Kehong Yuan

Tsinghua University, Peping, Beijing, China

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Publications (35)23.44 Total impact

  • Tong Wang · Ping Xiao · Shaowei Jia · Kehong Yuan · Hongjie Yang
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    ABSTRACT: Heavy-ions have the similar characteristic of depth-dose distribution with protons, but exhibit enhanced physical and radiobiological benefits. With increasing development in technical and clinical research, more facilities are being installed in the world. At the same time, many critical techniques of heavy-ion therapy facility were optimized and completed. This paper classified and reviewed the basic structure of heavy-ion system equipments, especially the accelerator, gantry, nozzle , TPS.
    No preview · Article · Nov 2014 · Zhongguo yi liao qi xie za zhi = Chinese journal of medical instrumentation
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    ABSTRACT: This paper proposes an adaptive window-setting scheme for noninvasive detection and segmentation of bladder tumor surface in T(1)-weighted magnetic resonance (MR) images. The inner border of the bladder wall is first covered by a group of ball-shaped detecting windows with different radii. By extracting the candidate tumor windows and excluding the false positive (FP) candidates, the entire bladder tumor surface is detected and segmented by the remaining windows. Different from previous bladder tumor detection methods that are mostly focusing on the existence of a tumor, this paper emphasizes segmenting the entire tumor surface in addition to detecting the presence of the tumor. The presented scheme was validated by ten clinical T(1)-weighted MR image datasets (five volunteers and five patients). The bladder tumor surfaces and the normal bladder wall inner borders in the ten datasets were covered by 223 and 10,491 windows, respectively. Such a large number of the detecting windows makes the validation statistically meaningful. In the FP reduction step, the best feature combination was obtained by using receiver operating characteristics or ROC analysis. The validation results demonstrated the potential of this presented scheme in segmenting the entire tumor surface with high sensitivity and low FP rate. This study inherits our previous results of automatic segmentation of the bladder wall and will be an important element in our MR-based virtual cystoscopy or MR cystography system.
    Full-text · Article · May 2012 · IEEE transactions on information technology in biomedicine: a publication of the IEEE Engineering in Medicine and Biology Society
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    ABSTRACT: Respiratory motion results in significant motion blur in thoracic and abdomen PET imaging. The extent of respiratory motion blur is mainly correlated with breathing amplitude, tumor size and location. In this paper we introduce a statistical study to quantitatively show the factors influencing the extent of respiratory motion blur in thoracic PET images. The study is centered on two regression models, one is linked with motion blur induced loss of mean intensity(LMI), tumor motion magnitude and tumor size, and another is to investigate the influence of tumor location, patient gender and patient height on tumor motion magnitude. We use the blur identification and image restoration technique to estimate the tumor motion and compute the LMI. The regression model was validated by simulation and phantom data before extended to 39 cases of clinical lung tumor PET images corrupted with blurring artifact. Results show that the motion magnitude of lung tumor during breathing is 10.9±3.7mm in transaxial plane, and it is significantly greater in lower lung lobes than in upper lobes. The LMI is 7.1±2.4% in the region of interest (ROI) above 40% of the image's maximum intensity. The least-square estimate of regression equations demonstrates that LMI is proportional to tumor motion magnitude and is inversely proportional to tumor size; the two factors play the same role in determining the extent of respiratory motion blur in thoraco-abdominal PET imaging. The location of tumor was shown as the major factor determining its motion magnitude, while the influencing of patient gender and height on tumor motion was not shown significant.
    No preview · Article · Nov 2011 · Computers in Biology and Medicine
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    ABSTRACT: This paper proposes a framework for detecting the suspected abnormal region of the bladder wall via magnetic resonance (MR) cystography. Volume-based features are used. First, the bladder wall is divided into several layers, based on which a path from each voxel on the inner border to the outer border is found. By using the path length to measure the wall thickness and a bent rate (BR) term to measure the geometry property of the voxels on the inner border, the seed voxels representing the abnormalities on the inner border are determined. Then, by tracing the path from each seed, a weighted BR term is constructed to determine the suspected voxels, which are on the path and inside the bladder wall. All the suspected voxels are grouped together for the abnormal region. This work is significantly different from most of the previous computer-aided bladder tumor detection reports on two aspects. First of all, the T<sub>1</sub>-weighted MR images are used which give better image contrast and texture information for the bladder wall, comparing with the computed tomography images. Second, while most previous reports detected the abnormalities and indicated them on the reconstructed 3-D bladder model by surface rendering, we further determine the possible region of the abnormality inside the bladder wall. This study aims at a noninvasive procedure for bladder tumor detection and abnormal region delineation, which has the potential for further clinical analysis such as the invasion depth of the tumor and virtual cystoscopy diagnosis. Five datasets including two patients and three volunteers were used to test the presented method, all the tumors were detected by the method, and the overlap rates of the regions delineated by the computer against the experts were measured. The results demonstrated the potential of the method for detecting bladder wall abnormal regions via MR cystography.
    Full-text · Article · Oct 2011 · IEEE Transactions on Biomedical Engineering
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    Zhen Tian · Xun Jia · Kehong Yuan · Tinsu Pan · Steve B Jiang
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    ABSTRACT: High radiation dose in computed tomography (CT) scans increases the lifetime risk of cancer and has become a major clinical concern. Recently, iterative reconstruction algorithms with total variation (TV) regularization have been developed to reconstruct CT images from highly undersampled data acquired at low mAs levels in order to reduce the imaging dose. Nonetheless, the low-contrast structures tend to be smoothed out by the TV regularization, posing a great challenge for the TV method. To solve this problem, in this work we develop an iterative CT reconstruction algorithm with edge-preserving TV (EPTV) regularization to reconstruct CT images from highly undersampled data obtained at low mAs levels. The CT image is reconstructed by minimizing energy consisting of an EPTV norm and a data fidelity term posed by the x-ray projections. The EPTV term is proposed to preferentially perform smoothing only on the non-edge part of the image in order to better preserve the edges, which is realized by introducing a penalty weight to the original TV norm. During the reconstruction process, the pixels at the edges would be gradually identified and given low penalty weight. Our iterative algorithm is implemented on graphics processing unit to improve its speed. We test our reconstruction algorithm on a digital NURBS-based cardiac-troso phantom, a physical chest phantom and a Catphan phantom. Reconstruction results from a conventional filtered backprojection (FBP) algorithm and a TV regularization method without edge-preserving penalty are also presented for comparison purposes. The experimental results illustrate that both the TV-based algorithm and our EPTV algorithm outperform the conventional FBP algorithm in suppressing the streaking artifacts and image noise under a low-dose context. Our edge-preserving algorithm is superior to the TV-based algorithm in that it can preserve more information of low-contrast structures and therefore maintain acceptable spatial resolution.
    Preview · Article · Aug 2011 · Physics in Medicine and Biology
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    Quansheng Xu · Kehong Yuan · Datian Ye
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    ABSTRACT: Respiratory motion results in significant motion blur in thoracic positron emission tomography (PET) imaging. Existing approaches to correct the blurring artifact involve acquiring the images in gated mode and using complicated reconstruction algorithms. In this paper, we propose a post-reconstruction framework to estimate respiratory motion and reduce the motion blur of PET images acquired in ungated mode. Our method includes two steps: one is to use minmax directional derivative analysis and local auto-correlation analysis to identify the two parameters blur direction and blur extent, respectively, and another is to employ WRL, à trous wavelet-denoising modified Richardson-Lucy (RL) deconvolution, to reduce the motion blur based on identified parameters. The mobile phantom data were first used to test the method before it was applied to 32 cases of clinical lung tumor PET data. Results showed that the blur extent of phantom images in different directions was accurately identified, and WRL can remove the majority of motion blur within ten iterations. The blur extent of clinical images was estimated to be 12.1 ± 3.7 mm in the direction of 74 ± 3° relative to the image horizontal axis. The quality of clinical images was significantly improved, both from visual inspection and quantitative evaluation after deconvolution. It was demonstrated that WRL outperforms RL and a Wiener filter in reducing the motion blur with one to two more iterations. The proposed method is easy to implement and thus could be a useful tool to reduce the effect of respiration in ungated thoracic PET imaging.
    Preview · Article · Jun 2011 · Physics in Medicine and Biology
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    Kehong Yuan · Zhen Tian · Jiying Zou · Yanling Bai · Qingshan You
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    ABSTRACT: Content-based image retrieval for medical images is a primary technique for computer-aided diagnosis. While it is a premise for computer-aided diagnosis system to build an efficient medical image database which is paid less attention than that it deserves. In this paper, we provide an efficient approach to develop the archives of large brain CT medical data. Medical images are securely acquired along with relevant diagnosis reports and then cleansed, validated and enhanced. Then some sophisticated image processing algorithms including image normalization and registration are applied to make sure that only corresponding anatomy regions could be compared in image matching. A vector of features is extracted by non-negative tensor factorization and associated with each image, which is essential for the content-based image retrieval. Our experiments prove the efficiency and promising prospect of this database building method for computer-aided diagnosis system. The brain CT image database we built could provide radiologists with a convenient access to retrieve pre-diagnosed, validated and highly relevant examples based on image content and obtain computer-aided diagnosis.
    Preview · Article · Mar 2011 · Information Processing & Management
  • Z. Tian · C. Duan · K. Yuan · W. Han · D. Ye
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    ABSTRACT: In this paper, a novel approach for dynamic organ tracking based on texture feature is proposed. The purpose of this work is to find an efficient way to delineate and track the contours of a specific organ such as mouth, eye and lung which has distinct iiiteiiRity from the nrroniidiiigR aiid whoRe Rhape arid poRitiori vary Rmoothiy. The initial organ contours were manually delineated as the reference. Control markers on the organ contours were chosen for automatically tracking the dynamic organ contours. The tracking procedure consists of two major steps. Firstly, texture features were extracted from the regions under these control markers by using Sobel operator; and secondly, a local searching strategy based on the texture feature was performed for marker matching and further the dynamic contours tracking. The advantage of this approach lies on that it makes use of the local contrast between the organ and its surroundings to capture efficient local texture features and match fast to the corresponding location on target images while considering the motion and deformation of the organ in target images. The proposed approach was tested by tracking eye, mouth contours in videos and lung contours in clinical 4D thoracic CT images respectively. The satisfied results were obtained. An accordance coefficient was proposed to quantitatively evaluate the tracking performance and it was found that our approach performed best in lung contour tracking with accordance coefficients about 95%.
    No preview · Article · Dec 2010 · International journal of innovative computing, information & control: IJICIC
  • C. Duan · K. Yuan · S. Bao · Z. Liang
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    ABSTRACT: Bladder cancer is reported to be the fifth leading cause of cancer deaths in the United States. Detection of the bladder abnormalities at the early stage is essential for the treatment of bladder cancer. Currently, cystoscopy is believed to be the most accurate diagnostic procedure for bladder abnormality detection, despite it is invasive, expensive, and uncomfortable with limited field-of-view (FOV). Recent advances in medical imaging technologies, such as magnetic resonance (MR) imaging, make virtual cystoscopy a potential alternative with advantages as being a safe and non-invasive method for evaluation of the entire bladder and detection of abnormalities. The purpose of this work is to present a method for bladder abnormality enhancement, which uses the bent rate to measure the protrusion on the inner border of bladder wall in different scale space. The method is applied to two T1-weighted MR datasets from a volunteer and a patient respectively. The results are encouraging. ICIC International
    No preview · Article · Dec 2010 · ICIC Express Letters
  • Weixiang Liu · Kehong Yuan · Tianfu Wang · Siping Chen
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    ABSTRACT: Recently nonnegative matrix factorization (NMF) has been proven powerful for nonnegative data analysis, especially in analyzing gene expression data. We propose an modified consensus clustering mechanism with soft sample assignment to improve the clustering accuracy. The idea is to use normalized inner product or cosine similarity matrix for the connectivity matrix of the consensus clustering. The experimental results demonstrate the effectiveness of the proposed method.
    No preview · Article · Oct 2010
  • C. Duan · Z. Tian · Z. Liang · S. Bao · K. Yuan
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    ABSTRACT: A level set method for bladder wall segmentation and wall thickness estimates was developed to extract the features of bladder abnormalities in a virtual cystoscopy system, using T1-weighted magnetic resonance (MR) images. The local intensity contrast information is used to construct the image energy with two level set functions to segment the inner and outer borders of the bladder wall. A path integration distance is used to estimate the bladder wall thickness, by mimicing the distribution of the electric field line between two iso-potential surfaces. The bladder wall thickness is then mapped to a pseudo-color space for rendering the reconstructed 3-D bladder model. Ten clinical cases including volunteers and patients were used to test the system. The results show that the system is robust and effective for automatically segmenting the bladder wall, estimating the wall thickness, capturing the abnormal variations of the wall thickness in the patient dataset, and further showing the abnormal variations on the 3-D model to offer reliable information for virtual cystoscopy aided diagnosis.
    No preview · Article · Sep 2010 · Qinghua Daxue Xuebao/Journal of Tsinghua University
  • Shu Feng · Kehong Yuan · Datian Ye
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    ABSTRACT: The noninvasive glucose measurement with optical coherence tomography (OCT) relies on the glucose induced change of OCT signal slope. The Monte Carlo simulation is performed to study the effect of multiple scattering on the measured signal losses. The OCT signal is divided into two categories: one is from the target layer in the medium (LSP); the other is from the rest of the medium (MSP). The results show the MSP signal decays much more slowly than the LSP signal. The LSP contribute to the precise OCT signal and the MSP degrades the OCT signal. Signal decays constantly and the decay constant increases with the increasing scattering coefficient. Experiment results are also presented to show how multiple scattering affects the measured OCT signal losses.
    No preview · Article · Sep 2010 · International journal of innovative computing, information & control: IJICIC
  • K. Yuan · C. Duan · W. Han · C. Chen
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    ABSTRACT: Object shape recognition is important in computer vision and machine learning. Previous works have used circularity to describe the shape feature for different objects. However, the circularity is insensitive to some objects, especially the objects with similar shape. In this paper, we propose an alternative shape feature, roundness curve, to represent the shape of object. The roundness curve is generated by applying the central projection method to binary images. First of all, centroid of each object is calculated. Then, the centroid projects radiate to acquire projection value in pre-defined directions. The roundness curve in an orthogonal coordinate system is established by the projection values and their indices on its two axes respectively. We use the proposed method in classification of kinds of microscopic cell images. Experimental results prove the effectiveness and robustness of our method, it outperforms the conventional circularity.
    No preview · Article · Apr 2010 · International journal of innovative computing, information & control: IJICIC
  • Kehong Yuan · Zhen Tian · Jiying Zou
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    ABSTRACT: Anatomy position annotation of brain CT axial slice is an important step in content-based image retrieval. In this paper, we provide an efficient approach to automatically estimate the approximate anatomy position of brain CT axial slices in two steps: First, decide whether the input image is encephalic image or nasal cavity image using vote scheme based on the classification results with features extracted by Gabor filter, Sobel operator and gray-level co-occurrence matrix (GLCM) respectively; Second, annotate the approximate anatomy position of encephalic images using nonnegative tensor factorization (NTF). The approach has 99% accuracy in distinguishing between encephalic images and nasal cavity images and over 90% accuracy in automatic position annotation of encephalic images.
    No preview · Conference Paper · Jan 2010
  • Kehong Yuan · Chaijie Duan · Zhen Tian
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    ABSTRACT: Respiration induces significant movements of thorax and abdomen, which shift target site and thus degrade the accuracy and efficacy of radiotherapy. 4D CT sorting is an indispensable and important step to retrospectively rearrange the reconstructed CT images sampled at scan positions to get a set of volume images corresponding to different respiratory phases. Currently, most current sorting methods depend on external surrogates of respiratory motion, which may not always accurately catch the internal motion especially when irregular breathing occurs and lead to severe image artifacts. The 4D CT images produced by our method present fewer artifacts than that by RPM phase-based sorting, which shows that our method is feasible to sort 4D CT images without using external motion monitoring system.
    No preview · Article · Jan 2010
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    ABSTRACT: This paper presents a level set based method for bladder abnormality detection on T1-weighted MR images. First, the bladder wall is segmented by using a coupled level set framework, in which the inner and outer borders of the bladder wall are extracted by two level set functions. Then, the middle layer of the bladder wall is founded and represented by a new level set function. Finally, the new level set function divides the bladder wall into several layers. The inter-layer intensity of all voxels in each layer is sorted in ascending order to generate the inter-layer intensity curve. The results prove the effectiveness of inter-layer intensity curve in indicating the emerging of the bladder abnormalities.
    No preview · Conference Paper · Jan 2010
  • Zhen Tian · Kehong Yuan · Yanling Bai
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    ABSTRACT: Four-dimensional computed tomography(4D CT) is significant in radiotherapy treatment planning for thorax and upper abdomen to take their motion induced by respiration into consideration, but its high radiation dose becomes a major concern and impedes its wide application. To solve the problem, we propose an image interpolation approach to get 4D CT simulation images. We simulate 4D CT images at arbitrary intermediate phases by B-Spline deformable model with cosine interpolation of the deformation field, which is obtained by deformable registration of two CT images at end-exhale and end-inhale phases. The mean of absolute differences computed between actual 4D CT images and simulation ones is used to evaluate the accuracy of simulation. Our experiment results show that both linear interpolation and cosine interpolation with proper parameters perform well and the latter performs a little better than the former in general.
    No preview · Article · Jan 2009 · Conference proceedings: ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Conference
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    Weixiang Liu · Aifa Tang · Kehong Yuan · Datian Ye

    Preview · Article · Dec 2008
  • Weixiang Liu · Kehong Yuan · Jian Wu · Datian Ye · Zhen Ji · Siping Chen
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    ABSTRACT: Classification of gene expression samples is a core task in microarray data analysis. How to reduce thousands of genes and to select a suitable classifier are two key issues for gene expression data classification. This paper introduces a framework on combining both feature extraction and classifier simultaneously. Considering the non-negativity, high dimensionality and small sample size, we apply a discriminative mixture model which is designed for non-negative gene express data classification via non-negative matrix factorization (NMF) for dimension reduction. In order to enhance the sparseness of training data for fast learning of the mixture model, a generalized NMF is also adopted. Experimental results on several real gene expression datasets show that the classification accuracy, stability and decision quality can be significantly improved by using the generalized method, and the proposed method can give better performance than some previous reported results on the same datasets.
    No preview · Article · Dec 2008 · International Journal of Pattern Recognition and Artificial Intelligence
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    Shu Feng · Kehong Yuan · Datian Ye
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    ABSTRACT: Measurement of tissue optical properties with optical coherence tomography (OCT) shows that scattering coefficient has an effect on the OCT signal slope. The slope is calculated by linear fitting with a certain range of OCT signal; hence, the effect of fitting range on the scattering induced change of slope is an important topic. In this paper, we study the scattering induced change of OCT signal slope versus depth in Intralipid suspensions with different concentrations based on Monte Carlo simulation. The results show that the OCT signal slope expresses a contrary change with scattering coefficient below a certain depth in high Intralipid concentrations, so there is an effective fitting depth. The effective fitting depth is approximately 600 mum for 10% Intralipid and increases with decreasing concentrations. The effective fitting depth is due to the face that multiple scattering photons of backscattered light become dominant below the depth, and it is related with the numeric aperture (NA) of the object lens.
    Preview · Article · Dec 2008 · Proceedings of SPIE - The International Society for Optical Engineering