Lung Imaging and Computer Aided Diagnosis

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.