Peripheral blood smear (PBS) analysis is a critical diagnostic tool for diseases including malaria, leukemia, and cancer. This monograph addresses the key challenges associated with image analysis of PBS images and highlights innovative systems that automate the identification of specific pathologies.
It reviews state-of-the-art algorithms for segmentation, color representation and normalization, image enhancement and restoration, and volume visualization, as well as relevant AI and machine learning techniques. The book also explains automated systems and workflows developed to address specific disease classes, supported by detailed case studies.
Key Features:
• Covers fundamental and advanced aspects of image analysis in the hematological field
• Focuses on blood cell segmentation and classification, along with practical guidelines for implementation
• Explores current challenges in integrating these technologies into real-world clinical settings
• Discusses ethical considerations and challenges associated with automation in medical diagnosis
• Includes relevant MATLAB and Python code, along with case studies on the segmentation of erythrocytes (RBCs) and leukocytes (WBCs)
This book is aimed at graduate students and researchers in bioengineering and image processing, as well as clinicians.
Table of Contents:
1. Characteristics of Blood Cells in Peripheral Blood Smear Images 2. Literature Survey 3. Color Image Analysis 4. Segmentation of Blood Cells 5. Feature Extraction 6. Classification Of Blood Cells 7. Case Studies
About the Author :
Roopa B. Hegde is an associate professor in the Department of Electronics and Communication Engineering, NMAM Institute of Technology, Udupi, India. She has published more than 30 articles in peer-reviewed journals with an H-index of 9. She has to her credit, two patents granted and four applications pending examination. She has won best paper award and the healthcare innovation hackathon. Her areas of interest include signal processing, image processing, pattern recognition, artificial intelligence, and machine learning.
Swathi Prabhu is an assistant professor in the Department of Information and Communication Technology at Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, India. She has published several articles in peer-reviewed journals and has an H-index of 5. Her areas of interest include medical image processing, artificial intelligence, machine learning, and deep learning.
Keerthana Prasad is a professor and director of the Manipal School of Information Sciences, Manipal. With a keen interest in application of technology in medicine, she has worked very closely with many medical researchers in India and abroad, mostly on computer aided diagnostic applications. She has to her credit, four patents granted and six patent applications pending examination in India. She has developed many in-house analysis tools for medical researchers of the university. She has won many awards including the Rural Innovation award, Innovation Day award at Philips, IDF Innovation award and best paper award. She has published about 75 research papers in indexed international journals and has an H-index of 20.
