Medical Image Fusion

Latha Parthiban

ISBN 10: 3845408871 / ISBN 13: 9783845408873

Verlag: LAP LAMBERT Academic Publishing Jul 2011, 2011

Beschreibung

Neuware -Image fusion has become a common term used within medical diagnostics and treatment. The term is used when multiple patient images are registered and overlaid or merged to provide additional information. Fused images may be created from multiple images from the same imaging modality, or by combining information from multiple modalities, such as magnetic resonance image (MRI), computed tomography (CT), positron emission tomography (PET), and single photon emission computed tomography (SPECT). In radiology and radiation oncology, these images serve different purposes. For example, CT images are used more often to ascertain differences in tissue density while MRI images are typically used to diagnose brain tumors. In this book, preprocessing or removing noise from the CT/MRI/US medical images with wavelets followed by fusing them based on two important techniques viz. lifting wavelet transform and double density dual tree discrete wavelet transform is analyzed for better performance.Books on Demand GmbH, Überseering 33, 22297 Hamburg 108 pp. Englisch.

Bestandsnummer des Verkäufers 9783845408873

Bibliografische Details

• Titel: Medical Image Fusion
• Verlag: LAP LAMBERT Academic Publishing Jul 2011
• Erscheinungsjahr: 2011
• Sprache: Englisch
• ISBN-10: 3845408871
• ISBN-13: 9783845408873
• Einband: Taschenbuch
• Zustand: Neu

Über diesen Titel

Inhaltsangabe

Image fusion has become a common term used within medical diagnostics and treatment. The term is used when multiple patient images are registered and overlaid or merged to provide additional information. Fused images may be created from multiple images from the same imaging modality, or by combining information from multiple modalities, such as magnetic resonance image (MRI), computed tomography (CT), positron emission tomography (PET), and single photon emission computed tomography (SPECT). In radiology and radiation oncology, these images serve different purposes. For example, CT images are used more often to ascertain differences in tissue density while MRI images are typically used to diagnose brain tumors. In this book, preprocessing or removing noise from the CT/MRI/US medical images with wavelets followed by fusing them based on two important techniques viz. lifting wavelet transform and double density dual tree discrete wavelet transform is analyzed for better performance.