Biomedical subjects

Xiuwen Liu

Publications and source records attributed to Xiuwen Liu.

8 recordsLinked to original sources

PubMed2006

Image and texture segmentation using local spectral histograms.

We present a method for segmenting images consisting of texture and nontexture regions based on local spectral histograms. Defined as a vector consisting of marginal distributions of chosen filter responses, local spectral histograms provide a feature statistic for both types of regions. Using local spectral histograms of homogeneous regions, we decompose the segmentation process into three stages. The first is the initial classification stage, where probability models for homogeneous texture and nontexture regions are derived and an initial segmentation result is obtained by classifying local windows. In the second stage, we give an algorithm that iteratively updates the segmentation using the derived probability models. The third is the boundary localization stage, where region boundaries are localized by building refined probability models that are sensitive to spatial patterns in segmented regions. We present segmentation results on texture as well as nontexture images. Our comparison with other methods shows that the proposed method produces more accurate segmentation results.

Algorithms↗

PubMed2005

Quantification of sifuvirtide in monkey plasma by an on-line solid-phase extraction procedure combined with liquid chromatography/electrospray ionization tandem mass spectrometry.

A simple, automated and rapid method has been developed for the determination of a novel antiviral peptide sifuvirtide in monkey plasma. Raw plasma samples were directly loaded onto an on-line solid-phase extraction (SPE) column, which removes the time-consuming and laborious sample pretreatment. Following a timed valve-switching event, the analyte was eluted on-line to a reversed-phase high-performance liquid chromatography (RP-HPLC) column and subsequently introduced into a linear ion trap mass spectrometer, LTQ-MS, via an electrospray ionization (ESI) interface. The multiply charged peptides were specified and quantitatively analyzed using selective reaction monitoring (SRM). A highly pure four iodine-sifuvirtide was synthesized using an optimized iodogen method and proved to be a suitable internal standard (IS). A single analysis run takes about 18 min. Validation of the method demonstrated that the linear calibration curves covered the range of 4.88-5000 ng/mL, and the correlation coefficients were above 0.9923. The limit of detection (LOD) with the signal-to-noise (S/N) ratio higher than 12 was calculated as 1.22 ng/mL. The intra- and inter-batch precisions were less than 12.7% and 9.1%, and the mean accuracy ranged from -5.2% to 3.6%, respectively. Any carry-over effect from the system was negligible. In a pharmacokinetic (PK) study of sifuvirtide after a single intravenous or subcutaneous dose in monkeys, the on-line SPE-LC/MS/MS system was successfully utilized to determine hundreds of samples with only one extraction column, which indicated the feasibility and the reliability of this method for application in preclinical and clinical PK studies of peptide drugs.

Amino Acid Sequence↗

PubMed2005

Statistical shape analysis: clustering, learning, and testing.

Using a differential-geometric treatment of planar shapes, we present tools for: 1) hierarchical clustering of imaged objects according to the shapes of their boundaries, 2) learning of probability models for clusters of shapes, and 3) testing of newly observed shapes under competing probability models. Clustering at any level of hierarchy is performed using a mimimum variance type criterion criterion and a Markov process. Statistical means of clusters provide shapes to be clustered at the next higher level, thus building a hierarchy of shapes. Using finite-dimensional approximations of spaces tangent to the shape space at sample means, we (implicitly) impose probability models on the shape space, and results are illustrated via random sampling and classification (hypothesis testing). Together, hierarchical clustering and hypothesis testing provide an efficient framework for shape retrieval. Examples are presented using shapes and images from ETH, Surrey, and AMCOM databases.

Algorithms↗

PubMed2005

Face detection using spectral histograms and SVMs.

We present a face detection method using spectral histograms and support vector machines (SVMs). Each image window is represented by its spectral histogram, which is a feature vector consisting of histograms of filtered images. Using statistical sampling, we show systematically the representation groups face images together; in comparison, commonly used representations often do not exhibit this necessary and desirable property. By using an SVM trained on a set of 4500 face and 8000 nonface images, we obtain a robust classifying function for face and non-face patterns. With an effective illumination-correction algorithm, our system reliably discriminates face and nonface patterns in images under different kinds of conditions. Our method on two commonly used data sets give the best performance among recent face-detection ones. We attribute the high performance to the desirable properties of the spectral histogram representation and good generalization of SVMs. Several further improvements in computation time and in performance are discussed.

Algorithms↗

PubMed2004

Optimal linear representations of images for object recognition.

Although linear representations are frequently used in image analysis, their performances are seldom optimal in specific applications. This paper proposes a stochastic gradient algorithm for finding optimal linear representations of images for use in appearance-based object recognition. Using the nearest neighbor classifier, a recognition performance function is specified and linear representations that maximize this performance are sought. For solving this optimization problem on a Grassmann manifold, a stochastic gradient algorithm utilizing intrinsic flows is introduced. Several experimental results are presented to demonstrate this algorithm.

Algorithms↗

PubMed2003

Independent spectral representations of images for recognition.

In recent years, studies have shown that independent components of local windows of natural images resemble the receptive fields of cells in the early stages of the mammalian visual pathway. However, the role of the independence in visual recognition is not well understood. We argue that the independence resolves the curse of dimensionality by reducing the complexity of probability models to the linear order of the dimension. In addition, we show empirically that the complexity reduction does not degrade the recognition performance on all the data sets that we have used with an independent spectral representation. In this representation, an input image is first decomposed into independent channels given by the estimated independent components from training images, and each channel's response is then summarised by using its histogram as an estimate of the underlying probability model along that dimension. We demonstrate the sufficiency of the proposed representation for image characterization by synthesizing textures and objects through sampling and for recognition by applying it to large data sets. Our comparisons show that the independent spectral representation often gives improved recognition performance.

Journal Article↗

PubMed2002

A spectral histogram model for texton modeling and texture discrimination.

We suggest a spectral histogram, defined as the marginal distribution of filter responses, as a quantitative definition for a texton pattern. By matching spectral histograms, an arbitrary image can be transformed to an image with similar textons to the observed. We use the chi(2)-statistic to measure the difference between two spectral histograms, which leads to a texture discrimination model. The performance of the model well matches psychophysical results on a systematic set of texture discrimination data and it exhibits the nonlinearity and asymmetry phenomena in human texture discrimination. A quantitative comparison with the Malik-Perona model is given, and a number of issues regarding the model are discussed.

Discrimination, Psychological↗

PubMedDate not supplied

Intrinsic generalization analysis of low dimensional representations.

Low dimensional representations of images impose equivalence relations in the image space; the induced equivalence class of an image is named as its intrinsic generalization. The intrinsic generalization of a representation provides a novel way to measure its generalization and leads to more fundamental insights than the commonly used recognition performance, which is heavily influenced by the choice of training and test data. We demonstrate the limitations of linear subspace representations by sampling their intrinsic generalization, and propose a nonlinear representation that overcomes these limitations. The proposed representation projects images nonlinearly into the marginal densities of their filter responses, followed by linear projections of the marginals. We use experiments on large datasets to show that the representations that have better intrinsic generalization also lead to better recognition performance.

Generalization, Psychological↗