Interactive Segmentation of MRI Brain Data
Visualization of Brain Activity Recorded in the Form of Electroencephalograms
Simulation and Visualization of Thermal Wave Propagation in Pico-Scales in Pico-scales
Interactive Segmentation of MRI Brain Data
Collaborators: Shamima Yasmin
Former collaborators: Konstantin Levinski
Research grant: SBIC Innovative Grant RP C-012/2006, "Improving Measurement Accuracy of Magnetic Resonance Brain Images to Support Change Detection in Large Cohort Studies", S$605,700, 2006-2009.
Novel visualization algorithms developed specifically for segmentation purposes have been proposed along with a method for 3D interactive correction of brain segmentation errors introduced by the fully automatic segmentation algorithms. We have developed the tool which is based on a 3D semi-automatic propagation algorithm. 3D visualization of the misclassification hints allows the user to focus attention on the problematic areas and avoid working with individual slices where it is not necessary. The proposed semi-automatic method uses a controlled propagation and allows for an efficient correction of the segmentation errors. The proposed software modules layout for the new interactive segmentation and visualization methods will allow for efficient development of advanced segmentation tools in further research and improvement of the initial software. We have also proposed an efficient method for hinting the user where a segmentation error might occur. This is done by averaging several layers of the image closest to the surface. This method is simple to implement and provides satisfactory results however it has high failure ratio and has to be replaced with a more robust approach. Click to see a video with the demo of the developed tool. Here is another video illustrating using a desktop haptic device for editing the pial surface of the brain.
Levinski K, Sourin A, Zagorodnov V. Interactive Surface-guided Segmentation of Brain MRI Data, Computer in Biology and Medicine, Elsevier, 39 (12) 1153-1160, 2009.
Levinski K, Sourin A, Zagorodnov V. 3D Visualization and Segmentation of Brain MRI Data. 2009 International Conference on Computer Graphics Theory and Applications (GRAPP 2009), Lisboa, Portugal, 5-8 February, pp. 111-118, 2009.
Levinski K, Sourin A, Zagorodnov V. 3D Interactive Segmentation of Brain MRI, International Workshop on Advanced Image Technology (IWAIT) 2009.
Visualization of brain activity recorded in the form of electroencephalograms
Collaborators: Vladimir Kulish
Olga Sourina (EEE)
Students: Ms. Lim Lay Na (SCE, FYP 2003/04), Mr. Ong Yean Xiong (SCE, FYP 2004/2005)
Research grant: RG27/06, "Investigation, modeling and quantification of brain response to external stimuli", 2006-2009.
We used 3D visualization of the electroencephalograms (EEG). We employed a concept of a dynamic 3D volumetric shape for illustrating how the electrical signal changes through time. Its size and appearance visually reflect the brain activity. The software developed is an interactive program, which visualizes one or several signals by modeling the respective time-dependent 3D surfaces around the 3D human head. The locations of electrodes and the surfaces of the moving EEG shapes are visualized with different colors. Two different semi-opaque moving shapes corresponding to two different EEG signals can be visualized concurrently to visually analyze the difference between the respective brain activities. In fact, this method of visualization let us notice several phenomena, which could not be possibly noticed if we used common ways of analyzing the EEG. Thus, we noticed that the brain is more active while giving positive responses. Also, we observed that although it requires less mental activity in the course of responding, giving a negative response is more stressful, because it is followed by prolonged activation of the cerebral cortex and partial activation of the visual cortex of the brain. Besides just a visual comparison, we can apply set-theoretic (“Boolean”) operations to the moving shapes to isolate activities common for both of them, as well as those that are unique for either one. Furthermore, the group set-theoretic operations applied to the individual frames of the moving shape allow us to isolate idle parts of the brain as well as to estimate an average level of the brain activity. Click here and here to see the streaming video illustrating the brain's reaction to two questions requiring answers "Yes" and "No".
See video: http://intune.ntu.edu.sg/SCE/courses/Alexei/webpage/visualbrain.wmv
O.Sourina. “Human Electroencephalograms Seen as Fractal Time Series:
Mathematical Analysis and Visualization.” Computers in Biology and Medicine,
V.Kulish, A.Sourin, O.Sourina, ''Analysis and visualization of human electroencephalograms seen as fractal time series,'' Journal of Mechanics in Medicine & Biology, 6:2 (2006) 175-188
Kulish VV, Sourin A, Sourina O. Fractal Spectra and Visualization of the Brain Activity Evoked by Olfactory Stimuli. The 9th Asian Symposium on Visualization Hong Kong, 4-9 June, 2007. pp.37-1 - 37-8.
Collaborators: Vladimir Kulish
Student: Mr. Bay Ai Meng, Nicky (SCE, UROP project, 2000)
Research grant ARP RG18/01, "Modeling and Investigation of the Pneumo-Olfactory Function of Humans", 2001-2004
A novel macroscopic gas transport model, derived from fundamental engineering principles, is used to simulate the three-dimensional unsteady respiration process within the alveolar region of the lungs. Volume visualization has been used for rendering the time-dependent model of the respiration process. The software tool lets us manipulate the volume on the screen with time-dependent affine transformations, control the visual appearance of the volume, and interpolate the in-between frames. The modeling software runs on the personal computer while the graphics subsystems runs on the SGI workstation remotely so that the resulting movie file can be downloaded back to the PC for further post-production processing. The user may either control the process of rendering visually, or run it in a batch mode. Click here to see the streaming video of 3-D unsteady simulation of alveolar respiration. Click here and here to see other streaming videos.
Vladimir Kulish, Sourin Alexei, J.L.Lage, "Simulation and visualization of gas diffusion in human lungs", IASTED International Conference on Applied Simulation and Modelling (ASM2001), September 4-7, 2001, in Marbella, Spain, pp. 158-163.
Kulish, V.V., Sourin, A.I., "Simulation and Visualization of the Micro-Particle Distribution Effects on Gas Diffusion in Human Lungs", 10th International Symposium on Flow Visualization, Kyoto, Japan, Aug 26 - 29, 2002, p.257, the whole article is on the CD ISBN4-906497-82-9.
Collaborator: Vladimir Kulish
Student: Mr. Tran Nguen Phuong (SCE, UROP project, 2001)
The relationship between the local temperature and the local heat flux has been established for the homogeneous hyperbolic heat equation. This relationship has been written in the form of a convolution integral involving the modified Bessel functions. The integral equation, relating the local temperature and the local heat flux, has been solved numerically for those processes of surface heating whose time scale is of the order of picoseconds. The scale analysis of the hyperbolic energy equation has then been performed and the dimensionless criterion for the mode of energy transport has been proposed which is similar to the Reynolds criterion to distinguish between the flow regimes. Finally, visualization of the thermal wave propagation has been rendered versus visualization of the classical (Fourier) heat conduction under the same conditions. Click here to see the streaming video of the classical (Fourier) heat conduction followed by the thermal wave propagation under the same conditions.
Vladimir Kulish, Alexei Sourin , ''Simulation and Visualization of Thermal Wave Propagation in Pico-Scales,'' 10th International Symposium on Flow Visualization, Kyoto, Japan, August 26-29, 2002, p.40, the whole article is on the CD ISBN4-906497-82-9.
Copyright © 2001-2006, Vladimir Kulish, Alexei
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