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Artificial Intelligence in Decision Support Systems for Diagnosis in Medical Imaging

  • Book
  • © 2018

Overview

Editors:
  1. Kenji Suzuki

    1. Medical Imaging Research Center and Department of Electrical and Computer Engineering, Illinois Institute of Technology, Chicago, USA

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  2. Yisong Chen

    1. Key Laboratory of Machine Perception (Ministry of Education), School of Electronics Engineering and Computer Science, Peking University, Beijing, China

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    You can also search for this editor in PubMed   Google Scholar

  • Presents a comprehensive overview of the state of the art in artificial intelligence for medical-image-based diagnosis and treatment
  • Demonstrates that intelligent technologies have now reached the practical level and are rapidly becoming available in clinical practices in hospitals
  • Medical imaging offers essential information on patients medical condition

Part of the book series: Intelligent Systems Reference Library (ISRL, volume 140)

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Table of contents (13 chapters)

  1. Front Matter

    Pages i-xxi
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  3. Computer-Aided Detection

    1. Front Matter

      Pages 85-85
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    2. Automated Lung Nodule Detection Using Positron Emission Tomography/Computed Tomography

      • Atsushi Teramoto, Hiroshi Fujita
      Pages 87-110

    3. Detecting Mammographic Masses via Image Retrieval and Discriminative Learning

      • Menglin Jiang, Shaoting Zhang, Dimitris N. Metaxas
      Pages 111-132

  4. Computer-Aided Diagnosis

    1. Front Matter

      Pages 133-133
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    2. High-Order Statistics of Micro-Texton for HEp-2 Staining Pattern Classification

      • Xian-Hua Han, Yen-Wei Chen
      Pages 135-164

    3. Intelligent Diagnosis of Breast Cancer Based on Quantitative B-Mode and Elastography Features

      • Chung-Ming Lo, Ruey-Feng Chang
      Pages 165-191

    4. Categorization of Lung Tumors into Benign/Malignant, Solid/GGO, and Typical Benign/Others

      • Yasushi Hirano
      Pages 193-208

    5. Fuzzy Object Growth Model for Neonatal Brain MR Understanding

      • Saadia Binte Alam, Syoji Kobashi, Jayaram K Udupa
      Pages 209-222

  5. Computer-Aided Prognosis

    1. Front Matter

      Pages 223-223
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    2. Computer-Aided Prognosis: Accurate Prediction of Patients with Neurologic and Psychiatric Diseases via Multi-modal MRI Analysis

      • Huiguang He, Hongwei Wen, Dai Dai, Jieqiong Wang
      Pages 225-265

    3. Radiomics in Medical Imaging—Detection, Extraction and Segmentation

      • Jie Tian, Di Dong, Zhenyu Liu, Yali Zang, Jingwei Wei, Jiangdian Song et al.
      Pages 267-333

  6. Computer-Aided Therapy and Surgery

    1. Front Matter

      Pages 335-335
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    2. Markerless Tumor Gating and Tracking for Lung Cancer Radiotherapy based on Machine Learning Techniques

      • Tong Lin, Yucheng Lin
      Pages 337-359

    3. Image Guided and Robot Assisted Precision Surgery

      • Fang Chen, Jia Liu, Hongen Liao
      Pages 361-387
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Keywords

About this book

This book offers the first comprehensive overview of artificial intelligence (AI) technologies in decision support systems for diagnosis based on medical images, presenting cutting-edge insights from thirteen leading research groups around the world.

Medical imaging offers essential information on patients’ medical condition, and clues to causes of their symptoms and diseases. Modern imaging modalities, however, also produce a large number of images that physicians have to accurately interpret. This can lead to an “information overload” for physicians, and can complicate their decision-making. As such, intelligent decision support systems have become a vital element in medical-image-based diagnosis and treatment.

Presenting extensive information on this growing field of AI, the book offers a valuable reference guide for professors, students, researchers and professionals who want to learn about the most recent developments and advances in the field.

Editors and Affiliations

  • Medical Imaging Research Center and Department of Electrical and Computer Engineering, Illinois Institute of Technology, Chicago, USA

    Kenji Suzuki

  • Key Laboratory of Machine Perception (Ministry of Education), School of Electronics Engineering and Computer Science, Peking University, Beijing, China

    Yisong Chen

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