Computational Intelligence in Biomedical Imaging

Brain Disease Classification and Progression using Machine Learning Techniques.- The Role of Content-Based Image Retrieval in Mammography CAD.- A Novel Image-based Approach for Early Detection of Prostate Cancer using DCE-MRI.- Computational Intelligent Image Analysis for Assisting Radiation Oncologists' Decision Making in Radiation Treatment Planning.- Computational Anatomy in the Abdomen: Automated Multi-Organ and Tumor Analysis from Computed Tomography.- Liver Volumetry in MRI by using Fast Marching Algorithm Coupled with 3D Geodesic Active Contour Segmentation.- Computer-aided Image Analysis for Vertebral Anatomy on X-ray CT Images.- Robust Segmentation of Challenging Lungs in CT using Multi-Stage Learning and Level Set Optimization.- Bone Suppression in Chest Radiographs by Means of Anatomically Specific Multiple Massive-Training ANNs Combined with Total Variation Minimization Smoothing and Consistency Processing.- Image Segmentation for Connectomics using Machine Learning.- Image Analysis Techniques for the Quantification of Brain Tumors on MR Images.- Respiratory and Cardiac Function Analysis on the Basis of Dynamic Chest Radiography.- Adaptive Noise Reduction and Edge Enhancement in Medical Images by using ICA.- Subtraction Techniques for CT and DSA and Automated Detection of Lung Nodules in 3D CT.

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.