CONTENTS -- Preface -- Chapter 1 An Introduction to Computer Vision in Medical Imaging Chi Hau Chen -- 1. Introduction -- 2. Some Medical Imaging Methods -- 2.1. X-ray -- 2.2. Magnetic Resonance Image (MRI) -- 2.3. Intravascular Ultrasound (IVUS) -- 3. Roles of Computer Vision, Image Processing and Pattern Recognition -- 4. Active Contours -- 4.1. Snakes -- 4.2. Level set methods -- 4.3. Geodesic active contours -- 4.4. Region-based active contours -- 4.5. Hybrid evolution method -- 4.6. IVUS image segmentation -- 5. Concluding Remarks -- Acknowledgment -- References -- Part 1 Theory and Methodologies -- Chapter 2 Distribution Matching Approaches to Medical Image Segmentation Ismail Ben Ayed -- 1. Introduction -- 2. Formulations -- 3. Optimization Aspects -- 3.1. Specialized optimizers -- 3.2. Derivative-based optimizers -- 3.2.1. Active curves and level sets -- 3.2.2. Line search and trust region methods -- 3.3. Bound optimizers -- 3.3.1. Graph cuts -- 3.3.2. Convex-relaxation techniques -- 4. Medical Imaging Applications -- 4.1. Left ventricle segmentation in cardiac images -- 4.1.1. Example -- 4.2. Vertebral-body segmentation in spine images -- 4.2.1. Example -- 4.3. Brain tumor segmentation -- 5. Conclusion and Outlook -- References -- Chapter 3 Digital Pathology in Medical Imaging Bikash Sabata, Chukka Srinivas, Pascal Bamford and Gerardo Fernandez -- 1. Introduction -- A. Subtyping and the role of digital pathology -- B. Quantification of IHC markers -- C. Tissue and stain variability -- D. Rules-based segmentation and identification -- E. Learning from image data examples -- F. Object-based learning models -- G. Membrane detection algorithms -- H. HER2 Dual ISH slide scoring algorithm -- 2. DP Enabled Applications -- 3. Multiplexed Quantification -- 4. Quantification Algorithms -- 5. Summary -- Acknowledgment -- References.

Chapter 4 Adaptive Shape Prior Modeling via Online Dictionary Learning Shaoting Zhang, Yiqiang Zhan, Yan Zhou and Dimitris Metaxas -- 1. Introduction -- 2. Relevant Work -- 3. Methodology -- 3.1. Sparse Shape Composition -- 3.2. Shape Dictionary Learning -- 3.3. Online Shape Dictionary Update -- 4. Experiments -- 4.1. Lung Localization -- 4.2. Real-time Left Ventricle Tracking -- 5. Conclusions -- References -- Chapter 5 Feature-Centric Lesion Detection and Retrieval in Thoracic Images Yang Song, Weidong Cai, Stefan Eberl, Michael J Fulham and David Dagan Feng -- 1. Lesion Detection -- 1.1. Review of State-of-the-art -- 1.2. Region-based Feature Classification -- 1.2.1. Region Type Identification -- 1.2.2. Region Type Refinement -- 1.2.3. 3D Object Localization -- 1.3. Multi-stage Discriminative Model -- 1.3.1. Abnormality Detection -- 1.3.2. Tumor and Lymph Node Differentiation -- 1.3.3. Tumor Region Refinement -- 1.3.4. Experimental Results -- 1.4. Data Adaptive Structure Estimation -- 1.4.1. Initial Abnormality Detection -- 1.4.2. Adaptive Structure Estimation -- 1.4.3. Feature Extraction and Classification -- 1.4.4. Experimental Results -- 2. Thoracic Image Retrieval -- 2.1. Review of State-of-the-art -- 2.2. Pathological Feature Description -- 2.3. Spatial Feature Encoding -- 2.3.1. Pathological Centroid Detection -- 2.3.2. Context-based Partitioning Model -- 2.3.3. Similarity Measure -- 2.3.4. Experimental Results -- 3. Summary -- References -- Chapter 6 A Novel Paradigm for Quantitation from MR Phase Joseph Dagher -- 1. Introduction to Phase Mapping in MRI -- 2. Theory -- 2.1. Novel paradigm: Phase Ambiguity functions -- 2.2. Proposed solution: MAGPI -- 2.3. Corresponding estimation method: CL2D -- 3. Results -- 3.1. Simulation results -- 3.2. Real phantom results -- 3.3. In Vivo results -- 4. Discussion and Conclusions -- References.

Chapter 7 A Multi-Resolution Active Contour Framework for Ultrasound Image Segmentation Weiming Wang, Jing Qin, Pheng-Ann Heng, Yim-Pan Chui, Liang Li and Bing Nan Li -- 1. Introduction -- 2. Methods -- 2.1. LGD-driven Active Contour Models -- 2.2. Phase-based Geodesic Active Contours -- 2.2.1. Monogenic Signal -- 2.2.2. FA-based Edge Stop Function -- 3. Experiments and Results -- 4. Conclusions -- Acknowledgments -- References -- Part 2 2-D, 3-D Reconstructions/Imaging Algorithms, Systems & Sensor Fusion -- Chapter 8 Model-Based Image Reconstruction in Optoacoustic Tomography Amir Rosenthal, Daniel Razansky and Vasilis Ntziachristos -- 1. Introduction -- 2. The model matrix -- 3. Image formation -- 4. Wavelet-packet formulation -- 5. Conclusions -- References -- Chapter 9 The Fusion of Three-Dimensional Quantitative Coronary Angiography and Intracoronary Imaging for Coronary Interventions Shengxian Tu, Niels R. Holm, Johannes P. Janssen and Johan H. C. Reiber -- 1. Introduction -- 2. Three-dimensional Angiographic Reconstruction and QCA -- 3. Fusion of 3D QCA and IVUS/OCT -- 4. Validations -- 4.1. Co-registration of X-ray angiography and IVUS/OCT -- Materials and methods -- Statistics -- Results -- 4.2. Comparison of lumen dimensions by 3D QCA and IVUS/OCT -- Materials and methods -- Statistics -- Results -- 5. Discussions -- Co-registration of X-ray angiography and IVUS/OCT -- Lumen sizing assessed by different imaging modalities -- 6. Conclusions -- Acknowledgements -- References -- Chapter 10 Three-Dimensional Reconstruction Methods in Near-Field Coded Aperture for SPECT Imaging System Stephen Baoming Hong -- 1. Introduction -- 2. Theory & Method -- 2.1. Coded aperture imaging and decoding -- 2.2. Iterative reconstruction of 2D coded aperture SPECT imaging -- 2.3. Iterative reconstruction of 3D coded aperture SPECT imaging.

3. Coded Aperture SPECT Imaging Systems and Phantom Experimental Results -- 3.1. Coded aperture SPECT imaging systems -- 3.2. Experimental setup -- 3.2(a) Reconstruct a simple 3D pyramid shaped phantom -- 3.2(b) Experimental setup and results for micro hot rod phantom -- 4. Discussions -- 5. Conclusion -- Acknowledgements -- References -- Chapter 11 Ultrasound Volume Reconstruction based on Direct Frame Interpolation Sergei Koptenko, Rachel Remlinger, Martin Lachaine,Tony Falco and Ulrich Scheipers -- 1. Introduction -- 2. Overview of volume reconstruction methods -- Voxel Nearest Neighbor -- Pixel Nearest Neighbor -- Description of Pixel Nearest Neighbor Methods Selected as a Test Benchmark -- Other Methods -- 3. Description of the direct frame interpolation method -- 4. Methods -- 5. Results -- Gray level error -- Computation Times -- 6. Conclusion -- Acknowledgements -- References -- Chapter 12 Deconvolution Technique for Enhancing and Classifying the Retinal Images Uvais A. Qidwai and Umair A. Qidwai -- 1. Introduction -- 2. Background -- 3. The Deconvolutional Model -- 4. Restoration of Images -- 5. Problem Formulation -- 5.1. The Retinal images -- 6. Proposed Algorithm -- 6.1. For RGB Images -- 6.2. For Fluorescence Angiographic Images -- 6.3. Region Growing for FA -- 7. Results and Discussion -- 7.1. Image Enhancement for RGB Images -- 8. Conclusion -- References -- Chapter 13 Medical Ultrasound Digital Signal Processing in the GPU Computing Era Marcin Lewandowski -- 1. Foreword -- 2. Signal architecture and processing in ultrasonography -- 3. Hardware architecture -- 4. GPUs -- 4.1. GPU Software & Hardware -- 4.2. Use in ultrasonography -- 4.3. GPU implementation issues -- 4.3.1. Optimisation of SAFT implementation on GPU -- 5. Summary and outlook -- Acknowledgments -- References.

Chapter 14 Developing Medical Image Processing Algorithms for GPU Assisted Parallel Computation Mathias Broxvall and Marios Daoutis -- 1. Introduction -- 2. Background -- 2.1. GPU parallel architectures -- 2.1.1. CUDA -- 2.1.2. OpenCL -- 2.2. Standard mathematical building blocks -- 3. Methods -- 3.1. Convolutions -- 3.1.1. Analysing the performance of a kernel -- 3.1.2. Reworking a memory bound algorithm -- 3.2. Multidimensional adaptive filtering -- 3.2.1. Mathematical model -- 3.2.2. GPU implementation -- 3.2.3. Computational performance -- 3.3. Line detection -- 4. Applications -- 4.1. Image de-noising in 4D echocardiography using adaptive filters -- 4.1.1. Hardware & Software -- 4.1.2. Handling border data -- 4.1.3. Results -- 4.2. Automatic needle detection for image guided HDR prostate brachytherapy treatment -- 5. Discussion -- 5.1. Applications -- 5.2. Numerical precision of arithmetic operations -- References -- Part 3 Specific Image Processing and Computer Vision Methods for Different Imaging Modalities Including IVUS, MRI, etc -- Chapter 15 Computer Vision in Interventional Cardiology Kendall R. Waters -- 1. Introduction -- 2. Coronary Artery Disease -- 2.1. To Treat or Not To Treat -- 2.2. Optimizing the Intervention -- 2.2.1. Angiography -- 2.2.2. Intravascular Ultrasound -- 2.2.3. Intracoronary Optical Coherence Technology -- 2.2.4. Intravascular Near-Infrared Spectroscopy -- 2.3. Emerging Technologies -- 2.3.1. FFRCT -- 2.3.2. Intravascular Photoacoustics -- 3. Valvular Heart Disease -- 3.1. Prevalence and Treatment -- 3.2. Echocardiographic Guidance and Computer Vision -- 4. Summary -- References -- Chapter 16 Pattern Classification of Brain Diffusion MRI: Application to Schizophrenia Diagnosis Ali Tabesh, Matthew J. Hoptman, Debra D'Angelo and Babak A. Ardekani -- 1. Introduction -- 2. Review of Pattern Classification.

2.1. Standardization and Dimensionality Reduction.