Data mining in biomedical imaging, signaling, and systems
Data mining has rapidly emerged as an enabling, robust, and scalable technique to analyze data for novel patterns, trends, anomalies, structures, and features that can be employed for a variety of biomedical and clinical domains. Approaching the techniques and challenges of image mining from a multi...
| Otros Autores: | , |
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| Formato: | Libro electrónico |
| Idioma: | Inglés |
| Publicado: |
Boca Raton :
Auerbach Publications
2011.
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| Edición: | 1st ed |
| Colección: | An Auerback Book Data mining in biomedical imaging, signaling, and systems
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| Materias: | |
| Ver en Biblioteca Universitat Ramon Llull: | https://discovery.url.edu/permalink/34CSUC_URL/1im36ta/alma991009629049806719 |
Tabla de Contenidos:
- Front Cover; Contents; Preface; Editors; Contributors; Chapter 1. Feature Extraction Methods in Biomedical Signaling and Imaging; Chapter 2. Supervised and Unsupervised Learning Methods in Biomedical Signaling and Imaging; Chapter 3. Data Mining of Acoustical Properties of Speech as Indicators of Depression; Chapter 4. Typicality Measure and the Creation of Predictive Models in Biomedicine; Chapter 5. Gaussian Mixture Model-Based Clustering Technique for Electrocardiogram Analysis; Chapter 6. Pattern Recognition Algorithms for Seizure Applications
- Chapter 7. Application of Parametric and Nonparametric Methods in Arrhythmia ClassificationChapter 8. Supervised and Unsupervised Metabonomic Techniques in Clinical Diagnosis: Classification of 677-MTHFR Mutations in Migraine Sufferers; Chapter 9. Automatic Grading of Adult Depression Using a Backpropagation Neural Network Classifier; Chapter 10. Alignment-Based Clustering of Gene Expression Time-Series Data; Chapter 11. Mining of Imaging Biomarkers for Quantitative Evaluation of Osteoarthritis; Chapter 12. Supervised Classification of Digital Mammograms
- Chapter 13. Biofilm Image Analysis: Automatic Segmentation Methods and ApplicationsChapter 14. Discovering Association of Diseases in the Upper Gastrointestinal Tract Using Text Mining Techniques; Chapter 15. Mental Health Informatics: Scopes and Challenges; Chapter 16. Systems Engineering for Medical Informatics; Back Cover
