Search

Contains fulltext : 71531.pdf (Publisher’s version ) (Closed access) BACKGROUND AND PURPOSE: Functional outcome in patients with minor head injury with neurocranial traumatic findings on CT is largely unknown. We hypothesized that certain CT findings may be predictive of poor functional outcome. Materials and METHODS: All patients from the CT in Head Injury Patients (CHIP) study with neurocranial traumatic CT findings were included. The CHIP study is a prospective, multicenter study of consecutive patients, > or =16 years of age, presenting within 24 hours of blunt head injury, with a Glasgow Coma Scale (GCS) score of 13-14 or a GCS score of 15 and a risk factor. Primary outcome was functional outcome according to the Glasgow Outcome Scale (GOS). Other outcome measures were the modified Rankin Scale (mRS), the Barthel Index (BI), and number and severity of postconcussive symptoms. The association between CT findings and outcome was assessed by using univariable and multivariable regression analysis. RESULTS: GOS was assessed in 237/312 patients (76%) at an average of 15 months after injury. There was full recovery in 150 patients (63%), moderate disability in 70 (30%), severe disability in 7 (3.0%), and death in 10 (4.2%). Outcome according to the mRS and BI was also favorable in most patients, but 82% of patients had postconcussive symptoms. Evidence of parenchymal damage was the only independent predictor of poor functional outcome (odds ratio = 1.89, P = .022). CONCLUSION: Patients with neurocranial complications after minor head injury generally make a good functional recovery, but postconcussive symptoms may persist. Evidence of parenchymal damage on CT was predictive of poor functional outcome.

Contains fulltext : 53711.pdf (Publisher’s version ) (Closed access) OBJECTIVE: A history of loss of consciousness (LOC) or post-traumatic amnesia (PTA) is commonly considered a prerequisite for minor head injury (MHI), although neurocranial complications also occur when LOC/PTA are absent, particularly in the presence of other risk factors. The purpose of this study was to evaluate whether known risk factors for complications after MHI in the absence of LOC/PTA have the same predictive value as when LOC/PTA are present. METHODS: A prospective multicentre study was performed in four university hospitals between February 2002 and August 2004 of consecutive blunt head injury patients (> or = 16 years) presenting with a normal level of consciousness and a risk factor. Outcome measures were any neurocranial traumatic CT finding and neurosurgical intervention. Common odds ratios (OR) were estimated for each of the risk factors and tested for homogeneity. RESULTS: 2462 patients were included: 1708 with and 754 without LOC/PTA. Neurocranial traumatic findings on CT were present in 7.5% and were more common when LOC/PTA was present (8.7%). Neurosurgical intervention was required in 0.4%, irrespective of the presence of LOC/PTA. ORs were comparable across the two subgroups (p>0.05), except for clinical evidence of a skull fracture, with high ORs both when LOC/PTA was present (OR = 37, 95% CI 17 to 80) or absent (OR = 6.9, 95% CI 1.8 to 27). LOC and PTA had significant ORs of 1.9 (95% CI 1.0 to 2.7) and 1.7 (95% CI 1.3 to 2.3), respectively. CONCLUSION: Known risk factors have comparable ORs in MHI patients with or without LOC or PTA. MHI patients without LOC or PTA need to be explicitly considered in clinical guidelines.

Blocking artifacts may appear in block based digital image and video coding systems like JPEG and MPEG, especially when compression is done at a low bit-rate. The blocking artifacts can degrade the perceived image quality. In this paper, a method for reducing the blocking artifacts in the decoded image using adaptive filters is proposed. The selection of the proper filter is based on the local image pattern and/or the relative pixel position in the coding block. The results show a significant image quality improvement. To evaluate the methods, both Mean Square Error (MSE) scores and Block-edge Impairment Metric (BIM) are given for objective comparison. Screen shots are also shown to demonstrate the subjective quality.

We propose a new class of non-linear filters for image interpolation, content-adaptive neural filters using pixel classification. In the proposed method, the neural filter coefficients depend on the local structure of the image data, which can be classified into classes based on the luminance pattern in the filter aperture. The optimal coefficients of the neural filters are obtained for each class by supervised learning which employs the original high resolution and the down-scaled images as the training set. As the proposed filters are considered an extension of the content-adaptive linear filters, we perform the evaluation and analysis of the proposed filters versus the content-adaptive linear filters. The results suggest that the proposed algorithm demonstrates more robust estimation in image interpolation and gives an additional improvement in the interpolated image quality. Since the number of classes using the pixel classification increases exponentially with the aperture size we also introduce a method to reduce the number of the classes.

In a Standard Definition (SD) Television system, the Y:U:V video format 4:2:2 with chrominance sub-sampling is widely used. With the advent of High Definition (HD) television, the 4:4:4 format is required for high-performance TV. High-quality up-sampling methods have been developed to perform a resolution conversion from Standard Definition (SD) signal to HD signal. Although these algorithms have been designed for spatial scaling of luminance, they may be adapted and used to up-sample the low-resolution components U,V (4:2:2) to a high-resolution UV-colour format (4:4:4). In this paper, a content-adaptive up-scaling method for chrominance is proposed, with interpolation filters that adapt to the local structure of both luminance and chrominance data. Optimal filters were computed from a large video data set in different colour formats, such that original high-resolution data in a 4:4:4 format was reconstructed from low-resolution colour data, on the basis of the Least Mean Square (LMS) criterion. By combining edge information of both luminance and chrominance, the edge in the chrominance signal can be detected more accurately, thus exploiting the wider bandwidth of the luminance signal.

Because the inner ear is not organized spatially but tonotopically, sound localization relies on the neural processing of implicit acoustic cues. To determine a sound’s position, the brain must learn and calibrate these individual cues, using accurate spatial feedback from other sensorimotor systems. Experimental evidence for such a system has been demonstrated in barn owls, but not in humans. In this study, the existence of ongoing spatial calibration in the adult human auditory system is demonstrated. The spectral elevation cues of human subjects were disrupted by modifying their outer ears (pinnae) with molds. Although localization of sound elevation was dramatically degraded immediately after the modification, accurate performance was steadily reacquired. Interestingly, learning the new spectral cues did not interfere with the neural representation of the original cues, as subjects could localize sounds with both normal and modified pinnae.

Simple probabilistic neural models can be used to study the information processing occurring in the brain. Suitable models have to reproduce complex Interspike Histograms (ISHs) observed experimentally, but have to be simple enough to allow theoretical analysis. The simple probabilistic integrate and fire model we present in this paper can be used to identify the origin of peaks appearing in complex multimodal ISHs.