Dr. Chih-Kuang Yeh Profile

Dr. Chih-Kuang Yeh

at National Tsing Hua Univ

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Author

Publications (4)

Proceedings Article | 4 March 2019 Presentation

Improved light delivery in biological tissue using HIFU heating tunnel: photoacoustic verification (Conference Presentation)

Zong Han Hsieh, Chih-Kuang Yeh, Meng-Lin Li

Proceedings Volume 10878, 108782P (2019) https://doi.org/10.1117/12.2507771

KEYWORDS: Photoacoustic spectroscopy, Tissue optics, Light scattering, Monte Carlo methods, Scattering, Optical imaging, Biomedical optics, Photon transport, Ultrasonography, Thermal effects

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Proceedings Article | 18 April 2017 Paper

Contrast-enhanced optical coherence microangiography with acoustic-actuated microbubbles

Yu-Hsuan Liu, Jia-Wei Zhang, Chih-Kuang Yeh, Kuo-Chen Wei, Hao-Li Liu, Meng-Tsan Tsai

Proceedings Volume 10251, 102511F (2017) https://doi.org/10.1117/12.2269408

KEYWORDS: Coherence (optics), Optical coherence tomography, Ultrasonography, Angiography, Acoustics, Particles, Capillaries, Tissues, Biomedical optics, Image contrast enhancement

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Proceedings Article | 12 March 2007 Paper

A new contrast-assisted method in microcirculation volumetric flow assessment

Sheng-Yi Lu, Yung-Sheng Chen, Chih-Kuang Yeh

Proceedings Volume 6513, 651316 (2007) https://doi.org/10.1117/12.708114

KEYWORDS: Toxic industrial chemicals, Blood circulation, Solids, Ultrasonography, Transducers, Blood, Tumors, Backscatter, Tissues, Acoustics

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Proceedings Article | 12 March 2007 Paper

Automatic ultrasonic breast lesions detection using support-vector-machine-based algorithm

Chih-Kuang Yeh, Shan-Jung Miao, Wei-Che Fan, Yung-Sheng Chen

Proceedings Volume 6513, 651317 (2007) https://doi.org/10.1117/12.702960

KEYWORDS: Speckle, Breast, Ultrasonography, Binary data, Ultrasonics, Tumors, Image classification, Image processing, Computer simulations, Signal processing

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It is difficult to automatically detect tumors and extract lesion boundaries in ultrasound images due to the variance in shape, the interference from speckle noise, and the low contrast between objects and background. The enhancement of ultrasonic image becomes a significant task before performing lesion classification, which was usually done with manual delineation of the tumor boundaries in the previous works. In this study, a linear support vector machine (SVM) based algorithm is proposed for ultrasound breast image training and classification. Then a disk expansion algorithm is applied for automatically detecting lesions boundary. A set of sub-images including smooth and irregular boundaries in tumor objects and those in speckle-noised background are trained by the SVM algorithm to produce an optimal classification function. Based on this classification model, each pixel within an ultrasound image is classified into either object or background oriented pixel. This enhanced binary image can highlight the object and suppress the speckle noise; and it can be regarded as degraded paint character (DPC) image containing closure noise, which is well known in perceptual organization of psychology. An effective scheme of removing closure noise using iterative disk expansion method has been successfully demonstrated in our previous works. The boundary detection of ultrasonic breast lesions can be further equivalent to the removal of speckle noise. By applying the disk expansion method to the binary image, we can obtain a significant radius-based image where the radius for each pixel represents the corresponding disk covering the specific object information. Finally, a signal transmission process is used for searching the complete breast lesion region and thus the desired lesion boundary can be effectively and automatically determined. Our algorithm can be performed iteratively until all desired objects are detected. Simulations and clinical images were introduced to evaluate the performance of our approach. Several types of cysts with different contours and contrast resolutions images were simulated with speckle characteristics. Four thousand sub-images of tumor objects and speckle-noised background were used for SVM training. Comparison with conventional algorithms such as active contouring, the proposed algorithm does not need to position any initial seed point within the lesion and is able to detect simultaneously multiple irregular shape lesions in a single image, thus it can be regarded as a fully automatic process. The results show that the mean normalized true positive area overlap between true contour and contour obtained by the proposed approach is 90%.

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