Blockade of IL-17 alleviated inflammation in rat arthritis and MMP-13 expression.

OBJECTIVE: Rheumatoid arthritis (RA) is one systemic auto-immune disorder featured as chronic synovitis and can destruct joint cartilage. Fibroblast-like synoviocyte (FLS) secretes various factors affecting chondrocyte matrix and degradation. This study thus investigated the effect of interleukin-17A (IL-17A) on FLS and osteoclast. MATERIALS AND METHODS: Type II collagen-induced arthritis (CIA) rats were assigned to CIA model, CIA + IgG1 isotype, and CIA + Anti-Rat IL-17A groups. Tissue volume and arthritis index (AI) evaluated arthritis condition. ELISA and flow cytometry measured IL-17A content and Th17 cell percentage in joint cavity fluid. Matrix metallopeptidase 13 (MMP-13) and collagen type II alpha 1 (COL2A1) expression in synovial tissues were compared. FLS-osteoclast co-culture system was treated with IL-17A + IgG1 Isotype or CIA + Anti-Rat IL-17A. MMP-13 and COL2A1 expression were compared. RESULTS: CIA model rats had significantly higher IL-17A and Th17 cell ratio in joint cavity fluid. Injection of Anti-Rat IL-17A decreased AI and tissue volume in model rats, decreased MMP-13 while increased COL2A1 expression in synovial or cartilage tissues. IL-17A treatment remarkably up-regulated MMP-13 mRNA or protein expression in chondrocytes. Anti-IL-17A weakened effects of IL-17A on FLS or chondrocytes. CONCLUSIONS: IL-17A inhibits COL2A1 mRNA and protein expression of chondrocyte in the co-culture system via inducing MMP-13 expression in FLS, thus enhancing collagen degradation and playing a role in RA-related cartilage injury.

The application of competitive Hopfield neural network to medical image segmentation.

In this paper, a parallel and unsupervised approach using the competitive Hopfield neural network (CHNN) is proposed for medical image segmentation. It is a kind of Hopfield network which incorporates the winner-takes-all (WTA) learning mechanism. The image segmentation is conceptually formulated as a problem of pixel clustering based upon the global information of the gray level distribution. Thus, the energy function for minimization is defined as the mean of the squared distance measures of the gray levels within each class. The proposed network avoids the onerous procedure of determining values for the weighting factors in the energy function. In addition, its training scheme enables the network to learn rapidly and effectively. For an image of n gray levels and c interesting objects, the proposed CHNN would consist of n by c neurons and be independent of the image size. In both simulation studies and practical medical image segmentation, the CHNN method shows promising results in comparison with two well-known methods: the hard and the fuzzy c-means (FCM) methods.

A PC-based imaging system for automated platelet identification.

In this communication, a PC-based imaging system was developed for automatically identifying fluorescence-labeled individual platelets adherent to protein-coated surface under flow conditions. It is to eliminate the laborious and time-consuming task, and the subjective error of manual measurements. Based upon the features of adherent platelets, three passes of the image processing were developed for platelet identification. From the results, 90-95% accuracy could be routinely obtained. The platelet distribution and other related parameters could be easily extracted and investigated.

Platelet-fibrin interaction in the suspension and under flow conditions.

Interactions between platelets and fibrin are important in hemostasis but often confused with platelet-fibrinogen interactions. A stirred mixture of solubilized fibrin and washed platelets at neutral pH range showed drastic reduction in turbidity and concomitant platelet adhesion onto newly formed fibrin strands. This platelet-fibrin interaction did not require platelet activation nor did it cause platelet aggregation. A device consisting of a parallel-plate flow chamber mounted on a fluorescence microscope has been constructed to allow direct visualization and recording of platelet-fibrin interaction under flow conditions. Platelets in whole blood adhered to the fibrin-coated portion but not to the uncoated portion of the flow chamber. Slow motion playback of video tapes indicated that the adhesion phenomenon was a dynamic process that involved attaching, detaching, relocation and transient contact. The fibrin coating influenced platelet adhesion both by increasing the number of cells making short-term attachments to the surface and by increasing the duration of cells attached to the surface. These observations provided basic characteristics of platelet-fibrin interaction.

Multiresolution registration of coronary artery image sequences.

Registration of coronary arterial images taken at different times is very important for obtaining better visibility of differences between sequential images. A typical image registration algorithm often employs a similarity measure to detect the differences generated from the relative motion or gray level changes between these images. Although a number of image registration approaches have been proposed to resolve the registration problem of digital angiography, they are either computationally expensive or not very robust in the application to practical images. This paper presents a feature-based sum of absolute values of difference (SAVD) using a coarse-to-fine strategy. The proposed algorithm was demonstrated to be capable to automatically registering the arterial structures in the areas of interest selected from a pair of sequential images as well as providing fractional pixel precision in registration. Compared to other existing methods, the algorithm improves the speed and the reliability of registration when a pair of coronary arterial images are acquired at the same or almost the same phase of cardiac motion.

Edge-shrinking interpolation for medical images.

A new algorithm for interpolating the missing data between two adjacent medical images is presented. Our method is useful for solving the interpolation of any region-represented images of an object to be reconstructed, even when the object is stretched abruptly, branched or hollow, as often occurs in medical images, which cases can not be handled well by existing methods. When this algorithm is applied, the nonoverlapped regions of the same object in the two base images are first extracted and encoded by chamfer distance code on every pixel in these regions. Then, the outer edges of the nonoverlapping regions are shrunk inward simultaneously so that the stretched edges reach the edges of the overlapping regions at the same time. The distance codes in nonoverlapping regions are used to limit the shrinking of these edges in the interpolation process. The proposed method also provides object centralization and enlargement operations to obtain stable and reasonable results in complicated case. The experimental results show that the proposed method is more efficient in resolving general interpolation tasks than the existing methods (S.P. Raya and J. K. Udupa, IEEE Trans. Med. Image. 9, 32-42, 1990; G. T. Herman et al., IEEE Comput. Graph. Appl. 12, 69-79, 1992; J.F. Guo et al., Comput. Med. Imag. Graph. 19, 267-279, 1995.)

Three-dimensional reconstruction of biliary tract from biplane projections.

Over the past few years, very little has been done in the area of 3-D reconstruction of the bile duct. Since the system in use for 3-D visualization of the biliary tree is built by surgical or autopsy materials, it generally cannot be applied to clinical diagnosis. In this paper, an algorithm for three-dimensional reconstruction of the biliary tree from two mutually orthogonal is presented to provide accurate and reproducible 3-D information of the biliary tree structure. It has been proven to be useful in diagnosis prior to operation or non-surgical treatment, particularly, obstructive stones can be visualized by using a transparency technique. As experiments demonstrated, the proposed method can be used as a useful tool for the visualization of 3-D structure of biliary tree in clinical applications.

Respiratory effect on the pulse spectrum.

During the last decade, progressive achievements in haemodynamics have shown that radial artery pulse pressure can be used to estimate the vascular properties of the internal organs. Clinical experiments have shown that slow and regular respiration has a large effect on the heart rate variability (HRV). This phenomenon is called respiratory sinus arrhythmia (RSA). It is known that respiration-related oscillations in venous return cause oscillations in stroke volume and blood pressure. It also can be inferred from cardiac output that systemic blood pressure has a similar respiration-related cycle. Moreover we found that the fluctuations of harmonics of arterial pulse are consistent with the fluctuation of HRV. This means that the whole cardiovascular system (CVS) makes rapid adaptation during respiration, and the harmonic proportions of arterial pulse were modified during different breath rates. This result shows that the regular respiration also has a large effect upon Windkessel properties of CVS.

Unstable Morse code recognition with adaptive variable-ratio threshold prediction for physically disabled persons.

With one or two switches, Morse code could provide an effective alternative communication channel for individuals with physical limitations. However, most of the physically disabled persons have difficulties in maintaining a stable typing of Morse code, and hence the automated recognition of unstable Morse code is becoming more on demand. In this study, an adaptive variable-ratio threshold prediction (AVRTP) algorithm is proposed to analyze the Morse code time series with variable unit time period and ratio. Two least-mean-square (LMS) predictors are applied to track the dot interval and the dot-dash difference concurrently, and then a predicted threshold based on a variable-ratio decision rule is used to distinguish between dots and dashes. The same method is also applied to identify character-spaces. By the adaptive prediction of variable-ratio threshold, AVRTP has successfully overcome the difficulty of analyzing severely unstable Morse code time series and outperformed the previously proposed adaptive unstable-speed prediction (AUSP) algorithm and LMS and matching (I,MS&M) algorithm. This study concludes with a computer simulation and a preliminary clinical evaluation that demonstrate AVRTP as an efficient and reliable method for unstable Morse code recognition.

Segmentation of multispectral magnetic resonance image using penalized fuzzy competitive learning network.

Segmentation (tissue classification) of the medical images obtained from Magnetic resonance (MR) images is a primary step in most applications of computer vision to medical image analysis. This paper describes a penalized fuzzy competitive learning network designed to segment multispectral MR spin echo images. The proposed approach is a new unsupervised and winner-takes-all scheme based on a neural network using the penalized fuzzy clustering technique. Its implementation consists of the combination of a competitive learning network and penalized fuzzy clustering methods in order to make parallel implementation feasible. The penalized fuzzy competitive learning network could provide an acceptable result for medical image segmentation in parallel processing using the hardware implementation. The experimental results show that a promising solution can be obtained using the penalized fuzzy competitive learning neural network based on least squares criteria.

Multispectral magnetic resonance images segmentation using fuzzy Hopfield neural network.

This paper demonstrates a fuzzy Hopfield neural network for segmenting multispectral MR brain images. The proposed approach is a new unsupervised 2-D Hopfield neural network based upon the fuzzy clustering technique. Its implementation consists of the combination of 2-D Hopfield neural network and fuzzy c-means clustering algorithm in order to make parallel implementation for segmenting multispectral MR brain images feasible. For generating feasible results, a fuzzy c-means clustering strategy is included in the Hopfield neural network to eliminate the need for finding weighting factors in the energy function which is formulated and based on a basic concept commonly used in pattern classification, called the 'within-class scatter matrix' principle. The suggested fuzzy c-means clustering strategy has also been proven to be convergent and to allow the network to learn more effectively than the conventional Hopfield neural network. The experimental results show that a near optimal solution can be obtained using the fuzzy Hopfield neural network based on the within-class scatter matrix.

A computer system for skeletal growth measurement.

In this paper, we have presented a new image-processing system for the measurement of skeletal growth in pediatric radiology. From a standard posterior and anterior view radiograph, taken from a left hand, the proposed system first automatically locates the phalangeal region of interest, and then measures the geometrical parameters associated with skeletal maturity. Finally, the bone age is estimated by using the standard phalangeal length table. Clinical studies reveal that the computer processing has resulted in an objective and accurate assessment of skeletal age. It greatly improves the shortcomings, including inter- and intraobserver variations and inaccuracy, reported in other research by manual methods. In conclusion, it is an inexpensive and useful tool for the evaluation of short-term abnormalities in the skeletal growth of children.

A fully automated identification of coronary borders from the tree structure of coronary angiograms.

The accurate assessment of variations in coronary arterial dimensions plays an important role in the evaluation of ischemic heart disease and the effectiveness of treatment. Although there exist a variety of edge detection algorithms in the literature, most of them are human interactive and may provide a poor estimate on coronary lesion. In this paper, we present a new method for automatic identification of arterial borders. The proposed algorithm makes use of mathematical morphology to segment blood vessels which follow a tree structure, based on a priori knowledge of coronary anatomy. Finally, an adaptive tracking strategy is applied to automatically identify 2-D arterial borders along both sides of the vessels. This is accomplished by using an edge detection model at a branching point, matched filters, and the tree structure of the coronary artery. Experimental results show that our approach not only is insensitive to the intensity variations of background and noise, but also can extract the boundary of the coronary artery accurately.