Chung-Ming Chen

National Taiwan University, T’ai-pei, Taipei, Taiwan

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Publications (46)77.36 Total impact

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    ABSTRACT: Recent studies suggest that structural and functional alterations of the language network are associated with auditory verbal hallucinations (AVHs) in schizophrenia. However, the ways in which the underlying structure and function of the network are altered and how these alterations are related to each other remain unclear. To elucidate this, we used diffusion spectrum imaging (DSI) to reconstruct the dorsal and ventral pathways and employed functional magnetic resonance imaging (fMRI) in a semantic task to obtain information about the functional activation in the corresponding regions in 18 patients with schizophrenia and 18 matched controls. The results demonstrated decreased structural integrity in the left ventral, right ventral and right dorsal tracts, and decreased functional lateralization of the dorsal pathway in schizophrenia. There was a positive correlation between the microstructural integrity of the right dorsal pathway and the functional lateralization of the dorsal pathway in patients with schizophrenia. Additionally, both functional lateralization of the dorsal pathway and microstructural integrity of the right dorsal pathway were negatively correlated with the scores of the delusion/hallucination symptom dimension. Our results suggest that impaired structural integrity of the right dorsal pathway is related to the reduction of functional lateralization of the dorsal pathway, and these alterations may aggravate AVHs in schizophrenia.
    Psychiatry Research Neuroimaging 09/2014; · 2.83 Impact Factor
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    ABSTRACT: 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.
    Journal of Biomedical Optics 04/2014; 19(4):46008. · 2.75 Impact Factor
  • Chia-Yun Hsu, Yi-Hong Chou, Chung-Ming Chen
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    ABSTRACT: A whole breast ultrasound tumor detection algorithm, which could help physicians determine and diagnose, has been proposed. First of all, we employ the multi-scale blob detection. Secondly, we discard most unlikely lesions by ultrasound confidence maps and sheet detection, according to the prior knowledge of breast anatomy. After discarding unlikely blob structures, we collect the survival blob-like structures from the results of four passes of multi-scale blob detection in a single set. The features of blobness, size, and probability of being at muscle layer of the all detected blob-like candidates are used in a classification process for the differentiation of true lesions from negative ones in the collection set. Mutual information based feature selection (MIFS) procedure is applied to select three most effective features for the purpose of dimension reduction with the aid of logistic regression classifier and the process of leave-one-out cross-validation (LOO-CV) method. 49 datasets were acquired from 29 patients, which contain 86 lesions in total. According to the area under the ROC curve (AUC), the technique shows promising future for computer aided detection and the superior results when compared to Moon's method.
    2014 International Conference on Computational Science and Computational Intelligence (CSCI); 03/2014
  • Computational and Mathematical Methods in Medicine 01/2013; 2013:790608. · 0.79 Impact Factor
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    ABSTRACT: This paper proposes a general boundary delineation method for 2D serial US images by modeling the spatial-temporal dynamics and generating the object boundary in each slice under the cell-based MAP scheme. The modeling of the spatial-temporal dynamics can serve as a prior to guide the cell-based MAP process and potentially maintain the contextual coherence. Experiments have been conducted on 8 sets of compression breast series and 5 sets of freehand breast acquisitions. The computer-generated results by our algorithm are compared to manual delineations prepared by experts. The experimental results suggest that the boundaries of the proposed method are not significantly different to manual outlines and are quite stable in terms of reproducibility.
    Image and Graphics (ICIG), 2013 Seventh International Conference on; 01/2013
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    ABSTRACT: 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 3-3.5 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. (© 2012 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim).
    Journal of Biophotonics 06/2012; · 3.86 Impact Factor
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    ABSTRACT: 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.
    IEEE Journal of Selected Topics in Quantum Electronics 05/2012; 18(3):1122-1129. · 3.47 Impact Factor
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    ABSTRACT: A probabilistic image segmentation algorithm called stochastic region competition is proposed for performing Doppler sonography segmentation. The image segmentation is conducted by maximizing a posteriori that models histogram likelihood, gradient likelihood, and a spatial prior. The optimization is done by a modified expectation and maximization (EM) method that aims to improve computation efficiency and avoid local optima. The algorithm was tested on 155 color Doppler sonograms and compared with manual delineations. The qualitative assessment shows that our algorithm is able to segment mass lesions under the condition of low image quality and the interference of the color-encoded Doppler information. The quantitative assessment analysis shows that the average distance between the algorithm-generated boundaries and manual delineations is statistically comparable to the variability of manual delineations. The ratio of the overlapping area between the algorithm-generated boundaries and manual delineations is also comparable to that between different sets of manual delineations. A reproductivity test was conducted to confirm that the result is statistically reproducible. The algorithm can be used to perform Doppler sonography segmentation and to replace the tedious manual delineation task in clinical application.
    Medical Physics 05/2012; 39(5):2867-76. · 3.01 Impact Factor
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    ABSTRACT: 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.
    IEEE transactions on bio-medical engineering 05/2012; 59(5):1454-61. · 2.15 Impact Factor
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    ABSTRACT: To investigate dynamic contrast-enhanced (DCE) magnetic resonance imaging (MRI) of advanced nonsmall-cell lung cancer (NSCLC) patients treated with the antiangiogenic agent bevacizumab combined with gemcitabine and cisplatin as first-line treatment. All patients were enrolled for MRI and computed tomography (CT) before and after the first three courses of bevacizumab combination chemotherapy. Pharmacokinetic parameters (K(trans), k(ep), v(e), v(p)) derived from DCE MRI were computed for the main mass. Parametric histogram analysis was obtained to evaluate changes of the internal tumor composition and for correlation with tumor response measured on CT. After three cycles of treatment, 11 patients showed decreased tumor size and a decreased value of all MR-derived pharmacokinetic parameters. Among these parameters, there was a significant decrease of mean and standard deviation of the K(trans) histogram as well as a decrease of mean of the k(ep) histogram (P < 0.05). Tumors with larger mean values of rate constant k(ep) (P < 0.0001) and smaller standard deviation of volume of extravascular extracellular space fraction v(e) (P < 0.0001) on histograms before chemotherapy were considered predictors for treatment response. DCE MRI enables a functional analysis of the treatment response of NSCLC. MRI parametric histogram has the potential to predict early treatment response of combined bevacizumab, gemcitabine, and cisplatin.
    Journal of Magnetic Resonance Imaging 04/2012; 36(2):387-96. · 2.57 Impact Factor
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    ABSTRACT: 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. 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. 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. 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.
    BioMedical Engineering OnLine 04/2012; 11:21. · 1.75 Impact Factor
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    ABSTRACT: Diffuse optical tomography (DOT) is an emerging technique for functional biological imaging. The imaging quality of DOT depends on the imaging reconstruction algorithm. The SIRT has been widely used for DOT image reconstruction but there is no criterion to truncate based on any kind of residual parameter. The iteration loops will always be decided by experimental rule. This work presents the CR calculation that can be great help for SIRT optimization. In this paper, four inhomogeneities with various shapes of absorption distributions are simulated as imaging targets. The images are reconstructed and analyzed based on the simultaneous iterative reconstruction technique (SIRT) method. For optimization between time consumption and imaging accuracy in reconstruction process, the numbers of iteration loop needed to be optimized with a criterion in algorithm, that is, the root mean square error (RMSE) should be minimized in limited iterations. For clinical applications of DOT, the RMSE cannot be obtained because the measured targets are unknown. Thus, the correlations between the RMSE and the convergence rate (CR) in SIRT algorithm are analyzed in this paper. From the simulation results, the parameter CR reveals the related RMSE value of reconstructed images. The CR calculation offers an optimized criterion of iteration process in SIRT algorithm for DOT imaging. Based on the result, the SIRT can be modified with CR calculation for self-optimization. CR reveals an indicator of SIRT image reconstruction in clinical DOT measurement. Based on the comparison result between RMSE and CR, a threshold value of CR (CRT) can offer an optimized number of iteration steps for DOT image reconstruction. This paper shows the feasibility study by utilizing CR criterion for SIRT in simulation and the clinical application of DOT measurement relies on further investigation.
    Optics Communications 04/2012; 285(8):2236–2241. · 1.54 Impact Factor
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    ABSTRACT: BACKGROUND: The purpose of this study was to analyze the relationship between cytologic features, clinical features, and recurrence of papillary thyroid cancer (PTC). We hoped to predict prognosis preoperatively. METHODS: We retrospectively studied cytologic features by using computerized morphometry and clinical data of 118 patients with usual-type PTC without initial metastasis, including 34 patients with cancer recurrence in 10 years after surgery and 84 patients who did not have recurrence for more than 10 years after surgery. Another 24 patients were recruited for validation. RESULTS: Multivariate logistic analysis indicated that nucleus-to-cell ratio, variation of nuclear area, tumor size, and patient age were significantly related to recurrence. Cox regression analysis showed that hazard ratios were 3.34, 1.53, 1.77, and 2.6, respectively. CONCLUSION: Cytologic features of PTC analyzed with computerized morphometry significantly correlated with recurrence. It helped to predict prognosis preoperatively and may be helpful for planning further treatment. © 2012 Wiley Periodicals, Inc. Head Neck, 2012.
    Head & Neck 01/2012; · 2.83 Impact Factor
  • Jie-Zhi Cheng, Chung-Ming Chen, Dinggang Shen
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    ABSTRACT: Computerized detection of vascular calcium depositions in mamagraphy is a new research topics, which is driven by the clinical hypothesis of the association with many related cardiovascular diseases. In several previous studies [7, 9], calcification cue plays a very important role in the computerized analysis. We observe that vascular calcium depositions can be identified with high confidence if they appear in a bright railway pattern. Accordingly, a linear structure analysis method is introduced in this study to detect most true calcifications and also keep the false positives as little as possible. The proposed method is tested with 40 mammograms and achieves performance of 93.8±1.3% in sensitivity and 84.7±3.9% in specificity. The output of this linear structure analysis may provide more reliable calcification cue for the subsequent vessel tracking process, which will be investigated in the future.
    Proceedings / IEEE International Symposium on Biomedical Imaging: from nano to macro. IEEE International Symposium on Biomedical Imaging 01/2012;
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    ABSTRACT: Genetic/transcriptional regulatory interactions are shown to predict partial components of signaling pathways, which have been recognized as vital to complex human diseases. Both activator (A) and repressor (R) are known to coregulate their common target gene (T). Xu et al. (2002) proposed to model this coregulation by a fixed second order response surface (called the RS algorithm), in which T is a function of A, R, and AR. Unfortunately, the RS algorithm did not result in a sufficient number of genetic interactions (GIs) when it was applied to a group of 51 yeast genes in a pilot study. Thus, we propose a data-driven second order model (DDSOM), an approximation to the non-linear transcriptional interactions, to infer genetic and transcriptional regulatory interactions. For each triplet of genes of interest (A, R, and T), we regress the expression of T at time t + 1 on the expression of A, R, and AR at time t. Next, these well-fitted regression models (viewed as points in R(3)) are collected, and the center of these points is used to identify triples of genes having the A-R-T relationship or GIs. The DDSOM and RS algorithms are first compared on inferring transcriptional compensation interactions of a group of yeast genes in DNA synthesis and DNA repair using microarray gene expression data; the DDSOM algorithm results in higher modified true positive rate (about 75%) than that of the RS algorithm, checked against quantitative RT-polymerase chain reaction results. These validated GIs are reported, among which some coincide with certain interactions in DNA repair and genome instability pathways in yeast. This suggests that the DDSOM algorithm has potential to predict pathway components. Further, both algorithms are applied to predict transcriptional regulatory interactions of 63 yeast genes. Checked against the known transcriptional regulatory interactions queried from TRANSFAC, the proposed also performs better than the RS algorithm.
    Frontiers in Genetics 01/2012; 3:71.
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    ABSTRACT: Pretibial myxedema (PM) is a manifestation of Graves' disease (GD). Currently, its diagnosis depends on physicians' observation and biopsy. No satisfactory, objective, and non-invasive tool is available to record and investigate lesions. Digital infrared thermal imaging (DITI) detects surface temperature, and sonography reflects composition changes in soft tissue. This study was aimed to observe changes in DITI and sonography in PM, and to evaluate their clinical usefulness. Nineteen GD patients with PM, 22 GD patients with mild diffuse non-pitting edema over lower legs, 46 GD patients with normal appearance of lower legs, and 14 normal volunteers were recruited for observation with DITI; 8, 21, 21, and 11 of them respectively also received soft tissue sonography for investigating the pathogenesis of DITI change. Lower leg temperatures of normal volunteers decreased gradually from proximal to distal parts. In all 19 patients with PM, DITI showed abnormally low focal temperatures over the lesions. In GD patients with mild diffuse non-pitting edema and GD patients with normal appearance of lower legs, DITI showed abnormally low focal temperature in 90.9 and 65.2% of the patients respectively. Areas of clinically visible PM and low focal temperature detected by DITI were sonographically characterized with increased skin thickness, hypoechoic substance deposition in the cutaneous tissue, and blurred boundary lines between dermis and subcutaneous tissue. TSH receptor antibody level correlated positively and significantly with skin thickness change and adjusted temperature difference between the center of temperature defect and the surrounding skin (P=0.046 and 0.033 respectively). By using DITI and sonography, we detected characteristic changes in PM. These techniques are helpful in recording and may be useful tools to detect early changes of PM.
    European Journal of Endocrinology 03/2011; 164(4):605-11. · 3.69 Impact Factor
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    ABSTRACT: Fully automatic and high-quality demarcation of sonographical breast lesions remains a far-reaching goal. This article aims to develop an image segmentation algorithm that provides quality delineation of breast lesions in sonography with a simple and friendly semiautomatic scheme. A data-driven image segmentation algorithm, named as augmented cell competition (ACCOMP) algorithm, is developed to delineate breast lesion boundaries in ultrasound images. Inspired by visual perceptual experience and Gestalt principles, the ACCOMP algorithm is constituted of two major processes, i.e., cell competition and cell-based contour grouping. The cell competition process drives cells, i.e., the catchment basins generated by a two-pass watershed transformation, to merge and split into prominent components. A prominent component is defined as a relatively large and homogeneous region circumscribed by a perceivable boundary. Based on the prominent component tessellation, cell-based contour grouping process seeks the best closed subsets of edges in the prominent component structure as the desirable boundary candidates. Finally, five boundary candidates with respect to five devised boundary cost functions are suggested by the ACCOMP algorithm for user selection. To evaluate the efficacy of the ACCOMP algorithm on breast lesions with complicated echogenicity and shapes, 324 breast sonograms, including 199 benign and 125 malignant lesions, are adopted as testing data. The boundaries generated by the ACCOMP algorithm are compared to manual delineations, which were confirmed by four experienced medical doctors. Four assessment metrics, including the modified Williams index, percentage statistic, overlapping ratio, and difference ratio, are employed to see if the ACCOMP-generated boundaries are comparable to manual delineations. A comparative study is also conducted by implementing two pixel-based segmentation algorithms. The same four assessment metrics are employed to evaluate the boundaries generated by two conventional pixel-based algorithms based on the same set of manual delineations. The ACCOMP-generated boundaries are shown to be comparable to the manual delineations. Particularly, the modified Williams indices of the boundaries generated by the ACCOMP algorithm and the first and second pixel-based algorithms are 1.069 +/- 0.024, 0.935 +/- 0.024, and 0.579 +/- 0.013, respectively. If the modified Williams index is greater than or equal to 1, the average distance between the computer-generated boundaries and manual delineations is deemed to be comparable to that between the manual delineations. The boundaries derived by the ACCOMP algorithm are shown to reasonably demarcate sonographic breast lesions, especially for the cases with complicated echogenicity and shapes. It suggests that the ACCOMP-generated boundaries can potentially serve as the basis for further morphological or quantitative analysis.
    Medical Physics 12/2010; 37(12):6240-52. · 3.01 Impact Factor
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    ABSTRACT: To develop an intensity inhomogeneity algorithm for breast sonograms in order to assist visual identification and automatic delineation of lesion boundaries. The proposed algorithm was composed of two essential ideas. One was decomposing the region of interest (ROI) into foreground and background regions by a cell-based segmentation algorithm, called constrained fuzzy cell-based bipartition-EM (CFCB-EM) algorithm. The CFCB-EM algorithm deformed the contour in a fuzzy cell-based deformation fashion with the cell structures derived by the fuzzy cell competition (FCC) algorithm as the deformation unit and the boundary estimated by the normalized cut (NC) algorithm as the reference contour. The other was modeling the intensity inhomogeneity in an ROI as a spatially variant normal distribution with a constant variance and spatially variant means, which formed a polynomial surface of order n. The proposed algorithm was formulated as a nested EM algorithm comprising the outer-layer EM algorithm, i.e., the intensity inhomogeneity correction-EM (IIC-EM) algorithm, and the inner-layer EM algorithm, i.e., the CFCB-EM algorithm. The E step of the IIC-EM algorithm was to provide a reasonably good bipartition separating the ROI into foreground and background regions, which included three major component algorithms, namely, the FCC, the NC, and the CFCB-EM. The M step of the IIC-EM algorithm was to estimate and correct the intensity inhomogeneity field by least-squared fitting the intensity inhomogeneity to an nth order polynomial surface. Forty-nine breast sonograms with intensity inhomogeneity, each from a different subject, were randomly selected for performance analysis. Three assessments were carried out to evaluate the effectiveness of the proposed algorithm. Based on the visual evaluation of two experienced radiologists, in the first assessment, 46 out of 49 breast lesions were considered to have better contrasts on the inhomogeneity-corrected images by both radiologists. The interrater reliability for the radiologists was found to be kappa = 0.479 (p = 0.001). In the second assessment, the mean gradients of the low-gradient boundary points before and after correction of the intensity inhomogeneity were compared by the paired t-test, yielding a p-value of 0.000, which suggested the proposed intensity inhomogeneity algorithm may enhance the mean gradient of the low-gradient boundary points. By using the paired t-test, the third assessment further showed that the Chan and Vese level set method could derive a much better lesion boundary on the inhomogeneity-corrected image than on the original image (p = 0.000). The proposed intensity inhomogeneity correction algorithm could not only augment the visibility of lesion boundary but also improve the segmentation result on a breast sonogram.
    Medical Physics 11/2010; 37(11):5645-54. · 3.01 Impact Factor
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    ABSTRACT: To develop a computer-aided diagnostic algorithm with automatic boundary delineation for differential diagnosis of benign and malignant breast lesions at ultrasonography (US) and investigate the effect of boundary quality on the performance of a computer-aided diagnostic algorithm. This was an institutional review board-approved retrospective study with waiver of informed consent. A cell-based contour grouping (CBCG) segmentation algorithm was used to delineate the lesion boundaries automatically. Seven morphologic features were extracted. The classifier was a logistic regression function. Five hundred twenty breast US scans were obtained from 520 subjects (age range, 15-89 years), including 275 benign (mean size, 15 mm; range, 5-35 mm) and 245 malignant (mean size, 18 mm; range, 8-29 mm) lesions. The newly developed computer-aided diagnostic algorithm was evaluated on the basis of boundary quality and differentiation performance. The segmentation algorithms and features in two conventional computer-aided diagnostic algorithms were used for comparative study. The CBCG-generated boundaries were shown to be comparable with the manually delineated boundaries. The area under the receiver operating characteristic curve (AUC) and differentiation accuracy were 0.968 +/- 0.010 and 93.1% +/- 0.7, respectively, for all 520 breast lesions. At the 5% significance level, the newly developed algorithm was shown to be superior to the use of the boundaries and features of the two conventional computer-aided diagnostic algorithms in terms of AUC (0.974 +/- 0.007 versus 0.890 +/- 0.008 and 0.788 +/- 0.024, respectively). The newly developed computer-aided diagnostic algorithm that used a CBCG segmentation method to measure boundaries achieved a high differentiation performance.
    Radiology 06/2010; 255(3):746-54. · 6.21 Impact Factor
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    ABSTRACT: Accurate measurement of the three-dimensional (3D) rigid body and surface kinematics of the natural human knee is essential for many clinical applications. Existing techniques are limited either in their accuracy or lack more realistic experimental evaluation of the measurement errors. The purposes of the study were to develop a volumetric model-based 2D to 3D registration method, called the weighted edge-matching score (WEMS) method, for measuring natural knee kinematics with single-plane fluoroscopy to determine experimentally the measurement errors and to compare its performance with that of pattern intensity (PI) and gradient difference (GD) methods. The WEMS method gives higher priority to matching of longer edges of the digitally reconstructed radiograph and fluoroscopic images. The measurement errors of the methods were evaluated based on a human cadaveric knee at 11 flexion positions. The accuracy of the WEMS method was determined experimentally to be less than 0.77 mm for the in-plane translations, 3.06 mm for out-of-plane translation, and 1.13 degrees for all rotations, which is better than that of the PI and GD methods. A new volumetric model-based 2D to 3D registration method has been developed for measuring 3D in vivo kinematics of natural knee joints with single-plane fluoroscopy. With the equipment used in the current study, the accuracy of the WEMS method is considered acceptable for the measurement of the 3D kinematics of the natural knee in clinical applications.
    Medical Physics 03/2010; 37(3):1273-84. · 3.01 Impact Factor

Publication Stats

235 Citations
77.36 Total Impact Points


  • 2003–2014
    • National Taiwan University
      • • Institute of Biomedical Engineering
      • • Department of Bio-Industrial Mechatronics Engineering
      T’ai-pei, Taipei, Taiwan
  • 2012
    • Academia Sinica
      • Institute of Statistical Science
      Taipei, Taipei, Taiwan
    • Carnegie Mellon University
      • Department of Biomedical Engineering
      Pittsburgh, PA, United States
    • National Yang Ming University
      T’ai-pei, Taipei, Taiwan
  • 2008
    • Chaoyang University of Technology
      臺中市, Taiwan, Taiwan