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Abstract: Transmission sources used for image attenuation correction, allowing image quantification, are collimated to reduce scatter. We propose the same effect can be achieved for an uncollimated source by increasing source to patient distance. The aim was to compare planar image performance characteristics and absorbed doses of uncollimated and collimated radioactive printed paper transmission sources. The scatter contribution to the uncollimated 99mTc source data was evaluated for different combinations of detector phantom distance, detector source distance and phantom source distance. Measurements were performed by increasing the Lucite phantom thickness in 1cm steps to 20 cm. Spatial resolution, detection efficiency and entrance absorbed dose rate were measured for the uncollimated and collimated transmission source images. Results derived from the energy spectra, obtained with the uncollimated transmission source indicate that scatter contribution increases with decreasing detector source distance. The scatter component in the uncollimated transmission images (detector source distances≥60cm; phantom source distances≥40cm) was comparable to that obtained with collimated transmission images. Attenuation coefficients obtained compared well (0.168cm−1 vs. 0.171cm−1). The full widths at half maxima differed by less than 0.9mm. The detection efficiency of the uncollimated source was 2.5 times higher than obtained with the collimated source. The entrance absorbed dose obtained from an uncollimated source was 3.75 times larger than that obtained from the collimated source. An uncollimated transmission source (detector source distance≥60cm) results in acceptable image characteristics and presents a low cost, low dose, high efficiency option for transmission imaging. [ABSTRACT FROM AUTHOR]

Abstract: This paper presents a novel algorithm for computing graph edit distance (GED) in image categorization. This algorithm is purely structural, i.e., it needs only connectivity structure of the graph and does not draw on node or edge attributes. There are two major contributions: (1) Introducing edge direction histogram (EDH) to characterize shape features of images. It is shown that GED can be employed as distance of EDHs. This algorithm is completely independent on cost function which is difficult to be defined exactly. (2) Computing distance of EDHs with earth mover distance (EMD) which takes neighborhood bins into account so as to compute distance of EDHs correctly. A set of experiments demonstrate that the newly presented algorithm is available for classifying and clustering images and is immune to the planar rotation of images. Compared with GED from spectral seriation, our algorithm can capture the structure change of graphs better and consume 12.79% time used by the former one. The average classification rate is 5% and average clustering rate is 25% higher than the spectral seriation method. [Copyright &y& Elsevier]