Browsing by Author "Ching, Yu-Tai"

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A Multilayer Steganography Method with High Embedding Efficiency for Palette Images
(2020-07-01)
Wu, Han-Yan
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Chen, Ling-Hwei
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Ching, Yu-Tai
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資訊工程學系
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Department of Computer Science
Embedding efficiency is an important issue in steganography methods. Matrix embedding (1, n, h) steganography was proposed by Crandall to achieve high embedding efficiency for palette images. This paper proposes a steganography method based on multilayer matrix embedding for palette images. First, a parity assignment is provided to increase the image quality. Then, a multilayer matrix embedding (k, 1, n, h) is presented to achieve high embedding efficiency and capacity. Without modifying the color palette, hk secret bits can be embedded into n pixels by changing at most k pixels. Under the same capacity, the embedding efficiency of the proposed method is compared with that of pixel-based steganography methods. The comparison indicates that the proposed method has higher embedding efficiency than pixel-based steganography methods. The experimental results also suggest that the proposed method provides higher image quality than some existing methods under the same embedding efficiency and capacity.
A semi-automatic method for neuron centerline extraction in confocal microscopic image stack
(2008)
Lee, Ping-Chang
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Ching, Yu-Tai
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Chang, H. M.
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Chiang, Ann-Shyn
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資訊工程學系
An algorithm for extracting the centerlines of neurons from 3-D image stack collected from a laser scanning confocal microscope is presented. Recovery of neuronal structure from image stack is critical for quantitative analysis of neuron-morphology. Many methods have been proposed to extract the centerline from the tubular structure in medical images, such as vessels. But the same methods do not work well in processing of neurons. One of the reasons is that the physical limitations of the spatial resolution of the image stack collect by confocal microscopy in the z-direction is much worse than the resolution in x and y directions. In our studied cases the mean voxel size of the image stack is 0.17 x 0.17 x 1.0 mu m(3) i.e., the resolution in z-direction cannot reflect the fact that the neuron has a tubular structure. In this paper, we propose an almost automatic neuron-tracing algorithm for a set of confocal microscopic images of neuron. The method is designed based on finding a minimal path in the volume to extract the centerlines of a neuron.
A study of grid artifacts formation and elimination in computed radiographic images
(2006-12-01)
Lin, Chih-Yang
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Lee, Wen-Jeng
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Chen, Shyh-Jye
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Tsai, Ching-Hwa
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Lee, Jei-Han
Computed radiography (CR) has many advantages such as filmless operations, efficiency, and convenience. Furthermore, it is easier to integrate with the picture archiving and communication systems. Another important advantage is that CR images generally have a wider dynamic range than conventional screen film. Unfortunately, grid artifacts and moire pattern artifacts may be present in CR images. These artifacts become a more serious problem when viewing CR images on a computer monitor when a clinic grade monitor is not available. Images produced using a grid with higher frequency or a Potter-Bucky grid (i.e., a moving grid, Bucky for short) can reduce occurrence but cannot guarantee elimination of these artifacts [CR & PACS (2000); Detrick F (2001), pp 7-8]. In this paper, the formation of the artifacts is studied. We show that the grid artifacts occur in a narrow band of frequency in the frequency domain. The frequency can be determined, accurately located, and thus removed from the frequency domain. When comparing the results obtained from the proposed method against the results obtained using previous computer methods, we show that our method can achieve better image quality.
Acceleration of Iterative Tomographic Image Reconstruction by Reference-based Back Projection
(2016-01-01)
Cheng, Chang-Chieh
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Li, Ping-Hui
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Ching, Yu-Tai
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資訊工程學系
;
Department of Computer Science
The purpose of this paper is to design and implement an efficient iterative reconstruction algorithm for computational tomography. We accelerate the reconstruction speed of algebraic reconstruction technique (ART), an iterative reconstruction method, by using the result of filtered back-projection (FBP), a wide used algorithm of analytical reconstruction, to be an initial guess and the reference for the first iteration and each back projection stage respectively. Both two improvements can reduce the error between the forward projection of each iteration and the measurements. We use three methods of quantitative analysis, root-mean square error (RMSE), peak signal to noise ratio (PSNR), and structural content (SC), to show that our method can reduce the number of iterations by more than half and the duality of tie. result is better than the original ART.
Automatic method to compare the lanes in gel electrophoresis images
(2007-03-01)
Lin, Chih-Yang
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Ching, Yu-Tai
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Yang, Yun-Liang
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生物科技學系
;
資訊工程學系
Gel electrophoresis (GE) is an important tool in genomic analysis. GE results are presented using images. Each image contains several vertical lanes. Each lane consists of several horizontal bands. Two lanes are identical if the relative positions of the bands are the same. We present a computer method designed to compare the lanes and identify identical lanes. This method, developed using many image-processing techniques, is applied to segment the lanes and bands in GE images. The lanes are then converted into "position vectors" that describe the positions of the bands. Comparing lanes becomes equivalent to comparing the position vectors. This method can accurately identify identical lanes, helping biologists to identify the identical lanes from many lanes with much less effort.
Computer Aided Alignment and Quantitative 4D Structural Plasticity Analysis of Neurons
(2013-04-01)
Lee, Ping-Chang
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He, Hai-yan
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Lin, Chih-Yang
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Ching, Yu-Tai
;
Cline, Hollis T.
The rapid development of microscopic imaging techniques has greatly facilitated time-lapse imaging of neuronal morphology. However, analysis of structural dynamics in the vast amount of 4-Dimensional data generated by in vivo or ex vivo time-lapse imaging still relies heavily on manual comparison, which is not only laborious, but also introduces errors and discrepancies between individual researchers and greatly limits the research pace. Here we present a supervised 4D Structural Plasticity Analysis (4D SPA) computer method to align and match 3-Dimensional neuronal structures across different time points on a semi-automated basis. We demonstrate 2 applications of the method to analyze time-lapse data showing gross morphological changes in dendritic arbor morphology and to identify the distribution and types of branch dynamics seen in a series of time-lapse images. Analysis of the dynamic changes of neuronal structure can be done much faster and with greatly improved consistency and reliability with the 4D SPA supervised computer program. Users can format the neuronal reconstruction data to be used for this analysis. We provide file converters for Neurolucida and Imaris users. The program and user manual are publically accessible and operate through a graphical user interface on Windows and Mac OSX.
Constructing Neuronal Structure from 3D Confocal Microscopic Images
(2009)
Lee, Ping-Chang
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Chang, Hsiu-Ming
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Lin, Chih-Yang
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Chiang, Ann-Shyn
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Ching, Yu-Tai
A semiautomatic method, based on the gradient flow (GVF) snake, to construct the neuronal structure from 3D confocal microscopic images is presented. A single neuron can be labeled by using green fluorescent protein (GFP) such that the 3D neuronal structure can be visualized in a stack of confocal microscopic images. To construct the neuronal structure, we traced a target fiber by providing a rough initial path to approximate that fiber. The path is then deformed under the gradient vector field to converge to the centerline of the fiber by using the GVF snake method. Using our developed software, a neuronal sturctural can be efficiently reconstructed and the tracing results can be reduced to the tree structure of the neuron. Given the tree structure of the neuron, quantitative information can be derived and further analysis can be carried out.
Continuous Human Location and Posture Tracking by Multiple Depth Sensors
(2014)
Qiu, Jun-Wei
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Chiang, Ting-Hui
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Lo, Chi Chung
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Lin, Li-Min
;
Van, Lan-Da
This paper proposes a continuous location and skeletal tracking system using multiple RGB-Depth sensors (such as Kinects) deployed along a corridor with overlapping coverages. First, we transform the coordinates of all sensor into a unified coordinate. Second, the system recognizes users (such as patients under rehabilitation) from different views of these sensors and classifies them by their patient IDs. Third, the patient information can be continuously handed over among sensors when they move around the area. Experiment results show that the skeletal association during handover achieves approximately 90.61% in accuracy and 96.89% in precision in 44,817 experiment trials. By our observation, injured people possess asymmetric gait parameters especially on the ratio of the duration of the swing/stance phase. For example, the injured foot generally has a longer swinging duration than the healthy side. The proposed system has potential in patient rehabilitation monitoring applications.
Correction of center of rotation and projection angle in synchrotron X-ray computed tomography
(2018-06-29)
Cheng, Chang-Chieh
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Ching, Yu-Tai
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Ko, Pai-Hung
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Hwu, Yeukuang
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資訊工程學系
An error in tomographic reconstruction parameters can result considerable artifacts in the reconstructed image, particularly in micro-computed tomography and nano-computed tomography. This study involved designing an automatic method for efficiently correcting errors resulting from incorrectly determined rotational axes and projection angles. In this method, errors are corrected by minimizing the "total variation" of a reconstructed image, and minimization is accomplished by using the gradient descent method. Compared with two previous methods, the proposed method achieved the best reconstruction results.
Detecting small lung tumors in mouse models by refractive-index microradiology
(2011-08-01)
Chien, Chia-Chi
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Zhang, Guilin
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Hwu, Y.
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Liu, Ping
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Yue, Weisheng
Refractive-index (phase-contrast) radiology was able to detect lung tumors less than 1 mm in live mice. Significant micromorphology differences were observed in the microradiographs between normal, inflamed, and lung cancer tissues. This was made possible by the high phase contrast and by the fast image taking that reduces the motion blur. The detection of cancer and inflammation areas by phase contrast microradiology and microtomography was validated by bioluminescence and histopathological analysis. The smallest tumor detected is less than 1 mm(3) with accuracy better than 1 x 10(-3) mm(3). This level of performance is currently suitable for animal studies, while further developments are required for clinical application.
Diagnosing Bladder Outlet Obstruction Assessed By Computational Fluid Dynamics and Medical Imaging Technique
(2010-06-01)
Yang, Cheng-Hsiu
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Chen, Wei-Ju
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Chen, Wen-Lih
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Lin, Chao-An
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Ching, Yu-Tai
Bladder outlet obstruction (BOO) suggestive benign prostatic hyperplasia is a common disease in elder men. The present golden standard diagnostics, however, is an invasive method, named pressure-flow studies. The aim of the study is to develop a non-invasive procedure, which combines computational fluid dynamics and the medical imaging techniques, as a new approach for diagnosing bladder outlet obstruction. Based on the computed tomography images, the lower urinary tract system is reconstructed. The flow resistance within the urinary tract is predicted using the computational fluid dynamics. It is shown that this quantity displayed a high correlation (r = 0.86) with the golden standard parameter, BOO index, i.e. clinically measured data for patients with BOO. This indicates that the present method may be potentially valid substitute for invasive BOO index.
EXTRACTION OF CORONARY ARTERIAL TREE USING CINE X-RAY ANGIOGRAMS
(2005-06-25)
Lin, Chih-Yang
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Ching, Yu-Tai
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資訊工程學系
;
Department of Computer Science
An efficient and robust method for identification of coronary arteries and evaluation of the severity of the stenosis on the routine X-ray angiograms is proposed. It is a challenging process to accurately identify coronary artery due to poor signal-to-noise ratio, vessel overlap, and superimposition with various anatomical structures such as ribs, spine, or heart chambers. The proposed method consists of two major stages: (a) signal-based image segmentation and (b) vessel feature extraction. The 3D Fourier and 3D Wavelet transforms are first employed to reduce the background and noisy structures in the images. Afterwards, a set of matched filters was applied to enhance the coronary arteries in the images. At the end, clustering analysis, histogram technique, and size filtering were utilized to obtain a binary image that consists of the final segmented coronary arterial tree. To extract vessel features in terms of vessel centerline and diameter, a gradient vector-flow based snake algorithm is applied to determine the medial axis of a vessel followed by the calculations of vessel boundaries and width associated with the detected medial axis.
Full Model for Sensors Placement and Activities Recognition
(2017-01-01)
Ching, Yu-Tai
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He, Guan-Wei
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Cheng, Chang-Chieh
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Yang, Yu-Jin
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資訊工程學系
We implemented a wired sensors system that supports activities identification. The system consists of Raspberry Pi, MPU6050 (accelerometers and gyrometers), and TCA9548 (1 to 8 multiplexer). Our experimental results show that when 6 MPU6050 attached to the right arm, right wrist. chest, waist, right thigh, and right ankle, the activities of standing, sitting, lying, walking, running, going upstairs, going downstairs. drinking water, and dumbbells activities could be identified with high accuracy. The system can connect up to 128 sensors, but under a practical sampling rate, the number of sensors should not be greater than 15. The system shall be used for finding the optimal locations for a multi -sensor wearable system (for examples, clothes or shoes).
Handwritten Signature Verification by Using a Six-Axis Motion Sensor and SVM
(2019-01-01)
Cheng, Chang-Chieh
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Chen, Yi-Chi
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Ching, Yu-Tai
;
資訊工程學系
;
資訊技術服務中心
Signature verification, if we consider the muscle memory, is a biometric for identification technology. To access muscle memory, we use a motion sensor that consists of accelerometer and gyroscope to implement a signature verification system. The motion sensor records six motion values including three-axis accelerations and angular velocities while name signing. 14 features of signature are extracted from the sequence of accelerations and angular velocities. A support vector machine (SVM) is then applied to verify the signatures. The proposed method was applied to verify the Chinese signatures. The SVM is trained by the training data from each person. The true positive rate of the proposed method can reach to 95.66%. Fake signatures generated by tracing from true signatures can also be recognized by the proposed method.
High-Resolution and Large-Volume Tomography Reconstruction for X-Ray Microscopy
(2016-01-01)
Cheng, Chang-Chieh
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Hwu, Yeukuang
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Ching, Yu-Tai
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資訊工程學系
;
Department of Computer Science
This paper presents a method of X-ray image acquisition for the high-resolution tomography reconstruction that uses a light source of synchrotron radiation to reconstruct a three-dimensional tomographic volume dataset for a nanoscale object. For large objects, because of the limited field-of-view, a projection image of an object should to be taken by several shots from different locations, and using an image stitching method to combine these image blocks together. In this study, the overlap of image blocks should be small because our light source is the synchrotron radiation and the X-ray dosage should be minimized as possible. We use the properties of synchrotron radiation to enable the image stitching and alignment success when the overlaps between adjacent image blocks are small. In this study, the size of overlaps can reach to 15% of the size of each image block. During the reconstruction, the mechanical stability should be considered because it leads the misalignment problem in tomography. We adopt the feature-based alignment method to solve this problem. Finally, we apply the proposed method for a kidney of mouse for verification.
High-throughput Computer Method for 3D Neuronal Structure Reconstruction from the Image Stack of the Drosophila Brain and Its Applications
(2012-09-01)
Lee, Ping-Chang
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Chuang, Chao-Chun
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Chiang, Ann-Shyn
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Ching, Yu-Tai
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生物資訊及系統生物研究所
Drosophila melanogaster is a well-studied model organism, especially in the field of neurophysiology and neural circuits. The brain of the Drosophila is small but complex, and the image of a single neuron in the brain can be acquired using confocal microscopy. Analyzing the Drosophila brain is an ideal start to understanding the neural structure. The most fundamental task in studying the neural network of Drosophila is to reconstruct neuronal structures from image stacks. Although the fruit fly brain is small, it contains approximately 100 000 neurons. It is impossible to trace all the neurons manually. This study presents a high-throughput algorithm for reconstructing the neuronal structures from 3D image stacks collected by a laser scanning confocal microscope. The proposed method reconstructs the neuronal structure by applying the shortest path graph algorithm. The vertices in the graph are certain points on the 2D skeletons of the neuron in the slices. These points are close to the 3D centerlines of the neuron branches. The accuracy of the algorithm was verified using the DIADEM data set. This method has been adopted as part of the protocol of the FlyCircuit Database, and was successfully applied to process more than 16 000 neurons. This study also shows that further analysis based on the reconstruction results can be performed to gather more information on the neural network.
Image Alignment for Tomography Reconstruction from Synchrotron X-Ray Microscopic Images
(2014-01-09)
Cheng, Chang-Chieh
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Chien, Chia-Chi
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Chen, Hsiang-Hsin
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Hwu, Yeukuang
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Ching, Yu-Tai
A synchrotron X-ray microscope is a powerful imaging apparatus for taking high-resolution and high-contrast X-ray images of nanoscale objects. A sufficient number of X-ray projection images from different angles is required for constructing 3D volume images of an object. Because a synchrotron light source is immobile, a rotational object holder is required for tomography. At a resolution of 10 nm per pixel, the vibration of the holder caused by rotating the object cannot be disregarded if tomographic images are to be reconstructed accurately. This paper presents a computer method to compensate for the vibration of the rotational holder by aligning neighboring X-ray images. This alignment process involves two steps. The first step is to match the "projected feature points" in the sequence of images. The matched projected feature points in the x-theta plane should form a set of sine-shaped loci. The second step is to fit the loci to a set of sine waves to compute the parameters required for alignment. The experimental results show that the proposed method outperforms two previously proposed methods, Xradia and SPIDER. The developed software system can be downloaded from the URL, http://www.cs.nctu.edu.tw/similar to chengchc/SCTA or http://goo.gl/s4AMx.
Increase in intrathoracic volume in pectus excavatum patients after the Nuss procedure
(2010-02-01)
Chang, Pei-Yeh
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Hsu, Zhen-Yu
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Lai, Jin-Yao
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Wang, Chao-Jan
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Ching, Yu-Tai
In this study, we present finite element analysis models to calculate the increase in intrathoracic volume of pectus excavatum patients after the Nuss procedure. One virtue of our approach is that the measurement of the intrathoracic volume has no time difference and is not affected by postoperative pain, which cannot be achieved with a 2-year difference between pre- and postoperative pulmonary function testing or any other clinical method. The calculations show that the intrathoracic volume of pectus excavatum patients increased by approximately 2.72-8.88% after the Nuss procedure. The increment curve was patient-dependent, although the increment behavior was similar among the six patients examined. The curve of the increase became flat when the elevating force exceeded 80 N or the displacement of the lower sternal end exceeded 2.6 cm in half of our cases.
IoT/M2M Wearable-based Activity-Calorie Monitoring and Analysis for Elders
(2017-01-01)
Soraya, Sabrina I.
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Chiang, Ting-Hui
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Chan, Guo-Jing
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Su, Yi-Juan
;
Yi, Chih-Wei
With the growth of aging population, elder care service has become an important part of the service industry of Internet of Things. Activity monitoring is one of the most important services in the field of the elderly care service. In this paper, we proposed a wearable solution to provide an activity monitoring service on elders for caregivers. The system uses wireless signals to estimate calorie burned by the walking and localization. In addition, it also uses wireless motion sensors to recognize physical activity, such as drinking and restroom activity. Overall, the system can be divided into four parts: wearable device, gateway, cloud server, and caregiver's android application. The algorithms we proposed for drinking activity are Decision Tree (J48) and Random Forest (RF). While for restroom activity, we proposed supervised Reduced Error Pruning (REP) Tree and Variable Order Hidden Markov Model (VOHMM). We developed a prototype service Android app to provide a life log for the recording of the activity sequence which would be useful for the caregiver to monitor elder activity and its calorie consumption.
Poster: Handwritten Signature Verification by Using a Six-Axis Motion Sensor and SVM
(2019-01-01)
Cheng, Chang-Chieh
;
Chen, Yi-Chi
;
Ching, Yu-Tai
;
資訊工程學系
;
資訊技術服務中心
Signature verification, if we consider the muscle memory, is a biometric for identification technology. To access muscle memory, we use a motion sensor that consists of accelerometer and gyroscope to implement a signature verification system. The motion sensor records six motion values including three-axis accelerations and angular velocities while name signing. 14 features of signature are extracted from the sequence of accelerations and angular velocities. A support vector machine (SVM) is then applied to verify the signatures. The proposed method was applied to verify the Chinese signatures. The SVM is trained by the training data from each person. The true positive rate of the proposed method can reach to 95.66%. Fake signatures generated by tracing from true signatures can also be recognized by the proposed method.