BM4203 Applied Medical Image Processing Syllabus:
BM4203 Applied Medical Image Processing Syllabus – Anna University PG Syllabus Regulation 2021
COURSE OBJECTIVES:
To understand the fundamentals of medical image processing techniques.
To understand the basic concepts of image enhancement, image restoration,
morphological image processing, image segmentation, feature recognition in medical images
To provide information about various medical imaging modalities
To provide information about classification and image visualization in medical image processing projects.
To familiarize the student with the image processing facilities in Matlab, Python and openCV
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. DFT, DCT, KLT, SVD
UNIT II MEDICAL IMAGE ENHANCEMENT AND RESTORATION
Image Enhancement operation, Noise distributions, Spatial averaging, Directional Smoothing, Median, Geometric mean, Harmonic mean, Contra harmonic mean filters, Homomorphic filtering, Color image enhancement. Image Restoration – degradation model, Unconstrained and constrained restoration, Inverse filtering- Wiener filtering
UNIT III MEDICAL IMAGE REPRESENTATION
Pixels and voxels – algebraic image operations – gray scale and color representation- depth color and look up tables – image file formats- DICOM- other formats- Analyze 7.5, NifTI and Interfile, Image quality and the signal to noise ratio
UNIT IV MEDICAL IMAGE ANALYSIS AND CLASSIFICATION
Image segmentation- pixel based, edge based, region based segmentation. Image representation and analysis, Feature extraction and representation, Statistical, Shape, Texture, feature and image classification – Statistical, Rule based, Neural Network approaches
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.
45 PERIODS
PRACTICAL EXERCISES: 30 PERIODS
The following experiments should be performed in OpenCV / Python / Scilab / Matlab Octave / other Open source software.
LIST OF EXPERIMENTS
1. Preprocessing of medical images
2. Filtering of medical images.
3. Edge detection using Python
4. Segmentation of ROI in medical images.
5. Feature extraction in medical images
6. Steganography using OpenCV.
7. Medical image fusion.
8. Statistical analysis of features
9. Neural network based classification.
COURSE OUTCOMES:
Upon Completion of the course, the students 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 and reconstruction of an image
CO3: Describe the image representation model
CO4: Analysis of image segmentation, feature extraction and image classification
CO5: Explore the knowledge in image registration and visualization and possibility of applying Image processing concepts in modern hospitals
TOTAL:75 PERIODS
REFERENCES
1. Atam P.Dhawan, Medical Image Analysis, 2nd Edition, John Wiley & Sons, Inc., Hoboken, New Jersey, 2011.
2. Anil K Jain, Fundamentals of Digital Image Processing, 1st Edition, Pearson Education India, 2015.
3. Rafael C.Gonzalez and Richard E.Woods, Digital Image Processing, 4th Edition, Pearson Education, 2018.
4. Wolfgang Birkfellner, “Applied Medical Image Processing – A Basic course”, CRC Press, 2011
5. Geoff Dougherty, Digital Image Processing for Medical Applications, 1st Edition, Cambridge University Press, 2010.
6. John L.Semmlow, “Biosignal and Biomedical Image Processing Matlab Based applications” Marcel Dekker Inc.,New York,2004
7. Kavyan Najarian and Robert Splerstor, “Biomedical signals and Image processing”,CRC – Taylor and Francis,New York,2006
8. Milan Sonka et aI, “Image Processing, Analysis and Machine Vision”, Brookes/Cole, Vikas Publishing House, 2nd edition, 1999.
9. Ravikanth Malladi, Geometric Methods in Bio-Medical Image Processing (Mathematics and Visualization), 1st Edition, Springer-Verlag Berlin Heidelberg 2002.
10. Joseph V. Hajnal, Derek L.G. Hill and David J. Hawkes, Medical Image Registration, CRC Press, 2001.