Signal and image processing techniques for the development of intelligent healthcare systems /

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Bibliographic Details
Imprint:Singapore : Springer, 2021.
Description:1 online resource
Language:English
Subject:Diagnostic imaging -- Digital techniques.
Diagnostic imaging -- Data processing.
Signal processing -- Digital techniques.
Image processing -- Digital techniques.
Artificial intelligence -- Medical applications.
Molecular biology.
Image processing.
Machine learning.
Biomedical engineering.
Science -- Life Sciences -- Anatomy & Physiology.
Computers -- Computer Graphics.
Computers -- Intelligence (AI) & Semantics.
Medical -- Allied Health Services -- Medical Technology.
Artificial intelligence -- Medical applications.
Diagnostic imaging -- Data processing.
Diagnostic imaging -- Digital techniques.
Image processing -- Digital techniques.
Signal processing -- Digital techniques.
Electronic books.
Electronic books.
Format: E-Resource Book
URL for this record:http://pi.lib.uchicago.edu/1001/cat/bib/12607767
Hidden Bibliographic Details
Other authors / contributors:Priya, E.
Rajinikanth, Venkatesan.
ISBN:9789811561412
9811561419
9811561400
9789811561405
Notes:Online resource; title from PDF title page (SpringerLink, viewed November 19, 2020).
Summary:This book comprehensively reviews the various automated and semi-automated signal and image processing techniques, as well as deep-learning-based image analysis techniques, used in healthcare diagnostics. It highlights a range of data pre-processing methods used in signal processing for effective data mining in remote healthcare, and discusses pre-processing using filter techniques, noise removal, and contrast-enhanced methods for improving image quality. The book discusses the status quo of artificial intelligence in medical applications, as well as its future. Further, it offers a glimpse of feature extraction methods for reducing dimensionality and extracting discriminatory information hidden in biomedical signals. Given its scope, the book is intended for academics, researchers and practitioners interested in the latest real-world technological innovations.
Other form:Original 9811561400 9789811561405
Standard no.:10.1007/978-981-15-6
10.1007/978-981-15-6141-2
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505 0 |a Chapter 1. An Integrated Design of Fuzzy C-Means and NCA based Multi-Properties Features Reduction for Brain Tumor Recognition.- Chapter 2. Hybrid Image Processing based Examination of 2D Brain MRI Slices to Detect Brain Tumour/Stroke Section - A Study.- Chapter 3. Edge Enhancing Coherence Diffusion Filter for Level Set Segmentation and Asymmetry Analysis using Curvelets in Breast Thermograms.- Chapter 4. Lung Cancer Diagnosis Based on Image Fusion and prediction using CT and PET image.- Chapter 5. Segmentation and Validation of Infrared Breast Images using Weighted Level Set and Phase Congruency Edge Map Framework.- Chapter 6. Analysis of Material Profile for Polymer Based Mechanical Microgripper for Thin Plate Holding.- Chapter 7. Design and Testing of Elbow Actuated Wearable Robotic Arm for Muscular Disorders.- Chapter 8. A Comprehensive Study of Image Fusion Techniques and Their Applications.- Chapter 9. Multilevel Mammogram Image Analysis for Identifying Outliers, Misclassification using Machine Learning.- Chapter 10. A Review on Automatic Detection of Retinal Lesions in Fundus Images for Diabetic Retinopathy.- Chapter 11. Medical Image Watermarking: A Review on Wavelet Based Methods.- Chapter 12. EEG Signal Extraction Analysis Techniques.- Chapter 13. Classification of sEMG Signal based Arm Action using Convolutional Neural Network.- Chapter 14. An Automated Approach for the Identification of TB Images Enhanced by Non-uniform Illumination Correction. 
520 |a This book comprehensively reviews the various automated and semi-automated signal and image processing techniques, as well as deep-learning-based image analysis techniques, used in healthcare diagnostics. It highlights a range of data pre-processing methods used in signal processing for effective data mining in remote healthcare, and discusses pre-processing using filter techniques, noise removal, and contrast-enhanced methods for improving image quality. The book discusses the status quo of artificial intelligence in medical applications, as well as its future. Further, it offers a glimpse of feature extraction methods for reducing dimensionality and extracting discriminatory information hidden in biomedical signals. Given its scope, the book is intended for academics, researchers and practitioners interested in the latest real-world technological innovations. 
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