Suwen Qi

Development of a rapid and high‐performance chemiluminescence immunoassay based on magnetic particles for protein S100B in human serum

Protein S100B is a clinically useful non-invasive biomarker for brain cell damage. A rapid chemiluminescence immunoassay (CLIA) for S100B in human serum has been developed. Fluorescein isothiocyanate (FITC) and N-(aminobutyl)-N-(ethylisoluminol) (ABEI) are used to label two different monoclonal antibodies of anti-S100B. Protein S100B in serum combines with labeled antibodies and can form a sandwiched immunoreaction. A simplified separation procedure based on the use of magnetic particles (MPs) that were coated with anti-FITC antibody is performed to remove the unwanted materials. After adding the substrate solution, the relative light unit (RLU) of ABEI is measured and is found to be directly proportional to the concentration of S100B in serum. The relevant variables involved in the CLIA signals are optimized and the parameters of the proposed method are evaluated. The results demonstrate that the method is linear to 25 ng/mL S100B with a detection limit of 0.02 ng/mL. The coefficient of variation (CV) is < 5% and < 6% for intra- and interassay precision, respectively. The average recoveries are between 97 and 107%. The linearity-dilution effect produces a linear correlation coefficient of 0.9988. Compared with the commercial kit, the proposed method shows a correlation of 0.9897. The proposed method displays acceptable performance for quantification of S100B and is appropriate for use in clinical diagnosis.

Multispectral Image Alignment With Nonlinear Scale-Invariant Keypoint and Enhanced Local Feature Matrix

The scale space-based method has been recently studied for multispectral alignment; however, due to the significant intensity difference between the image pairs, there are usually not enough keypoint correspondences found, and the robustness of the alignment tends to be compromised. In this letter, we attempt to improve the performance from the following two aspects: 1) to avoid the boundary blurring of Gaussian scale space, we adopt nonlinear scale space to explore more keypoints with potential of being correctly matched, and 2) a robust feature descriptor is proposed, and the resulting feature matrix is matched using the previously proposed rotation-invariant distance to obtain more correct keypoint correspondences. Experimental results for multispectral remote images indicate that the proposed method improves the matching performance compared to state-of-the-art methods in terms of correctly matched number of keypoints, aligning accuracy, and rate of correctly matched image pairs. It is also revealed in this letter that, if the descriptor is carefully designed, the local features are distinctive enough for produce good matching even when the main orientation is not present.

A supervised method using convolutional neural networks for retinal vessel delineation

Retinal vessel delineation is a hot research topic owing to its importance in a lot of clinic application. Several methods have been proposed in the past decades. Here we will present a new supervised method for retinal vessel segmentation. The method is designed to explore the complex relationship between retinal images and their corresponding vessel label maps. Specifically, in order to build a model describing the direct transformation from retinal image to vessel map, we introduce a deep convolutional neural network (abbreviation as CNN), which has strong enough induction ability. For the purpose of constructing the whole vessel probability map, we also design a synthesis method. Our method shows better performance on DRIVE dataset than state-of-the-art of reported approaches in the light of sensitivity (abbreviation as Se), specificity (abbreviation as Sp) and accuracy (abbreviation as Acc). Our proposed method has great potential to be applied in existing computer-assisted diagnostic system of ophthalmologic diseases. Meanwhile, the method may offer a novel, general computing framework for segmentation in other fields.

Continuous Detection of Muscle Aspect Ratio Using Keypoint Tracking in Ultrasonography

Muscle aspect ratio of cross-sectional area is one of the most widely used parameters for quantifying muscle function in both diagnosis and rehabilitation assessment. Ultrasound imaging has been frequently used to noninvasively study the characteristics of human muscles as a reliable method. However, the aspect ratio measurement is traditionally conducted by the manual digitization of reference points; thus, it is subjective, time-consuming, and prone to errors. In this paper, a novel method is proposed to continuously detect the muscle aspect ratio. Two keypoint pairs are manually digitized on the lateral and longitudinal borders at the first frame, and automatically tracked by an optical flow technique at the subsequent frames. The muscle aspect ratio is thereby obtained based on the estimated muscle width and thickness. Six ultrasound sequences from different subjects are used to evaluate this method, and the overall coefficient of multiple correlation of the results between manual and proposed methods is 0.97 ± 0.02. The linear regression shows that a good linear correlation between the results of the two methods is obtained (R2 = 0.974), with difference -0.01 ± 0.16. The method proposed here provides an accurate, high repeatable, and efficient approach for estimating muscle aspect ratio during human motion, thus justifying its application in biological sciences.

An automatic calibration system for binocular stereo imaging

In this paper, an automatic calibration system for binocular stereo imaging based on Zhangs 2D flat calibration method is proposed. Firstly, the interface of system is designed. After images being collected, the result of Harris corners detection extracted from the 2D flat are displayed on the interface, meanwhile, the system begins to compute intrinsic and extrinsic parameters of the stereo camera and then optimizes them by maximum likelihood estimate. Finally, mean reprojection error and the visualization of camera intrinsic parameters shown on the interface is convenient for users to analyze factors of effecting stereo camera calibration.The results appear that the proposed system not only operates easily but also obtains high precision of calibration, which improves the traditional method of camera calibration.

Multi-view face detector using a single cascade classifier

In this work, a cascade classifier is trained to detect multi-view face samples. Comparing with most of face detection system which use different classifier to classify frontal face and profile face, our system has advantage in detection speed. The proposed face detector extracts the Haar-like feature from the training samples and train a cascade classifier by using Adaboost learing algorithm. Different from the existing algorithms, our detection system only contains a cascade classifier model. Our preliminary experiments demonstrate that our cascade classifier can achieve similiar accuracy and 60% higher speed detection than the multi-view face detection system which consist of two sparate cascade classifiers.

Fundus imagemosaic based on the SIFT feature

Fundus image mosaic can expand the visual field of view and provide auxiliary diagnosis for fundus diseases. Due to the low contrast and uneven grayscale of fundus images, traditional methods based on gray characteristic are no longer applicable. We propose a fundus image mosaic algorithm based on Scale-invariant feature transform (SIFT), along with a quantitative evaluation criterion. We detect feature points in Gaussian scale space and generate feature descriptors with scale invariance for all feature points. The matching feature point pairs are obtained by evaluating the similarity of their feature descriptors and outliers are eliminated using Random sample consensus (RANSAC) algorithm employed the projective transformation model. After that, the transformation matrix can be calculated based on the obtained matching point pairs. Finally, the multi-view fundus images are fused based on the transformation matrix. To the best of our knowledge, there is no quantitative evaluation reported for fundus image mosaic. Here we propose a criterion based on manual marker to evaluate the mosaic results. The experimental results show that the average accuracy is 2.27±0.5 pixels. Our proposed method has great potential to be applied in existing computer-assisted diagnostic system of ophthalmologic diseases.

Determination of the concentration of a-fetoprotein using an Alexa fluor-based fluorescence immunoassay

Alpha fetoprotein (AFP) has been identified as a specific marker of hepatocellular carcinoma (HCC). Our perpose is to develop a new fluorescence immunoassay based on Alexa fluor-647 to determine the concentration of AFP in serum. Alexa fluor-647 and magnetic beads were applied in labeling two different anti-AFP monoclonal antibodies. Both labeled antibodies and AFP antigen formed a sandwiched immunocomplex. After washing in a magnetic field, the fluorescent intensity of Alexa fluor-647 in the immunocomplex was measured and the value was directly in proportion to the levels of AFP present in the samples. The influence of two physicochemical parameters involved in the assay signals were optimized and the parameters of the proposed method were assessed. The consequence showed the detection limit of the proposed method was 1.1 ng/mL. The coefficient of variation (CV) was less than 6% for intra-assay precision. An entire assay could be finished in 25 min. This assay provided a new way to quantitatively measure AFP in serum for the diagnosis of HCC.