Course

Syllabus

Imaging Modalities: Brief survey of major modalities for medical imaging: Ultrasound, X-ray, CT, MRI, PET, and SPECT. 

Objectives of biomedical image analysis – Computer aided diagnosis, Removal of artifacts – Image Enhancement – Gray level transforms – Histogram transformation. 

Spatial domain filters – Frequency domain filters – Morphological image processing – Binary morphological operations and properties – Morphological algorithms – Medical Image Segmentation, Thresholding – Region growing – Region splitting and merging – Edge detection. Analysis of shape and texture – Representation of shapes and contours – Shape factors – Models for generation of texture – Statistical analysis of texture – Fractal analysis – Fourier domain analysis of texture – Applications – Contrast enhancement of mammograms – Detection of calcifications by region growing – Shape and texture analysis of tumours. 

Reconstruction Techniques, Classification and Clustering, Examples of Image Classification for Diagnostic/Assistive Technologies, Case studies. 

Image processing practical exercises: 

1. Basic operations on images 
2. Image enhancement using point operations. 
3. Image enhancement using spatial domain filters. 
4. Histogram processing of images. 
5. Image enhancement using frequency domain filters. 
6. Denoising of medical images. 
7. Medical image segmentation using edge and region-based methods. 
8. Extraction of shape and texture features from a medical image. 
9. Design of pattern classification system for biomedical images. 
10. Performance metrics in bioimages. Recommended Tools MATLAB, Python 

Learning Objectives 

LO1 To familiarize with major signal and image acquisition modalities in healthcare. 

LO2 To understand instrumentation and signal characteristics associated with each modality. 

Course Outcomes 

CO1 To get familiarized with (a) biomedical signal acquisition modalities like ECG, EEG, EMG, (2) biomedical imaging modalities like x-ray, MRI, CT and (3) surgical and other analytic equipment. 

CO3 Ability to read and interpret data from diverse modalities.

1. Suetens, P. Fundamentals of Medical Imaging, Cambridge University Press 
2. Dougherty, G, Digital Image Processing for Medical Applications, Cambridge University Press 
3. Prince, J. & Links, J. Medical Imaging Signals and Systems, Prentice Hall 
4. Bankman, Isaac., Handbook of Medical Imaging: Processing and Analysis, Academic Press 
5. Yoo, Terry S. Insight into Images: Principles and Practice for Segmentation, Registration and Image Analysis, CRC Press 
6. Sethian, J.A., Level-set Methods, Cambridge University Press, 2000