Lung Imaging and Computer Aided Diagnosis

Ayman El-Baz received BSc and MS degrees in electrical engineering from Mansoura University, Egypt, in 1997 and 2000, respectively, and a PhD degree in electrical engineering from University of Louisville, Kentucky. He joined the Bioengineering Department of the University of Louisville in August 2006. His current research is focused on developing new computer-assisted diagnosis systems for different diseases and brain disorders. Jasjit S. Suri is an innovator, a scientist, a visionary, an industrialist, and an internationally known world leader in biomedical engineering. Dr. Suri has spent over 20 years in the field of biomedical engineering/devices and its management. He received his doctorate from the University of Washington, Seattle, and a business management sciences degree from Weatherhead, Case Western Reserve University, Cleveland, Ohio. Dr. Suri was awarded the President’s Gold Medal in 1980 and the Fellow of American Institute of Medical and Biological Engineering for his outstanding contributions.

A Novel Three-Dimensional Framework for Automatic Lung Segmentation from Low- Dose Computed Tomography Images. Incremental Engineering of Lung Segmentation Systems. 3D MGRF-Based Appearance Modeling for Robust Segmentation of Pulmonary Nodules in 3D LDCT Chest Images. Ground-Glass Nodule Characterization in High-Resolution Computed Tomography Scans. Four-Dimensional Computed Tomography Lung Registration Methods. Pulmonary Kinematics via Registration of Serial Lung Images. Acquisition and Automated Analysis of Normal and Pathological Lungs in Small Animals Using Computed Microtomography. Airway Segmentation and Analysis from Computed Tomography. Pulmonary Vessel Segmentation for Multislice CT Data: Methods and Applications. A Novel Level Set-Based Computer-Aided Detection System for Automatic Detection of Lung Nodules in Low-Dose Chest Computed Tomography Scans. Model-Based Methods for Detection of Pulmonary Nodules. Concept and Practice of Genetic Algorithm Template Matching and Higher Order Local Autocorrelation Schemes in Automated Detection of Lung Nodules. Computer-Aided Detection of Lung Nodules in Chest Radiographs and Thoracic CT. Lung Nodule and Tumor Detection and Segmentation. Texture Classification in Pulmonary CT. Computer-Aided Assessment and Stenting of Tracheal Stenosis. Appearance Analysis for the Early Assessment of Detected Lung Nodules. Validation of a New Image-Based Approach for the Accurate Estimating of the Growth Rate of Detected Lung Nodules Using Real Computed Tomography Images and Elastic Phantoms Generated by State-of-the-Art Microfluidics Technology.Three-Dimensional Shape Analysis Using Spherical Harmonics for Early Assessment of Detected Lung Nodules. Index.