בחרו את שפת העמוד:
שפה נוכחית
כניסה עם גוגל
כניסה עם פייסבוק
Computer vision in medical imaging
להגדלת הטקסט להקטנת הטקסט- ספר
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.
| כותר |
Computer vision in medical imaging / editor, C.H. Chen. |
|---|---|
| מוציא לאור |
New Jersey : World Scientific |
| שנה |
[2014] |
| הערות |
Description based upon print version of record. Includes bibliographical references and index. English |
| הערת תוכן ותקציר |
Preface CONTENTS 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 Ayed1. 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 ReferencesChapter 3 Digital Pathology in Medical Imaging Bikash Sabata, Chukka Srinivas, Pascal Bamford and Gerardo Fernandez 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 Chapter 4 Adaptive Shape Prior Modeling via Online Dictionary Learning Shaoting Zhang, Yiqiang Zhan, Yan Zhou and Dimitris Metaxas1. 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 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 Classification1.2.1. 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 |
| סדרה |
Series in computer vision volume 2 |
| היקף החומר |
1 online resource (xiii, 393 pages) : illustrations (some color). |
| שפה |
אנגלית |
| שנת זכויות יוצרים |
�2014 |
| מספר מערכת |
997010710126905171 |
תצוגת MARC
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