Local noise estimation in low-dose chest CT images.

PURPOSE: Image noise in computed tomography (CT) images may have significant local variation due to tissue properties, dose, and location of the X-ray source. We developed and tested an automated tissue-based estimator method for estimating local noise in CT images. METHOD: An automated TBE method for estimating the local noise in CT image in 3 steps was developed: (1) Partition the image into homogeneous and transition regions, (2) For each pixel in the homogeneous regions, compute the standard deviation in a 15 x 15 x 1 voxel local region using only pixels from the same homogeneous region, and (3) Interpolate the noise estimate from the homogeneous regions in the transition regions. Noise-aware fat segmentation was implemented. Experiments were conducted on the anthropomorphic phantom and in vivo low-dose chest CT scans to validate the TBE, characterize the magnitude of local noise variation, and determine the sensitivity of noise estimates to the size of the region in which noise is computed. The TBE was tested on all scans from the Early Lung Cancer Action Program public database. The TBE was evaluated quantitatively on the phantom data and qualitatively on the in vivo data. RESULTS: The results show that noise can vary locally by over 200 Hounsfield units on low-dose in vivo chest CT scans and that the TBE can characterize these noise variations within 5 %. The new fat segmentation algorithm successfully improved segmentation on all 50 scans tested. CONCLUSION: The TBE provides a means to estimate noise for image quality monitoring, optimization of denoising algorithms, and improvement of segmentation algorithms. The TBE was shown to accurately characterize the large local noise variations that occur due to changes in material, dose, and X-ray source location.

A public image database to support research in computer aided diagnosis.

The Public Lung Database to address drug response (PLD) has been developed to support research in computer aided diagnosis (CAD). Originally established for applications involving the characterization of pulmonary nodules, the PLD has been augmented to provide initial datasets for CAD research of other diseases. In general, the best performance for a CAD system is achieved when it is trained with a large amount of well documented data. Such training databases are very expensive to create and their lack of general availability limits the targets that can be considered for CAD applications and hampers development of the CAD field. The approach taken with the PLD has been to make available small datasets together with both manual and automated documentation. Furthermore, datasets with special properties are provided either to span the range of task complexity or to provide small change repeat images for direct calibration and evaluation of CAD systems. This resource offers a starting point for other research groups wishing to pursue CAD research in new directions. It also provides an on-line reference for better defining the issues relating to specific CAD tasks.

Production and genetic characterization of near-isogenic lines in the bread-wheat cultivar Alpe.

Two biotypes of the bread-wheat cultivar Alpe were shown to possess contrasting alleles at each of the glutenin (Glu-B1, Glu-D1, Glu-B3 and Glu-D3) and gliadin (Gli-B1 and Gli-D1) loci on chromosomes 1B and 1D. Fourteen near-isogenic lines (NILs) were produced by crossing these biotypes and used to determine the genetic control of both low-molecular-weight (LMW) glutenin subunits and gliadins by means of one-dimensional or two-dimensional electrophoresis. Genes coding for the B, C and D groups of EMW subunits were found to be inherited in clusters tightly linked with those controlling gliadins. Southern-blot analysis of total genomic DNAs hybridized to a γ-gliadin-specific cDNA clone revealed that seven NILs lack both the Gli-D1 and Glu-D3 loci on chromosome 1D. Segregation data indicated that these "null" alleles are normally inherited. Comparison of the "null" NILs with those possessing allele b at the Glu-D3 locus showed one B subunit, seven C subunits and two D subunits, as fractionated by two-dimensional A-PAGExSDS-PAGE, to be encoded by this allele. Alleles b and k at Glu-B3 were found to code for two C subunits plus eight and six B subunits respectively, whereas alleles b and k at Gli-B1 each controlled the synthesis of two ß-gliadins, one γ and two ω-gliadins. The novel Gli-B5 locus coding for two ω-gliadins was shown to recombine with the Gli-B1 locus on chromosome 1B. The two-dimensional map of glutenin subunits showed α-gliadins encoded at the Gli-A2 locus on chromosome 6A. The use of Alpe NILs in the study of the individual and combined effects of glutenin subunits on dough properties is discussed.

Genetics of gliadins coded by the group 1 chromosomes in the high-quality bread wheat cultivar Neepawa.

The inheritance and biochemical properties of gliadins controlled by the group 1 chromosomes of the high-quality bread wheat cultivar Neepawa were studied in the progeny of the cross Neepawa x Costantino by six different electrophoretic procedures. Chromosome 1B of Neepawa contains two gliadin loci, one (Gli-B1) coding for at least six ω- or γ-gliadins, the other (Gli-B3) controlling the synthesis of gliadin N6 only. The map distance between these loci was calculated as 22.1 cM. Amongst the chromosome 1A gliadins, three proteins are encoded at the Gli-A1 locus whereas polypeptides N14-N15-N16 are controlled by a remote locus which recombines with Gli-A1. Six other gliadins are controlled by a gene cluster at Gli-D1 on chromosome 1D. Canadian wheat cultivars sharing the Gli-B1 allele of Neepawa were found to differ in the presence or absence of gliadin N6. The electrophoretic mobilities of proteins N6 and N14-N15-N16 were unaffected by the addition of a reducing agent during two-dimensional sodium dodecyl sulphate polyacrylamid-gel electrophoresis, suggesting the absence of intra-chain disulphide bonds in their structure.