Dr. Jun Feng Profile

Dr. Jun Feng

Associate Prof. at City Univ of Hong Kong

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Proceedings Article | 12 May 2004 Paper

Adaptive thresholding image series from fluorescence confocal scanning laser microscope using orientation intensity profiles

Judy Feng, Horace Ip, Shuk H. Cheng

Proceedings Volume 5370, (2004) https://doi.org/10.1117/12.530850

KEYWORDS: Image analysis, Image segmentation, Luminescence, Confocal microscopy, Microscopes, Medical imaging, Organisms, 3D image reconstruction, Tissues, Image processing

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Many grey-level thresholding methods based on histogram or other statistic information about the interest image such as maximum entropy and so on have been proposed in the past. However, most methods based on statistic analysis of the images concerned little about the characteristics of morphology of interest objects, which sometimes could provide very important indication which can help to find the optimum threshold, especially for those organisms which have special texture morphologies such as vasculature, neuro-network etc. in medical imaging. In this paper, we propose a novel method for thresholding the fluorescent vasculature image series recorded from Confocal Scanning Laser Microscope. After extracting the basic orientation of the slice of vessels inside a sub-region partitioned from the images, we analysis the intensity profiles perpendicular to the vessel orientation to get the reasonable initial threshold for each region. Then the threshold values of those regions near the interest one both in x-y and optical directions have been referenced to get the final result of thresholds of the region, which makes the whole stack of images look more continuous. The resulting images are characterized by suppressing both noise and non-interest tissues conglutinated to vessels, while improving the vessel connectivities and edge definitions. The value of the method for idealized thresholding the fluorescence images of biological objects is demonstrated by a comparison of the results of 3D vascular reconstruction.

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