Medical image recognition, segmentation and parsing : machine learning and multiple object approaches
This book describes the technical problems and solutions for automatically recognizing and parsing a medical image into multiple objects, structures, or anatomies. It gives all the key methods, including state-of-the-art approaches based on machine learning, for recognizing or detecting, parsing or...
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| Other Authors | |
|---|---|
| Format | Electronic eBook |
| Language | English |
| Published |
Amsterdam :
Elsevier,
[2016]
|
| Series | Elsevier and MICCAI Society book series.
|
| Subjects | |
| Online Access | Full text |
| ISBN | 9780128026762 0128026766 9780128025819 0128025816 |
| Physical Description | 1 online resource : illustrations |
Cover
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| 020 | |a 9780128026762 |q (electronic bk.) | ||
| 020 | |a 0128026766 |q (electronic bk.) | ||
| 020 | |z 9780128025819 | ||
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| 245 | 0 | 0 | |a Medical image recognition, segmentation and parsing : |b machine learning and multiple object approaches / |c edited by S. Kevin Zhou. |
| 264 | 1 | |a Amsterdam : |b Elsevier, |c [2016] | |
| 264 | 4 | |c ©2016 | |
| 300 | |a 1 online resource : |b illustrations | ||
| 336 | |a text |b txt |2 rdacontent | ||
| 337 | |a computer |b c |2 rdamedia | ||
| 338 | |a online resource |b cr |2 rdacarrier | ||
| 490 | 1 | |a The Elsevier and MICCAI society book series | |
| 504 | |a Includes bibliographical references and index. | ||
| 506 | |a Plný text je dostupný pouze z IP adres počítačů Univerzity Tomáše Bati ve Zlíně nebo vzdáleným přístupem pro zaměstnance a studenty | ||
| 520 | |a This book describes the technical problems and solutions for automatically recognizing and parsing a medical image into multiple objects, structures, or anatomies. It gives all the key methods, including state-of-the-art approaches based on machine learning, for recognizing or detecting, parsing or segmenting, a cohort of anatomical structures from a medical image. You will learn how to: research challenges and problems in medical image recognition, segmentation and parsing of multiple objects; methods and theories for medical image recognition, segmentation and parsing of multiple objects; efficient and effective machine learning solutions based on big datasets; selected applications of medical image parsing using proven algorithms. -- |c Edited summary from book. | ||
| 505 | 0 | |a Front Cover; Medical Image Recognition, Segmentation and Parsing: Machine Learning and Multiple Object Approaches; Copyright; Contents; Foreword; Acknowledgments; Contributors; Chapter 1: Introduction to Medical Image Recognition; 1.1 Introduction; 1.2 Challenges and Opportunities; 1.3 Rough-to-Exact Object Representation; 1.4 Simple-to-Complex Probabilistic Modeling; 1.4.1 Chain Rule; 1.4.2 Bayes' Rule and the Equivalence of Probabilistic Modelingand Energy-Based Method; 1.4.3 Practical Medical Image Recognition, Segmentation, and Parsing Algorithms. | |
| 505 | 8 | |a 1.5 Medical Image Recognition Using Machine Learning Methods1.5.1 Object Detection and Context; 1.5.2 Machine Learning Methods; 1.5.2.1 Classification; 1.5.2.2 Regression; 1.6 Medical Image Segmentation Methods; 1.6.1 Simple Image Segmentation Methods; 1.6.2 Active Contour Method; 1.6.3 Variational Methods; 1.6.4 Level Set Methods; 1.6.5 Active Shape Models and Active Appearance Models; 1.6.6 Graph Cut Method; 1.7 Conclusions; Recommended Notations; Notes; References; Part 1: AutomaticRecognition and DetectionAlgorithms; Chapter 2: A Survey of Anatomy Detection; 2.1 Introduction. | |
| 505 | 8 | |a 2.2 Methods for Detecting an Anatomy2.2.1 Classification-Based Detection Methods; 2.2.1.1 Boosting detection cascade; 2.2.1.2 Probabilistic boosting tree; 2.2.1.3 Randomized decision forest; 2.2.1.4 Exhaustive search to handle pose variation; 2.2.1.5 Parallel, pyramid, and tree structures; 2.2.1.6 Network structure: Probabilistic boosting network; 2.2.1.7 Marginal space learning; 2.2.1.8 Probabilistic, hierarchical, and discriminant framework; 2.2.1.9 Multiple instance boosting to handle inaccurate annotation; 2.2.2 Regression-Based Detection Methods; 2.2.2.1 Shape regression machine. | |
| 505 | 8 | |a 2.2.2.2 Hough forest2.2.3 Classification-Based vs Regression-Based Object Detection; 2.3 Methods for Detecting Multiple Anatomies; 2.3.1 Classification-Based Methods; 2.3.1.1 Discriminative anatomical network; 2.3.1.2 Active scheduling; 2.3.1.3 Submodular detection; 2.3.1.4 Integrated detection network; 2.3.2 Regression-Based Method: Regression Forest; 2.3.3 Combining Classification and Regression: Context Integration; 2.4 Conclusions; References; Chapter 3: Robust Multi-Landmark Detection Based on Information Theoretic Scheduling; 3.1 Introduction; 3.2 Literature Review; 3.3 Methods. | |
| 505 | 8 | |a 3.3.1 Problem Statement3.3.2 Scheduling Criterion Based on Information Gain; 3.3.3 Monte-Carlo Simulation Method for the Evaluation of Information Gain; 3.3.4 Implementation; Learning-based landmark detection; Spatial correlation across landmarks; 3.4 Applications; 3.4.1 Automatic View Identification of Radiographs; 3.4.2 Auto-Alignment for MR Knee Scan Planning; 3.4.3 Auto-Navigation for Anatomical Measurement in CT; 3.4.4 Automatic Vertebrae Labeling; 3.4.5 Virtual Attenuation Correction of Brain PET Images; 3.4.6 Bone Segmentation in MR for PET-MR Attenuation Correction; 3.5 Conclusion. | |
| 590 | |a Knovel |b Knovel (All titles) | ||
| 650 | 0 | |a Imaging systems in medicine. | |
| 650 | 0 | |a Machine learning. | |
| 650 | 0 | |a Image reconstruction. | |
| 650 | 0 | |a Pattern recognition systems. | |
| 655 | 7 | |a elektronické knihy |7 fd186907 |2 czenas | |
| 655 | 9 | |a electronic books |2 eczenas | |
| 700 | 1 | |a Zhou, S. Kevin, |e editor. | |
| 776 | 0 | 8 | |i Print version: |z 0128025816 |z 9780128025819 |w (OCoLC)919014709 |
| 830 | 0 | |a Elsevier and MICCAI Society book series. | |
| 856 | 4 | 0 | |u https://proxy.k.utb.cz/login?url=https://app.knovel.com/hotlink/toc/id:kpMIRSPML2/medical-image-recognition?kpromoter=marc |y Full text |