In view of better results expected from examination of medical datasets (images) with hybrid (integration of thresholding and segmentation) image processing methods, this work focuses on implementation of possible hybrid image examination techniques for medical images. It describes various image thresholding and segmentation methods which are essential for the development of such a hybrid processing tool. Further, this book presents the essential details, such as test image preparation, implementation of a chosen thresholding operation, evaluation of threshold image, and implementation of segmentation procedure and its evaluation, supported by pertinent case studies. Aimed at researchers/graduate students in the medical image processing domain, image processing, and computer engineering, this book:

• Provides broad background on various image thresholding and segmentation techniques
• Discusses information on various assessment metrics and the confusion matrix
• Proposes integration of the thresholding technique with the bio-inspired algorithms
• Explores case studies including MRI, CT, dermoscopy, and ultrasound images
• Includes separate chapters on machine learning and deep learning for medical image processing

In view of better results expected from examination of medical datasets (images) with hybrid (integration of thresholding and segmentation) image processing methods, this work focuses on implementation of possible hybrid image examination techniques for medical images. It describes various image thresholding and segmentation methods which are essential for the development of such a hybrid processing tool. Further, this book presents the essential details, such as test image preparation, implementation of a chosen thresholding operation, evaluation of threshold image, and implementation of segmentation procedure and its evaluation, supported by pertinent case studies. Aimed at researchers/graduate students in the medical image processing domain, image processing, and computer engineering, this book:

• Provides broad background on various image thresholding and segmentation techniques
• Discusses information on various assessment metrics and the confusion matrix
• Proposes integration of the thresholding technique with the bio-inspired algorithms
• Explores case studies including MRI, CT, dermoscopy, and ultrasound images
• Includes separate chapters on machine learning and deep learning for medical image processing

In view of better results expected from examination of medical datasets (images) with hybrid (integration of thresholding and segmentation) image processing methods, this work focuses on implementation of possible hybrid image examination techniques for medical images. It describes various image thresholding and segmentation methods which are essential for the development of such a hybrid processing tool. Further, this book presents the essential details, such as test image preparation, implementation of a chosen thresholding operation, evaluation of threshold image, and implementation of segmentation procedure and its evaluation, supported by pertinent case studies. Aimed at researchers/graduate students in the medical image processing domain, image processing, and computer engineering, this book:

• Provides broad background on various image thresholding and segmentation techniques
• Discusses information on various assessment metrics and the confusion matrix
• Proposes integration of the thresholding technique with the bio-inspired algorithms
• Explores case studies including MRI, CT, dermoscopy, and ultrasound images
• Includes separate chapters on machine learning and deep learning for medical image processing