Using Learning Methods and Level Sets
Chapter 9 – LOGISMOS: A Family of Graph-Based Optimal Image Segmentation Methods
Chapter 10 – A Context Integration Framework for Rapid Multiple Organ Parsing
Chapter 11 – Multiple-Atlas Segmentation in Medical Imaging
Chapter 12 – An Overview of the Multi-Object Geometric Deformable Model Approach in Biomedical Imaging
Chapter 13 – Robust and Scalable Shape Prior Modeling via Sparse Representation and Dictionary Learning

Part 3: Recognition, Segmentation and Parsing of Specific Objects

Chapter 14 – Semantic Parsing of Brain MR Images
Chapter 15 – Parsing of the Lungs and Airways
Chapter 16 – Aortic and Mitral Valve Modeling From Multi-Modal Image Data
Chapter 17 – Model-Based 3D Cardiac Image Segmentation With Marginal Space Learning
Chapter 18 – Spine Disk and RIB Centerline Parsing
Chapter 19 – Data-Driven Detection and Segmentation of Lymph Nodes
Chapter 20 – Polyp Segmentation on CT Colonography
Chapter 21 – Detect Cells and Cellular Behaviors in Phase Contrast Microscopy Images

Book Description

This book describes the technical problems and solutions for automatically recognizing and parsing a medical image into multiple objects, structures, or anatomies. It gives all the key methods, including state-of- the-art approaches based on machine learning, for recognizing or detecting, parsing or segmenting, a cohort of anatomical structures from a medical image.

Written by top experts in Medical Imaging, this book is ideal for university researchers and industry practitioners in medical imaging who want a complete reference on key methods, algorithms and applications in medical image recognition, segmentation and parsing of multiple objects.


  • Research challenges and problems in medical image recognition, segmentation and parsing of multiple objects
  • Methods and theories for medical image recognition, segmentation and parsing of multiple objects
  • Efficient and effective machine learning solutions based on big datasets
  • Selected applications of medical image parsing using proven algorithms

Key Features

  • Provides a comprehensive overview of state-of-the-art research on medical image recognition, segmentation, and parsing of multiple objects
  • Presents efficient and effective approaches based on machine learning paradigms to leverage the anatomical context in the medical images, best exemplified by large datasets
  • Includes algorithms for recognizing and parsing of known anatomies for practical applications


  • ISBN: 978-0-12-802581-9
  • Language: English
  • Published: 2016
  • Copyright: Copyright © 2016 Elsevier Inc. All rights reserved.
  • Imprint: Academic Press
  • No. of pages: 542
  • DOI:
  • Editors: S. Kevin Zhou

PDF Downloading

NOTE: The zip file is encrypted. Whoever has interested in the passcode please email public[at] with the following format. We will send the passcode to your e-mail within 3 days. If you have any problems, please contact us.

Title: MIRSP Book passcode

[Main message as below]

Name: First name Last name

E-mail:  xxxx@your_organization

Organization: xxxxxxx”.

We agree that

1) we use the pdf files only for research purposes,

2) we will not distribute the PDF under any circumstances,

3) we will read the chapters and cite the book in an appropriate context, and

4) we will share our success stories of using the experiences learned from the book.