A Beginner's Guide to Multilevel Image Thresholding

Detalles Bibliográficos
Clasificación:Libro Electrónico
Autor principal: Rajinikanth, Venkatesan
Otros Autores: Madhava Raja, Nadaradjane Sri, Dey, Nilanjan
Formato: Electrónico eBook
Idioma:Inglés
Publicado: Milton : Taylor & Francis Group, 2020.
Colección:Intelligent Signal Processing and Data Analysis Ser.
Temas:
Image segmentation.
Segmentation d'image.
Acceso en línea:Texto completo
Tabla de Contenidos:
  • Cover
  • Half Title
  • Series Page
  • Title Page
  • Copyright Page
  • Contents
  • Preface
  • Acknowledgements
  • Author Biographies
  • Chapter 1: Introduction
  • 1.1. Introduction to Image Enhancement
  • 1.2. Importance of Image Enhancement
  • 1.3. Introduction to Enhancement Techniques
  • 1.3.1. Artifact Removal
  • 1.3.2. Filtering
  • 1.3.3. Contrast Enrichment
  • 1.3.4. Edge Detection
  • 1.3.5. Thresholding
  • 1.3.6. Smoothening
  • 1.4. Recent Advancements
  • 1.5. Need for Multilevel Thresholding
  • 1.6. Implementation and Evaluation of Thresholding Process
  • 1.7. Summary
  • References
  • Chapter 2: Thresholding Approaches
  • 2.1. Need for Image Thresholding
  • 2.2. Bilevel and Multilevel Threshold
  • 2.3. Common Thresholding Methods
  • 2.3.1. Otsu's Approach
  • 2.3.2. Tsallis Approach
  • 2.3.3. Fuzzy-tsallis entropy Approach
  • 2.3.4. Shannon's Approach
  • 2.3.5. Kapur's Approach
  • 2.4. Selection of Thresholding Method
  • 2.5. Implementation Issues
  • 2.6. Summary
  • References
  • Chapter 3: Grayscale and RGB-Scale Image Examination
  • 3.1. Image Selection
  • 3.2. Grayscale and RGB-Scale Image
  • 3.3. Complexity due to Image Dimension
  • 3.4. Complexity due to Pixel Distribution
  • 3.5. Implementation Steps
  • 3.6. Summary
  • References
  • Chapter 4: Heuristic-Algorithm-Assisted Thresholding
  • 4.1. Thresholding Methods
  • 4.2. Limitations in Traditional Thresholding
  • 4.3. Need for Heuristic Algorithm
  • 4.4. Selection of Heuristic Algorithm
  • 4.4.1. Particle Swarm Optimization
  • 4.4.2. Bacterial Foraging Optimization
  • 4.4.3. Firefly Algorithm
  • 4.4.4. Bat Algorithm
  • 4.4.5. Cuckoo Search
  • 4.4.6. Social Group Optimization
  • 4.5. Implementation Steps
  • 4.6. Summary
  • References
  • Chapter 5: Objective Function and Image Quality Measures
  • 5.1. Introduction to Implementation
  • 5.2. Monitoring Parameter
  • 5.2.1. Objective Function
  • 5.2.2. Single and Multiple Objective Function
  • 5.3. Assessment of Thresholding Process
  • 5.4. Summary
  • References
  • Chapter 6: Assessment of Images with Constraints
  • 6.1. Selection of Test Images
  • 6.2. Abnormalities in Test Images
  • 6.3. Test Image Stained with Noise
  • 6.3.1. Gaussian Noise
  • 6.3.2. Local variance Noise
  • 6.3.3. Poisson Noise
  • 6.3.4. Salt & pepper Noise
  • 6.3.5. Speckle Noise
  • 6.4. Impact of Noise in Thresholding Outcome
  • 6.5. Summary
  • References
  • Chapter 7: Thresholding of Benchmark Images
  • 7.1. Selection of Test Image
  • 7.3. Selection of Objective Function
  • 7.4. Assessment of Outcome and Comparison
  • 7.5. Summary
  • References
  • Chapter 8: Thresholding of Biomedical Images
  • 8.1. Need of Thresholding for Medical Images
  • 8.2. Thresholding for MRI
  • 8.3. Thresholding for Ultrasound Image
  • 8.4. Thresholding for RGB-Scale Medical Images
  • 8.5. Thresholding of Lung CT Scan Slice Infected With Covid-19
  • 8.6. Summary
  • References
  • Index

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