CEI347 Digital Image Processing Syllabus:
CEI347 Digital Image Processing Syllabus – Anna University Regulation 2021
OBJECTIVES:
1. To provide information about various medical imaging modalities.
2. To understand the basic concepts of image enhancement, image restoration, morphological Image processing, image segmentation, feature recognition in medical images.
3. To provide information about classification and image visualization in medical image processing projects.
4. To familiarize the student with the image processing facilities in Matlab and its equivalent open source tools.
UNIT I FUNDAMENTALS OF IMAGE PROCESSING
Image perception, MTF of the visual system, Image fidelity criteria, Image model, Image sampling and quantization – two dimensional sampling theory, Image quantization, Optimum mean square quantizer, Image transforms – 2D-DFT and other transforms.
UNIT II BIO-MEDICAL IMAGE PREPROCESSING
Image Enhancement operations – Image noise and modeling, Image restoration – Image degradation model, Inverse and Wiener filtering, Geometric transformations and correction.
UNIT III MEDICAL IMAGE RECONSTRUCTION
Mathematical preliminaries and basic reconstruction methods, Image reconstruction in CT scanners, MRI, fMRI, Ultrasound imaging. 3D Ultrasound imaging, Nuclear Medical Imaging modalities – SPECT, PET, Molecular Imaging.
UNIT IV IMAGE ANALYSIS AND CLASSIFICATION
Image segmentation- pixel based, edge based, region based segmentation. Active contour models and Level sets for medical image segmentation, Image representation and analysis, Feature Extraction and Representation-Statistical, Shape, Texture features. Statistical and Neural Network based image classification.
UNIT V IMAGE REGISTRATIONS AND VISUALIZATION
Image Registration: Rigid body transformation – Affine transformation, Principal axes registration, Iterative principal axes registration, Feature based registration, Elastic deformation based registration, Registration of Images from Different modalities, Evaluation of Registration Methods. Image visualization: 2-D display methods, 3-D display methods, surface and volume based 3-D display methods – Surface Visualization and Volume visualization, 3-D Echocardiography, 3D+time Echocardiography, virtual reality based interactive visualization.
TOTAL : 45 PERIODS
SKILL DEVELOPMENT ACTIVITIES (Group Seminar/MiniProject/Assignment/Content Preparation / Quiz/ Surprise Test / Solving GATEquestions/ etc)
1. Survey different algorithms for segmentation of various medical images.
2. Identify suitable open source software for 2D and 3D visualization of medical images.
3. Compare various segmentation techniques and its suitability for the given medical image.
4. Conduct a literature survey and find the best preprocessing technique for medical images.
5. Develop the best Machine learning algorithm to preprocess and classify different images.
TEXT BOOKS:
1. Atam P.Dhawan, Medical Image Analysis, 2nd Edition, John Wiley & Sons, Inc., Hoboken, New Jersey, 2011.
2. Rafael C.Gonzalez and Richard E.Woods, Digital Image Processing, 4th Edition, Pearson Education, 2018.
REFERENCES:
1. Anil K Jain, Fundamentals of Digital Image Processing, 1st Edition, Pearson Education India, 2015.
2. Geoff Dougherty, Digital Image Processing for Medical Applications, 1st Edition, Cambridge University Press, 2010.
3. Jerry L.Prince and Jonathan M.Links, Medical Imaging Signals and Systems, 2ndEdition, Pearson Education, 2014.
4. Kavyan Najarian and Robert Splerstor, Biomedical signals and Image processing, 2nd Edition, CRC Press, 2012.
5. Ravikanth Malladi, Geometric Methods in Bio-Medical Image Processing (Mathematics and Visualization), 1st Edition, Springer-Verlag Berlin Heidelberg 2002.
6. A. Ardeshir Goshtasby, Image Registration Principles, Tools and Methods (Advances in Computer Vision and Pattern Recognition), Springer 2014.
7. Joseph V. Hajnal, Derek L.G. Hill and David J. Hawkes, Medical Image Registration, CRC Press, 2001.
COURSE OUTCOMES:
Upon completion of the course, the student should be able to:
CO1 Apply basic medical image processing algorithms.
CO2 Image pre-processing applications that incorporates different concepts of filters for medical Image Processing.
CO3 Summarize about medical imaging and reconstruction for high dimensionality visualization.
CO4 Analysis of image segmentation, feature extraction and image classification.
CO5 Relate the knowledge in image registration and visualization and possibility of applying Image processing concepts in modern hospitals.