Content Based Image Retrieval Using Low Level Features of Automatically Extracted Regions of Interest

This paper proposes a region based image retrieval system using the local colour and texture features of image sub regions. The regions of interest (ROI) are roughly identified by segmenting the image into fixed partitions, finding the edge map and applying morphological dilation. The colour and texture features of the ROIs are computed from the histograms of the quantized HSV colour space and Gray Level co- occurrence matrix (GLCM) respectively. Each ROI of the query image is compared with same number of ROIs of the target image that are arranged in the descending order of white pixel density in the regions, using Euclidean distance measure for similarity computation. Preliminary experimental results show that the proposed method provides better retrieving result than retrieval using some of the existing methods.

On the efficacy of texture analysis for crowd monitoring

The goal of this work is to assess the efficacy of texture measures for estimating levels of crowd densities ill images. This estimation is crucial for the problem of crowd monitoring. and control. The assessment is carried out oil a set of nearly 300 real images captured from Liverpool Street Train Station. London, UK using texture measures extracted from the images through the following four different methods: gray level dependence matrices, straight lille segments. Fourier analysis. and fractal dimensions. The estimations of dowel densities are given in terms of the classification of the input images ill five classes of densities (very low, low. moderate. high and very high). Three types of classifiers are used: neural (implemented according to the Kohonen model). Bayesian. and an approach based on fitting functions. The results obtained by these three classifiers. using the four texture measures. allowed the conclusion that, for the problem of crowd density estimation. texture analysis is very effective.

In vitro study on radiographic gray levels of biomaterials using two digital image methods

PURPOSE: To compare the direct and indirect radiographic methods for assessing the gray levels of biomaterials employing the Digora for Windows and the Adobe Photoshop CS2 systems. METHODS: Specimens of biomaterials were made following manusfacturer's instructions and placed on phosphor storage plates (PSP) and on radiographic film for subsequent gray level assessment using the direct and indirect radiographic method, respectively. The radiographic density of each biomaterial was analyzed using Adobe Photoshop CS2 and Digora for Windows software. RESULTS: The distribution of gray levels found using the direct and indirect methods suggests that higher exposure times are correlated to lower reproducibility rates between groups. CONCLUSION: The indirect method is a feasible alternative to the direct method in assessing the radiographic gray levels of biomaterials, insofar as significant reproducibility was observed between groups for the exposure times of 0.2 to 0.5 seconds.

Wolf habitat selection at the territory level : seasonal and interannual variation and influence on reproductive success

Habitat selection has been one of the main research topics in ecology for decades. Nevertheless, many aspects of habitat selection still need to be explored. In particular, previous studies have overlooked the importance of temporal variation in habitat selection and the value of including data on reproductive success in order to describe the best quality habitat for a species. We used data collected from radiocollared wolves in Yellowstone National Park (USA), between 1996 and 2008, to describe wolf habitat selection. In particular, we aimed to identify i) seasonal differences in wolf habitat selection, ii) factors influencing interannual variation in habitat selection, and iii) the effect of habitat selection on wolf reproductive success. We used probability density functions to describe wolf habitat use and habitat coverages to represent the habitat available to wolves. We used regression analysis to connect habitat use with habitat characteristics and habitat selection with reproductive success. Our most relevant result was discovering strong interannual variability in wolf habitat selection. This variability was in part explained by pack identity and differences in litter size and leadership of a pack between two years (summer) and in pack size and precipitation (winter). We also detected some seasonal differences. Wolves selected open habitats, intermediate elevations, intermediate distances from roads, and avoided steep slopes in late winter. They selected areas close to roads and avoided steep slopes in summer. In early winter, wolves selected wetlands, herbaceous and shrub vegetation types, and areas at intermediate elevation and distance from roads. Surprisingly, the habitat characteristics selected by wolves were not useful in predicting reproductive success. We hypothesize that interannual variability in wolf habitat selection may be too strong to detect effects on reproductive success. Moreover, prey availability and competitor pressure may also have an influence on wolf reproductive success, which we did not assess. This project demonstrated how important temporal variation is in shaping patterns of habitat selection. We still believe in the value of running long-term studies, but the effect of temporal variation should always be taken into account.

Using data mining techniques to support breast cancer diagnosis

More than ever, there is an increase of the number of decision support methods and computer aided diagnostic systems applied to various areas of medicine. In breast cancer research, many works have been done in order to reduce false-positives when used as a double reading method. In this study, we aimed to present a set of data mining techniques that were applied to approach a decision support system in the area of breast cancer diagnosis. This method is geared to assist clinical practice in identifying mammographic findings such as microcalcifications, masses and even normal tissues, in order to avoid misdiagnosis. In this work a reliable database was used, with 410 images from about 115 patients, containing previous reviews performed by radiologists as microcalcifications, masses and also normal tissue findings. Throughout this work, two feature extraction techniques were used: the gray level co-occurrence matrix and the gray level run length matrix. For classification purposes, we considered various scenarios according to different distinct patterns of injuries and several classifiers in order to distinguish the best performance in each case described. The many classifiers used were Naïve Bayes, Support Vector Machines, k-nearest Neighbors and Decision Trees (J48 and Random Forests). The results in distinguishing mammographic findings revealed great percentages of PPV and very good accuracy values. Furthermore, it also presented other related results of classification of breast density and BI-RADS® scale. The best predictive method found for all tested groups was the Random Forest classifier, and the best performance has been achieved through the distinction of microcalcifications. The conclusions based on the several tested scenarios represent a new perspective in breast cancer diagnosis using data mining techniques.

Trabecular Bone Score: A Noninvasive Analytical Method Based Upon the DXA Image.

The trabecular bone score (TBS) is a gray-level textural metric that can be extracted from the two-dimensional lumbar spine dual-energy X-ray absorptiometry (DXA) image. TBS is related to bone microarchitecture and provides skeletal information that is not captured from the standard bone mineral density (BMD) measurement. Based on experimental variograms of the projected DXA image, TBS has the potential to discern differences between DXA scans that show similar BMD measurements. An elevated TBS value correlates with better skeletal microstructure; a low TBS value correlates with weaker skeletal microstructure. Lumbar spine TBS has been evaluated in cross-sectional and longitudinal studies. The following conclusions are based upon publications reviewed in this article: 1) TBS gives lower values in postmenopausal women and in men with previous fragility fractures than their nonfractured counterparts; 2) TBS is complementary to data available by lumbar spine DXA measurements; 3) TBS results are lower in women who have sustained a fragility fracture but in whom DXA does not indicate osteoporosis or even osteopenia; 4) TBS predicts fracture risk as well as lumbar spine BMD measurements in postmenopausal women; 5) efficacious therapies for osteoporosis differ in the extent to which they influence the TBS; 6) TBS is associated with fracture risk in individuals with conditions related to reduced bone mass or bone quality. Based on these data, lumbar spine TBS holds promise as an emerging technology that could well become a valuable clinical tool in the diagnosis of osteoporosis and in fracture risk assessment. © 2014 American Society for Bone and Mineral Research.

Trabecular bone score: a noninvasive analytical method based upon the DXA image.

The trabecular bone score (TBS) is a gray-level textural metric that can be extracted from the two-dimensional lumbar spine dual-energy X-ray absorptiometry (DXA) image. TBS is related to bone microarchitecture and provides skeletal information that is not captured from the standard bone mineral density (BMD) measurement. Based on experimental variograms of the projected DXA image, TBS has the potential to discern differences between DXA scans that show similar BMD measurements. An elevated TBS value correlates with better skeletal microstructure; a low TBS value correlates with weaker skeletal microstructure. Lumbar spine TBS has been evaluated in cross-sectional and longitudinal studies. The following conclusions are based upon publications reviewed in this article: 1) TBS gives lower values in postmenopausal women and in men with previous fragility fractures than their nonfractured counterparts; 2) TBS is complementary to data available by lumbar spine DXA measurements; 3) TBS results are lower in women who have sustained a fragility fracture but in whom DXA does not indicate osteoporosis or even osteopenia; 4) TBS predicts fracture risk as well as lumbar spine BMD measurements in postmenopausal women; 5) efficacious therapies for osteoporosis differ in the extent to which they influence the TBS; 6) TBS is associated with fracture risk in individuals with conditions related to reduced bone mass or bone quality. Based on these data, lumbar spine TBS holds promise as an emerging technology that could well become a valuable clinical tool in the diagnosis of osteoporosis and in fracture risk assessment.

Correlations between trabecular bone score, measured using anteroposterior dual-energy x-ray absorptiometry acquisition, and 3-dimensional parameters of bone microarchitecture: an experimental study on human cadaver vertebrae.

Developing a novel technique for the efficient, noninvasive clinical evaluation of bone microarchitecture remains both crucial and challenging. The trabecular bone score (TBS) is a new gray-level texture measurement that is applicable to dual-energy X-ray absorptiometry (DXA) images. Significant correlations between TBS and standard 3-dimensional (3D) parameters of bone microarchitecture have been obtained using a numerical simulation approach. The main objective of this study was to empirically evaluate such correlations in anteroposterior spine DXA images. Thirty dried human cadaver vertebrae were evaluated. Micro-computed tomography acquisitions of the bone pieces were obtained at an isotropic resolution of 93μm. Standard parameters of bone microarchitecture were evaluated in a defined region within the vertebral body, excluding cortical bone. The bone pieces were measured on a Prodigy DXA system (GE Medical-Lunar, Madison, WI), using a custom-made positioning device and experimental setup. Significant correlations were detected between TBS and 3D parameters of bone microarchitecture, mostly independent of any correlation between TBS and bone mineral density (BMD). The greatest correlation was between TBS and connectivity density, with TBS explaining roughly 67.2% of the variance. Based on multivariate linear regression modeling, we have established a model to allow for the interpretation of the relationship between TBS and 3D bone microarchitecture parameters. This model indicates that TBS adds greater value and power of differentiation between samples with similar BMDs but different bone microarchitectures. It has been shown that it is possible to estimate bone microarchitecture status derived from DXA imaging using TBS.

A retrospective case-control study assessing the role of trabecular bone score in postmenopausal Caucasian women with osteopenia: analyzing the odds of vertebral fracture.

This case-control study assessed whether the trabecular bone score (TBS), determined from gray-level analysis of DXA images, might be of any diagnostic value, either alone or combined with bone mineral density (BMD), in the assessment of vertebral fracture risk among postmenopausal women with osteopenia. Of 243 postmenopausal Caucasian women, 50-80 years old, with BMD T-scores between -1.0 and -2.5, we identified 81 with osteoporosis-related vertebral fractures and compared them with 162 age-matched controls without fractures. Primary outcomes were BMD and TBS. For BMD, each incremental decrease in BMD was associated with an OR = 1.54 (95% CI = 1.17-2.03), and the AUC was 0.614 (0.550-0.676). For TBS, corresponding values were 2.53 (1.82-3.53) and 0.721 (0.660-0.777). The difference in the AUC for TBS vs. BMD was statistically significant (p = 0.020). The OR for (TBS + BMD) was 2.54 (1.86-3.47) and the AUC 0.732 (0.672-0.787). In conclusion, the TBS warrants a closer look to see whether it may be of clinical usefulness in the determination of fracture risk in postmenopausal osteopenic women.

Effects of anti-resorptive agents on trabecular bone score (TBS) in older women.

We evaluated the longitudinal effects of anti-resorptive agents (534 treated women vs. 1,150 untreated) on lumbar spine bone mineral density (BMD) and trabecular bone score (TBS). TBS was responsive to treatment in women over age 50. The treatment-related increase in TBS was less than the increase in BMD, which is consistent with bone texture preservation. INTRODUCTION: In addition to inducing an increase in BMD, anti-resorptive agents also help to preserve bone architecture. TBS, a new gray-level texture measurement, correlates with 3D parameters of bone micro-architecture independent of BMD. Our objective was to evaluate the longitudinal effects of anti-resorptive agents on lumbar spine BMD and TBS. METHODS: Women (≥50 years), from the BMD program database for the province of Manitoba, Canada, who had not received any anti-resorptive drug prior to their initial dual X-ray absorptiometry (DXA) exam were divided into two groups: untreated, those without any anti-resorptive drug over the course of follow-up, and treated, those with a non-estrogen anti-resorptive drug (86 % bisphosphonates, 10 % raloxifene, and 4 % calcitonin). Lumbar spine TBS was calculated for each lumbar spine DXA examination. Changes in TBS and BMD between baseline and follow-up (mean follow-up 3.7 years), expressed in percentage per year, were compared between the two groups. RESULTS: A total of 1,150 untreated women and 534 treated women met the inclusion criteria. Only a weak correlation was seen between BMD and TBS in either group. Significant intergroup differences in BMD change and TBS change were observed over the course of follow-up (p < 0.001). Similar mean decreases in BMD and TBS (-0.36 %/year and -0.31 %/year, respectively) were seen for untreated subjects (both p < 0.001). Conversely, treated subjects exhibited a significant mean increase in BMD (+1.86 %/year, p < 0.002) and TBS (+0.20 %/year, p < 0.001). CONCLUSION: TBS is responsive to treatment with non-estrogen anti-resorptive drug therapy in women over age 50. The treatment-related increase in TBS is less than the increase in BMD, which is consistent with bone texture preservation.

Trabecular Bone Score (TBS)--A Novel Method to Evaluate Bone Microarchitectural Texture in Patients With Primary Hyperparathyroidism.

Context: In the milder form of primary hyperparathyroidism (PHPT), cancellous bone, represented by areal bone mineral density at the lumbar spine by dual-energy x-ray absorptiometry (DXA), is preserved. This finding is in contrast to high-resolution peripheral quantitative computed tomography (HRpQCT) results of abnormal trabecular microstructure and epidemiological evidence for increased overall fracture risk in PHPT. Because DXA does not directly measure trabecular bone and HRpQCT is not widely available, we used trabecular bone score (TBS), a novel gray-level textural analysis applied to spine DXA images, to estimate indirectly trabecular microarchitecture. Objective: The purpose of this study was to assess TBS from spine DXA images in relation to HRpQCT indices and bone stiffness in radius and tibia in PHPT. Design and Setting: This was a cross-sectional study conducted in a referral center. Patients: Participants were 22 postmenopausal women with PHPT. Main Outcome Measures: Outcomes measured were areal bone mineral density by DXA, TBS indices derived from DXA images, HRpQCT standard measures, and bone stiffness assessed by finite element analysis at distal radius and tibia. Results: TBS in PHPT was low at 1.24, representing abnormal trabecular microstructure (normal ≥1.35). TBS was correlated with whole bone stiffness and all HRpQCT indices, except for trabecular thickness and trabecular stiffness at the radius. At the tibia, correlations were observed between TBS and volumetric densities, cortical thickness, trabecular bone volume, and whole bone stiffness. TBS correlated with all indices of trabecular microarchitecture, except trabecular thickness, after adjustment for body weight. Conclusion: TBS, a measurement technology readily available by DXA, shows promise in the clinical assessment of trabecular microstructure in PHPT.

Minimum error contrast enhancement

Contrast enhancement is an image processing technique where the objective is to preprocess the image so that relevant information can be either seen or further processed more reliably. These techniques are typically applied when the image itself or the device used for image reproduction provides poor visibility and distinguishability of different regions of interest inthe image. In most studies, the emphasis is on the visualization of image data,but this human observer biased goal often results to images which are not optimal for automated processing. The main contribution of this study is to express the contrast enhancement as a mapping from N-channel image data to 1-channel gray-level image, and to devise a projection method which results to an image with minimal error to the correct contrast image. The projection, the minimum-error contrast image, possess the optimal contrast between the regions of interest in the image. The method is based on estimation of the probability density distributions of the region values, and it employs Bayesian inference to establish the minimum error projection.

Elicitation of subjective crop yield PDF

En este trabajo se investiga la coherencia y confiabilidad de estimaciones de funciones de densidad de probabilidad (FDP) subjetivas de rendimientos de cultivos realizadas por un amplio grupo de agricultores. Se utilizaron tres técnicas de elicitación diferentes: el método de estimación de FDP en dos pasos, la distribución Triangular y la distribución Beta. Los sujetos entrevistados ofrecieron estimaciones para los valores puntuales de rendimientos de cultivos (medio, máximo posible, más frecuente y mínimo posible) y para las FDP basadas en la estimación de intervalos. Para evaluar la persistencia, se utilizaron los conceptos de persistencia temporal y persistencia metodológica. Los resultados son interesantes para juzgar la adecuación de las técnicas de estimación de probabilidades subjetivas a los sistemas de ayuda en la toma de decisiones en agricultura.

A Meta-Analysis of Trabecular Bone Score in Fracture Risk Prediction and Its Relationship to FRAX.

Trabecular bone score (TBS) is a gray-level textural index of bone microarchitecture derived from lumbar spine dual-energy X-ray absorptiometry (DXA) images. TBS is a bone mineral density (BMD)-independent predictor of fracture risk. The objective of this meta-analysis was to determine whether TBS predicted fracture risk independently of FRAX probability and to examine their combined performance by adjusting the FRAX probability for TBS. We utilized individual-level data from 17,809 men and women in 14 prospective population-based cohorts. Baseline evaluation included TBS and the FRAX risk variables, and outcomes during follow-up (mean 6.7 years) comprised major osteoporotic fractures. The association between TBS, FRAX probabilities, and the risk of fracture was examined using an extension of the Poisson regression model in each cohort and for each sex and expressed as the gradient of risk (GR; hazard ratio per 1 SD change in risk variable in direction of increased risk). FRAX probabilities were adjusted for TBS using an adjustment factor derived from an independent cohort (the Manitoba Bone Density Cohort). Overall, the GR of TBS for major osteoporotic fracture was 1.44 (95% confidence interval [CI] 1.35-1.53) when adjusted for age and time since baseline and was similar in men and women (p > 0.10). When additionally adjusted for FRAX 10-year probability of major osteoporotic fracture, TBS remained a significant, independent predictor for fracture (GR = 1.32, 95% CI 1.24-1.41). The adjustment of FRAX probability for TBS resulted in a small increase in the GR (1.76, 95% CI 1.65-1.87 versus 1.70, 95% CI 1.60-1.81). A smaller change in GR for hip fracture was observed (FRAX hip fracture probability GR 2.25 vs. 2.22). TBS is a significant predictor of fracture risk independently of FRAX. The findings support the use of TBS as a potential adjustment for FRAX probability, though the impact of the adjustment remains to be determined in the context of clinical assessment guidelines. © 2015 American Society for Bone and Mineral Research.

Fragmentation of massive dense cores down to <~ 1000 AU: relation between fragmentation and density structure

In order to shed light on the main physical processes controlling fragmentation of massive dense cores, we present a uniform study of the density structure of 19 massive dense cores, selected to be at similar evolutionary stages, for which their relative fragmentation level was assessed in a previous work. We inferred the density structure of the 19 cores through a simultaneous fit of the radial intensity profiles at 450 and 850 μm (or 1.2 mm in two cases) and the spectral energy distribution, assuming spherical symmetry and that the density and temperature of the cores decrease with radius following power-laws. Even though the estimated fragmentation level is strictly speaking a lower limit, its relative value is significant and several trends could be explored with our data. We find a weak (inverse) trend of fragmentation level and density power-law index, with steeper density profiles tending to show lower fragmentation, and vice versa. In addition, we find a trend of fragmentation increasing with density within a given radius, which arises from a combination of flat density profile and high central density and is consistent with Jeans fragmentation. We considered the effects of rotational-to-gravitational energy ratio, non-thermal velocity dispersion, and turbulence mode on the density structure of the cores, and found that compressive turbulence seems to yield higher central densities. Finally, a possible explanation for the origin of cores with concentrated density profiles, which are the cores showing no fragmentation, could be related with a strong magnetic field, consistent with the outcome of radiation magnetohydrodynamic simulations.