Details

Subject(s)

• Image segmentation [Browse]
• Threshold logic [Browse]
• Imaging systems in medicine [Browse]

Author

• Raja, Nadaradjane Sri Madhava [Browse]
• Dey, Nilanjan, 1984- [Browse]

Series

• CRC focus series. [More in this series]
• Intelligent signal processing and data analysis. [More in this series]

Summary note

"Choosing appropriate thresholding based on the existing image (Grayscale/RGB) and the assigned task is essential to attain better result and the complexity of the image thresholding is largely reduced with the help of a chosen heuristic algorithm technique. This book emphasizes various image thresholding methods that are necessary for image pre-processing and initial level enhancement. It presents a detailed evaluation, such as choosing the threshold function, need for the heuristic algorithm, choice of objective function, and evaluation of the outcome along with real-time applications in detail"-- Provided by publisher.

Bibliographic references

Includes bibliographical references and index.

Source of description

Description based on print version record.

Contents

• 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
• 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
• 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
• 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
• 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
• 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
• 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
• Index.

Show 85 more Contents items

ISBN

• 1-00-304944-3
• 1-000-22833-9
• 1-003-04944-3
• 1-000-22845-2

LCCN

2020028732

OCLC

1161997043

Doi

• 10.1201/9781003049449

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