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Expression 11000xl photo scanner

Manufactured by Epson
Sourced in Japan

The Epson Expression 11000XL is a high-performance photo scanner. It features a maximum optical resolution of 11200 dpi and can scan film, slides, and photographic prints up to 12 x 17 inches in size.

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Lab products found in correlation

2 protocols using expression 11000xl photo scanner

1

Standardized Lateral Cephalogram Analysis

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All lateral cephalograms were taken in a standardized position in centric occlusion with the same device (copy of the Broadbent cephalometer). The position was stabilized with ear rods and nasal support to prevent variations in the head position. The focus‐coronal plane distance was 150 cm, and the film‐coronal plane distance was 9 cm,22 which resulted in a constant magnification factor of 6%. The patients were asked to refrain from swallowing during the radiological examination, with tongue posture subsequently assessed on the cephalograms to ascertain that no children swallowed during the radiographic examination. The films were digitally scanned using an Epson Expression 11000XL – Photo Scanner (Epson America, Inc, Long Beach, CA) at a resolution of 300 dpi and 16‐bit grayscale.
After anonymization of all documents with a unique code, the scanned radiographs were traced by two trained and calibrated persons (MHZ and SNP) using OnyxCeph™ (Image Instruments, Chemnitz, Germany). Seven widely used soft tissue measurements were made on the films of 98 siblings at two time points: N‐Sn‐Pog', Cotg‐Sn‐Ls, Pr‐Ls, LbSup_I_Sn‐Pog', LbInf_I_Sn‐Pog', Id‐Li and Sn‐Pr (ǁFH) (Figure 1).
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2

Digitizing Leaf Morphometry Using Scanners

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All samples were scanned using an Epson Expression 11000XL Photo Scanner (Seiko Epson Inc., Tokyo, Japan), with the 600 dpi needed to clearly display leaf shape features was adopted (Fig. S1a). In order to acquire complete representations of leaf shape, sample images were with the help of a commercial graphic editor (AutoCAD, Autodesk Inc. San Rafael, CA, USA) to further clear backgrounds by smoothing and binary coding (Fig. S1b). A great effort was devoted to the image content digitization in this work. Data were obtained by applying a georeferencing framework with ARCGIS v9.3. (ESRI, Redlands, CA, USA) to the vector representation of leaf length and width based on artificial neural network (Fig. S1c) [69 ]. Leaf length was the distance between leaf base to blade tip along leaf midrib, and leaf length was divided into a number of sections per 0.1 or 0.2 cm, while leaf widths were the distances perpendicular to midrib at every leaf length intervals (Fig. 1).
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