Browsing by Author "Chen, Chung-Ming"

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A Modified Harris Corner Detection for Breast IR Image
(2014)
Lee, Chia-Yen
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Wang, Hao-Jen
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Chen, Chung-Ming
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Chuang, Ching-Cheng
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Chang, Yeun-Chung
Harris corner detectors, which depend on strong invariance and a local autocorrelation function, display poor detection performance for infrared (IR) images with low contrast and nonobvious edges. In addition, feature points detected by Harris corner detectors are clustered due to the numerous nonlocal maxima. This paper proposes a modified Harris corner detector that includes two unique steps for processing IR images in order to overcome the aforementioned problems. Image contrast enhancement based on a generalized form of histogram equalization (HE) combined with adjusting the intensity resolution causes false contours on IR images to acquire obvious edges. Adaptive nonmaximal suppression based on eliminating neighboring pixels avoids the clustered features. Preliminary results show that the proposed method can solve the clustering problem and successfully identify the representative feature points of IR breast images.
Brain structure and spatial sensitivity profile assessing by near-infrared spectroscopy modeling based on 3D MRI data
(2013-03-01)
Chuang, Ching-Cheng
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Chen, Chung-Ming
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Hsieh, Yao-Sheng
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Liu, Tsan-Chi
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Sun, Chia-Wei
The goal of this study is to prove that the light propagation in the head by used the 3-D optical model from in vivo MRI data set can also provide significant characteristics on the spatial sensitivity of cerebral cortex folding geometry based on Monte Carlo simulation. Thus, we proposed a MRI based approach for 3-D brain modeling of near-infrared spectroscopy (NIRS). In the results, the spatial sensitivity profile of the cerebral cortex folding geometry and the arrangement of source-detector separation have being necessarily considered for applications of functional NIRS. The optimal choice of source-detector separation is suggested within 33.5 cm by the received intensity with different source-detector separations and the ratio of received light from the gray and white matter layer is greater than 50%. Additionally, this study has demonstrated the capability of NIRS in not only assessing the functional but also detecting the structural change of the brain by taking advantage of the low scattering and absorption coefficients observed in CSF of sagittal view. ((c) 2013 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim)
Diffuser-Aided Diffuse Optical Imaging for Breast Tumor: A Feasibility Study Based on Time-Resolved Three-Dimensional Monte Carlo Modeling
(2012-05-01)
Chuang, Ching-Cheng
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Lee, Chia-Yen
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Chen, Chung-Ming
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Hsieh, Yao-Sheng
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Liu, Tsan-Chi
This study proposed diffuser-aided diffuse optical imaging (DADOI) as a new approach to improve the performance of the conventional diffuse optical tomography (DOT) approach for breast imaging. The 3-D breast model for Monte Carlo simulation is remodeled from clinical MRI image. The modified Beer-Lambert's law is adopted with the DADOI approach to substitute the complex algorithms of inverse problem for mapping of spatial distribution, and the depth information is obtained based on the time-of-flight estimation. The simulation results demonstrate that the time-resolved Monte Carlo method can be capable of performing source-detector separations analysis. The dynamics of photon migration with various source-detector separations are analyzed for the characterization of breast tissue and estimation of optode arrangement. The source-detector separations should be less than 4 cm for breast imaging in DOT system. Meanwhile, the feasibility of DADOI was manifested in this study. In the results, DADOI approach can provide better imaging contrast and faster imaging than conventional DOT measurement. The DADOI approach possesses great potential to detect the breast tumor in early stage and chemotherapy monitoring that implies a good feasibility for clinical application.
Diffuser-aided time-domain diffuse optical imaging: a phantom study
(2014-04-01)
Sun, Chia-Wei
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Chuang, Ching-Cheng
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Lee, Chia-Yen
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Chen, Chung-Ming
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光電工程學系
We present the first experimental results of time-resolved diffuser-aided diffuse optical imaging (DADOI) method in this paper. A self-manufactured diffuser plate was inserted between the optode and the surface of a scattering medium. The diffuser was utilized to enhance the multiple scattering that destroys the image information for baseline measurement of turbid medium. Therefore, the abnormality can be detected with the modified optical density calculation. The time-domain DADOI method can provide better imaging contrast and simpler imaging than the conventional diffuse optical tomography measurement. Besides, it also reveals rich depth information with temporal responses. Therefore, the DADOI offers a great potential to detect the breast tumor and chemotherapy monitoring in clinical diagnosis. (C) 2014 Society of Photo-Optical Instrumentation Engineers (SPIE)
Muscle oxygenation dynamics in response to electrical stimulation as measured with near-infrared spectroscopy: A pilot study
(2019-03-01)
Huang, Yi-Hua
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Chuang, Ming-Lung
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Wang, Pay-Zen
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Chen, Yueh-Chi
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Chen, Chung-Ming
Neuromuscular electrical stimulation (NMES) is used for preventing muscle atrophy and improving muscle siren in patients and healthy people. However, the current intensity of NMES is usually set at a level that causes the stimulated muscles to contract. This typically causes pain. Quantifying the instantaneous changes in muscle microcirculation and metabolism during NMES before muscle contraction occurs is crucial, because it enables the current intensity to be optimally tuned, thereby reducing the NMES-induced muscle pain and fatigue. We applied near-infrared spectroscopy (MRS) to measure instantaneous tissue oxygenation and deoxygenation changes in 43 healthy young adults during NMES at 10, 15, 20, 25, 30, and 35 mA. Having been stabilized at the NIRS signal baseline, the tissue oxygenation and total hemoglobin concentration increased immediately after stimulation in a dose-dependent manner (P < 0.05) until stimulation was stopped at the level causing muscle contraction without pain. Tissue deoxygenation appeared relatively unchanged during NMES. We conclude that NIRS can be used to determine the optimal NMES current intensity by monitoring oxygenation changes.
Near-Infrared Brain Volumetric Imaging Method: A Monte Carlo Study
(2012-05-01)
Chuang, Ching-Cheng
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Wang, Pei-Ning
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Chen, Wei-Ta
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Lan, Tsuo-Hung
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Chen, Chung-Ming
"The symptom of brain volumetric changes may provide significant biomarker to predict progressive dementia. The brain volumetric changes of prefrontal cortex are highly associated with many neurodegenerative diseases. Besides, brain atrophy reveals the expanded interhemispheric fissure and the concomitant increasing cerebrospinal fluid volume. Thus, the quantitative assessment of brain volumetric changes is an important consideration for clinical studies of neurodegenerative diseases. In this study, we first proposed an approach that uses near-infrared brain volumetric imaging to detect brain volumetric changes. The healthy, aged, and typical Alzheimer's disease (AD) brains were modeled with different characterization of brain volumetric changes from in vivo MRI data based on time-resolved 3-D Monte Carlo simulation. In the results, the significant difference of prefrontal cortex structure can be observed among healthy, aged, and AD brain with various source-detector separations in sagittal view. Our study shows that the near-infrared brain volumetric imaging can be an indicator of brain atrophy for clinical application of neurodegenerative diseases with patient-oriented measurement."
Patient-oriented simulation based on Monte Carlo algorithm by using MRI data
(2012-04-17)
Chuang, Ching-Cheng
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Lee, Yu-Tzu
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Chen, Chung-Ming
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Hsieh, Yao-Sheng
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Liu, Tsan-Chi
Background: Although Monte Carlo simulations of light propagation in full segmented three-dimensional MRI based anatomical models of the human head have been reported in many articles. To our knowledge, there is no patient-oriented simulation for individualized calibration with NIRS measurement. Thus, we offer an approach for brain modeling based on image segmentation process with in vivo MRI T1 three-dimensional image to investigate the individualized calibration for NIRS measurement with Monte Carlo simulation. Methods: In this study, an individualized brain is modeled based on in vivo MRI 3D image as five layers structure. The behavior of photon migration was studied for this individualized brain detections based on three-dimensional time-resolved Monte Carlo algorithm. During the Monte Carlo iteration, all photon paths were traced with various source-detector separations for characterization of brain structure to provide helpful information for individualized design of NIRS system. Results: Our results indicate that the patient-oriented simulation can provide significant characteristics on the optimal choice of source-detector separation within 3.3 cm of individualized design in this case. Significant distortions were observed around the cerebral cortex folding. The spatial sensitivity profile penetrated deeper to the brain in the case of expanded CSF. This finding suggests that the optical method may provide not only functional signal from brain activation but also structural information of brain atrophy with the expanded CSF layer. The proposed modeling method also provides multi-wavelength for NIRS simulation to approach the practical NIRS measurement. Conclusions: In this study, the three-dimensional time-resolved brain modeling method approaches the realistic human brain that provides useful information for NIRS systematic design and calibration for individualized case with prior MRI data.
Study on dynamics of photon migration in human breast based on three-dimensional Monte Carlo modeling
(2010-01-01)
Chuang, Ching-Cheng
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Chen, Chung-Ming
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Lee, Chia-Yen
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Tsai, Jui-che
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Lu, Chih-Wei
The scattering and absorption properties of human breast are very important for cancer diagnosis based on diffuse optical tomography (DOT). In this study, the dynamics of photon migration in three-dimensional human breast model with various source-detector separations is simulated based on a Monte Carlo algorithm. The three-dimensional human breast structure is obtained from in vivo MRI image. The breast model consists of skin, fatty tissue, glandular tissue, sternum and ribcage. The backscattered diffuse photons from each layer in breast are recorded by marking the deepest layer which every photon can reach. The experimental results indicate that the re-emitted photons contain more information from deep tissue regions with the source-detector separations because of the strong dependence to the resolution and sensitivity in DOT imaging. The geometric position of the source-detector separations were optimized in this study. The different sizes of breast tumor were modeled to analysis of optical image characterizations. Finally, the tumor images from different deep information were obtained with temporal profiles.