Hybrid Image Processing Methods for Medical Image Examination (Hardcover)

Chapter 1: Introduction Introduction to Disease Screening Screening for blood level infection Screening for skin melanoma Stomach ulcer screening Screening for breast abnormality Screening for brain abnormality Screening for the fetal growth Screening for retinal abnormality Screening for lung abnormality Heart disease screening Osteoporosis Screening of COVID-19 infection Medical Image Recording Procedures Summary References Chapter 2: Image Examination Clinical Image Enhancement Techniques Importance of Image Enhancement Introduction to Enhancement Techniques Artefact removal Noise removal Contrast enrichment Edge detection Restoration Colour space correction Image edge smoothening Recent Advancements Hybrid image examination technique Need for multi-level thresholding Thresholding Implementation and evaluation of thresholding process Summary References Chapter 3: Image Thresholding Need for Thresholding of Medical Images Bi-level and Multi-level threshold Common Thresholding Methods Thresholding for Grey Scale and RGB Images 3.4.1 Thresholding with Between-Class Variance 3.4.2 Thresholding with Entropy Functions Choice of Threshold Scheme Performance Issues Evaluation and Confirmation of Thresholding Technique Thresholding Methods Restrictions in Traditional Threshold Selection Process Need for Heuristic Algorithm Selection of Heuristic Algorithm Particle Swarm Optimization Bacterial Foraging Optimization Firefly Algorithm Bat Algorithm Cuckoo Search Social Group Optimization Teaching-Learning-Based Optimization Jaya Algorithm Introduction to Implementation Monitoring Parameter Objective function Single and Multiple Objective function Summary References Chapter 4: Image Segmentation Requirement of Image Segmentation Extraction of Image Regions with Segmentation Morphological approach Circle Detection Watershed algorithm Seed Region Growing Principal Component Analysis Local Binary Pattern Graph Cut approach Contour Based Approach CNN based segmentation Assessment and Validation of Segmentation Construction of Confusion Matrix Summary References Chapter 5: Medical Image Processing with Hybrid Image Processing Method 5.1 Introduction Context Methodology Database Thresholding Otsu’s function Brain Storm Optimization Segmentation Performance evaluation and validation Results and Discussion Summary References Chapter 6: Deep Learning for Medical Image Processing Introduction Implementation of CNN for image assessment Transfer learning concepts AlexNet VGG-16 VGG-19 Medical Image Examination with Deep-Learning: Case study Brain abnormality detection Lung abnormality detection Retinal abnormality detection COVID-19 lesion detection Summary References Chapter 7: Conclusion