This course has two components. On the one hand, it is an introduction to digital image analysis presenting selected fundamental problems in medical image analysis, computer vision, photo/video editing, and graphics. We cover such basic concepts as image segmentation, registration, object recognition/matching, tracking, texture, etc. On the other hand, this is an applied course on standard computer science algorithms where students develop practical understanding of dynamic programming, graph based algorithms, computational geometry methods, etc. In fact, image analysis provides a stimulating environment for studying algorithms as their outputs can be intuitively visualized. Students with previous background in algorithms will be exposed to applications in image analysis, while students already familiar with problems in imaging will learn efficient methods based on standard CS algorithms. The course emphasizes the design, analysis, and implementation of algorithms in the context of 2D/3D medical images, photo and video data.
Electroencephalography (EEG) records the electrical signals produced by the brain using an array of electrodes placed on the scalp. Computers use an algorithm called common spatial pattern (CSP) to translate these signals into commands for the control of various devices.
Haiping Lu at the A*A*STAR Institute for Infocomm Research and co-workers have now developed an improved version of CSP for classifying EEG signals. The new algorithm will facilitate the development of advanced brain–computer interfaces that may one day enable paralyzed patients to control devices such as computers and robotic arms.
This article describes a biomedical signal processing (BSP) toolbox for the analysis
of physiologic signals. The BSP toolbox is designed to enable researchers to conduct
preliminary analysis of physiologic time series, such as the electrocardiogram (ECG),
intracranial pressure (ICP), arterial blood pressure (ABP), and oxygen saturation (SpO2).
The toolbox includes detection algorithms for the ECG and pressure waveforms, spectral
analysis, nonlinear filtering, multi-signal analysis, and nonstationary signal visualization.
The following sections discuss the functionality of this toolbox and provide examples of its