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Computer Science

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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.

Computer Vision In Medical Imaging

POST SECONDARY FOUR YEAR

COLFR

COLSO

COLJR

COLSR

COMPUTER VISION IN MEDICAL IMAGING

Duration

Duration Unit Of Measure

Transaction Type

Front Cover.
Half Title Page.
Other Frontmatter.
Title Page.
Copyright Page.
Dedication.
Preface.
Contents.
1: An Introduction to Computer Vision in Medical Imaging.
2: Theory and Methodologies.
3: Distribution Matching Approaches to Medical Image Segmentation.
4: Digital Pathology in Medical Imaging.
5: Adaptive Shape Prior Modeling Via Online Dictionary Learning.
6: Feature-Centric Lesion Detection and Retrieval in Thoracic Images.
7: A Novel Paradigm for Quantitation from MR Phase.
8: A Multi-Resolution Active Contour Framework for Ultrasound Image Segmentation.
9: 2D, 3D, Reconstructions/Imaging Algorithms, Systems &#x0026; Sensor Fusion.
10: Model-Based Image Reconstruction in Optoacoustic Tomography.
11: The Fusion of Three-Dimensional Quantitative Coronary Angiography and Intracoronary Imaging for Coronary Interventions.
12: Three-Dimensional Reconstruction Methods in Near-Field Coded Aperture for Spect Imaging System.
13: Ultrasound Volume Reconstruction Based on Direct Frame Interpolation.
14: Deconvolution Technique for Enhancing and Classifying the Retinal Images.
15: Medical Ultrasound Digital Signal Processing in the Gpu Computing ERA.
16: Developing Medical Image Processing Algorithms for GPU Assisted Parallel Computation.
17: Specific Image Processing and Computer Vision Methods for Different Imaging Modalities Including IVUS, MRI, Etc..
18: Computer Vision in Interventional Cardiology.
19: Pattern Classification of Brain Diffusion MRI: Application to Schizophrenia Diagnosis.
20: On Compressed Sensing Reconstruction for Magnetic Resonance Imaging.
21: On Hierarchical Statistical Shape Models with Application to Brain Mri.
22: Advanced PDE-Based Methods for Automatic Quantification of Cardiac Function and Scar from Magnetic Resonance Imaging.
23: Automated IVUS Segmentation Using Deformable Template Model with Feature Tracking.
Index.

Computer Vision In Medical Imaging.css-1p8rvzy{position:absolute;padding-left:8px;}

Computer Vision In Medical Imaging