The aim of the study was to estimate the prevalence, characteristics, and prognostic factors of interstitial lung disease (ILD) in patients with polymyositis (PM) and dermatomyositis (DM). The medical records of 151 PM/DM patients treated at Chang Gung Memorial Hospital between January, 2000 and June, 2007 were retrospectively reviewed. Thirty of 151 (19.9%) PM/DM patients had developed ILD. Older age at PM/DM onset, anti-Jo-1 antibody, and arthritis/arthralgia were associated with the presence of ILD (p = 0.004, p = 0.008, and p = 0.026, respectively). Anti-Jo-1 was initially excluded from the multivariate analysis because only 80 patients underwent the test. An older age at onset above 45 years (odds ratio 3.28, 95% confidence interval (CI) 1.15-9.34, p = 0.026) and arthritis/arthralgia at onset (odds ratio (OR) 2.57, 95% CI 1.09-6.08, p = 0.032) were the two independent risk factors for developing ILD. If anti-Jo-1 was included in the multivariate analysis (n = 80), then an older age at onset above 45 years (OR 7.30, 95% CI 1.70-31.40, p = 0.008) and anti-Jo-1 positive (OR 7.89, 95% CI 1.18-52.87, p = 0.033) were associated with ILD, while arthritis/arthralgia was no longer significant (OR 2.64, 95% CI 0.70-10.01, p = 0.153). Of the 30 ILD patients, 16 (53.3%) died. The survival time was significantly shorter in ILD patients than in patients without ILD (p < 0.001). Poor survival in ILD patients was associated with male gender (p = 0.039), a Hamman-Rich-like presentation (p = 0.039), and a clinical diagnosis of acute interstitial pneumonia (p = 0.007).

In this paper, a novel human visual system (HVS)-directed neural-network-based adaptive interpolation scheme for natural image is proposed. A fuzzy decision system built from the characteristics of the HVS is proposed to classify pixels of the input image into human perception nonsensitive class and sensitive class. Bilinear interpolation is used to interpolate the nonsensitive regions and a neural network is proposed to interpolate the sensitive regions along edge directions. High-resolution digital images along with supervised learning algorithms are used to automatically train the proposed neural network. Simulation results demonstrate that the proposed new resolution enhancement algorithm can produce a higher visual quality for the interpolated image than the conventional interpolation methods.Index Terms-Fuzzy decision system, human visual system, image interpolation, neural network, resolution enhancement.

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Machine vision is a key technology used in an intelligent transportation system (ITS) to augment human drivers' visual capabilities. For the in-car applications, additional motion components are usually induced by disturbances such as the bumpy ride of the vehicle or the steering effect, and they will affect the image interpretation processes that is required by the motion field (motion vector) detection in the image. In this paper, a novel robust in-car digital image stabilization (DIS) technique is proposed to stably remove the unwanted shaking phenomena in the image sequences captured by in-car video cameras without the influence caused by moving object (front vehicles) in the image or intentional motion of the car, etc. In the motion estimation process, the representative point matching (RPM) module combined with the inverse triangle method is used to determine and extract reliable motion vectors in plain images that lack features or contain a large low-contrast area to increase the robustness in different imaging conditions, since most of the images captured by in-car video cameras include large low-contrast sky areas. An adaptive background evaluation model is developed to deal with irregular images that contain large moving objects (front vehicles) or a low-contrast area above the skyline. In the motion compensation processing, a compensating motion vector (CMV) estimation method with an inner feedbackloop integrator is proposed to stably remove the unwanted shaking phenomena in the images without losing the effective area of the images with a constant motion condition. The proposed DIS technique was applied to the on-road captured video sequences with various irregular conditions for performance demonstrations.Index Terms-Adaptive background-based evaluation function, in-car digital image stabilizer (ICDIS), intelligent transportation system (ITS), inverse triangle method, representative point matching (RPM), smoothness index (SI).