Correction of soil acidity in the subsurface of an oxisol with sandy loam texture under no-tillage

In Rio Grande do Sul State (RS), Southern Brazil, aluminum saturation in many areas under no-till system is high and base saturation low in the 0.10-0.20 m layer (subsurface), which may reduce the grain yield of annual crops. The objective of this study was to evaluate if the occurrence of high aluminum saturation and low base saturation in the subsurface, under a no-till system, represents a restrictive environment for crop production, as well as to evaluate forms of lime incorporation for soil acidity correction in the subsurface. For this purpose, an experiment was carried out with soybean (2005/2006), corn (2006/2007), wheat (2007) and soybean (2007/2008) crops, in a Rhodic Hapludox (USDA, 1999) with sandy loam texture, under no-till for four years in the county of Tupanciretã (RS). The six treatments were: no-tillage with and without lime, plowing with and without lime, and chiseling with and without lime. The values of pH-H2O, aluminum saturation and base saturation were evaluated 24 months after treatment application in the layers 0-0.05; 0.05-0.10; 0.10-0.15; 0.15-0.20 and 0.20-0.30 m. The yields of soybean (2005/2006), corn (2006/2007), wheat (2007) and soybean (2007/2008) were evaluated. Soil acidity in the subsurface did not affect crop yield when the acidity in the layer from 0-0.10 m was at levels for which lime application is not recommended, according to CQFSRS/SC (2004). Lime incorporation through plowing was the most efficient way of correcting acidity at deeper levels.

Phosphorous forms in cultivated indian black earth (anthrosols) of varying texture in the brazilian Amazon

Despite the agricultural importance of Indian Black Earth (IBE) in the Amazon region, there are few studies that report on the relation between soil texture and chemical fertility of IBE. These soils of pre-Colombian origin, with high contents of P, Ca and other nutrients are found across the Amazon valley. IBE profiles were studied to evaluate the total contents of P, its primary chemical forms and the P transformation phases in areas with IBE soils of variable texture and in adjacent reference soils. The soil texture strongly influenced soil fertility, changing in terms of transformation of the primary P forms and, consequently, predominant P forms in IBE. Soils with texture varying between clay and heavy clay had higher total P contents and primary Ca-P forms. Highest P-Al and lowest total P amounts were observed at the site Rio Preto da Eva, where texture varies from sandy loam to sandy clay loam. In the IBE with clay texture the amounts of soluble P, extracted with NH4Cl were highest, although different from Mehlich 1-extractable amounts.

Effects of Exemestane and Tamoxifen Treatment on Bone Texture Analysis Assessed by TBS in Comparison With Bone Mineral Density Assessed by DXA in Women With Breast Cancer.

We performed an analysis of a substudy of the randomized Tamoxifen Exemestane Adjuvant Multinational trial to determine the effects of exemestane (EXE) and tamoxifen (TAM) adjuvant treatment on bone mineral density (BMD) measured by dual-energy X-ray absorptiometry compared with the trabecular bone score, a novel grey-level texture measurement that correlates with 3-dimensional parameters of bone texture in postmenopausal women with hormone receptor-positive breast cancer for the first time. In total, 36 women were randomized to receive TAM (n = 17) or EXE (n = 19). Patients receiving TAM showed a mean increase of BMD in lumbar spine from baseline of 1.0%, 1.5%, and 1.9% and in trabecular bone score of 2.2%, 3.5%, and 3.3% at 6-, 12-, and 24-mo treatment, respectively. Conversely, patients receiving EXE showed a mean decrease from baseline in lumbar spine BMD of -2.3%, -3.6%, and -5.3% and in trabecular bone score of -0.9%, -1.7%, and -2.3% at 6-, 12-, and 24-mo treatment, respectively. Changes in trabecular bone score from baseline at spine were also significantly different between EXE and TAM: p = 0.05, 0.007, and 0.006 at 6, 12, and 24mo, respectively. TAM induced an increase in BMD and bone texture analysis, whereas EXE resulted in decreases. The results were independent from each other.

Multi-Atlas based Segmentation of Head and Neck CT Images using Active Contour

This paper presents the segmentation of bilateral parotid glands in the Head and Neck (H&N) CT images using an active contour based atlas registration. We compare segmentation results from three atlas selection strategies: (i) selection of "single-most-similar" atlas for each image to be segmented, (ii) fusion of segmentation results from multiple atlases using STAPLE, and (iii) fusion of segmentation results using majority voting. Among these three approaches, fusion using majority voting provided the best results. Finally, we present a detailed evaluation on a dataset of eight images (provided as a part of H&N auto segmentation challenge conducted in conjunction with MICCAI-2010 conference) using majority voting strategy.

Effects of Pavement Surface Texture on Noise and Frictional Characteristics HR-281, February 1987

An experimental modification of the transverse groove surface texture of a section of an urban interstate highway was performed by the Iowa Department of Transportation. Transverse groove texturing i s a design feature required by the Federal Highway Administration t o reduce skidding under wet pavement conditions. Adjacent residents claimed the texturing was the cause of especially annoying tonal characteristics within the traffic noise. A research proposal to modify the existing texture pattern by surface grinding and to study the noise and friction effects was approved for funding by the Iowa Highway Research Board. Results i n the form of a comparison between traffic noise before modification and traffic noise immediately after and 15 months after modification indicate that the change in surface texture has lowered overall traffic noise levels by reducing a high frequency component of the traffic noise spectrum. Fraffic testing data show reduced capacity of the roadway to inhibit wet pavement skidding as a result of the surface modification.

A fully-automatic caudate nucleus segmentation of brain MRI: application in volumetric analysis of pediatric attention-deficit/hyperactivity disorder

Background Accurate automatic segmentation of the caudate nucleus in magnetic resonance images (MRI) of the brain is of great interest in the analysis of developmental disorders. Segmentation methods based on a single atlas or on multiple atlases have been shown to suitably localize caudate structure. However, the atlas prior information may not represent the structure of interest correctly. It may therefore be useful to introduce a more flexible technique for accurate segmentations. Method We present Cau-dateCut: a new fully-automatic method of segmenting the caudate nucleus in MRI. CaudateCut combines an atlas-based segmentation strategy with the Graph Cut energy-minimization framework. We adapt the Graph Cut model to make it suitable for segmenting small, low-contrast structures, such as the caudate nucleus, by defining new energy function data and boundary potentials. In particular, we exploit information concerning the intensity and geometry, and we add supervised energies based on contextual brain structures. Furthermore, we reinforce boundary detection using a new multi-scale edgeness measure. Results We apply the novel CaudateCut method to the segmentation of the caudate nucleus to a new set of 39 pediatric attention-deficit/hyperactivity disorder (ADHD) patients and 40 control children, as well as to a public database of 18 subjects. We evaluate the quality of the segmentation using several volumetric and voxel by voxel measures. Our results show improved performance in terms of segmentation compared to state-of-the-art approaches, obtaining a mean overlap of 80.75%. Moreover, we present a quantitative volumetric analysis of caudate abnormalities in pediatric ADHD, the results of which show strong correlation with expert manual analysis. Conclusion CaudateCut generates segmentation results that are comparable to gold-standard segmentations and which are reliable in the analysis of differentiating neuroanatomical abnormalities between healthy controls and pediatric ADHD.

Improved segmentation of deep brain grey matter structures using magnetization transfer (MT) parameter maps.

Basal ganglia and brain stem nuclei are involved in the pathophysiology of various neurological and neuropsychiatric disorders. Currently available structural T1-weighted (T1w) magnetic resonance images do not provide sufficient contrast for reliable automated segmentation of various subcortical grey matter structures. We use a novel, semi-quantitative magnetization transfer (MT) imaging protocol that overcomes limitations in T1w images, which are mainly due to their sensitivity to the high iron content in subcortical grey matter. We demonstrate improved automated segmentation of putamen, pallidum, pulvinar and substantia nigra using MT images. A comparison with segmentation of high-quality T1w images was performed in 49 healthy subjects. Our results show that MT maps are highly suitable for automated segmentation, and so for multi-subject morphometric studies with a focus on subcortical structures.

MBIS: multivariate Bayesian image segmentation tool.

We present MBIS (Multivariate Bayesian Image Segmentation tool), a clustering tool based on the mixture of multivariate normal distributions model. MBIS supports multichannel bias field correction based on a B-spline model. A second methodological novelty is the inclusion of graph-cuts optimization for the stationary anisotropic hidden Markov random field model. Along with MBIS, we release an evaluation framework that contains three different experiments on multi-site data. We first validate the accuracy of segmentation and the estimated bias field for each channel. MBIS outperforms a widely used segmentation tool in a cross-comparison evaluation. The second experiment demonstrates the robustness of results on atlas-free segmentation of two image sets from scan-rescan protocols on 21 healthy subjects. Multivariate segmentation is more replicable than the monospectral counterpart on T1-weighted images. Finally, we provide a third experiment to illustrate how MBIS can be used in a large-scale study of tissue volume change with increasing age in 584 healthy subjects. This last result is meaningful as multivariate segmentation performs robustly without the need for prior knowledge.

Active Contour-Based Segmentation of Head and Neck with Adaptive Atlas Selection

This paper presents automated segmentation of structuresin the Head and Neck (H\&N) region, using an activecontour-based joint registration and segmentation model.A new atlas selection strategy is also used. Segmentationis performed based on the dense deformation fieldcomputed from the registration of selected structures inthe atlas image that have distinct boundaries, onto thepatient's image. This approach results in robustsegmentation of the structures of interest, even in thepresence of tumors, or anatomical differences between theatlas and the patient image. For each patient, an atlasimage is selected from the available atlas-database,based on the similarity metric value, computed afterperforming an affine registration between each image inthe atlas-database and the patient's image. Unlike manyof the previous approaches in the literature, thesimilarity metric is not computed over the entire imageregion; rather, it is computed only in the regions ofsoft tissue structures to be segmented. Qualitative andquantitative evaluation of the results is presented.

Segmentation of Head and Neck Lymph Node Regions for Radiotherapy Planning Using Active Contour-Based Atlas Registration

In this paper, we present the segmentation of the headand neck lymph node regions using a new active contourbased atlas registration model. We propose to segment thelymph node regions without directly including them in theatlas registration process; instead, they are segmentedusing the dense deformation field computed from theregistration of the atlas structures with distinctboundaries. This approach results in robust and accuratesegmentation of the lymph node regions even in thepresence of significant anatomical variations between theatlas-image and the patient's image to be segmented. Wealso present a quantitative evaluation of lymph noderegions segmentation using various statistical as well asgeometrical metrics: sensitivity, specificity, dicesimilarity coefficient and Hausdorff distance. Acomparison of the proposed method with two other state ofthe art methods is presented. The robustness of theproposed method to the atlas selection, in segmenting thelymph node regions, is also evaluated.

Model-based Segmentation and Image Fusion of 3D Computed Tomography and 3D Ultrasound of the Eye for Radiotherapy Planning

For radiotherapy treatment planning of retinoblastoma inchildhood, Computed Tomography (CT) represents thestandard method for tumor volume delineation, despitesome inherent limitations. CT scan is very useful inproviding information on physical density for dosecalculation and morphological volumetric information butpresents a low sensitivity in assessing the tumorviability. On the other hand, 3D ultrasound (US) allows ahigh accurate definition of the tumor volume thanks toits high spatial resolution but it is not currentlyintegrated in the treatment planning but used only fordiagnosis and follow-up. Our ultimate goal is anautomatic segmentation of gross tumor volume (GTV) in the3D US, the segmentation of the organs at risk (OAR) inthe CT and the registration of both. In this paper, wepresent some preliminary results in this direction. Wepresent 3D active contour-based segmentation of the eyeball and the lens in CT images; the presented approachincorporates the prior knowledge of the anatomy by usinga 3D geometrical eye model. The automated segmentationresults are validated by comparing with manualsegmentations. Then, for the fusion of 3D CT and USimages, we present two approaches: (i) landmark-basedtransformation, and (ii) object-based transformation thatmakes use of eye ball contour information on CT and USimages.