Database Of Scans Research Articles

Shape analysis of female torsos based on discrete cosine transform

Purpose – The purpose of this paper is to present results of shape analysis of female torso shape using the discrete cosine transform (DCT) from a three-dimensional (3D) whole body scan database. Design/methodology/approach – Torso shape is a central part of body shape and difficult to describe by linear measurements. In order to analyze body shape variation within a population the authors employed a DCT-based shape description method to compresses a dense 3D body scan surface into a small vector that preserves shape and removes size. The DCT-based shape descriptors of torso surfaces are further fed to principal component analysis (PCA) that decompose shape variation into constituent shape components. A visualization program was developed to observe principal components of torso shape and interpret their meanings. Findings – Extreme shapes of the first ten principal components summarize major shape variations and identify shapes that are difficult to capture with traditional anthropometric measurements. PCA results also help to find and retrieve similar shapes from a population-level database. Originality/value – Using the DCT for PCA of torso shape is a unique and original approach. It provides a basis for the description and classification of torso shape in 3D and the results from the shape analysis are potentially useful for designers of clothing and personal protective equipment.

Development of HeadForms and an Anthropometric Sizing Analysis System for Head-Related Product Designs

The present study developed a sizing analysis system based on the Civilian American and European Surface Anthropometry Resource (CAESAR) database of North Americans ( n = 2,299) for head-related product designs. To find representative heads from a huge amount of 3D human scan database, a sizing analysis system is required for efficient analysis of sizing systems based on anthropometric measurements. The head of the CAESAR 3D scan were manually edited to improve a quality for better use to the product design. Twenty one anthropometric landmarks were marked on the edited 3D heads to measure 40 anthropometric dimensions related to the head product designs. All head and face dimensions were automatically measured by applying a measurement system coded using Matlab. Fifteen representative headforms were generated in terms of 5 ethnic groups (composite group, Caucasians, American Africans, Asians, and Hispanics) and 3 gender groups (composite gender, males, and females). Finally, the sizing analysis system was developed based on the measurement of the CAESAR for analysis of head and facial measurements and generation of sizing systems.

Multi-atlas and label fusion approach for patient-specific MRI based skull estimation.

MRI-based skull segmentation is a useful procedure for many imaging applications. This study describes a methodology for automatic segmentation of the complete skull from a single T1-weighted volume. The skull is estimated using a multi-atlas segmentation approach. Using a whole head computed tomography (CT) scan database, the skull in a new MRI volume is detected by nonrigid image registration of the volume to every CT, and combination of the individual segmentations by label-fusion. We have compared Majority Voting, Simultaneous Truth and Performance Level Estimation (STAPLE), Shape Based Averaging (SBA), and the Selective and Iterative Method for Performance Level Estimation (SIMPLE) algorithms. The pipeline has been evaluated quantitatively using images from the Retrospective Image Registration Evaluation database (reaching an overlap of 72.46 ± 6.99%), a clinical CT-MR dataset (maximum overlap of 78.31 ± 6.97%), and a whole head CT-MRI pair (maximum overlap 78.68%). A qualitative evaluation has also been performed on MRI acquisition of volunteers. It is possible to automatically segment the complete skull from MRI data using a multi-atlas and label fusion approach. This will allow the creation of complete MRI-based tissue models that can be used in electromagnetic dosimetry applications and attenuation correction in PET/MR.

Efficient scan matching method using direction distribution

An efficient scan matching method that uses direction distributions of two-dimensional laser scans is proposed. Using line fitting with each point in the scan, together with the neighbouring two points, the distribution of these line directions is computed. The direction distribution represents a measure of the geometric tendency of the scan, and this can be used to compute similarities between scans. This approach can be applied to select scans in a scan database that have high probabilities of being closely aligned with the current scan. Moreover, an initial estimate of the rotational displacement between two scans can be obtained, and scan matching can be accomplished efficiently even with a large rotational displacement between scans.

Hierarchical Pictorial Structures for Simultaneously Localizing Multiple Organs in Volumetric Pre-Scan CT.

Parsing volumetric computed tomography (CT) into 10 or more salient organs simultaneously is a challenging task with many applications such as personalized scan planning and dose reporting. In the clinic, pre-scan data can come in the form of very low dose volumes acquired just prior to the primary scan or from an existing primary scan. To localize organs in such diverse data, we propose a new learning based framework that we call hierarchical pictorial structures (HPS) which builds multiple levels of models in a tree-like hierarchy that mirrors the natural decomposition of human anatomy from gross structures to finer structures. Each node of our hierarchical model learns (1) the local appearance and shape of structures, and (2) a generative global model that learns probabilistic, structural arrangement. Our main contribution is two fold. First we embed the pictorial structures approach in a hierarchical framework which reduces test time image interpretation and allows for the incorporation of additional geometric constraints that robustly guide model fitting in the presence of noise. Second we guide our HPS framework with the probabilistic cost maps extracted using random decision forests using volumetric 3D HOG features which makes our model fast to train and fast to apply to novel test data and posses a high degree of invariance to shape distortion and imaging artifacts. All steps require approximate 3 mins to compute and all organs are located with suitably high accuracy for our clinical applications such as personalized scan planning for radiation dose reduction. We assess our method using a database of volumetric CT scans from 81 subjects with widely varying age and pathology and with simulated ultra low dose cadaver pre-scan data.

MicroRNA-370 Attenuates Hepatic Fibrogenesis by Targeting Smoothened.

Recent research shows that abnormal expression of microRNA plays an important role in the process of hepatic fibrosis . miR-370 has been reported to be involved in liver function and is suppressed during hepatic carcinogenesis. The aim of this study was to investigate the role of miR-370 in hepatic fibrosis. The expression levels of miR-370 in rat fibrotic livers and activated hepatic stellate cells (HSCs) were evaluated by quantitative real-time PCR. The effect of miR-370 on the activation of HSCs was analyzed by flow cytometric analyses, real-time PCR and Western blot. Adenovirus carrying miR-370 was injected through the tail vein to access the effect of miR-370 on hepatic fibrosis induced by CCl4 in rats. The downstream targets of miR-370 were predicted by the Target Scan database and verified by luciferase assays, real-time PCR and Western blot in HSCs and were further confirmed by immunohistochemistry in vivo. Real-time PCR showed that miR-370 expression was significantly reduced in rat fibrotic livers and TGFβ1-stimulated HSCs. Overexpression of miR-370 inhibited the proliferation of HSC-T6 cells via inducing cell apoptosis and suppressed the activation of HSCs. Upregulation of miR-370 obviously attenuated the CCl4-induced liver fibrosis in rats. miR-370 was directly bound to the 3'UTR of Smoothened (SMO) and suppressed the expression of SMO in HSCs and fibrotic livers. Our study demonstrated that miR-370 plays an inhibitory role in hepatic fibrogenesis by targeting SMO. Restoration of miR-370 may have beneficial effects on the treatment of liver fibrosis.

Low dose CT image restoration using a database of image patches

Reducing the radiation dose in CT imaging has become an active research topic and many solutions have been proposed to remove the significant noise and streak artifacts in the reconstructed images. Most of these methods operate within the domain of the image that is subject to restoration. This, however, poses limitations on the extent of filtering possible. We advocate to take into consideration the vast body of external knowledge that exists in the domain of already acquired medical CT images, since after all, this is what radiologists do when they examine these low quality images. We can incorporate this knowledge by creating a database of prior scans, either of the same patient or a diverse corpus of different patients, to assist in the restoration process. Our paper follows up on our previous work that used a database of images. Using images, however, is challenging since it requires tedious and error prone registration and alignment. Our new method eliminates these problems by storing a diverse set of small image patches in conjunction with a localized similarity matching scheme. We also empirically show that it is sufficient to store these patches without anatomical tags since their statistics are sufficiently strong to yield good similarity matches from the database and as a direct effect, produce image restorations of high quality. A final experiment demonstrates that our global database approach can recover image features that are difficult to preserve with conventional denoising approaches.

Association Rules Algorithm Based on the Intersection

Mining association rules in the database is one of important study in data mining research. Traditional association rules consist of some redundant information, and need scan database many times and generate lots of candidate item sets. Aiming at low efficiency in association rules mining using traditional methods, this paper proposes the algorithm (ISMFP), which is based on intersection for mining the maximum frequent patterns. Firstly, applying the intersection theory of mathematics, put forwards a number of concepts and definitions. Then gives the process of association rules mining, and analyzes its performance. After that, the example describes the process of implementation of the algorithm. Finally, the experimental results show that the algorithm ISMFP is efficient on mining frequent patterns, especially there exists low threshold of support degree or long patterns.

A Novel and Improved Apriori Algorithm

Apriori algorithm is the classical algorithm in data mining association rules. Because the Apriori algorithm needs scan database for many times, it runs too slowly. In order to improve the running efficiency, this paper improves the Apriori algorithm based on the Apriori analysis. The improved idea is that it transforms the transaction database into corresponding 0-1 matrix. Whose each vector and subsequent vector does inner product operation to receive support. And comparing with the given minsupport, the rows and columns will be deleted if vector are less than the minsupport, so as to reduce the size of the rating matrix, improve the running speeding. Because the improved algorithm only needs to scan the database once when running, therefore the running speeding is more quickly. The experiment also shows that this improved algorithm is efficient and feasible.

Content-Based Image Retrieval by Metric Learning From Radiology Reports: Application to Interstitial Lung Diseases.

Content-based image retrieval (CBIR) is a search technology that could aid medical diagnosis by retrieving and presenting earlier reported cases that are related to the one being diagnosed. To retrieve relevant cases, CBIR systems depend on supervised learning to map low-level image contents to high-level diagnostic concepts. However, the annotation by medical doctors for training and evaluation purposes is a difficult and time-consuming task, which restricts the supervised learning phase to specific CBIR problems of well-defined clinical applications. This paper proposes a new technique that automatically learns the similarity between the several exams from textual distances extracted from radiology reports, thereby successfully reducing the number of annotations needed. Our method first infers the relation between patients by using information retrieval techniques to determine the textual distances between patient radiology reports. These distances are subsequently used to supervise a metric learning algorithm, that transforms the image space accordingly to textual distances. CBIR systems with different image descriptions and different levels of medical annotations were evaluated, with and without supervision from textual distances, using a database of computer tomography scans of patients with interstitial lung diseases. The proposed method consistently improves CBIR mean average precision, with improvements that can reach 38%, and more marked gains for small annotation sets. Given the overall availability of radiology reports in picture archiving and communication systems, the proposed approach can be broadly applied to CBIR systems in different medical problems, and may facilitate the introduction of CBIR in clinical practice.

Comparison of the Quantitative CT Imaging Biomarkers of Idiopathic Pulmonary Fibrosis at Baseline and Early Change with an Interval of 7 Months

Median survival of patients with idiopathic pulmonary fibrosis (IPF) is 2-5years. Sensitive imaging metrics can play a role in detecting early changes in therapeutic development. The aim of the present study was to compare known computed tomography (CT) histogram kurtosis and a classifier-based quantitative score to assess baseline severity and change over time in patients with IPF. A total of 57 patients with at least baseline and paired follow-up scans were selected from an imaging database of standardized CT scans obtained from patients with IPF. CT histogram measurement of kurtosis and quantitative lung fibrosis (QLF) and quantitative interstitial lung disease (QILD) scores from a classification algorithm were calculated. Spearman rank correlations were used to assess associations between baseline severity and changes for all CT-derived measures compared to forced vital capacity (FVC) and carbon monoxide diffusion capacity (DLCO) (percent predicted). At baseline, mean (±SD) of kurtosis was 2.43 (±1.83). Mean (±SD) values of QLF and QILD scores were 20.7% (±13.4) and 43.3% (±20.0), respectively. All baseline histogram indices and QLF and QILD scores were correlated well with baseline FVC and DLCO. When assessing associations with changes in FVC and DLCO over time, only QLF score was statistically significant (ρ=-0.57; P<.0001 for FVC and ρ=-0.34; P=.025 for DLCO), whereas kurtosis was not. Classifier-model-derived scores (QLF and QILD), based on a set of texture features, are associated with baseline disease extent and are also a sensitive measure of change over time. A QLF score can be used for measuring the extent of disease severity and longitudinal changes.

Outcome of FDG‐Avid Areas on PET‐CT Scans Organized for Investigation of Head and Neck Malignancy

Objectives:Positron emission tomography (PET) scans may highlight areas of increased fludeoxyglucose (FDG) uptake outside the head and neck (HN) region. These may represent a synchronous malignancy or an area of inflammation or infection. Our study aims to investigate the rate of increased FDG uptake PET scans organized for the investigation of head and neck squamous cell carcinoma (HNSCC) and report on the outcome of these FDG‐avid areas.Methods:A prospective database of PET scans maintained by the West of Scotland PET Centre was reviewed to identify all scans organized by the head and neck teams between January 2008 and December 2011. Areas with high FDG uptake, above the normal limits for different body tissues at which the radiologists advised further investigation, were noted. Case notes were reviewed and relevant head and neck consultants were contacted to identify further investigations and outcome of these “hot spots.”Results:Of 299 PET scans during the study period, 40 FDG‐avid areas outside the HN and lung were identified, the majority originating in the colon (56%), followed by the prostate and pelvic region. There were 9 confirmed second primary tumors (22.5%), which is a significant proportion of the group, and half of the group had an incidental high standardized uptake value (SUV; negative investigation). Eleven patients were not investigated due to poor health and palliative management.Conclusions:All increased SUV areas outside the head and neck region should be investigated to identify a synchronous primary which may determine a patient's overall management and long‐term outcome.

English

Now a days high utility item sets specially from large transaction databases is required task to process many day to day operations in quick time. In many relevant algorithms presented those are surface the problem of generating large number of candidate item set and thus degrades the mining performance in terms of execution time and space. In this paper mainly three algorithms are presented, such as UP-Growth (utility pattern) for mining high utility item set for pruning candidate item sets. In these algorithms, compact tree structure (UP-Tree) is used for discovering the useful item-set so that candidate item is generated with only two scan of database. The performance of UP-Growth was evaluated in comparison with the state-of-theart algorithms on different types of datasets. We have used this approach with existing UP-Growth and UP-Growth+ with aim of improving their performances further. The experimental results show that UP-Growth not only reduces the number of candidates effectively but also outperforms other algorithms substantially in terms of execution time, especially when the database contains lots of long transactions. If we did not use one of the above algorithm then we will face number of problems like if we are retrieve items then that operation takes more time to scan database. It is a time consuming process. This process reduces performance of the project. So we are prefer other alternatives like using UP growth algorithm, FP growth algorithm etc. In these algorithms also have some drawbacks like time consuming i.e. scans database for multiple times. So all aspects we considered and proposed one algorithm in this project i.e. UP Growth algorithm. It is very efficient algorithm for mining high utility compare to FP growth algorithm and etc.

Automatic detection and measurement of structures in fetal head ultrasound volumes using sequential estimation and Integrated Detection Network (IDN).

Routine ultrasound exam in the second and third trimesters of pregnancy involves manually measuring fetal head and brain structures in 2-D scans. The procedure requires a sonographer to find the standardized visualization planes with a probe and manually place measurement calipers on the structures of interest. The process is tedious, time consuming, and introduces user variability into the measurements. This paper proposes an automatic fetal head and brain (AFHB) system for automatically measuring anatomical structures from 3-D ultrasound volumes. The system searches the 3-D volume in a hierarchy of resolutions and by focusing on regions that are likely to be the measured anatomy. The output is a standardized visualization of the plane with correct orientation and centering as well as the biometric measurement of the anatomy. The system is based on a novel framework for detecting multiple structures in 3-D volumes. Since a joint model is difficult to obtain in most practical situations, the structures are detected in a sequence, one-by-one. The detection relies on Sequential Estimation techniques, frequently applied to visual tracking. The interdependence of structure poses and strong prior information embedded in our domain yields faster and more accurate results than detecting the objects individually. The posterior distribution of the structure pose is approximated at each step by sequential Monte Carlo. The samples are propagated within the sequence across multiple structures and hierarchical levels. The probabilistic model helps solve many challenges present in the ultrasound images of the fetus such as speckle noise, signal drop-out, shadows caused by bones, and appearance variations caused by the differences in the fetus gestational age. This is possible by discriminative learning on an extensive database of scans comprising more than two thousand volumes and more than thirteen thousand annotations. The average difference between ground truth and automatic measurements is below 2 mm with a running time of 6.9 s (GPU) or 14.7 s (CPU). The accuracy of the AFHB system is within inter-user variability and the running time is fast, which meets the requirements for clinical use.

Trends in preventive care among insured adolescents

The goal of the report published in this issue of The Journal by Tsai et al from the Centers for Disease Control and Prevention was to investigate patterns of preventive care visits among commercially-insured adolescents before and after the recommendation of the Advisory Committee on Immunization Practices (ACIP) of three vaccines targeted toward adolescents. The data were derived from the Market Scan database. Eleven to 21-year-olds who were continuously enrolled in the same insurance plan during 2003-2010 were included in the study. The proportion of individuals making at least one preventive visit increased from 25% to 41% between 2003 and 2010, and the rate of vaccination-related visits increased from 13% to 26%. The increases were greater during the years in which ACIP issued recommendations. Rates of preventive and vaccination-related visits were considerably higher among females, early adolescents, and those in managed-care plans. Without this stimulus for attendance, only 2.4% of 11- to 21-year-olds had annual preventive visits during the 8 years. The data from this study show that yearly improvements in preventive care visits were substantial and that the recommendations of the ACIP may have stimulated this improvement. However, rates of immunizations are still relatively low and rates of attendance for preventive care are low in this age group, so there is still important work to be done. Article page 625▶ Trends and Characteristics of Preventive Care Visits among Commercially Insured Adolescents, 2003-2010The Journal of PediatricsVol. 164Issue 3PreviewTo examine preventive care visit patterns among commercially insured adolescents during 2003-2010. In 2005-2007, the Advisory Committee on Immunization Practices (ACIP) recommended 3 vaccines targeted at adolescents. We also investigate the relationship between preventive care visits and immunization. Full-Text PDF

DOP014 The role of optineurin in macrophage cytokine secretion and Crohn's disease

Human Scan database” (www.targetscan.org), and their impact on biological activity was searched in “GeneCodis database” (http://genecodis.cnb.csic.es). Results: 15 out of 24 tissue samples (8 responders and 7 non-responders) reached a RIN value 8 allowing miRNA sequencing. We found more than 1,300 known miRNA and about 70 new miRNA. Responders to CS had an up-regulated expression of has-miR-5701 and has-miR-625 3p, and downregulated expression of has-miR-1246 and has-miR-1291 as compared to non-responders. Bioanalysis using miRNA targets database showed up to 2,000 potential targets for the aforementioned miRNA, most of them involved in MAPK signalling pathways, cytoskeleton organization pathway, and cell differentiation endocytosis and autophagy mechanisms. Conclusions: Patients with active UC not responding to CS show a differential mucosal miRNA expression profile before starting therapy. These findings suggest that regulation of gene expression by miRNA might play a role in the response to treatment in UC patients.

Organ Localization Using Joint AP/LAT View Landmark Consensus Detection and Hierarchical Active Appearance Models.

Parsing 2D radiographs into anatomical regions is a challenging task with many applications. In the clinic, scans routinely include anterior-posterior (AP) and lateral (LAT) view radiographs. Since these orthogonal views provide complementary anatomic information, an integrated analysis can afford the greatest localization accuracy. To solve this integration we propose automatic landmark candidate detection, pruned by a learned geometric consensus detector model and refined by fitting a hierarchical active appearance organ model (H-AAM). Our main contribution is twofold. First, we propose a probabilistic joint consensus detection model which learns how landmarks in either or both views predict landmark locations in a given view. Second, we refine landmarks by fitting a joint H-AAM that learns how landmark arrangement and image appearance can help predict across views. This increases accuracy and robustness to anatomic variation. All steps require just seconds to compute and compared to processing the scouts separately, joint processing reduces mean landmark distance error from 27.3 mm to 15.7 mm in LAT view and from 12.7 mm to 11.2 mm in the AP view. The errors are comparable to human expert inter-observer variability and suitable for clinical applications such as personalized scan planning for dose reduction. We assess our method using a database of scout CT scans from 93 subjects with widely varying pathology.

BrainPrint: identifying subjects by their brain.

Introducing BrainPrint, a compact and discriminative representation of anatomical structures in the brain. BrainPrint captures shape information of an ensemble of cortical and subcortical structures by solving the 2D and 3D Laplace-Beltrami operator on triangular (boundary) and tetrahedral (volumetric) meshes. We derive a robust classifier for this representation that identifies the subject in a new scan, based on a database of brain scans. In an example dataset containing over 3000 MRI scans, we show that BrainPrint captures unique information about the subject's anatomy and permits to correctly classify a scan with an accuracy of over 99.8%. All processing steps for obtaining the compact representation are fully automated making this processing framework particularly attractive for handling large datasets.

Insurer Market Structure and Variation in Commercial Health Care Spending

To examine the relationship between insurance market structure and health care prices, utilization, and spending. Claims for 37.6 million privately insured employees and their dependents from the Truven Health Market Scan Database in 2009. Measures of insurer market structure derived from Health Leaders Inter study data. Regression models are used to estimate the association between insurance market concentration and health care spending, utilization, and price, adjusting for differences in patient characteristics and other market-level traits. Insurance market concentration is inversely related to prices and spending, but positively related to utilization. Our results imply that, after adjusting for input price differences, a market with two equal size insurers is associated with 3.9 percent lower medical care spending per capita (p = .002) and 5.0 percent lower prices for health care services relative to one with three equal size insurers (p < .001). Greater fragmentation in the insurance market might lead to higher prices and higher spending for care, suggesting some of the gains from insurer competition may be absorbed by higher prices for health care. Greater attention to prices and utilization in the provider market may need to accompany procompetitive insurance market strategies.

How to produce personality neuroscience research with high statistical power and low additional cost

Personality neuroscience involves examining relations between cognitive or behavioral variability and neural variables like brain structure and function. Such studies have uncovered a number of fascinating associations but require large samples, which are expensive to collect. Here, we propose a system that capitalizes on neuroimaging data commonly collected for separate purposes and combines it with new behavioral data to test novel hypotheses. Specifically, we suggest that groups of researchers compile a database of structural (i.e., anatomical) and resting-state functional scans produced for other task-based investigations and pair these data with contact information for the participants who contributed the data. This contact information can then be used to collect additional cognitive, behavioral, or individual-difference data that are then reassociated with the neuroimaging data for analysis. This would allow for novel hypotheses regarding brain-behavior relations to be tested on the basis of large sample sizes (with adequate statistical power) for low additional cost. This idea can be implemented at small scales at single institutions, among a group of collaborating researchers, or perhaps even within a single lab. It can also be implemented at a large scale across institutions, although doing so would entail a number of additional complications.