Browsing by Author "Liu, Li-chang"

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A Novel Binary Method for Fingerprint Optical Coherence Tomography Images
(2014)
Hsu, Yu-wei
;
Lee, Jiann-der
;
Tseng, Ellen
;
Liu, Li-chang
;
電子工程學系及電子研究所
This paper described a novel binary method for inhomogeneously lighted fingerprint optical coherence tomography (OCT) images. To reduce the light effect problem, a block size is calculated by the median value of the length values of white pixels between black pixels in the initial binary result and used to rescale the gray values of the input image. The rescaled gray image is used to calculate the binary candidate image and the binary gradient direction image. The final ridges are calculated by using the relationship between the binary candidate image and the binary gradient direction image.
A Region Segmentation Method on 2-D Vessel Optical Coherence Tomography Images
(2013)
Liu, Li-chang
;
Lee, Jiann-der
;
Hsu, Yu-wei
;
Liu, Carol T.
;
Tseng, Ellen
This paper describes a novel region segmentation method designed to avoid complications of the threshold process used in traditional segmentation methods in 2-D optical coherence tomography (OCT) images. Analysis of the layers and regions in OCT images is used to diagnose the presence of cancer and identify the stage of the cancer if present. However, scattering during OCT images generates a speckle effect and creates diffusion problems which are also captured; these problems cause traditional image processing methods such as the Canny edge and Otsu methods to fail in finding the proper layer and region edges. The proposed method uses the mean value and an enhanced-fuzzy-c-mean algorithm to cluster pixels in 2-D OCT images and find the edge between different clustered regions. Low-resolution vessel OCT and high-resolution oral cancer OCT images are tested in the experiment, and the experimental results show that the proposed method performs with more robust and accurate segmentation results than does the overcomplete-wavelet-frame-based fractal signature method.
Vessel segmentation in 2-D optical coherence tomography images
(2013-01-01)
Liu, Li-chang
;
Lee, Jiann-der
;
Hsu, Yu-wei
;
Tseng, Scott
;
Tseng, Ellen
This paper described a novel region segmentation method to avoid difficulties of the threshold process used in traditional segmentation methods in 2-D optical coherence tomography (OCT) images. The speckle effect and diffusion problems make traditional image processing methods such as Canny edge and Otsu methods fail on finding layers and region edges in OCT images. The overcomplete-wavelet-frame-based fractal signature method based on high-pass information and a fuzzy-c-mean algorithm is considered to avoid the threshold processing, but the high-pass information is distorted because of noises and diffusions. To improve the high-pass information distortion problem, the proposed method uses the mean value and an enhanced-fuzzy-c-mean algorithm to cluster pixels in 2-D OCT images and find the edge between different clustered regions. The vessel OCT images are tested in the experiment, and the experimental results show that the proposed method performs with more accurate segmentation results than the overcomplete-wavelet-frame-based fractal signature method.