Biomedical Texture Analysis (e-bok)
Format
E-bok
Filformat
EPUB med Adobe-kryptering
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Nedladdning
Kan laddas ned under 24 månader, dock max 3 gånger.
Språk
Engelska
Antal sidor
430
Utgivningsdatum
2017-08-25
Förlag
Elsevier Science
ISBN
9780128123218
Biomedical Texture Analysis (e-bok)

Biomedical Texture Analysis (e-bok)

Fundamentals, Tools and Challenges

E-bok (EPUB - DRM), Engelska, 2017-08-25
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Biomedical Texture Analysis: Fundamentals, Applications, Tools and Challenges describes the fundamentals and applications of biomedical texture analysis (BTA) for precision medicine. It defines what biomedical textures (BTs) are and why they require specific image analysis design approaches when compared to more classical computer vision applications. The fundamental properties of BTs are given to highlight key aspects of texture operator design, providing a foundation for biomedical engineers to build the next generation of biomedical texture operators. Examples of novel texture operators are described and their ability to characterize BTs are demonstrated in a variety of applications in radiology and digital histopathology. Recent open-source software frameworks which enable the extraction, exploration and analysis of 2D and 3D texture-based imaging biomarkers are also presented. This book provides a thorough background on texture analysis for graduate students and biomedical engineers from both industry and academia who have basic image processing knowledge. Medical doctors and biologists with no background in image processing will also find available methods and software tools for analyzing textures in medical images.Defines biomedical texture precisely and describe how it is different from general texture information considered in computer visionDefines the general problem to translate 2D and 3D texture patterns from biomedical images to visually and biologically relevant measurementsDescribes, using intuitive concepts, how the most popular biomedical texture analysis approaches (e.g., gray-level matrices, fractals, wavelets, deep convolutional neural networks) work, what they have in common, and how they are differentIdentifies the strengths, weaknesses, and current challenges of existing methods including both handcrafted and learned representations, as well as deep learning. The goal is to establish foundations for building the next generation of biomedical texture operatorsShowcases applications where biomedical texture analysis has succeeded and failedProvides details on existing, freely available texture analysis software, helping experts in medicine or biology develop and test precise research hypothesis
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