Classes

Stochastic Processes

Semester: 
Spring

Introduction to probability, random variables, and stochastic processes. Modeling and analysis of stochastic systems. Apply knowledge of stochastic modeling to solve computational problems in systems theory, medicine and biology.

Introduction to Image Processing

Semester: 
Fall

Human visual system; Spatial and temporal resolution; Sampling and quantization; Linear systems and convolution; Spatial filters; Histogram equalization and specification; Discrete Fourier Transform (DFT); Walsh Transform; Frequency domain filters; Homomorphic filtering; Spatial filters from frequency domain specs; Color Image Processing; Optimal thresholding; Region-oriented segmentation; Hough Transform.

Mathematical Methods in Medical Image Computing

Semester: 
Spring

This course provides a comprehensive overview on the mathematical techniques and methods used in the image processing science. The course contains two central themes: inverse problems in image processing; and stochastic image analysis. The first theme includes regularization methods for ill-posed problems and solutions to large scale inverse problems. The second theme covers modeling of image intensity distribution, local smoothing filters, wiener filters, image segmentation, and shape analysis.