CALL FOR BOOK CHAPTERS
BOOK TITLE: Decoding Medical Images: Enhancement, Restoration, Reconstruction BY RIVER PUBLISHER
This book offers a comprehensive study of medical image processing, with an emphasis on methods to enhance the accuracy and readability of medical images for diagnosis and therapy. Its main goals are to improve the visual clarity of images by sharpening details, lowering noise, and altering contrast to better visualize subtle elements. Also, it guarantees that anatomical features are accurately represented by correcting distortions, artefacts, and defects resulting from capture or transmission. Reconstruction is the main topic, including how to mix data from different modalities (such as CT and MRI) to build a more complete perspective or recreate missing or incomplete data from fragmented images. The book will cover a wide range of topics, including deep learning methodologies, image processing methods, clinical applications, evaluation, and validation. The goal of the book is to provide readers a thorough understanding of medical image processing methods for enhancing, restoring, and reconstructing medical images. It will describe the different algorithms' theoretical foundations and real-world applications. It will highlight the practical uses and promise of these methods for enhancing medical diagnosis and care.
Topics (Proposed Tentative Chapters)
- Examining various medical imaging modalities (such as X-ray, CT, MRI, etc.), their underlying concepts, advantages, and constraints.
- Exploring Image Quality and Challenges: Understanding noise, artefacts, distortions, and their influence on diagnosis.
- Introduction to essential operations in Basic Medical Image Processing Techniques, including image filtering, segmentation, histogram analysis, and other related procedures.
- Contrast Enhancement: Methods for enhancing visual contrast, such as histogram equalisation, adaptive filtering, and unsharp masking.
- Methods for Noise Reduction: Techniques for reducing noise while maintaining fine image details, including conventional, non-conventional methods and deep learning methodologies.
- Exploring Sharpening and edge detection: methods used to improve image clarity and identify boundaries to accurately outline anatomical structures.
- Artefact Correction: Approaches to mitigate artefacts resulting from faults in acquisition, transmission, or reconstruction.
- Aligning medical images for analysis and comparison across modalities or time periods for efficient image registration.
- Reconstructing high-resolution images from lower resolution ones, particularly useful for older scans or limited acquisition data.
- Creating three-dimensional models from medical images to enhance comprehension of anatomy and pathophysiology.
- Investigating methods for differentiating various tissue types and structures in medical imaging to facilitate further analysis.
- Fusion and Multimodal Imaging: Combining information from different modalities (e.g., PET/CT) to create more comprehensive views and enhance diagnostic accuracy.
- Clinical Applications in Radiology: Investigating for the purpose of diagnosing different illnesses, planning treatments, and performing image-guided procedures.
- Exploring the potential of artificial intelligence and deep learning in medical imaging for automating image interpretation, providing diagnostic assistance, and enabling personalized treatment.
- Future Trends and Challenges: Exploring emerging technologies along with ethical considerations and regulatory challenges.
