Lung cancer remains the leading cause of cancer-related deaths worldwide. Early diagnosis can improve the effectiveness of treatment and increase a patient’s chances of survival. Thus, there is an urgent need for new technology to diagnose small, malignant lung nodules early as well as large nodules located away from large diameter airways because the current technology—namely, needle biopsy and bronchoscopy—fail to diagnose those cases. However, the analysis of small, indeterminate lung masses is fraught with many technical difficulties. Often patients must be followed for years with serial CT scans in order to establish a diagnosis, but inter-scan variability, slice selection artifacts, differences in degree of inspiration, and scan angles can make comparing serial scans unreliable.
Lung Imaging and Computer Aided Diagnosis brings together researchers in pulmonary image analysis to present state-of-the-art image processing techniques for detecting and diagnosing lung cancer at an early stage. The book addresses variables and discrepancies in scans and proposes ways of evaluating small lung masses more consistently to allow for more accurate measurement of growth rates and analysis of shape and appearance of the detected lung nodules.
Dealing with all aspects of image analysis of the data, this book examines:
- Lung segmentation
- Nodule segmentation
- Vessels segmentation
- Airways segmentation
- Lung registration
- Detection of lung nodules
- Diagnosis of detected lung nodules
- Shape and appearance analysis of lung nodules
Contributors also explore the effective use of these methodologies for diagnosis and therapy in clinical applications. Arguably the first book of its kind to address and evaluate image-based diagnostic approaches for the early diagnosis of lung cancer, Lung Imaging and Computer Aided Diagnosis constitutes a valuable resource for biomedical engineers, researchers, and clinicians in lung disease imaging.
Table of Contents
A Novel Three-Dimensional Framework for Automatic Lung Segmentation from Low- Dose Computed Tomography Images; Ayman El-Baz, Georgy Gimel’farb, Robert Falk, and Mohamed Abo El-Ghar
Incremental Engineering of Lung Segmentation Systems; Avishkar Misra, Arcot Sowmya, and Paul Compton
3D MGRF-Based Appearance Modeling for Robust Segmentation of Pulmonary Nodules in 3D LDCT Chest Images; Ayman El-Baz, Georgy Gimel’farb, Robert Falk, and Mohamed Abo El-Ghar
Ground-Glass Nodule Characterization in High-Resolution Computed Tomography Scans; Kazunori Okada
Four-Dimensional Computed Tomography Lung Registration Methods; Anand P. Santhanam, Yugang Min, Jannick P. Rolland, Celina Imielinska, and Patrick A. Kupelian
Pulmonary Kinematics via Registration of Serial Lung Images; Tessa Cook, Gang Song, Nicholas J. Tustison, Drew Torigian, Warren B. Gefter, and James Gee
Acquisition and Automated Analysis of Normal and Pathological Lungs in Small Animals Using Computed Microtomography; Xabier Artaechevarria, Mario Ceresa, Arrate Muñoz- Barrutia, and Carlos Ortiz-de- Solorzano
Airway Segmentation and Analysis from Computed Tomography; Benjamin Irving, Andrew Todd-Pokropek, and Paul Taylor
Pulmonary Vessel Segmentation for Multislice CT Data: Methods and Applications; Jens N. Kaftan and Til Aach
A Novel Level Set-Based Computer-Aided Detection System for Automatic Detection of Lung Nodules in Low-Dose Chest Computed Tomography Scans; Ayman El-Baz, Aly Farag, Georgy Gimel’farb, Robert Falk, and Mohamed Abo El-Ghar
Model-Based Methods for Detection of Pulmonary Nodules; Paulo R. S. Mendonça, Rahul Bhotika, and Robert Kaucic
Concept and Practice of Genetic Algorithm Template Matching and Higher Order Local Autocorrelation Schemes in Automated Detection of Lung Nodules; Yongbum Lee, Takeshi Hara, DuYih Tsai, and Hiroshi Fujita
Computer-Aided Detection of Lung Nodules in Chest Radiographs and Thoracic CT; Kenji Suzuki
Lung Nodule and Tumor Detection and Segmentation; Jinghao Zhou and Dimitris N. Metaxas
Texture Classification in Pulmonary CT; Lauge Sørensen, Mehrdad J. Gangeh, Saher B. Shaker, and Marleen de Bruijne
Computer-Aided Assessment and Stenting of Tracheal Stenosis; Rômulo Pinho, Kurt G. Tournoy, and Jan Sijbers
Appearance Analysis for the Early Assessment of Detected Lung Nodules; Ayman El-Baz, Georgy Gimel’farb, Robert Falk, Mohamed Abo El-Ghar, and Jasjit Suri
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; Ayman El-Baz, Palaniappan Sethu, Georgy Gimel’farb, Fahmi Khalifa, Ahmed Elnakib, Robert Falk, Mohamed Abo El-Ghar, and Jasjit Suri
Three-Dimensional Shape Analysis Using Spherical Harmonics for Early Assessment of Detected Lung Nodules; Ayman El-Baz, Matthew Nitzken, Georgy Gimel’farb, Eric Van Bogaert, Robert Falk, Mohamed Abo El-Ghar, and Jasjit Suri
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.