Browsing by Author "Lu, HHS"

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A discrete region competition approach incorporating weak edge enhancement for ultrasound image segmentation
(2003-02-01)
Chen, CM
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Lu, HHS
;
Chen, YL
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統計學研究所
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Institute of Statistics
Ultrasound images are inherently difficult to analyze due to their echo texture, speckle noise and weak edges. Taking into account these characteristics, we present a new region-based approach for ultrasound image segmentation. It is composed of two primary algorithms, discrete region competition and weak edge enhancement. The discrete region competition features four techniques, region competition, statistical modeling of speckle, early vision modeling, and discrete concepts. In addition, to prevent regions from leaking out of the desired area across weak edges, edges located on the slowly varying slope are enhanced according to their position on the slope and the length of the slope. This new approach has been implemented and verified on clinical ultrasound images. (C) 2002 Elsevier Science B.V. All rights reserved.
A dual-snake model of high penetrability for ultrasound image boundary extraction
(2001-12-01)
Chen, CM
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Lu, HHS
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Hsiao, AT
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統計學研究所
;
Institute of Statistics
Most deformable models require the initial contour to be placed close to the boundary of the object of interest for boundary extraction of ultrasound (US) images, which is impractical in many clinical applications. To allow a distant initial contour, a new dual-snake model promising high penetrability through the interference of the noises is proposed in this paper. The proposed dual-snake model features a new far-reaching external force, called the discrete gradient flow, a connected component-weighted image force, and an effective stability evaluation of two underlying snakes. The experimental results show that, with a distant initial contour, the mean distance from the derived boundary to the desired boundary is less than 1.4 pixels, and most snake elements are within 2.7 pixels of the desired boundaries for the synthetic images with CNR greater than or equal to 1. For the clinical US images, the mean distance is less than 1.9 pixels, and most snake elements are within 3 pixels of the desired boundaries. (E-mail: chung@lotus.mc.ntu.edu.tw) (C) 2002 World Federation for Ultrasound in Medicine Biology.
A textural approach based on Gabor functions for texture edge detection in ultrasound images
(2001-04-01)
Chen, CM
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Lu, HHS
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Han, KC
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統計學研究所
;
Institute of Statistics
Edge detection is an important, but difficult, step in quantitative ultrasound (US) image analysis. In this paper, we present a new textural approach for detecting a class of edges in US images; namely, the texture edges with a weak regional mean gray-level difference (RMGD) between adjacent regions. The proposed approach comprises a vision model-based texture edge detector using Gabor functions and a new texture-enhancement scheme. The experimental results on the synthetic edge images have shown that the performances of the four tested textural and nontextural edge detectors are about 20%-95% worse than that of the proposed approach. Moreover, the texture enhancement may improve the performance of the proposed texture edge detector by as much as 40%, The experiments on 20 clinical US images have shown that the proposed approach can find reasonable edges for real objects of interest with the performance of 0.4 +/- 0.08 in terms of the Pratt's figure. (C) 2001 World Federation for Ultrasound in Medicine & Biology.
Adaptive symmetric mean filter: A new noise-reduction approach based on the slope facet model
(2001-10-10)
Huang, HC
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Chen, CM
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Wang, SD
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Lu, HHS
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統計學研究所
Two new noise-reduction algorithms, namely, the adaptive symmetric mean filter (ASMF) and the hybrid filter, are presented in this paper. The idea of the ASMF is to find the largest symmetric region on a slope facet by incorporation of the gradient similarity criterion and the symmetry constraint into region growing. The gradient similarity criterion allows more pixels to be included for a statistically better estimation, whereas the symmetry constraint promises an unbiased estimate if the noise is completely removed. The hybrid filter combines the advantages of the ASMF, the double-window modified-trimmed mean filter, and the adaptive mean filter to optimize noise reduction on the step and the ramp edges. The experimental results have shown the ASMF and the hybrid filter are superior to three conventional filters for the synthetic and the natural images in terms of the root-mean-squared error, the root-mean-squared difference of gradient, and the visual presentation. (C) 2001 Optical Society of America.
An adaptive snake model for ultrasound image segmentation: Modified trimmed mean filter, ramp integration and adaptive weighting parameters
(2000-10-01)
Chen, CM
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Lu, HHS
;
統計學研究所
;
Institute of Statistics
The snake model is a widely-used approach to finding the boundary of the object of interest in an ultrasound image. However, due to the speckles, the weak edges and the tissue-related textures in an ultrasound image, conventional snake models usually cannot obtain the desired boundary satisfactorily. In this paper, we propose a new adaptive snake model for ultrasound image segmentation. The proposed snake model is composed of three major techniques, namely, the modified trimmed mean (MTM) filtering, ramp integration and adaptive weighting parameters. With the advantages of the mean and median filters, the MIM filter is employed to alleviate the speckle interference in the segmentation process. The weak edge enhancement by ramp integration attempts to capture the slowly varying edges, which are hard to capture by conventional snake models. The adaptive weighting parameter allows weighting of each energy term to change adaptively during the deformation process. The proposed snake model has been verified on the phantom and clinical ultrasound images. The experimental results showed that the proposed snake model achieves a reasonable performance with an initial contour placed 10 to 20 pixels away from the desired boundary. The mean minimal distances from the derived boundary to the desired boundary have been shown to be less than 3.5 (for CNR greater than or equal to 0.5) and 2.5 pixels, respectively, for the phantom and ultrasound images.
An early vision-based snake model for ultrasound image segmentation
(2000-02-01)
Chen, CM
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Lu, HHS
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Lin, YC
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統計學研究所
;
Institute of Statistics
Due to the speckles and the ill-defined edges of the object of interest, the classic image-segmentation techniques are usually ineffective in segmenting ultrasound (US) images. In this paper, we present a new algorithm for segmenting general US images that is composed of two major techniques; namely, the early-vision model and the discrete-snake model, By simulating human early vision, the early-vision model can capture both grey-scale and textural edges while the speckle noise is suppressed. By performing deformation only on the peaks of the distance map, the discrete-snake model promises better noise immunity and more accurate convergence. Moreover, the constraint for most conventional snake models that the initial contour needs to be located very close to the actual boundary has been relaxed substantially. The performance of the proposed snake model has been shown to be comparable to manual delineation and superior to that of the gradient vector flow (GVF) snake model. (C) 2000 World Federation for Ultrasound in Medicine & Biology.
Bayesian wavelet shrinkage for nonparametric mixed-effects models
(2000-10-01)
Huang, SY
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Lu, HHS
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統計學研究所
;
Institute of Statistics
The main purpose of this article is to study the wavelet shrinkage method from a Bayesian viewpoint. Nonparametric mixed-effects models are proposed and used for interpretation of the Bayesian structure. Bayes and empirical Bayes estimation are discussed. The latter is shown to have the Gauss-Markov type optimality (i.e., BLUP), to be equivalent to a method of regularization estimator (MORE), and to be minimax in a certain class. Characterization of prior and posterior regularity is discussed. The smoothness of posterior estimators is controlled via prior parameters. Computational issues including the use of generalized cross validation are discussed, and examples are presented.
Cell-based dual snake model: A new approach to extracting highly winding boundaries in the ultrasound images
(2002-08-01)
Chen, CM
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Lu, HHS
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Huang, YS
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統計學研究所
;
Institute of Statistics
Two common deficiencies of most conventional deformable models are the need to place the initial contour very close to the desired boundary and the incapability of capturing a highly winding boundary for sonographic boundary extraction. To remedy these two deficiencies, a new deformable model (namely, the cell-based dual snake model) is proposed in this paper. The basic idea is to apply the dual snake model in the cell-based deformation manner. While the dual snake model provides an effective mechanism allowing a distant initial contour, the cell-based deformation makes it possible to catch the winding characteristics of the desired boundary. The performance of the proposed cell-based dual snake model has been evaluated on synthetic images with simulated speckles and on the clinical ultrasound (US) images. The experimental results show that the mean distances from the derived to the desired boundary points are 0.9 +/- 0.42 pixels and 1.29 +/- 0.39 pixels for the synthetic and the clinical US images, respectively.
Cross-reference weighted least square estimates for positron emission tomography
(1998-02-01)
Lu, HHS
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Chen, CM
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Yang, IH
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統計學研究所
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Institute of Statistics
An efficient new method, termed as the crossreference weighted least square estimate (WLSE) [CRWLSE], is proposed to integrate the incomplete local smoothness information to improve the reconstruction of positron emission tomography (PET) images in the presence of accidental coincidence events and attenuation. The algebraic reconstruction technique (ART) is applied to this new estimate and the convergence is proved. This numerical technique is based on row operations. The computational complexity is only linear in the sizes of pixels and detector tubes, Hence, it is efficient in storage and computation for a large and sparse system. Moreover, the easy incorporation of range limits and spatially variant penalty will not deprive the efficiency. All this makes the new method practically applicable. An automatically data-driven selection method for this new estimate based on the generalized cross validation is also studied, The Monte Carlo studies demonstrate the advantages of this new method.
Evolution of the yeast protein interaction network
(2003-10-28)
Qin, H
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Lu, HHS
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Wu, WB
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Li, WH
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統計學研究所
To study the evolution of the yeast protein interaction network, we first classified yeast proteins by their evolutionary histories into isotemporal categories, then analyzed the interaction tendencies within and between the categories, and finally reconstructed the main growth path. We found that two proteins tend to interact with each other if they are in the same or similar categories, but tended to avoid each other otherwise, and that network evolution mirrors the universal tree of life. These observations suggest synergistic selection during network evolution and provide insights into the hierarchical modularity of cellular networks.
Explore biological pathways from noisy array data by directed acyclic Boolean networks
(2005)
Li, LM
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Lu, HHS
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統計學研究所
;
Institute of Statistics
We consider the structure of directed acyclic Boolean (DAB) networks as a tool for exploring biological pathways. In a DAB network, the basic objects are binary elements and their Boolean duals. A DAB is characterized by two kinds of pairwise relations: similarity and prerequisite. The latter is a partial order relation, namely, the on-status of one element is necessary for the on-status of another element. A DAB network is uniquely determined by the state space of its elements. We arrange samples from the state space of a DAB network in a binary array and introduce a random mechanism of measurement error. Our inference strategy consists of two stages. First, we consider each pair of elements and try to identify their most likely relation. In the meantime, we assign a score, s-p-score, to this relation. Second, we rank the s-p-scores obtained from the first stage. We expect that relations with smaller s-p-scores are more likely to be true, and those with larger s-p-scores are more likely to be false. The key idea is the definition of s-scores (referring to similarity), p-scores (referring to prerequisite), and s-p-scores. As with classical statistical tests, control of false negatives and false positives are our primary concerns. We illustrate the method by a simulated example, the classical arginine biosynthetic pathway, and show some exploratory results on a published microarray expression dataset of yeast Saccharomyces cerevisiae obtained from experiments with activation and genetic perturbation of the pheromone response MAPK pathway.
Extended Gauss-Markov theorem for nonparametric mixed-effects models
(2001-02-01)
Huang, SY
;
Lu, HHS
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交大名義發表
;
National Chiao Tung University
The Gauss-Markov theorem provides a golden standard for constructing the best linear unbiased estimation for linear models. The main purpose of this article is to extend the Gauss-Markov theorem to include nonparametric mixed-effects models. The extended Gauss-Markov estimation (or prediction) is shown to be equivalent to a regularization method and its minimaxity is addressed. The resulting Gauss-Markov estimation serves as an oracle to guide the exploration for effective nonlinear estimators adaptively. Various examples are discussed. Particularly, the wavelet nonparametric regression example and its connection with a Sobolev regularization is presented. (C) 2001 Academic Press.
Generalized cross-validation for wavelet shrinkage in nonparametric mixed effects models
(2003-09-01)
Lu, HHS
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Huang, SY
;
Lin, FH
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統計學研究所
;
Institute of Statistics
A nonlinear wavelet shrinkage estimator was proposed in an earlier article by Huang and Lu. Such an estimator combined the asymptotic equivalence to the best linear unbiased prediction and the Bayesian estimation in nonparametric mixed-effects models. In this article, a data-driven GCV method is proposed to select hyperparameters. The proposed GCV method has low computational cost and can be applied to one or higher dimensional data. It can be used for selecting hyperparameters for either level independent or level dependent shrinkage. It can also be used for selecting the primary resolution level and the number of vanishing moments in the wavelet basis. The strong consistency of the GCV method is proved.
Gridding spot centers of smoothly distorted microarray images
(2006-02-01)
Ho, J
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Hwang, WL
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Lu, HHS
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Lee, DT
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統計學研究所
We use an optimization technique to accurately locate a distorted grid structure in a microarray image. By assuming that spot centers deviate smoothly from a checkerboard grid structure, we show that the process of gridding spot centers can be formulated as a constrained optimization problem. The constraint is equal to the variations of the transform parameter. We demonstrate the accuracy of our algorithm on two sets of microarray images. One set consists of some images from the Stanford Microarray Database; we compare our centers with those annotated in the Database. The other set consists of oligonucleotide images, and we compare our results with those obtained by GenePix Pro 5.0. Our experiments were performed completely automatically.
On accelerated cross-reference maximum likelihood estimates for positron emission tomography
(1998)
Lu, HHS
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Tseng, WJ
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統計學研究所
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Institute of Statistics
The state of art of positron emission tomography (PET) takes into account the accidental coincidence events and attenuation. The maximum likelihood estimator can handle this kind of random variation in the reconstruction of a PET image. However, the reconstruction is ill-posed and needs regularization. The boundary information, either from an expert or from the other medical modality of the same object, like the X-ray CT scan, MRI, and so forth, can be used to regularize the reconstruction. We have investigated new, efficient and robust approaches to extract the related but incomplete boundary information. Fast algorithms adapted from the expectation/conditional maximization (ECM) and space alternating generalized expectation maximization (SAGE) algorithms are proposed to accelerate the computation. The method of generalized approximate cross validation (GACV) is adjusted to select the penalty parameter from observed data quickly. The Monte Carlo studies demonstrate the improvement.
Patterns of segmental duplication in the human genome
(2005-01-01)
Zhang, LQ
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Lu, HHS
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Chung, WY
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Yang, J
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Li, WH
We analyzed the completed human genome for recent segmental duplications (size greater than or equal to 1 kb and sequence similarity greater than or equal to 90%). We found that approximately 4% of the genome is covered by duplications and that the extent of segmental duplication varies from 1% to 14% among the 24 chromosomes. Intrachromosomal duplication is more frequent than interchromosomal duplication in 15 chromosomes. The duplication frequencies in pericentromeric and subtelomeric regions are greater than the genome average by approximately threefold and fourfold. We examined factors that may affect the frequency of duplication in a region. Within individual chromosomes, the duplication frequency shows little correlation with local gene density, repeat density, recombination rate, and GC content, except chromosomes 7 and Y. For the entire genome, the duplication frequency is correlated with each of the above factors. Based on known genes and Ensembl genes, the proportion of duplications containing complete genes is 3.4% and 10.7%, respectively. The proportion of duplications containing genes is higher in intrachromosomal than in interchromosomal duplications, and duplications containing genes have a higher sequence similarity and tend to be longer than duplications containing no genes. Our simulation suggests that many duplications containing genes have been selectively maintained in the genome.
Random-coincidence corrections using iterative reconstruction for PET images
(2000)
Chen, JC
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Liu, RS
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Tu, KY
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Lu, HHS
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Chen, TB
Iterative reconstruction (IR) algorithms can reduce artifacts caused by filtered backprojection (FBP) or convolution backprojection (CBP). Recently, the computational effects required for IR of positron emission tomography (PET) studies have been reduced to make it practically appealing. We have made an implementation of the improved Maximum Likelihood-Expectation Maximization (ML-EM) algorithm. The transition matrix was generated based on the geometry of the instrument. Phantoms of 6 line sources and 19 line sources were used to test our accelerated ML-EM algorithms against the FBP method. The singles were used to calculate the random coincidence rates by a well known formula and were compared to the randoms obtained by another geometric method. We also designed a new model using two line sources to determine the ratio of random events to true events. The artifacts near those line sources were eliminated with the ML-EM method. With decay correction, the RC events were uniformly distributed in whole field after 10 iterations. The ML-EM reconstructed images are superior to those obtained with FBP. The patterns of randoms provide insightful information for random correction, which the hardware correction by the delay window can not provide. This information is particular valuable when the delay window correction is not available in the old fashion PET scanner.
Rapid divergence in expression between duplicate genes inferred from microarray data
(2002-12-01)
Gu, ZL
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Nicolae, D
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Lu, HHS
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Li, WH
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統計學研究所
For more than 30 years, expression divergence has been considered as a major reason for retaining duplicated genes in a genome, but how often and how fast duplicate genes diverge in expression has not been studied at the genomic level. Using yeast microarray data, we show that expression divergence between duplicate genes is significantly correlated with their synonymous divergence (K-S) and also with their nonsynonymous divergence (K-A) if K(A)less than or equal to0.3. Thus, expression divergence increases with evolutionary time, and K-A is initially coupled with expression divergence. More interestingly, a large proportion of duplicate genes have diverged quickly in expression and the vast majority of gene pairs eventually become divergent in expression. Indeed, more than 40% of gene pairs show expression divergence even when K-S isless than or equal to0.10, and this proportion becomes >80% for K-S > 1.5. Only a small fraction of ancient gene pairs do not show expression divergence.
Statistical methods for identifying yeast cell cycle transcription factors
(2005-09-20)
Tsai, HK
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Lu, HHS
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Li, WH
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統計學研究所
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Institute of Statistics
Knowing transcription factors (TFs) involved in the yeast cell cycle is helpful for understanding the regulation of yeast cell cycle genes. We therefore developed two methods for predicting (i) individual cell cycle TFs and (h) synergistic TF pairs. The essential idea is that genes regulated by a cell cycle TF should have higher (lower, if it is a repressor) expression levels than genes not regulated by it during one or more phases of the cell cycle. This idea can also be used to identify synergistic interactions of TFs. Applying our methods to chromatin immunoprecipitation data and microarray data, we predict 50 cell cycle TFs and 80 synergistic TF pairs, including most known cell cycle TFs and synergistic TF pairs. Using these and published results, we describe the behaviors of 50 known or inferred cell cycle TFs in each cell cycle phase in terms of activation/repression and potential positive/negative interactions between TFs. In addition to the cell cycle, our methods are also applicable to other functions.
Supervised motion segmentation by spatial-frequential analysis and dynamic sliced inverse regression
(2004-04-01)
Wu, HM
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Lu, HHS
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統計學研究所
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Institute of Statistics
lit this paper, we propose a new method for supervised motion segmentation based on spatial-frequential analysis and dimension reduction techniques. A sequence of images could contain non-ridge motion in the region of interest and the segmentation of these moving objects with deformation is challenging. The aim is to extract feature vectors that capture the spatial-frequential information in the training set and then to monitor the variations of the feature vectors over time in the test set. Given successive images in the training set, we consider a dynamic model that extends the sliced inverse regression in Li (1991). It is designed to capture the intrinsic dimension of feature vectors that holds over a local time scale. These projected features are then used to classify training images and predict forthcoming images in the test set into distinct categories. Theoretic properties are addressed. Simulation studies and clinical studies of a sequence of magnetic resonance images are reported, which confirm the practical feasibility of this new approach.