Computer Vision In Medical Imaging: (2 Series in Computer Vision)

The major progress in computer vision allows us to make extensive use of medical imaging data to provide us better diagnosis, treatment and predication of diseases. Computer vision can exploit texture, shape, contour and prior knowledge along with contextual information from image sequence and provide 3D and 4D information that helps with better human understanding. Many powerful tools have been available through image segmentation, machine learning, pattern classification, tracking, reconstruction to bring much needed quantitative information not easily available by trained human specialists. The aim of the book is for both medical imaging professionals to acquire and interpret the data, and computer vision professionals to provide enhanced medical information by using computer vision techniques. The final objective is to benefit the patients without adding to the already high medical costs.

Table of Contents:
Introduction (C H Chen); Theory and Methodologies (including Classification, Tracking, etc.): Distribution Matching Approaches to Medical Image Segmentation (Ismail Ben Ayed); Digital Pathology Imaging - A New Frontier in Medical Imaging (Bikash Sabata, Chukka Srinivas, Pascal Bamford and Jerry Fernandez); Adaptive Shape Prior Modeling via Online Dictionary Learning (Shaoting Zhang, Yiqiang Zhan, Yan Zhou and Dimitris Metaxas); Feature-Centric Lesion Detection and Retrieval for Assisting Lung Disease Diagnosis(Yang Song, etc.); A Joint Acquisition, Reconstruction and Estimation Paradigm for Quantitative Magnetic Resonance Imaging (Joseph Dagher); Fuzzy Level Set Methods for Medical Image Segmentation (Li, Bing Nan); 2D, 3D Reconstructions/Imaging Algorithms, Systems & Sensor Fusion: Model-Based Tomographic Image Reconstruction (Amir Rosenthal); The Fusion of Three-Dimensional Quantitative Coronary Angiography and Intracoronary Imaging Devices for Coronary Interventions (S T Tu); Iterative 3D Reconstruction Approach for SPECT Imaging (Baoming Hong); 3D Ultrasound Volume Reconstruction Using the Direct Frame Interpolation Method (Sergei Koptenko, etc.); Deconvolution Technique for Enhancing and Classifying the Retinal Images (Uvais Qidwai); Medical Ultrasound Digital Signal Processing in the GPU Computing Era (Marcin Lewandowski); Designing Medical Imaging Processing Algorithms for GPU Based Computing (Mathias Broxvall and Marios Daotis); Specific Image Processing and Computer Vision Methods for Different Imaging Modalities Including IVUS, PET, MRI, etc.: Computer Vision in Interventional Cardiology (Kendall Waters); On Reproducible Coronary Plaque Quantification by Multiscale Computed Tomography (Nico Bruining, et al.); Image Processing for Brain Diffusion MRI (Ali Tabesh); On Compressive Sensing in MRI (Ali Bilgin); Kinetic and Parametric Analysis of Dynamic Brain PET Studies (Ronald Boellaard); On Hierarchical Statistical Shape Models with Application to Brain MRI (Juan Cerrolaza, etc.); Advanced PDE based Methods for Automatic Quantification of Cardiac Function and Scar from MRI (Cristiana Corsi, et al.); On Automated IVUS Segmentation using a Deformable Template Model with Feature Tracking (Prakash Manandhar and C H Chen).