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Hybrid and advanced compression tech...
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Kothari, Ashish.
Hybrid and advanced compression techniques for medical images
Record Type:
Electronic resources : Monograph/item
Title/Author:
Hybrid and advanced compression techniques for medical imagesby Rohit M. Thanki, Ashish Kothari.
Author:
Thanki, Rohit M.
other author:
Kothari, Ashish.
Published:
Cham :Springer International Publishing :2019.
Description:
xvii, 95 p. :ill., digital ;24 cm.
Contained By:
Springer eBooks
Subject:
Diagnostic imaging.
Online resource:
https://doi.org/10.1007/978-3-030-12575-2
ISBN:
9783030125752$q(electronic bk.)
Hybrid and advanced compression techniques for medical images
Thanki, Rohit M.
Hybrid and advanced compression techniques for medical images
[electronic resource] /by Rohit M. Thanki, Ashish Kothari. - Cham :Springer International Publishing :2019. - xvii, 95 p. :ill., digital ;24 cm.
Chapter 1. Data Compression and its Application in Medical Imaging -- Chapter 2. Classification in Data Compression -- Chapter 3.Mathematical Preliminaries -- Chapter 4.Conventional Compression Techniques for Medical Images -- Chapter 5. CS Theory based Compression Techniques for Medical Images -- Chapter 6. Color Medical Image Compression Techniques.
This book introduces advanced and hybrid compression techniques specifically used for medical images. The book discusses conventional compression and compressive sensing (CS) theory based approaches that are designed and implemented using various image transforms, such as: Discrete Fourier Transform (DFT), Discrete Cosine Transform (DCT), Discrete Wavelet Transform (DWT), and Singular Value Decomposition (SVD) and greedy based recovery algorithm. The authors show how these techniques provide simulation results of various compression techniques for different types of medical images, such as MRI, CT, US, and x-ray images. Future research directions are provided for medical imaging science. The book will be a welcomed reference for engineers, clinicians, and research students working with medical image compression in the biomedical imaging field. Covers various algorithms for data compression and medical image compression; Provides simulation results of compression algorithms for different types of medical images; Provides study of compressive sensing theory for compression of medical images.
ISBN: 9783030125752$q(electronic bk.)
Standard No.: 10.1007/978-3-030-12575-2doiSubjects--Topical Terms:
214977
Diagnostic imaging.
LC Class. No.: RC78.7.D53 / T436 2019
Dewey Class. No.: 616.0754
Hybrid and advanced compression techniques for medical images
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Chapter 1. Data Compression and its Application in Medical Imaging -- Chapter 2. Classification in Data Compression -- Chapter 3.Mathematical Preliminaries -- Chapter 4.Conventional Compression Techniques for Medical Images -- Chapter 5. CS Theory based Compression Techniques for Medical Images -- Chapter 6. Color Medical Image Compression Techniques.
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This book introduces advanced and hybrid compression techniques specifically used for medical images. The book discusses conventional compression and compressive sensing (CS) theory based approaches that are designed and implemented using various image transforms, such as: Discrete Fourier Transform (DFT), Discrete Cosine Transform (DCT), Discrete Wavelet Transform (DWT), and Singular Value Decomposition (SVD) and greedy based recovery algorithm. The authors show how these techniques provide simulation results of various compression techniques for different types of medical images, such as MRI, CT, US, and x-ray images. Future research directions are provided for medical imaging science. The book will be a welcomed reference for engineers, clinicians, and research students working with medical image compression in the biomedical imaging field. Covers various algorithms for data compression and medical image compression; Provides simulation results of compression algorithms for different types of medical images; Provides study of compressive sensing theory for compression of medical images.
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Engineering (Springer-11647)
based on 0 review(s)
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EB RC78.7.D53 T367 2019 2019
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https://doi.org/10.1007/978-3-030-12575-2
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