Computational Intelligence in Biomedical Imaging

1. Front Matter

   Pages i-xv

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2. Decision Support

   1. Front Matter

      Pages 1-1

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   2. Brain Disease Classification and Progression Using Machine Learning Techniques

      • Bo Cheng, Chong-Yaw Wee, Manhua Liu, Daoqiang Zhang, Dinggang Shen

      Pages 3-32

   3. The Role of Content-Based Image Retrieval in Mammography CAD

      • Issam El Naqa, Yongyi Yang

      Pages 33-53

   4. A Novel Image-Based Approach for Early Detection of Prostate Cancer Using DCE-MRI

      • Ahmad Firjani, Fahmi Khalifa, Ahmad Elnakib, Georgy Gimel’farb, Mohammed Abou El-Ghar, Adel Elmaghraby et al.

      Pages 55-82

   5. Computational Intelligent Image Analysis for Assisting Radiation Oncologists’ Decision Making in Radiation Treatment Planning

      • Hidetaka Arimura, Taiki Magome, Genyu Kakiuchi, Jumpei Kuwazuru, Asumi Mizoguchi

      Pages 83-103

3. Computational Anatomy

   1. Front Matter

      Pages 105-105

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   2. Computational Anatomy in the Abdomen: Automated Multi-Organ and Tumor Analysis from Computed Tomography

      • Marius George Linguraru, Ronald M. Summers

      Pages 107-139

   3. Liver Volumetry in MRI by Using Fast Marching Algorithm Coupled with 3D Geodesic Active Contour Segmentation

      • Hieu Trung Huynh, Ibrahim Karademir, Aytekin Oto, Kenji Suzuki

      Pages 141-157

   4. Computer-Aided Image Analysis for Vertebral Anatomy on X-Ray CT Images

      • T. Hayashi, H. Chen, K. Miyamoto, X. Zhou, T. Hara, H. Fujita

      Pages 159-184

   5. Robust Segmentation of Challenging Lungs in CT Using Multi-stage Learning and Level Set Optimization

      • Neil Birkbeck, Michal Sofka, Timo Kohlberger, Jingdan Zhang, Jens Wetzl, Jens Kaftan et al.

      Pages 185-208

4. Image Processing and Analysis

   1. Front Matter

      Pages 209-209

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   2. Bone Suppression in Chest Radiographs by Means of Anatomically Specific Multiple Massive-Training ANNs Combined with Total Variation Minimization Smoothing and Consistency Processing

      • Sheng Chen, Kenji Suzuki

      Pages 211-235

   3. Image Segmentation for Connectomics Using Machine Learning

      • T. Tasdizen, M. Seyedhosseini, T. Liu, C. Jones, E. Jurrus

      Pages 237-278

   4. Image Analysis Techniques for the Quantification of Brain Tumors on MR Images

      • Nishant Verma, Matthew C. Cowperthwaite, Mark G. Burnett, Mia K. Markey

      Pages 279-316

   5. Respiratory and Cardiac Function Analysis on the Basis of Dynamic Chest Radiography

      • Rie Tanaka, Shigeru Sanada

      Pages 317-345

   6. Adaptive Noise Reduction and Edge Enhancement in Medical Images by Using ICA

      • Xian-Hua Han, Yen-Wei Chen

      Pages 347-375

   7. Subtraction Techniques for CT and DSA and Automated Detection of Lung Nodules in 3D CT

      • Takayuki Ishida, Megumi Yamamoto, Yasuhiko Okura

      Pages 377-397

5. Back Matter

   Pages 399-406

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Computational Intelligence in Biomedical Imaging is a comprehensive overview of the state-of-the-art computational intelligence research and technologies in biomedical images with emphasis on biomedical decision making. Biomedical imaging offers useful information on patients’ medical conditions and clues to causes of their symptoms and diseases. Biomedical images, however, provide a large number of images which physicians must interpret. Therefore, computer aids are demanded and become indispensable in physicians’ decision making. This book discusses major technical advancements and research findings in the field of computational intelligence in biomedical imaging, for example, computational intelligence in computer-aided diagnosis for breast cancer, prostate cancer, and brain disease, in lung function analysis, and in radiation therapy. The book examines technologies and studies that have reached the practical level, and those technologies that are becoming available in clinical practices in hospitals rapidly such as computational intelligence in computer-aided diagnosis, biological image analysis, and computer-aided surgery and therapy.

• artificial neural networks
• biomedical imaging
• computational intelligence
• computer-aided diagnosis
• computer-aided surgery
• computerized medical support
• machine learning
• medical decision making
• medical image analysis
• medical informatics
• support vector machines

• Department of Radiology, University of Chicago, Chicago, USA

  Kenji Suzuki

Dr. Kenji Suzuki is an Assistant Professor of Radiology at the University of Chicago. He has written over 240 papers including 95 peer-reviewed journal papers. Dr. Suzuki received his PhD in Engineering in 2001. His research interests include computer-aided diagnosis of lesions in the abdomen, thorax, and heart, medical image analysis and machine learning in medical imaging.

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