Does anticholinergics drug burden relate to global neuro-disability outcome measures and length of hospital stay?

Primary objective: To assess the relationship between disability, length of stay (LOS) and anticholinergic burden (ACB) with people following acquired brain or spinal cord injury. Research design: A retrospective case note review assessed total rehabilitation unit admission. Methods and procedures: Assessment of 52 consecutive patients with acquired brain/spinal injury and neuropathy in an in-patient neuro-rehabilitation unit of a UK university hospital. Data analysed included: Northwick Park Dependency Score (NPDS), Rehabilitation complexity Scale (RCS), Functional Independence Measure and Functional Assessment Measure FIM-FAM (UK version 2.2), LOS and ACB. Outcome was different in RCS, NPDS and FIM-FAM between admission and discharge. Main outcomes and results: A positive change was reported in ACB results in a positive change in NPDS, with no significant effect on FIM-FAM, either Motor or Cognitive, or on the RCS. Change in ACB correlated to the length of hospital stay (regression correlation = −6.64; SE = 3.89). There was a significant harmful impact of increase in ACB score during hospital stay, from low to high ACB on NPDS (OR = 9.65; 95% CI = 1.36–68.64) and FIM-FAM Total scores (OR = 0.03; 95% CI = 0.002–0.35). Conclusions: There was a statistically significant correlation of ACB and neuro-disability measures and LOS amongst this patient cohort

Inspecting Colour Tonality on Textured Surfaces

We present a multidimensional histogram method to inspect tonality on colour textured surfaces, e.g. ceramic tiles. Comparison in the noise dominated chromatic channels is error prone. We perform vectorordered colour smoothing and generate a PCA-based reconstruction of a query tile based on a reference tile eigenspace. Histograms of local feature vectors are then compared for tonality defect detection. The proposed method is compared and evaluated on a data set with groundtruth

Ceramic Tile Inspection for Colour and Structural Defects

The ceramic tiles manufacturing process has now been completely automated with the exception of the final stage of production concerned with visual inspection. This paper is concerned with the problem of automatic inspection of ceramic tiles using computer vision. It must be noted that the detection of defects in textured surfaces is an important area of automatic industrial inspection that has been largely overlooked by the recent wave of research in machine vision applications. Initially, We outline the benefits to the tile manufacturing industry. This is followed by a categorisation of typical tile defects. Next, we review a number of techniques recently developed to detect various kinds of defects in plain and textured tiles. The techniques range from pin hole and crack detectors for plain tiles based on a set of separable line filters, through textured tile crack detector based on the Wigner distribution and a novel cojoint spatial-spatial frequency representation of texture, to a..