Emphasis, particularly at the BTech level, will be on BASICS. Mathematical basics are like bricks - if the bricks are strong, the walls built out of them will be strong.
A topic may be considered really well-understood only when one can implement it on a computer and verify. Today, with the easy access to powerful computers and software platforms like MATLAB, this is very much feasible. Towards such a goal, theory pertaining to many topics in the courses will be followed by pseudo-codes - so that one can implement it on a platform of choice.
The curricula provided in the following are designed to cater to the students at the MIT, Manipal, and is not to be considered generic.
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BME 3203: MEDICAL IMAGE PROCESSING [4-0-0-4]
Pre-requisite: Digital Signal Processing
Image Processing: Review of signals, systems & transforms; 2D signals & systems, 2D DFT and its computation. 2D convolution/filtering. Image perception – the human vision system, psycho-visual experiments, monochrome vision model, temporal properties. Image compression – the discrete cosine transform (DCT), properties, computation, practical compression algorithm. Image Enhancement: Point operations, Spatial filtering: linear filters & the median filter.
Medical Imaging: Imaging modalities; Computed tomography (CT): mathematical basis, the Radon transform & the central slice theorem; Image reconstruction from projections: the Direct Fourier Method, convolution backprojection (CBP) algorithm, reconstruction from fan-beam projections; X-rays: utility, generation and detection; X-ray CT systems - 5 generations. Emission CT: principles, Positron emission tomography (PET); Magnetic resonance imaging: Principles of data-generation, resolving the tissues, resolving the spatial locations.
References:
1) R.C Gonzalez and R.E. Woods, Digital Image Processing, 2nd Ed., Pearson Education Inc., Eighth Indian Reprint, 2002.
2) Jae S. Lim, Two-dimensional Signal and Image Processing, Prentice-Hall, Englewood Cliffs, New Jersey, 1990.
3) A.K. Jain, Fundamentals of Digital Image Processing, Prentice-Hall, 1989, Fourth Indian Reprint.
4) A.C. Kak and M. Slaney, Principles of Computerized Tomographic Imaging, SIAM’s Classics in Applied Mathematics, Philadelphia, SIAM, 2001.
5) Kline Jacob, Handbook of Biomedical Engineering, Academic Press, 1988.
BME 5201: MEDICAL IMAGE PROCESSING [4-0-0-4] (for MTech Students)
Review of signals, systems & transforms; 2D signals & systems, 2D DFT and its computation. Image perception – the human vision system, psycho-visual experiments, monochrome vision model, temporal properties. Image compression – the discrete cosine transform (DCT), properties, computation, practical compression algorithm.
Image Enhancement: Point operations, Spatial filtering: linear filters & the median filter. Histogram Equalization.
Image Segmentation: Thresholding, Edge Detection, Color Image Processing (with emphasis on Applications), Hough Transform and Connected Component Labeling.
Medical Imaging: Imaging modalities; Computed tomography (CT): mathematical basis, the Radon transform & the central slice theorem; Image reconstruction from projections: the Direct Fourier Method, convolution backprojection (CBP) algorithm, reconstruction from fan-beam projections; X-rays: utility, generation and detection; X-ray CT systems. Emission CT: principles, Positron emission tomography (PET); Magnetic resonance imaging: Principles of data-generation, resolving the tissues, resolving the spatial locations.
References:
1) R.C Gonzalez and R.E. Woods, Digital Image Processing, 2nd Ed., Pearson Education Inc., Eighth Indian Reprint, 2002.
2) Jae S. Lim, Two-dimensional Signal and Image Processing, Prentice-Hall, Englewood Cliffs, New Jersey, 1990.
3) A.K. Jain, Fundamentals of Digital Image Processing, Prentice-Hall, 1989, Fourth Indian Reprint.
4) A.C. Kak and M. Slaney, Principles of Computerized Tomographic Imaging, SIAM’s Classics in Applied Mathematics, Philadelphia, SIAM, 2001.
5) Kline Jacob, Handbook of Biomedical Engineering, Academic Press, 1988.
BME 4103: ADVANCED MEDICAL IMAGE PROCESSING [4-0-0-4] (BTech)
Mathematical Preliminaries: Review of 2D Signals & systems, Probability Theory & Random Variables; Linear Algebra; Matrix-representation of Filtering/convolution.
Orthogonal and unitary transforms, examples, Affine transformation and applications: Geometric transformation of objects in images.
Image enhancement: Histogram Equalization, Thresholding, Morphological Approach to image processing/enhancement.
Image Segmentation: Hough transform: Detection of edges/boundary, lines and curves; Connected Component labeling. Object representation & recognition: Boundary-description, Fourier descriptor, moments, invariants; elements of Pattern-recognition/classification. Colour-image processing: Fundamentals, Colour Models, Biomedical Engineering Applications.
Image Restoration: Introduction to Stochastic Processes; Image degradation model, pseudo-inverse & Wiener filters.
References: