Bio-Medical Image Processing has become a major aspect of engineering sciences and radiology has become a dominant player in the medical field. This field has been an interdisciplinary research field incorporating applied mathematics, computer sciences, engineering, statistics, physics, biology, and medicine. More challenges arise with the new development of high technology and the use of various imaging modalities.  Way of processing and analyzing a significant volume of images is important, leading to high-quality information for disease diagnoses and treatment.  Computer-aided diagnostic processing has already become an important part of the clinical routine. With the increasing use of direct digital imaging systems for medical diagnostics, digital image processing becomes more and more important in health care. This course is particularly important for knowing the introduction to basic concepts and techniques for medical image processing and to promote interest for further study and research in medical imaging processing. It will aid the participants to do research work in this area and as well as state of art techniques.

Imaging science is experiencing tremendous growth in all over the world. Digital medical imaging and its analysis are fundamental to understanding, visualizing, and quantifying medical images in clinical applications. With the help of automated and quantitative image analysis techniques, disease diagnosis will be easier/faster and more accurate and leading to significant development in medicine in general. The goal of this course is to help students develop skills in computational radiology, radiological image analysis, and biomedical image processing fields.

This course gives an overview of medical image formation, enhancement, segmentation, analysis, visualization, and communication with many examples from medical applications. It starts with a brief introduction to medical imaging modalities and acquisition systems. As a focus, image enhancement techniques, segmentation, registration, texture analysis, DICOM and their application in diagnostic imaging will be discussed. To complete this overview, storage, retrieval, and communication of medical images are also introduced.

• Introduction (Structure of images, image types, programming environments (Matlab/Octave, Python)
• Image Enhancement
• Segmentation Part I
• Segmentation Part II
• Segmentation Part III
• Feature Extraction & Classification
• Image Registration
• Color Image Processing
• Hyperspectral Image Processing
• DICOM
• Medical Image Sources
• Applied Medical Image Processing