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ویرایش:
نویسندگان: Isabelle Bloch. Anca Ralescu
سری:
ISBN (شابک) : 3031194241, 9783031194245
ناشر: Springer
سال نشر: 2023
تعداد صفحات: 310
[311]
زبان: English
فرمت فایل : PDF (درصورت درخواست کاربر به PDF، EPUB یا AZW3 تبدیل می شود)
حجم فایل: 7 Mb
در صورت تبدیل فایل کتاب Fuzzy Sets Methods in Image Processing and Understanding: Medical Imaging Applications به فرمت های PDF، EPUB، AZW3، MOBI و یا DJVU می توانید به پشتیبان اطلاع دهید تا فایل مورد نظر را تبدیل نمایند.
توجه داشته باشید کتاب روشهای مجموعههای فازی در پردازش و درک تصویر: کاربردهای تصویربرداری پزشکی نسخه زبان اصلی می باشد و کتاب ترجمه شده به فارسی نمی باشد. وبسایت اینترنشنال لایبرری ارائه دهنده کتاب های زبان اصلی می باشد و هیچ گونه کتاب ترجمه شده یا نوشته شده به فارسی را ارائه نمی دهد.
این کتاب یک مرور کلی از روشهای اخیر با استفاده از اطلاعات سطح بالاتر (سطح شی یا صحنه) برای کارهای پیشرفته مانند درک تصویر همراه با کاربردهای آنها در تصاویر پزشکی ارائه میکند. روشهای پیشرفتهای برای پردازش و درک تصویر فازی ارائه شدهاند، از جمله اشیاء فضایی فازی، هندسه و توپولوژی، ریختشناسی ریاضی، یادگیری ماشین، توصیفهای کلامی محتوای تصویر، ترکیب، روابط فضایی، و نمایشهای ساختاری. برای هر جنبه روش شناختی پوشش داده شده، تصاویری از حوزه تصویربرداری پزشکی ارائه شده است. این یک کتاب ایده آل برای دانشجویان تحصیلات تکمیلی و محققان در زمینه پردازش تصویر پزشکی است.
This book provides a thorough overview of recent methods using higher level information (object or scene level) for advanced tasks such as image understanding along with their applications to medical images. Advanced methods for fuzzy image processing and understanding are presented, including fuzzy spatial objects, geometry and topology, mathematical morphology, machine learning, verbal descriptions of image content, fusion, spatial relations, and structural representations. For each methodological aspect covered, illustrations from the medical imaging domain are provided. This is an ideal book for graduate students and researchers in the field of medical image processing.
Acknowledgments Contents 1 Introduction 1.1 Fuzzy Sets and Image Understanding Under Imprecision 1.1.1 Sources of Imprecision 1.1.2 Advantages and Usefulness of Fuzzy Sets 1.1.3 Semantic Gap 1.1.4 A Short Review of Existing Books 1.2 Representations 1.3 Low Level—Clustering, Enhancement, Filtering, Edge Detection 1.4 Intermediate Level 1.5 Higher Level 1.5.1 Representations of Structural Information 1.5.2 Fusion 1.5.3 Scene Understanding 1.6 Emerging Topics 1.6.1 Mining and Retrieval 1.6.2 Towards Bipolarity 1.6.3 Towards More Interactions Between Knowledge and Image Information 1.6.4 Deep Neuro-Fuzzy Systems References 2 Preliminaries 2.1 Imprecision in Images and Related Knowledge 2.2 Basic Definitions of Fuzzy Sets Theory 2.2.1 Fuzzy Sets 2.2.2 Set Theoretical Operations: Original Definitions of L. Zadeh 2.2.3 Structure and Types of Fuzzy Sets 2.2.4 α-Cuts 2.2.5 Cardinality 2.2.6 Convexity 2.2.7 Fuzzy Number 2.3 Main Operators on Fuzzy Sets 2.3.1 Fuzzy Complementation 2.3.2 Triangular Norms and Conorms 2.3.3 Mean Operators 2.3.4 Symmetric Sums 2.3.5 Adaptive Operators 2.3.6 Logical Connectives 2.4 Linguistic Variable 2.4.1 Definition 2.4.2 Example of Linguistic Variable 2.4.3 Modifiers 2.5 Translating a Crisp Operation into a Fuzzy Operation 2.5.1 Extension Principle Definition Application to the Compatibility of Two Fuzzy Sets Application to Fuzzy Numbers 2.5.2 Combination of Results on α-Cuts Reconstruction from α-Cuts Extension Principle Based on α-Cuts 2.5.3 Translating Binary Terms into Functional Ones 2.5.4 Comparison 2.6 Summary of the Main Notations References 3 Fuzzy Spatial Objects 3.1 Fuzzy Sets in the Spatial Domain 3.2 Set Theoretical Operations 3.2.1 Degree of Intersection Crisp Case Direct Extension Introducing the Volume of the Overlapping Domain Properties Application to the Non-contradiction Principle 3.2.2 Degree of Union and Covering 3.2.3 Degree of Inclusion Inclusion from Other Set Operations Inclusion from Fuzzy Implication Other Axiomatic Definitions for the Fuzzy Inclusion Inclusion and Fuzzy Entropy 3.2.4 Degree of Equality 3.3 Topology: Neighborhood, Boundary, and Connectedness of a Fuzzy Set 3.3.1 Fuzzy Neighborhood 3.3.2 Boundary of a Fuzzy Set 3.3.3 Connectedness 3.4 Fuzzy Geometry 3.4.1 Fuzzy Points and Lines 3.4.2 Fuzzy Rectangles and Fuzzy Convex Polygons 3.4.3 Fuzzy Disks 3.4.4 Fuzzy Geometrical Measures Area of a Fuzzy Set Perimeter of a Fuzzy Set Compactness of a Fuzzy Set Height, Width, and Diameter of a Fuzzy Set Intersection and Parallelism Between Fuzzy Lines Geometrical Measures as Fuzzy Numbers 3.5 Fuzzy Geometric Transformations 3.5.1 Transformation of a Fuzzy Set by a Crisp Operation 3.5.2 Transformation of a Fuzzy Set by a Fuzzy Operation References 4 Fuzzy Mathematical Morphology 4.1 Lattice Structure of ps: [/EMC pdfmark [/Subtype /Span /ActualText (script upper F) /StPNE pdfmark [/StBMC pdfmarkFps: [/EMC pdfmark [/StPop pdfmark [/StBMC pdfmark 4.2 Algebraic Operators 4.3 Structuring Elements and Basic Morphological Operators 4.4 An Example in Medical Imaging 4.5 Towards a Fuzzy Mathematical Morphology Toolbox 4.5.1 Neighborhood and Boundary from Fuzzy Dilation and Erosion 4.5.2 Fuzzy Morphological Filters 4.5.3 Conditioning and Fuzzy Geodesic Operators 4.5.4 Fuzzy Skeleton and Skeleton by Influence Zones Distance-Based Approaches Morphological Approaches to Compute the Centers of Maximal Balls Morphological Thinning Fuzzy Skeleton of Influence Zones Discussion 4.5.5 Fuzzy Median, Application to Interpolation Between Fuzzy Sets 4.5.6 Extensions References 5 Fusion 5.1 Definitions 5.2 Fusion Systems and Architectures Types 5.3 Fuzzy Modeling in Fusion 5.4 Defining and Estimating Membership Functions 5.5 Fuzzy Combination 5.6 Decision in Fuzzy Fusion 5.7 Exploiting Spatial Information 5.8 Illustrative Examples References 6 Spatial Relations 6.1 Set Theoretical and Topological Relations 6.1.1 Adjacency 6.1.2 Fuzzy Region Connection Calculus 6.2 Distances Between Image Regions or Objects 6.2.1 Representations 6.2.2 Comparison of Membership Functions 6.2.3 Combination of Spatial and Membership Comparisons 6.2.4 Discussion and Examples 6.3 Fuzzy Hamming Distance 6.4 Directional Relations 6.4.1 Fuzzy Relations Describing Relative Position 6.4.2 Centroid Method 6.4.3 Histogram of Angles: Compatibility Method 6.4.4 Aggregation Method 6.4.5 Histogram of Forces 6.4.6 Projection Based Approach 6.4.7 Morphological Approach 6.4.8 Discussion and Examples 6.5 Complex Relations: Surround, Between, Along, Across, Parallel, Aligned 6.5.1 Surround 6.5.2 Between 6.5.3 Across 6.5.4 Along 6.5.5 Aligned 6.5.6 Parallel 6.6 Fuzzy Perceptual Organization for Image Understanding 6.6.1 Fuzzy Grouping Operator to Produce Straight LineSegments 6.6.2 Discrimination: Overlap of Two Segments 6.6.3 Obtaining Junctions 6.6.4 Obtaining Symmetric Line Structures Symmetry of Non-parallel Line Segments Symmetry of Parallel Line Structures 6.6.5 Obtaining Curves and Closed Regions 6.7 Comparison of Spatial Relations 6.7.1 Relations Represented as Numbers or Intervals 6.7.2 Relations Represented as Distributions 6.7.3 Relations Represented as Spatial Fuzzy Sets References 7 Fuzzy Sets and Machine Learning 7.1 Fuzzy IF-THEN Rules 7.2 Unsupervised Learning 7.2.1 Fuzzy Clustering 7.2.2 Spatial Information and Bias 7.3 Fuzzy Sets and Connectionist Approaches 7.3.1 Conventional 2D Hopfield Neural Network 7.3.2 Fuzzy Sets and Deep Learning References 8 Structural and Linguistic Representations 8.1 Fuzzy Representation of Image Information and of Related Knowledge 8.1.1 Image Features 8.1.2 Knowledge and Semantics 8.1.3 Semantic Gap 8.2 Linguistic Representations 8.2.1 Description of Some Properties or Characteristics 8.2.2 Quantifiers 8.2.3 Associating Linguistic Representations and the Spatial Domain 8.3 Knowledge-Based Systems 8.4 Fuzzy Graphs and Hypergraphs 8.5 Fuzzy Logics and Fuzzy Rules 8.6 Ontologies 8.7 Fuzzy Decision Trees 8.8 Fuzzy Association Rules 8.9 Fuzzy Formal Concept Analysis References 9 Structural and Linguistic Reasoning for Image Understanding 9.1 From Linguistic Descriptions to Image Understanding 9.1.1 Representations of Structural Information 9.1.2 Fusion 9.1.3 Scene Understanding 9.2 From Image Analysis to Image Content Descriptions 9.3 A Few Examples in Medical Image Understanding 9.3.1 Interpretation as Graph Reasoning 9.3.2 Interpretation as Constraint Satisfaction Problem 9.3.3 Recognition Based on Ontological Reasoning 9.3.4 Interpretation as Abductive Reasoning 9.3.5 Deriving Linguistic Descriptions 9.4 Interpretability and Explainability References Index