Computational Intelligence in Biomedical Imaging

✍ Scribed by Bo Cheng, Chong-Yaw Wee, Manhua Liu, Daoqiang Zhang, Dinggang Shen (auth.), Kenji Suzuki (eds.)

• Publisher: Springer-Verlag New York
• Year: 2014
• Tongue: English
• Leaves: 410
• Edition: 1
• Category: Library

✦ Synopsis

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.

✦ Table of Contents

Front Matter....Pages i-xv
Front Matter....Pages 1-1
Brain Disease Classification and Progression Using Machine Learning Techniques....Pages 3-32
The Role of Content-Based Image Retrieval in Mammography CAD....Pages 33-53
A Novel Image-Based Approach for Early Detection of Prostate Cancer Using DCE-MRI....Pages 55-82
Computational Intelligent Image Analysis for Assisting Radiation Oncologists’ Decision Making in Radiation Treatment Planning....Pages 83-103
Front Matter....Pages 105-105
Computational Anatomy in the Abdomen: Automated Multi-Organ and Tumor Analysis from Computed Tomography....Pages 107-139
Liver Volumetry in MRI by Using Fast Marching Algorithm Coupled with 3D Geodesic Active Contour Segmentation....Pages 141-157
Computer-Aided Image Analysis for Vertebral Anatomy on X-Ray CT Images....Pages 159-184
Robust Segmentation of Challenging Lungs in CT Using Multi-stage Learning and Level Set Optimization....Pages 185-208
Front Matter....Pages 209-209
Bone Suppression in Chest Radiographs by Means of Anatomically Specific Multiple Massive-Training ANNs Combined with Total Variation Minimization Smoothing and Consistency Processing....Pages 211-235
Image Segmentation for Connectomics Using Machine Learning....Pages 237-278
Image Analysis Techniques for the Quantification of Brain Tumors on MR Images....Pages 279-316
Respiratory and Cardiac Function Analysis on the Basis of Dynamic Chest Radiography....Pages 317-345
Adaptive Noise Reduction and Edge Enhancement in Medical Images by Using ICA....Pages 347-375
Subtraction Techniques for CT and DSA and Automated Detection of Lung Nodules in 3D CT....Pages 377-397
Back Matter....Pages 399-406

✦ Subjects

Computational Intelligence; Biomedical Engineering; Computer Imaging, Vision, Pattern Recognition and Graphics; Signal, Image and Speech Processing; Imaging / Radiology