Median Filtering in Constant Time Perreault, S.; Hebert, P.
IEEE transactions on image processing,
09/2007, Letnik:
16, Številka:
9
Journal Article
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The median filter is one of the basic building blocks in many image processing situations. However, its use has long been hampered by its algorithmic complexity O(tau) of in the kernel radius. With ...the trend toward larger images and proportionally larger filter kernels, the need for a more efficient median filtering algorithm becomes pressing. In this correspondence, a new, simple, yet much faster, algorithm exhibiting O(1) runtime complexity is described and analyzed. It is compared and benchmarked against previous algorithms. Extensions to higher dimensional or higher precision data and an approximation to a circular kernel are presented, as well.
Distinct molecular subgroups of pediatric medulloblastoma confer important differences in prognosis and therapy. Currently, tissue sampling is the only method to obtain information for ...classification. Our goal was to develop and validate radiomic and machine learning approaches for predicting molecular subgroups of pediatric medulloblastoma.
In this multi-institutional retrospective study, we evaluated MR imaging datasets of 109 pediatric patients with medulloblastoma from 3 children's hospitals from January 2001 to January 2014. A computational framework was developed to extract MR imaging-based radiomic features from tumor segmentations, and we tested 2 predictive models: a double 10-fold cross-validation using a combined dataset consisting of all 3 patient cohorts and a 3-dataset cross-validation, in which training was performed on 2 cohorts and testing was performed on the third independent cohort. We used the Wilcoxon rank sum test for feature selection with assessment of area under the receiver operating characteristic curve to evaluate model performance.
Of 590 MR imaging-derived radiomic features, including intensity-based histograms, tumor edge-sharpness, Gabor features, and local area integral invariant features, extracted from imaging-derived tumor segmentations, tumor edge-sharpness was most useful for predicting sonic hedgehog and group 4 tumors. Receiver operating characteristic analysis revealed superior performance of the double 10-fold cross-validation model for predicting sonic hedgehog, group 3, and group 4 tumors when using combined T1- and T2-weighted images (area under the curve = 0.79, 0.70, and 0.83, respectively). With the independent 3-dataset cross-validation strategy, select radiomic features were predictive of sonic hedgehog (area under the curve = 0.70-0.73) and group 4 (area under the curve = 0.76-0.80) medulloblastoma.
This study provides proof-of-concept results for the application of radiomic and machine learning approaches to a multi-institutional dataset for the prediction of medulloblastoma subgroups.
•Neutron diffraction studies on rare earth metal holmium to 20 GPa and 10 K.•Our studies combines large volume diamond cell with spallation neutron source.•Novel magnetic structures are identified in ...rare earth metals at high pressures.
The magnetic ordering in rare earth metals is well established for ambient pressure crystal structures, however, little is known about the magnetic ordering in their corresponding high-pressure crystalline modifications. Holmium (Ho) was studied in a large-volume diamond anvil cell at the Spallation Neutron Source to high-pressure up to 20 GPa and to low-temperature to 10 K. We have conducted two independent high-pressure low-temperature experiments under non-hydrostatic and quasi-hydrostatic pressure conditions respectively. The ambient pressure hexagonal close packed (hcp) phase of holmium shows two magnetic transitions below 10 GPa one to an incommensurate Antiferromagnetic (AFM) phase and another to a conical-Ferromagnetic (c-FM) phase. In contrast, alpha-Samarium-type (α-Sm) phase above 10 GPa and the double hexagonal close packed (dhcp) phase above 19 GPa show only one FM transition marked by the appearance of a magnetic peak at 3 Å and the concurrent enhancement of nuclear peaks below 30 K. These new transitions observed by neutron diffraction can be accounted by a commensurate superlattice formation along c-axis in both the α-Sm-type phase and the dhcp phase.
Recently identified molecular subgroups of medulloblastoma have shown potential for improved risk stratification. We hypothesized that distinct MR imaging features can predict these subgroups.
All ...patients with a diagnosis of medulloblastoma at one institution, with both pretherapy MR imaging and surgical tissue, served as the discovery cohort (n = 47). MR imaging features were assessed by 3 blinded neuroradiologists. NanoString-based assay of tumor tissues was conducted to classify the tumors into the 4 established molecular subgroups (wingless, sonic hedgehog, group 3, and group 4). A second pediatric medulloblastoma cohort (n = 52) from an independent institution was used for validation of the MR imaging features predictive of the molecular subtypes.
Logistic regression analysis within the discovery cohort revealed tumor location (P < .001) and enhancement pattern (P = .001) to be significant predictors of medulloblastoma subgroups. Stereospecific computational analyses confirmed that group 3 and 4 tumors predominated within the midline fourth ventricle (100%, P = .007), wingless tumors were localized to the cerebellar peduncle/cerebellopontine angle cistern with a positive predictive value of 100% (95% CI, 30%-100%), and sonic hedgehog tumors arose in the cerebellar hemispheres with a positive predictive value of 100% (95% CI, 59%-100%). Midline group 4 tumors presented with minimal/no enhancement with a positive predictive value of 91% (95% CI, 59%-98%). When we used the MR imaging feature-based regression model, 66% of medulloblastomas were correctly predicted in the discovery cohort, and 65%, in the validation cohort.
Tumor location and enhancement pattern were predictive of molecular subgroups of pediatric medulloblastoma and may potentially serve as a surrogate for genomic testing.
We report high-pressure structural studies (52 GPa) at room temperature combined with magnetic (
M(T
):1GPa and electrical resistivity (
ρ(T)
:0-21GPa) measurements down to 2 K on Fe
0.99
Ni
0.01
Se
...0.5
Te
0.5
superconductor using designer diamond anvils (D-DAC) pressure cell. The
M(T
) data show huge enhancement of superconducting transition temperature (
T
c
) from 8.62 to 14.8 K (1 GPa) and
ρ(T)
reveal maximum enhancement of
T
c
~ 30.5 K at 3 GPa (d
T
c
/d
P
= ~ 7.19 K/GPa) followed by moderate decrease of
T
c
up to 19 K at 7.5 GPa, and further increasing pressure
T
c
gets vanished at 10.6 GPa. The reduction of
T
c
due to the occurrence of structural transition that is likely associated with possible reduction of charge carriers in the density of states in Fermi surface. The high-pressure XRD measurement shows tetragonal phase exists up to 7 GPa, followed by mixed phase which is visible between 7.5 GPa and 14.5 GPa. The structural transformation occurs at 15 GPa from tetragonal (
P
4
/nmm
) to NiAs -type hexagonal phase (
P6
3
/mmc
) and it is stable up to 52 GPa, confirmed from the equation of state (EOS) and it can be correlated with variation of
T
c
under pressure for Fe
0.99
Ni
0.01
Se
0.5
Te
0.5
chalcogenide superconductors.
After allogeneic stem cell transplant, severe grade III-IV gastrointestinal (GI) acute GvHD is associated with significant morbidity and mortality, and generally results in poor outcomes. Salvage ...therapy for patients who fail steroid therapy is not well defined in the literature. In the current retrospective study, we reviewed our experience with the combination of basiliximab and infliximab in 21 patients with severe, grade III-IV GI acute GvHD of whom 16 met the definition for steroid-refractory disease. The overall response rate was 76%, with 43% CR at a median time of 21 days after beginning treatment. The survival at 1 year was 24%, with most deaths due to complications from GvHD and recurrence of primary disease. All five of the long-term survivors have chronic GvHD. On the basis of a review of the literature, this regimen does not seem to be significantly more effective than other strategies for severe GI GvHD and seems to be worse than the results reported for basiliximab alone. Future studies of single-agent basiliximab and newer agents are required.
Soil moisture and temperature conditions play an important role in plant growth. Modeling soil moisture and temperature is useful for predicting crop yields and risks. In this study, the Soil ...Temperature and Moisture Model (STM2) was used to predict soil moisture and temperature at several depths: 15, 30, 45, and 60 cm for soil moisture and 10, 25, and 50 cm for soil temperature. The objective of this study was to assess the prediction efficiency of STM2 according to soil depth and phenology. The STM2 uses soil texture data along with average daily weather data (maximum and minimum air temperature and precipitation) as inputs. During the 2008 and 2010 growing seasons, soil moisture and temperature data were measured using monitoring stations located in four agricultural fields in southern Quebec. These fields represent the range of soil texture diversity found in this agricultural area: gravelly, sandy, loamy, and clayey soils. The measurements were used to validate STM2 predictions. The overall performance of soil temperature prediction was better than that for soil moisture. Estimation quality decreased with increasing depth and was higher during the first and third phenological periods for soil moisture. Good performances were observed for the sandy and loamy soils, moderate for the clayey soil, and mostly weak for the gravelly soil. A sensitivity analysis was performed on STM2 data inputs. For soil moisture, bulk density, saturated hydraulic conductivity, and weather data have a great impact while for soil temperature, only weather data have an impact on model estimates. This study showed that STM2 can be used in combination with soil and climatic data sets to reliably predict surface soil moisture and temperature variations in southern Quebec.
This study aims to evaluate the effectiveness of primary care interventions to improve the detection and treatment of osteoporosis. Eight electronic databases and six gray literature sources were ...searched. Randomized controlled trials, controlled clinical trials, quasi-randomized trials, controlled before–after studies, and interrupted time series written in English or French from 1985 to 2009 were considered. Eligible studies had to include patients at risk (women ≥ 65 years, men ≥ 70 years, and men/women ≥ 50 years with at least one major risk factor for osteoporosis) or at high risk (men/women using oral glucocorticoids or with previous fragility fractures) for osteoporosis and fractures. Outcomes included bone mineral density (BMD) testing, osteoporosis treatment initiation, and fractures. Data were pooled using a random effects model when applicable. Thirteen studies were included. The majority were multifaceted and involved patient educational material, physician notification, and/or physician education. Absolute differences in the incidence of BMD testing ranged from 22% to 51% for high-risk patients only and from 4% to 18% for both at-risk and high-risk patients. Absolute differences in the incidence of osteoporosis treatment initiation ranged from 18% to 29% for high-risk patients only and from 2% to 4% for at-risk and high-risk patients. Pooling the results of six trials showed an increased incidence of osteoporosis treatment initiation (risk difference (RD) = 20%; 95% CI: 7–33%) and of BMD testing and/or osteoporosis treatment initiation (RD = 40%; 95% CI: 32–48%) for high-risk patients following intervention. Multifaceted interventions targeting high-risk patients and their primary care providers may improve the management of osteoporosis, but improvements are often clinically modest.
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