Course Information

CS 736: Medical Image Computing

The course builds on fundamental theory in advanced data science, image processing, and machine learning for applications in medical image analysis.

1. Introduction to imaging modalities.
Image visualization (sectioning, multimodal images, overlays, rendering surfaces and volumes, glyphs).

2. Mathematical imaging models.
Models for: physical signals, measurements, sampling, image noise and image artefacts.
X-ray, computed tomography (CT), magnetic resonance imaging (MRI), ultrasound, microscopy.

3. Image Reconstruction and Denoising.
Mathematical models for image regularity, statistical image models, Markov random fields (MRFs), practical sampling schemes, noise models (MRI, CT, ultrasound), Bayesian modeling and inference, optimization algorithms.

4. Image Segmentation and Object Delineation.
Clustering, segmentation priors, hidden MRFs, Bayesian modeling and estimation, expectation maximization, graph cuts, kernel methods, variational level-set methods.

5. Image Classification.
Abnormality detection, image categorization.

6. Statistical Shape Analysis.
Shape descriptors, shape similarity, learning statistical shape models: means and modes of variation.

7. Image Registration.
Measures of image similarity, spatial transformations, optimization methods.

8. Image Retrieval.
Image descriptors, image similarity.

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Other Details

Duration : Full Semester Total Credit : 6
Type : Theory
Autumn Semester 2018-19

Status : Not Offered Instructor : ---
Spring Semester 2018-19

Status : Offered Instructor : Prof. Suyash P. Awate

Last Modified Date: 15-Jul-2013


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