Mixture Of Principal Components Research Articles

The anti-inflammation effect of Baige capsule and its principal components mixture in MCAO rats

Background: Baige (BG) is a compound Chinese herbal preparation, constituted of different position extracts (ethanol extracts from Pueraria lobate and SFE-CO2 extracts from Radix Angelicae dahuricae) of P. lobata and A. dahurica to treat the brain injury in patients.Aim: The goal of this study was to identify the neuroprotective properties of BG and its principal component mixture (PCM) and verify whether the material basis for BG is its PCM.Methods: Middle cerebral artery occlusion (MCAO) was operated on male Sprague-Dawley rat for 2 h, different doses of BG or PCM or vehicle were gavaged after 3 h of MCAO. Rats were sacrificed after 30 days treatment. Blood serum inflammation factors and NGF were detected by ELISA.Results: After 30 days of treatment, both BG and PCM interventions reduced the infarct volume, modified neurological severity score (mNSS) in rats, declined IL-1β and IL-6 levels in the serum, increased NGF level in the serum and recovered the number of Nissl body in injured brain.Conclusions: Both BG and PCM exert equivalent levels of recovery effect in MCAO on rats; and PCM is the material foundation of BG. This recovery effect is associated with inflammatory inhibition and NGF production.

Model-based error concealment for wireless video

We introduce model-based schemes for error concealment of networked video. We build appearance models for specific objects in the scene and use these models to replenish any lost information. Due to the models being designed specifically for the object, they are able to capture the statistical variations in the object appearance more effectively, thereby leading to better error-concealment performance. We examine statistical modeling techniques from the literature and introduce a new efficient and accurate linear model for data representation, called the mixture of principal components (MPC), and use these models for error concealment. We simulate lossy network conditions and show that these model-based concealment schemes outperform traditional concealment schemes across a variety of loss probabilities and bit rates for the coded video.

Image Compression Using KLT, Wavelets and an Adaptive Mixture of Principal Components Model

In this paper, we present preliminary results comparing the nature of the errors introduced by the mixture of principal components (MPC) model with a wavelet transform and the Karhunen Loeve transform (KLT) for the lossy compression of brain magnetic resonance (MR) images. MPC, wavelets and KLT were applied to image blocks in a block transform coding scheme. The MPC model partitions the space of image blocks into a set of disjoint classes and computes a separate KLT for each class. In our experiments, though both the wavelet transform and KLT obtained a higher peak signal to noise ratio (PSNR) than MPC, according to radiologists, MPC preserved the texture and boundaries of gray and white matter better than the wavelet transform or KLT.

Compression of SAR images using KLT, VQ and mixture of principal components

Owing to the very high-resolution nature of synthetic aperture radar (SAR), any use of image compression on such data must minimise the degree of distortion introduced. Two common methods for compressing images are linear block transform coding, such as the Karhunen–Loeve transform, and vector quantisation. However, the implicit assumption of stationarity for these techniques is far from valid for most images. As a result, they introduce distortions in regions within an image that are significantly different from its global statistics. A new approach to data representation, referred to as the mixture of principal components (MPC), is proposed which combines advantages of both transform coding and vector quantisation. Like vector quantisation, it partitions the input space into a number of non-overlapping regions, and each region is represented by a number of basis vectors in the manner of transform coding. When applied to the compression of SAR images, the MPC method introduces less distortion for a given compression ratio compared with the other two techniques. For example, at 0.25 bits per pixel (a compression ratio of 64:1), the degree of distortion is reduced by close to 3 dB. When the resulting images are compared, the visibility of the distortion is also reduced when the new method is used.

Compression of digital chest radiographs with a mixture of principal components neural network: evaluation of performance.

The performance of a new, neural network-based image compression method was evaluated on digital radiographs for use in an educational environment. The network uses a mixture of principal components (MPC) representation to effect optimally adaptive transform coding of an image and has significant computational advantages over other techniques. Nine representative digital chest radiographs were compressed 10:1, 20:1, 30:1, and 40:1 with the MPC method. The five versions of each image, including the original, were shown simultaneously, in random order, to each of seven radiologists, who rated each one on a five-point scale for image quality and visibility of pathologic conditions. One radiologist also ranked four versions of each of the nine images in terms of the severity of distortion: The four versions represented 30:1 and 40:1 compression with the MPC method and with the classic Karhunen-Loève transform (KLT). Only for the images compressed 40:1 with the MPC method were there any unacceptable ratings. Nevertheless, the images compressed 40:1 received a top score in 26%-33% of the evaluations. Images compressed with the MPC method were rated better than or as good as images compressed with the KLT technique 17 of 18 times. Four of nine times, images compressed 40:1 with the MPC method were rated as good as or better than images compressed 30:1 with the KLT technique.