CSE 320/420 Biomedical Image Computing and Modeling (3)

Instructor

Miaomiao Zhang (Fall 2017)

Course Description

Biomedical image modalities, image computing techniques, and imaging informatics systems. Understanding, using, and developing algorithms and software to analyze biomedical image data and extract useful quantitative information: Biomedical image modalities and format; image processing and analysis; geometric and statistical modeling; image informatics systems in biomedicine. Credit will not be given for both CSE 320 and CSE 420. Prerequisite: (Math 205 or Math 43) and CSE 017, or consent of instructor.

Textbook (recommended):

COURSE OUTCOMES

Student will have

1. Understand the principles of biomedical imaging modalities such as X-ray, CT, MRI, Ultrasound, Nuclear Medicine, and Microscopy

2. Understand the definition and usage of standard biomedical image formats such as DICOM and TIFF

3. Have working knowledge and ability to write computer programs to implement algorithms for processing, enhancing and analyzing images, including image noise reduction, morphology, binary image analysis, image segmentation, image registration, image reconition, learning and statistical inference methods, functional and time series data analysis

4. Write graphics applications with graphical user interfaces (GUI) to implement image analysis, shape analysis and modeling, volume rendering, 3D surface reconstruction

5. Have a grasp of computer systems techniques for the storage, distribution, and retrieval of biomedical images

6. Understand guideline for the usage of display devices in medical diagnosis, electronic health records, and computer-aided diagnosis

7. Propose a course project integrating multiple techniques learned in class, develop a software demo for the coures project, perform system evaluation, present results in both technical report and oral presentation

RELATIONSHIP BETWEEN COURSE OUTCOMES AND STUDENT ENABLED CHARACTERISTICS

CSE 320 substantially supports the following student enabled characteristics

A. An ability to apply knowledge of computing and mathematics appropriate to the discipline

B. An ability to analyze a problem and identify and define the computing requirements appropriate to its solution

I. An ability to use current techniques, skills, and tools necessary for computing practices

J. An ability to apply mathematical foundations, algorithmic principles, and computer science theory in the modeling and design of computer-based systems in a way that demonstrates comprehension of the tradeoffs involved in design choices

K. An ability to apply design and development principles in the construction of software systems of varying complexity

Prerequisites by Topic

  • Programming and data structures: algorithm and implementation in a high level language such as Java, C/C++, Matlab; classes, subclasses, recursion, searching, sorting, arrays, linked lists, trees, stacks, queues, hash tables; practical methods for implementing medium-scale programs
  • Linear algebra: matrices, vectors, vector spaces, elementary matrix transformations, linear differential equations, systems of linear equations, eigenvalues and application to linear systems of differential equations

Major Topics Covered in the Course

  • Introduction to biomedical imaging modalities
    • X-ray
    • Computed tomography (CT)
    • Magnetic resonance imaging (MRI)
    • Ultrasound
    • Nuclear medicine including positron emission tomography (PET), single-photon emission computed tomography (SPECT)
    • Microscopy: Fluorescence microscopy, electron microscopy, confocal microscopy
  • Introduction to standard imaging formats: DICOM, TIFF, Analyze, NIFTI
  • Image representation, image processing and enhancement, image manipulation
  • Image segmentation techniques
  • Image matching and registration, convex optimization, stochastic optimization
  • Image recognition: feature extraction, artificial neural networks, convolutional neural networks, support vector machines, boosting, Bayesian methods, ensemble methods, decision tree, clustering, sparse dictionary learning
  • Motion estimation and tracking, action recognition
  • Content based image retrieval techniques
  • Shape Analysis, object modeling, curves and surfaces, 3D surface reconstruction, visualization, volume rendering
  • Applications including brain image analysis, cell image analysis, computer-aided diagnosis, functional and time series data analysis
  • High performance computing and parallel algorithm implementation using graphics processing unit (GPU) programming; CUDA as an example
  • Electronic health records, HIPAA, PACS, Display devices
  • User interface design principles and software tools
  • System performance evaluation metrics
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