Introduction to Computational Analysis in Medical Imaging

Abstract

The exponential growth of digital imaging modalities like CT, MRI, Ultrasound, PET and digital pathology has produced huge amount of high dimensional data. This data having powerful insights need advanced computational techniques for accurate interpretation and clinical decision making. In the clinical setting, sophisticated computational analysis models play an important role in supporting correct diagnosis, the monitoring of disease progression, and the treatment decision. The chapter starts with an explanation of the development of medical image analysis from the traditional image processing methods to the modern ML models. Core tasks such as image acquisition modelling, pre-processing, segmentation, registration, feature extraction, classification and quantitative assessment are systematically described. Particular attention is given towards integrating radiomics and artificial intelligence approaches. In addition to methodological foundations, the chapter covers practical considerations such as data variability, multimodal integration, reproducibility, validation strategies and regulatory aspects. Emerging trends such as self-supervised learning, federated learning, explainable artificial intelligence, and foundation models are introduced in this book as well to give readers a window into the future of the field. Overall this chapter provides a conceptual and technical framework for the understanding of computational medical imaging. It provides a basic reference for researchers, graduate students and clinicians who wish to work with advanced image analysis methodologies.