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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.
Computers in biology and medicine 11/2011; 42(1):8-18. · 1.27 Impact Factor
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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.
Physics in Medicine and Biology 08/2011; 56(18):5949-67. · 2.83 Impact Factor
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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.
Physics in Medicine and Biology 06/2011; 56(14):4481-98. · 2.83 Impact Factor
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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.
Innovative Computing, Information and Control (ICICIC), 2009 Fourth International Conference on; 01/2010
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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.
Conference proceedings: ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Conference 01/2009; 2009:3541-4.
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ABSTRACT: In microarray data analysis, each gene expression sample has thousands of genes and reducing such high dimensionality is useful for both visualization and further clustering of samples. Traditional principal component analysis (PCA) is a commonly used method which has problems. Nonnegative Matrix Factorization (NMF) is a new dimension reduction method. In this paper we compare NMF and PCA for dimension reduction. The reduced data is used for visualization, and clustering analysis via k-means on 11 real gene expression datasets. Before the clustering analysis, we apply NMF and PCA for reduction in visualization. The results on one leukemia dataset show that NMF can discover natural clusters and clearly detect one mislabeled sample while PCA cannot. For clustering analysis via k-means, NMF most typically outperforms PCA. Our results demonstrate the superiority of NMF over PCA in reducing microarray data.
Journal of Biomedical Informatics 09/2008; 41(4):602-6. · 1.79 Impact Factor
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ABSTRACT: Nonnegative matrix factorization (NMF) has been proven to be a powerful clustering method. Recently Cichocki and coauthors have proposed a family of new algorithms based on the alpha-divergence for NMF. However, it is an open problem to choose an optimal alpha.
In this paper, we tested such NMF variant with different alpha values on clustering cancer gene expression data for optimal alpha selection experimentally with 11 datasets.
Our experimental results show that alpha=1 and 2 are two special optimal cases for real applications.
Artificial Intelligence in Medicine 08/2008; 44(1):1-5. · 1.35 Impact Factor
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ABSTRACT: Optical coherence tomography (OCT) presents its capability of noninvasive glucose measurement in tissue-simulating phantoms and biological tissues. However, speckle noise substantially deteriorates the accuracy of the measurements with this technique. The speckle noise can be suppressed by averaging of in-depth scans. We studied the suppression of speckle noise for accurate measurement of backscattering signal and the maximum precision of glucose measurement in Intralipid suspensions with the OCT technique. Our results show that the precision of glucose measurement can achieve plusmn4.4 mM for 10% Intralipid and plusmn2.2 mM for 3% Intralipid after the averaging of 50,000 scans.
Information Technology and Applications in Biomedicine, 2008. ITAB 2008. International Conference on; 07/2008
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ABSTRACT: In this paper, we propose an effective pre-processing method of CT brain images to provide consistent feature for content-based medical image retrieval. The key steps in pre-processing include cutting out background region, using ellipse to correct lean imaging angle, and normalization. We take vast CT brain images as experimental data to evaluate the method. Experimental results show the effect of pre-processing.
BioMedical Engineering and Informatics, 2008. BMEI 2008. International Conference on; 06/2008
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ABSTRACT: Content-based image retrieval for medical image is a main way for computer-aided diagnosis. To storage medical image into the huge databases is its premise. In this paper, we provides efficient approach to develop the archives of large brain CT medical data, including quality control, ensuring consistency/integrity of information, expert verification of institutional findings, image correction, the validation for registration, and region of interest focusing on attention mechanism. The database is highly promising and shows the profit when it is used to implement brain CT image assistant diagnosis systems.
Bioinformatics and Biomedical Engineering, 2008. ICBBE 2008. The 2nd International Conference on; 06/2008
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ABSTRACT: Nonnegative Matrix Factorization (NMF) is a powerful tool for gene expression data analysis as it reduces thousands of genes to a few compact metagenes, especially in clustering gene expression samples for cancer class discovery. Enhancing sparseness of the factorisation can find only a few dominantly coexpressed metagenes and improve the clustering effectiveness. Sparse p-norm (p > 1) Nonnegative Matrix Factorization (Sp-NMF) is a more sparse representation method using high order norm to normalise the decomposed components. In this paper, we investigate the benefit of high order normalisation for clustering cancer-related gene expression samples. Experimental results demonstrate that Sp-NMF leads to robust and effective clustering in both automatically determining the cluster number, and achieving high accuracy.
International Journal of Data Mining and Bioinformatics 02/2008; 2(3):236-49. · 0.43 Impact Factor
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IJPRAI. 01/2008; 22:1587-1598.
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Medical Biometrics, First International Conference, ICMB 2008, Hong Kong, China, January 4-5, 2008, Proceedings; 01/2008
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ABSTRACT: There exist two classical linear methods for feature extraction, i.e. principal component analysis (PCA) and Fisher discriminant analysis (FDA). PCA best represents the data while FDA best separates the data in the least squares sense with different scatter measures from samples. This paper discusses a regularized scatter measure (RSM) as a linear combination of within-class and between-class scatters for feature extraction. The tradeoff between for representation and for discrimination is controlled via some suitable regularization parameters and the corresponding eigenvalue problem is resolved without singularity. Experiments on two different size data sets demonstrate the effectiveness of the method. In addition, we can see that the counterpart of PCA, i.e. minor component analysis (MCA), is to optimize one special case of RSM. And this provides another easy way for understanding why MCA outperforms PCA for feature extraction in one-class classification problem.
Innovative Computing, Information and Control, 2007. ICICIC '07. Second International Conference on; 10/2007
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ABSTRACT: This paper considers gene expression data classification by discriminative mixture models in which sparseness of training data features controls the learning rate. Our goal is to improve the sparseness of training features reduced by nonnegative matrix factorization (NMF). We use the generalized L<sub>p</sub>-norm NMF for reducing the high dimensional gene expression data. Experimental results on four real gene expression datasets show that, the classification accuracy can be significantly improved by using the generalized method, and especially that it is first to adopt L<sub>2</sub>-norm NMF for dimension reduction.
Bioinformatics and Biomedical Engineering, 2007. ICBBE 2007. The 1st International Conference on; 08/2007
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ABSTRACT: With contrast to conventional MRI, Diffusion tensor im- age (DTI) can demonstrate the anatomic structure and pathological process of white matter tracts and it is widely used by clinician. However, at present, DTI and MRI im- ages are diagnosed respectively and clinician gets the im- age information objectively, and this leads to different diag- nosis conclusions. In this article, we develop an integrated approach that aims to maximize the amount of image in- formation for clinician by combining anatomical and func- tional MR images with nerve fiber bundles derived from dif- fusion tensor imaging to assist treatment solution for brain tumor. Firstly, segmentation on MRI T2 is operated to de- cide solid tumor border or edema area et al.; and then DTI and MRI images are fused together, which can supplied not only individual information but also fusion information. This new visualization facility which is intuitive for clini- cian to understand and use can adequately mine this source of image information. Finally, white matter fibers affected by tumor were analyzed quantitatively. The combined visu- alization and computational methods have the potential to assist in preoperative surgery and radiotherapy planning as well as postoperative treatment evaluation.
Innovative Computing ,Information and Control, International Conference on.
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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.
Information Processing & Management.
