Suyash P.
Awate Asha and Keshav Bhide Chair Professor Computer Science and Engineering Department, Indian Institute of Technology (IIT) Bombay Office: A214, Kanwal Rekhi Building Email: myfirstname @cse.iitb.ac.in 

Research  Publications  Teaching  Students  CV  Personal 
Medical Image Computing (CS 736) 
Departmental Course Information 
Necessary background
(i.e., prerequisite): Basics in linear algebra,
probability and statistics, computing. Sufficient background: Some course in data processing (e.g., image, signal, geometry, speech, natural language), machine learning, artificial intelligence, or equivalent. 
The course focuses on the mathematical theory,
and the associated algorithms, within advanced topics in data science, image analysis and processing, and machine learning, for a wide range of applications in medical image computing (also known as medical vision; related to computer vision). The course assignments involve several computing experiments to explore the behaviour of the theories and algorithms on realworld image data. 
Topics (tentative list): 
1) Introduction to imaging modalities.
 Mathematical imaging models for physical signals, sampling, noise and artefact models. Signal modelling and model fitting.  X ray, computed tomography (CT), positron emission tomography (PET), magnetic resonance imaging (MRI) (including diffusion MRI, functional MRI), microscopy, ultrasound, 2D and 3D imaging. 2) Visualization.  Methods: sectioning, multimodal images, overlays, rendering surfaces and volumes, using glyphs. 3) Image reconstruction.  Methods: mathematical models of image regularity, random fields, practical data sampling and acquisition schemes, problem formulations, optimization algorithms.  Application domains: MRI, CT, PET, others. 4) Image restoration.  Methods: degradation models for corrupted and missing data, Bayesian graphical modeling and inference, regression methods, learning based methods.  Application domains: MRI, CT, PET, microscopy, ultrasound, others. 5) Image segmentation, object delineation, classification.  Methods: clustering, graph partitioning, classification, mixture models, expectation maximization, hidden Markov random fields, multivariate Gaussian, kernel methods, variational methods using geometric and statistical modeling, abnormality detection, image categorization, computer aided diagnosis.  Applications across biological structures and imaging modalities. 6) Statistical shape analysis.  Methods: descriptors, shape spaces, learning shape models, learning shape mean and modes of variation.  Application domains: organs and substructures, hypothesis testing, segmentation. 7) Image registration.  Methods: similarity models, deformation models, energy functions, optimization algorithms.  Applications: anatomical atlas generation, coregistration, motion correction. 8) Image retrieval.  Methods: image descriptors, image similarity, searching, databases, hierarchical methods.  Applications. 9) Machine learning methods in medical image computing. 
Credit Structure (tentative): 
Assignments (about 45): 30% Course Project: 10% Quizzes: 10% Midsemester examination: 25% Endsemester examination: 25% 
Reading List: 
Basics: Introduction to Mathematical Statistics. Robert V. Hogg, Joseph W. McKean, Allen Craig. Pearson 2012. A First Course in Probability. Sheldon Ross. Pearson. Probability, Random Variables and Stochastic Processes. Athanasios Papoulis, S. Unnikrishna Pillai. McGrawHill 2002. Introduction To Linear Algebra. Gilbert Strang. Wellesley Cambridge Press 2009. Applied Numerical Linear Algebra. James W. Demmel. SIAM 1997. Practical Methods of Optimization. R Fletcher. WileyInterscience 2000. 
Basic Image
Processing: Digital Image Processing: An Algorithmic Introduction Using Java. Willhelm Burger, Mark J. Burge. SpringerVerlag 2009. Digital Image Processing. Rafael C. Gonzales, Richard E. Woods. Prentice Hall 2008. Fundamentals of Digital Image Processing. Anil K. Jain. Prentice Hall 1988. 
For This Course: Pattern Recognition and Machine Learning. Christopher Bishop. Springer 2006. Markov Random Field Modeling in Image Analysis. Stan Z Li. Springer 2009. Machine Learning and Medical Imaging. Guorong Wu, Dinggang Shen, Mert Sabuncu. Elsevier 2016. Guide to Medical Image Analysis: Methods and Algorithms. Klaus D. Toennies. Springer 2012. Mathematics of Medical Imaging. Charles L. Epstein. Prentice Hall 2003. Medical Image Reconstruction: A Conceptual Tutorial. Gengsheng L. Zeng. Springer 2010. Statistical Models of Shape: Optimisation and Evaluation. Rhodri H. Davies, Carole J. Twining, Chris J. Taylor. Springer, 2010. Medical Image Registration. Joseph V. Hajnal, Derek L.G. Hill, David Hawkes. CRC Press, 2001. Visual Computing for Medicine: Theory, Algorithms, and Applications. Bernhard Preim, Charl Botha. Morgan Kaufmann 2014. 
Learning Material: In addition to the lectures, material will be provided by the instructor in the form of slides or online tutorials or publications. 
Computing Resources: 
Matlab at IITB Matlab Tutorials: 1, 2, 3, 4, 5, 6, 7 Insight Toolkit (ITK), more information ITK SNAP 
See moodle for course slides, notes, assignments, etc. 
Some public feedback 