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Your search keyword '"Luk Chan, Kap"' showing total 8 results
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8 results on '"Luk Chan, Kap"'

3. Denoising of MR Images using Non Linear Anisotropic Diffusion Filtering as a Preprocessing Step

Author

Seramani, Sankar, Jiayin, Zhou, Luk, Chan Kap, Malmurugan, N., and Nagappan, A.

Subjects
Tumor, Segmentation, Quantitative Biology::Tissues and Organs, Computer Science::Computer Vision and Pattern Recognition, Physics::Medical Physics, Brain, Diffusion Filtering, Nonlinear, MRI
Abstract

Magnetic resonance imaging (MRI) is a noninvasive method for producing tomographic images of the human body and it is most often used for the detection of tumors(lesions) and other abnormalities in soft tissues such as the brain. MR Images often requires application of noise filtering techniques before visual inspection or application of noise sensitive post processing methods, so a filtering which is desired to significantly decrease image noise and simultaneously to preserve some fine details in the image is required .Therefore good denoising technique shoud be developed on MRI for further processing the Lymph node for automatic segmentation.. In this paper Non Linear Anisotropic Diffusion Filtering denoising technique is discussed and its results were compared to the existing wavelet based denoising technique. Index Terms: MRI, tumor, brain, segmentation, difussion filtering, non linear

Published
2008
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8. Incorporating temporal information for ventricular contour detection in echocardiographic image sequences.

Author

Fang W, Luk Chan K, Fu S, and Muthu Krishnan S

Abstract

A novel way to incorporate temporal information with level set algorithm is proposed to counter the dropout problem when detecting ventricular contours in echocardiographic raphic image sequences. The temporal information ided embed- ed into the speed term of the level set equation. By identifying the ventricular contours as strong or weak segments, the weak segments are strengthened based on temporal information from neighboring frames. Hence disrupted heart wall boundary structure information due to dropout can be recovered. A Gaussian Mixture Model (GMM) is employed to compute thresholds separating the segments. A weight and a strengthening ng factor are used to control the information recovery process. Experimental results show the proposed method exhibits good performance when tracking the ventricular boundary in real echocardiographic data.

Published
2005
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