Sample records for accurate automatic delineation

  1. An automatic, stagnation point based algorithm for the delineation of Wellhead Protection Areas

    NASA Astrophysics Data System (ADS)

    Tosco, Tiziana; Sethi, Rajandrea; di Molfetta, Antonio

    2008-07-01

    Time-related capture areas are usually delineated using the backward particle tracking method, releasing circles of equally spaced particles around each well. In this way, an accurate delineation often requires both a very high number of particles and a manual capture zone encirclement. The aim of this work was to propose an Automatic Protection Area (APA) delineation algorithm, which can be coupled with any model of flow and particle tracking. The computational time is here reduced, thanks to the use of a limited number of nonequally spaced particles. The particle starting positions are determined coupling forward particle tracking from the stagnation point, and backward particle tracking from the pumping well. The pathlines are postprocessed for a completely automatic delineation of closed perimeters of time-related capture zones. The APA algorithm was tested for a two-dimensional geometry, in homogeneous and nonhomogeneous aquifers, steady state flow conditions, single and multiple wells. Results show that the APA algorithm is robust and able to automatically and accurately reconstruct protection areas with a very small number of particles, also in complex scenarios.

  2. A deep convolutional neural network-based automatic delineation strategy for multiple brain metastases stereotactic radiosurgery

    PubMed Central

    Stojadinovic, Strahinja; Hrycushko, Brian; Wardak, Zabi; Lau, Steven; Lu, Weiguo; Yan, Yulong; Jiang, Steve B.; Zhen, Xin; Timmerman, Robert; Nedzi, Lucien

    2017-01-01

    Accurate and automatic brain metastases target delineation is a key step for efficient and effective stereotactic radiosurgery (SRS) treatment planning. In this work, we developed a deep learning convolutional neural network (CNN) algorithm for segmenting brain metastases on contrast-enhanced T1-weighted magnetic resonance imaging (MRI) datasets. We integrated the CNN-based algorithm into an automatic brain metastases segmentation workflow and validated on both Multimodal Brain Tumor Image Segmentation challenge (BRATS) data and clinical patients' data. Validation on BRATS data yielded average DICE coefficients (DCs) of 0.75±0.07 in the tumor core and 0.81±0.04 in the enhancing tumor, which outperformed most techniques in the 2015 BRATS challenge. Segmentation results of patient cases showed an average of DCs 0.67±0.03 and achieved an area under the receiver operating characteristic curve of 0.98±0.01. The developed automatic segmentation strategy surpasses current benchmark levels and offers a promising tool for SRS treatment planning for multiple brain metastases. PMID:28985229

  3. A deep convolutional neural network-based automatic delineation strategy for multiple brain metastases stereotactic radiosurgery.

    PubMed

    Liu, Yan; Stojadinovic, Strahinja; Hrycushko, Brian; Wardak, Zabi; Lau, Steven; Lu, Weiguo; Yan, Yulong; Jiang, Steve B; Zhen, Xin; Timmerman, Robert; Nedzi, Lucien; Gu, Xuejun

    2017-01-01

    Accurate and automatic brain metastases target delineation is a key step for efficient and effective stereotactic radiosurgery (SRS) treatment planning. In this work, we developed a deep learning convolutional neural network (CNN) algorithm for segmenting brain metastases on contrast-enhanced T1-weighted magnetic resonance imaging (MRI) datasets. We integrated the CNN-based algorithm into an automatic brain metastases segmentation workflow and validated on both Multimodal Brain Tumor Image Segmentation challenge (BRATS) data and clinical patients' data. Validation on BRATS data yielded average DICE coefficients (DCs) of 0.75±0.07 in the tumor core and 0.81±0.04 in the enhancing tumor, which outperformed most techniques in the 2015 BRATS challenge. Segmentation results of patient cases showed an average of DCs 0.67±0.03 and achieved an area under the receiver operating characteristic curve of 0.98±0.01. The developed automatic segmentation strategy surpasses current benchmark levels and offers a promising tool for SRS treatment planning for multiple brain metastases.

  4. Automatic delineation of functional lung volumes with 68Ga-ventilation/perfusion PET/CT.

    PubMed

    Le Roux, Pierre-Yves; Siva, Shankar; Callahan, Jason; Claudic, Yannis; Bourhis, David; Steinfort, Daniel P; Hicks, Rodney J; Hofman, Michael S

    2017-10-10

    Functional volumes computed from 68 Ga-ventilation/perfusion (V/Q) PET/CT, which we have shown to correlate with pulmonary function test parameters (PFTs), have potential diagnostic utility in a variety of clinical applications, including radiotherapy planning. An automatic segmentation method would facilitate delineation of such volumes. The aim of this study was to develop an automated threshold-based approach to delineate functional volumes that best correlates with manual delineation. Thirty lung cancer patients undergoing both V/Q PET/CT and PFTs were analyzed. Images were acquired following inhalation of Galligas and, subsequently, intravenous administration of 68 Ga-macroaggreted-albumin (MAA). Using visually defined manual contours as the reference standard, various cutoff values, expressed as a percentage of the maximal pixel value, were applied. The average volume difference and Dice similarity coefficient (DSC) were calculated, measuring the similarity of the automatic segmentation and the reference standard. Pearson's correlation was also calculated to compare automated volumes with manual volumes, and automated volumes optimized to PFT indices. For ventilation volumes, mean volume difference was lowest (- 0.4%) using a 15%max threshold with Pearson's coefficient of 0.71. Applying this cutoff, median DSC was 0.93 (0.87-0.95). Nevertheless, limits of agreement in volume differences were large (- 31.0 and 30.2%) with differences ranging from - 40.4 to + 33.0%. For perfusion volumes, mean volume difference was lowest and Pearson's coefficient was highest using a 15%max threshold (3.3% and 0.81, respectively). Applying this cutoff, median DSC was 0.93 (0.88-0.93). Nevertheless, limits of agreement were again large (- 21.1 and 27.8%) with volume differences ranging from - 18.6 to + 35.5%. Using the 15%max threshold, moderate correlation was demonstrated with FEV1/FVC (r = 0.48 and r = 0.46 for ventilation and perfusion images, respectively

  5. Patient-specific and global convolutional neural networks for robust automatic liver tumor delineation in follow-up CT studies.

    PubMed

    Vivanti, Refael; Joskowicz, Leo; Lev-Cohain, Naama; Ephrat, Ariel; Sosna, Jacob

    2018-03-10

    Radiological longitudinal follow-up of tumors in CT scans is essential for disease assessment and liver tumor therapy. Currently, most tumor size measurements follow the RECIST guidelines, which can be off by as much as 50%. True volumetric measurements are more accurate but require manual delineation, which is time-consuming and user-dependent. We present a convolutional neural networks (CNN) based method for robust automatic liver tumor delineation in longitudinal CT studies that uses both global and patient specific CNNs trained on a small database of delineated images. The inputs are the baseline scan and the tumor delineation, a follow-up scan, and a liver tumor global CNN voxel classifier built from radiologist-validated liver tumor delineations. The outputs are the tumor delineations in the follow-up CT scan. The baseline scan tumor delineation serves as a high-quality prior for the tumor characterization in the follow-up scans. It is used to evaluate the global CNN performance on the new case and to reliably predict failures of the global CNN on the follow-up scan. High-scoring cases are segmented with a global CNN; low-scoring cases, which are predicted to be failures of the global CNN, are segmented with a patient-specific CNN built from the baseline scan. Our experimental results on 222 tumors from 31 patients yield an average overlap error of 17% (std = 11.2) and surface distance of 2.1 mm (std = 1.8), far better than stand-alone segmentation. Importantly, the robustness of our method improved from 67% for stand-alone global CNN segmentation to 100%. Unlike other medical imaging deep learning approaches, which require large annotated training datasets, our method exploits the follow-up framework to yield accurate tumor tracking and failure detection and correction with a small training dataset. Graphical abstract Flow diagram of the proposed method. In the offline mode (orange), a global CNN is trained as a voxel classifier to segment liver

  6. Automatic delineation of tumor volumes by co-segmentation of combined PET/MR data

    NASA Astrophysics Data System (ADS)

    Leibfarth, S.; Eckert, F.; Welz, S.; Siegel, C.; Schmidt, H.; Schwenzer, N.; Zips, D.; Thorwarth, D.

    2015-07-01

    Combined PET/MRI may be highly beneficial for radiotherapy treatment planning in terms of tumor delineation and characterization. To standardize tumor volume delineation, an automatic algorithm for the co-segmentation of head and neck (HN) tumors based on PET/MR data was developed. Ten HN patient datasets acquired in a combined PET/MR system were available for this study. The proposed algorithm uses both the anatomical T2-weighted MR and FDG-PET data. For both imaging modalities tumor probability maps were derived, assigning each voxel a probability of being cancerous based on its signal intensity. A combination of these maps was subsequently segmented using a threshold level set algorithm. To validate the method, tumor delineations from three radiation oncologists were available. Inter-observer variabilities and variabilities between the algorithm and each observer were quantified by means of the Dice similarity index and a distance measure. Inter-observer variabilities and variabilities between observers and algorithm were found to be comparable, suggesting that the proposed algorithm is adequate for PET/MR co-segmentation. Moreover, taking into account combined PET/MR data resulted in more consistent tumor delineations compared to MR information only.

  7. SU-D-16A-02: A Novel Methodology for Accurate, Semi-Automated Delineation of Oral Mucosa for Radiation Therapy Dose-Response Studies

    DOE Office of Scientific and Technical Information (OSTI.GOV)

    Dean, J; Welsh, L; Gulliford, S

    Purpose: The significant morbidity caused by radiation-induced acute oral mucositis means that studies aiming to elucidate dose-response relationships in this tissue are a high priority. However, there is currently no standardized method for delineating the mucosal structures within the oral cavity. This report describes the development of a methodology to delineate the oral mucosa accurately on CT scans in a semi-automated manner. Methods: An oral mucosa atlas for automated segmentation was constructed using the RayStation Atlas-Based Segmentation (ABS) module. A radiation oncologist manually delineated the full surface of the oral mucosa on a planning CT scan of a patient receivingmore » radiotherapy (RT) to the head and neck region. A 3mm fixed annulus was added to incorporate the mucosal wall thickness. This structure was saved as an atlas template. ABS followed by model-based segmentation was performed on four further patients sequentially, adding each patient to the atlas. Manual editing of the automatically segmented structure was performed. A dose comparison between these contours and previously used oral cavity volume contours was performed. Results: The new approach was successful in delineating the mucosa, as assessed by an experienced radiation oncologist, when applied to a new series of patients receiving head and neck RT. Reductions in the mean doses obtained when using the new delineation approach, compared with the previously used technique, were demonstrated for all patients (median: 36.0%, range: 25.6% – 39.6%) and were of a magnitude that might be expected to be clinically significant. Differences in the maximum dose that might reasonably be expected to be clinically significant were observed for two patients. Conclusion: The method developed provides a means of obtaining the dose distribution delivered to the oral mucosa more accurately than has previously been achieved. This will enable the acquisition of high quality dosimetric data for

  8. Fast automatic delineation of cardiac volume of interest in MSCT images

    NASA Astrophysics Data System (ADS)

    Lorenz, Cristian; Lessick, Jonathan; Lavi, Guy; Bulow, Thomas; Renisch, Steffen

    2004-05-01

    Computed Tomography Angiography (CTA) is an emerging modality for assessing cardiac anatomy. The delineation of the cardiac volume of interest (VOI) is a pre-processing step for subsequent visualization or image processing. It serves the suppression of anatomic structures being not in the primary focus of the cardiac application, such as sternum, ribs, spinal column, descending aorta and pulmonary vasculature. These structures obliterate standard visualizations such as direct volume renderings or maximum intensity projections. In addition, outcome and performance of post-processing steps such as ventricle suppression, coronary artery segmentation or the detection of short and long axes of the heart can be improved. The structures being part of the cardiac VOI (coronary arteries and veins, myocardium, ventricles and atria) differ tremendously in appearance. In addition, there is no clear image feature associated with the contour (or better cut-surface) distinguishing between cardiac VOI and surrounding tissue making the automatic delineation of the cardiac VOI a difficult task. The presented approach locates in a first step chest wall and descending aorta in all image slices giving a rough estimate of the location of the heart. In a second step, a Fourier based active contour approach delineates slice-wise the border of the cardiac VOI. The algorithm has been evaluated on 41 multi-slice CT data-sets including cases with coronary stents and venous and arterial bypasses. The typical processing time amounts to 5-10s on a 1GHz P3 PC.

  9. Automatic segmentation software in locally advanced rectal cancer: READY (REsearch program in Auto Delineation sYstem)-RECTAL 02: prospective study.

    PubMed

    Gambacorta, Maria A; Boldrini, Luca; Valentini, Chiara; Dinapoli, Nicola; Mattiucci, Gian C; Chiloiro, Giuditta; Pasini, Danilo; Manfrida, Stefania; Caria, Nicola; Minsky, Bruce D; Valentini, Vincenzo

    2016-07-05

    To validate autocontouring software (AS) in a clinical practice including a two steps delineation quality assurance (QA) procedure.The existing delineation agreement among experts for rectal cancer and the overlap and time criteria that have to be verified to allow the use of AS were defined.Median Dice Similarity Coefficient (MDSC), Mean slicewise Hausdorff Distances (MSHD) and Total-Time saving (TT) were analyzed.Two expert Radiation Oncologists reviewed CT-scans of 44 patients and agreed the reference-CTV: the first 14 consecutive cases were used to populate the software Atlas and 30 were used as Test.Each expert performed a manual (group A) and an automatic delineation (group B) of 15 Test patients.The delineations were compared with the reference contours.The overlap between the manual and automatic delineations with MDSC and MSHD and the TT were analyzed.Three acceptance criteria were set: MDSC ≥ 0.75, MSHD ≤1mm and TT sparing ≥ 50%.At least 2 criteria had to be met, one of which had to be TT saving, to validate the system.The MDSC was 0.75, MSHD 2.00 mm and the TT saving 55.5% between group A and group B. MDSC among experts was 0.84.Autosegmentation systems in rectal cancer partially met acceptability criteria with the present version.

  10. Interactive contour delineation and refinement in treatment planning of image‐guided radiation therapy

    PubMed Central

    Zhou, Wu

    2014-01-01

    The accurate contour delineation of the target and/or organs at risk (OAR) is essential in treatment planning for image‐guided radiation therapy (IGRT). Although many automatic contour delineation approaches have been proposed, few of them can fulfill the necessities of applications in terms of accuracy and efficiency. Moreover, clinicians would like to analyze the characteristics of regions of interests (ROI) and adjust contours manually during IGRT. Interactive tool for contour delineation is necessary in such cases. In this work, a novel approach of curve fitting for interactive contour delineation is proposed. It allows users to quickly improve contours by a simple mouse click. Initially, a region which contains interesting object is selected in the image, then the program can automatically select important control points from the region boundary, and the method of Hermite cubic curves is used to fit the control points. Hence, the optimized curve can be revised by moving its control points interactively. Meanwhile, several curve fitting methods are presented for the comparison. Finally, in order to improve the accuracy of contour delineation, the process of the curve refinement based on the maximum gradient magnitude is proposed. All the points on the curve are revised automatically towards the positions with maximum gradient magnitude. Experimental results show that Hermite cubic curves and the curve refinement based on the maximum gradient magnitude possess superior performance on the proposed platform in terms of accuracy, robustness, and time calculation. Experimental results of real medical images demonstrate the efficiency, accuracy, and robustness of the proposed process in clinical applications. PACS number: 87.53.Tf PMID:24423846

  11. Automatic delineation of brain regions on MRI and PET images from the pig.

    PubMed

    Villadsen, Jonas; Hansen, Hanne D; Jørgensen, Louise M; Keller, Sune H; Andersen, Flemming L; Petersen, Ida N; Knudsen, Gitte M; Svarer, Claus

    2018-01-15

    The increasing use of the pig as a research model in neuroimaging requires standardized processing tools. For example, extraction of regional dynamic time series from brain PET images requires parcellation procedures that benefit from being automated. Manual inter-modality spatial normalization to a MRI atlas is operator-dependent, time-consuming, and can be inaccurate with lack of cortical radiotracer binding or skull uptake. A parcellated PET template that allows for automatic spatial normalization to PET images of any radiotracer. MRI and [ 11 C]Cimbi-36 PET scans obtained in sixteen pigs made the basis for the atlas. The high resolution MRI scans allowed for creation of an accurately averaged MRI template. By aligning the within-subject PET scans to their MRI counterparts, an averaged PET template was created in the same space. We developed an automatic procedure for spatial normalization of the averaged PET template to new PET images and hereby facilitated transfer of the atlas regional parcellation. Evaluation of the automatic spatial normalization procedure found the median voxel displacement to be 0.22±0.08mm using the MRI template with individual MRI images and 0.92±0.26mm using the PET template with individual [ 11 C]Cimbi-36 PET images. We tested the automatic procedure by assessing eleven PET radiotracers with different kinetics and spatial distributions by using perfusion-weighted images of early PET time frames. We here present an automatic procedure for accurate and reproducible spatial normalization and parcellation of pig PET images of any radiotracer with reasonable blood-brain barrier penetration. Copyright © 2017 Elsevier B.V. All rights reserved.

  12. A multimodality segmentation framework for automatic target delineation in head and neck radiotherapy

    DOE Office of Scientific and Technical Information (OSTI.GOV)

    Yang, Jinzhong; Aristophanous, Michalis, E-mail: MAristophanous@mdanderson.org; Beadle, Beth M.

    2015-09-15

    Purpose: To develop an automatic segmentation algorithm integrating imaging information from computed tomography (CT), positron emission tomography (PET), and magnetic resonance imaging (MRI) to delineate target volume in head and neck cancer radiotherapy. Methods: Eleven patients with unresectable disease at the tonsil or base of tongue who underwent MRI, CT, and PET/CT within two months before the start of radiotherapy or chemoradiotherapy were recruited for the study. For each patient, PET/CT and T1-weighted contrast MRI scans were first registered to the planning CT using deformable and rigid registration, respectively, to resample the PET and magnetic resonance (MR) images to themore » planning CT space. A binary mask was manually defined to identify the tumor area. The resampled PET and MR images, the planning CT image, and the binary mask were fed into the automatic segmentation algorithm for target delineation. The algorithm was based on a multichannel Gaussian mixture model and solved using an expectation–maximization algorithm with Markov random fields. To evaluate the algorithm, we compared the multichannel autosegmentation with an autosegmentation method using only PET images. The physician-defined gross tumor volume (GTV) was used as the “ground truth” for quantitative evaluation. Results: The median multichannel segmented GTV of the primary tumor was 15.7 cm{sup 3} (range, 6.6–44.3 cm{sup 3}), while the PET segmented GTV was 10.2 cm{sup 3} (range, 2.8–45.1 cm{sup 3}). The median physician-defined GTV was 22.1 cm{sup 3} (range, 4.2–38.4 cm{sup 3}). The median difference between the multichannel segmented and physician-defined GTVs was −10.7%, not showing a statistically significant difference (p-value = 0.43). However, the median difference between the PET segmented and physician-defined GTVs was −19.2%, showing a statistically significant difference (p-value =0.0037). The median Dice similarity coefficient between the multichannel

  13. A multimodality segmentation framework for automatic target delineation in head and neck radiotherapy.

    PubMed

    Yang, Jinzhong; Beadle, Beth M; Garden, Adam S; Schwartz, David L; Aristophanous, Michalis

    2015-09-01

    To develop an automatic segmentation algorithm integrating imaging information from computed tomography (CT), positron emission tomography (PET), and magnetic resonance imaging (MRI) to delineate target volume in head and neck cancer radiotherapy. Eleven patients with unresectable disease at the tonsil or base of tongue who underwent MRI, CT, and PET/CT within two months before the start of radiotherapy or chemoradiotherapy were recruited for the study. For each patient, PET/CT and T1-weighted contrast MRI scans were first registered to the planning CT using deformable and rigid registration, respectively, to resample the PET and magnetic resonance (MR) images to the planning CT space. A binary mask was manually defined to identify the tumor area. The resampled PET and MR images, the planning CT image, and the binary mask were fed into the automatic segmentation algorithm for target delineation. The algorithm was based on a multichannel Gaussian mixture model and solved using an expectation-maximization algorithm with Markov random fields. To evaluate the algorithm, we compared the multichannel autosegmentation with an autosegmentation method using only PET images. The physician-defined gross tumor volume (GTV) was used as the "ground truth" for quantitative evaluation. The median multichannel segmented GTV of the primary tumor was 15.7 cm(3) (range, 6.6-44.3 cm(3)), while the PET segmented GTV was 10.2 cm(3) (range, 2.8-45.1 cm(3)). The median physician-defined GTV was 22.1 cm(3) (range, 4.2-38.4 cm(3)). The median difference between the multichannel segmented and physician-defined GTVs was -10.7%, not showing a statistically significant difference (p-value = 0.43). However, the median difference between the PET segmented and physician-defined GTVs was -19.2%, showing a statistically significant difference (p-value =0.0037). The median Dice similarity coefficient between the multichannel segmented and physician-defined GTVs was 0.75 (range, 0.55-0.84), and the

  14. Interactive Cadastral Boundary Delineation from Uav Data

    NASA Astrophysics Data System (ADS)

    Crommelinck, S.; Höfle, B.; Koeva, M. N.; Yang, M. Y.; Vosselman, G.

    2018-05-01

    Unmanned aerial vehicles (UAV) are evolving as an alternative tool to acquire land tenure data. UAVs can capture geospatial data at high quality and resolution in a cost-effective, transparent and flexible manner, from which visible land parcel boundaries, i.e., cadastral boundaries are delineable. This delineation is to no extent automated, even though physical objects automatically retrievable through image analysis methods mark a large portion of cadastral boundaries. This study proposes (i) a methodology that automatically extracts and processes candidate cadastral boundary features from UAV data, and (ii) a procedure for a subsequent interactive delineation. Part (i) consists of two state-of-the-art computer vision methods, namely gPb contour detection and SLIC superpixels, as well as a classification part assigning costs to each outline according to local boundary knowledge. Part (ii) allows a user-guided delineation by calculating least-cost paths along previously extracted and weighted lines. The approach is tested on visible road outlines in two UAV datasets from Germany. Results show that all roads can be delineated comprehensively. Compared to manual delineation, the number of clicks per 100 m is reduced by up to 86 %, while obtaining a similar localization quality. The approach shows promising results to reduce the effort of manual delineation that is currently employed for indirect (cadastral) surveying.

  15. Laparoscopy-guided intracorporeal ultrasound accurately delineates hepatobiliary anatomy.

    PubMed

    Yamamoto, M; Stiegmann, G V; Durham, J; Berguer, R; Oba, Y; Fujiyama, Y; McIntyre, R C

    1993-01-01

    The purpose of this study was to develop a technique and assess the ability of a laparoscopic ultrasound probe to delineate biliary antomy and to determine the presence or absence of duct stones. Five pigs had ultrasonography of biliary structures and liver at laparoscopy followed by cholangiograms and anatomical dissection. Five patients had ultrasonography of the biliary tract at laparoscopic cholecystectomy. All animals had adequate visualization of important hepatobiliary structure, and an optimal method of accessing these structures at laparoscopy was established. Patients had ultrasonography which used methods developed in the animal trial. All had adequate visualization of the entire common bile duct confirmed by cholangiography. Limitations in demonstrating the relationship of the cystic duct to the common duct were technical and can be corrected. Laparoscopic ultrasonography has significant potential for delineation of biliary anatomy and determination of presence or absence of duct calculi. Clinical implementation could minimize the risk of iatrogenic duct injury and the need for operative cholangiography.

  16. Radiotherapy treatment planning: benefits of CT-MR image registration and fusion in tumor volume delineation.

    PubMed

    Djan, Igor; Petrović, Borislava; Erak, Marko; Nikolić, Ivan; Lucić, Silvija

    2013-08-01

    Development of imaging techniques, computed tomography (CT), magnetic resonance imaging (MRI), and positron emission tomography (PET), made great impact on radiotherapy treatment planning by improving the localization of target volumes. Improved localization allows better local control of tumor volumes, but also minimizes geographical misses. Mutual information is obtained by registration and fusion of images achieved manually or automatically. The aim of this study was to validate the CT-MRI image fusion method and compare delineation obtained by CT versus CT-MRI image fusion. The image fusion software (XIO CMS 4.50.0) was applied to delineate 16 patients. The patients were scanned on CT and MRI in the treatment position within an immobilization device before the initial treatment. The gross tumor volume (GTV) and clinical target volume (CTV) were delineated on CT alone and on CT+MRI images consecutively and image fusion was obtained. Image fusion showed that CTV delineated on a CT image study set is mainly inadequate for treatment planning, in comparison with CTV delineated on CT-MRI fused image study set. Fusion of different modalities enables the most accurate target volume delineation. This study shows that registration and image fusion allows precise target localization in terms of GTV and CTV and local disease control.

  17. Crowdsourcing for error detection in cortical surface delineations.

    PubMed

    Ganz, Melanie; Kondermann, Daniel; Andrulis, Jonas; Knudsen, Gitte Moos; Maier-Hein, Lena

    2017-01-01

    With the recent trend toward big data analysis, neuroimaging datasets have grown substantially in the past years. While larger datasets potentially offer important insights for medical research, one major bottleneck is the requirement for resources of medical experts needed to validate automatic processing results. To address this issue, the goal of this paper was to assess whether anonymous nonexperts from an online community can perform quality control of MR-based cortical surface delineations derived by an automatic algorithm. So-called knowledge workers from an online crowdsourcing platform were asked to annotate errors in automatic cortical surface delineations on 100 central, coronal slices of MR images. On average, annotations for 100 images were obtained in less than an hour. When using expert annotations as reference, the crowd on average achieves a sensitivity of 82 % and a precision of 42 %. Merging multiple annotations per image significantly improves the sensitivity of the crowd (up to 95 %), but leads to a decrease in precision (as low as 22 %). Our experiments show that the detection of errors in automatic cortical surface delineations generated by anonymous untrained workers is feasible. Future work will focus on increasing the sensitivity of our method further, such that the error detection tasks can be handled exclusively by the crowd and expert resources can be focused on error correction.

  18. Ultrasound image-based thyroid nodule automatic segmentation using convolutional neural networks.

    PubMed

    Ma, Jinlian; Wu, Fa; Jiang, Tian'an; Zhao, Qiyu; Kong, Dexing

    2017-11-01

    Delineation of thyroid nodule boundaries from ultrasound images plays an important role in calculation of clinical indices and diagnosis of thyroid diseases. However, it is challenging for accurate and automatic segmentation of thyroid nodules because of their heterogeneous appearance and components similar to the background. In this study, we employ a deep convolutional neural network (CNN) to automatically segment thyroid nodules from ultrasound images. Our CNN-based method formulates a thyroid nodule segmentation problem as a patch classification task, where the relationship among patches is ignored. Specifically, the CNN used image patches from images of normal thyroids and thyroid nodules as inputs and then generated the segmentation probability maps as outputs. A multi-view strategy is used to improve the performance of the CNN-based model. Additionally, we compared the performance of our approach with that of the commonly used segmentation methods on the same dataset. The experimental results suggest that our proposed method outperforms prior methods on thyroid nodule segmentation. Moreover, the results show that the CNN-based model is able to delineate multiple nodules in thyroid ultrasound images accurately and effectively. In detail, our CNN-based model can achieve an average of the overlap metric, dice ratio, true positive rate, false positive rate, and modified Hausdorff distance as [Formula: see text], [Formula: see text], [Formula: see text], [Formula: see text], [Formula: see text] on overall folds, respectively. Our proposed method is fully automatic without any user interaction. Quantitative results also indicate that our method is so efficient and accurate that it can be good enough to replace the time-consuming and tedious manual segmentation approach, demonstrating the potential clinical applications.

  19. A wavelet-based ECG delineation algorithm for 32-bit integer online processing.

    PubMed

    Di Marco, Luigi Y; Chiari, Lorenzo

    2011-04-03

    Since the first well-known electrocardiogram (ECG) delineator based on Wavelet Transform (WT) presented by Li et al. in 1995, a significant research effort has been devoted to the exploitation of this promising method. Its ability to reliably delineate the major waveform components (mono- or bi-phasic P wave, QRS, and mono- or bi-phasic T wave) would make it a suitable candidate for efficient online processing of ambulatory ECG signals. Unfortunately, previous implementations of this method adopt non-linear operators such as root mean square (RMS) or floating point algebra, which are computationally demanding. This paper presents a 32-bit integer, linear algebra advanced approach to online QRS detection and P-QRS-T waves delineation of a single lead ECG signal, based on WT. The QRS detector performance was validated on the MIT-BIH Arrhythmia Database (sensitivity Se = 99.77%, positive predictive value P+ = 99.86%, on 109010 annotated beats) and on the European ST-T Database (Se = 99.81%, P+ = 99.56%, on 788050 annotated beats). The ECG delineator was validated on the QT Database, showing a mean error between manual and automatic annotation below 1.5 samples for all fiducial points: P-onset, P-peak, P-offset, QRS-onset, QRS-offset, T-peak, T-offset, and a mean standard deviation comparable to other established methods. The proposed algorithm exhibits reliable QRS detection as well as accurate ECG delineation, in spite of a simple structure built on integer linear algebra.

  20. A tree canopy height delineation method based on Morphological Reconstruction—Open Crown Decomposition

    NASA Astrophysics Data System (ADS)

    Liu, Q.; Jing, L.; Li, Y.; Tang, Y.; Li, H.; Lin, Q.

    2016-04-01

    For the purpose of forest management, high resolution LIDAR and optical remote sensing imageries are used for treetop detection, tree crown delineation, and classification. The purpose of this study is to develop a self-adjusted dominant scales calculation method and a new crown horizontal cutting method of tree canopy height model (CHM) to detect and delineate tree crowns from LIDAR, under the hypothesis that a treetop is radiometric or altitudinal maximum and tree crowns consist of multi-scale branches. The major concept of the method is to develop an automatic selecting strategy of feature scale on CHM, and a multi-scale morphological reconstruction-open crown decomposition (MRCD) to get morphological multi-scale features of CHM by: cutting CHM from treetop to the ground; analysing and refining the dominant multiple scales with differential horizontal profiles to get treetops; segmenting LiDAR CHM using watershed a segmentation approach marked with MRCD treetops. This method has solved the problems of false detection of CHM side-surface extracted by the traditional morphological opening canopy segment (MOCS) method. The novel MRCD delineates more accurate and quantitative multi-scale features of CHM, and enables more accurate detection and segmentation of treetops and crown. Besides, the MRCD method can also be extended to high optical remote sensing tree crown extraction. In an experiment on aerial LiDAR CHM of a forest of multi-scale tree crowns, the proposed method yielded high-quality tree crown maps.

  1. Evaluation of an automatic segmentation algorithm for definition of head and neck organs at risk.

    PubMed

    Thomson, David; Boylan, Chris; Liptrot, Tom; Aitkenhead, Adam; Lee, Lip; Yap, Beng; Sykes, Andrew; Rowbottom, Carl; Slevin, Nicholas

    2014-08-03

    The accurate definition of organs at risk (OARs) is required to fully exploit the benefits of intensity-modulated radiotherapy (IMRT) for head and neck cancer. However, manual delineation is time-consuming and there is considerable inter-observer variability. This is pertinent as function-sparing and adaptive IMRT have increased the number and frequency of delineation of OARs. We evaluated accuracy and potential time-saving of Smart Probabilistic Image Contouring Engine (SPICE) automatic segmentation to define OARs for salivary-, swallowing- and cochlea-sparing IMRT. Five clinicians recorded the time to delineate five organs at risk (parotid glands, submandibular glands, larynx, pharyngeal constrictor muscles and cochleae) for each of 10 CT scans. SPICE was then used to define these structures. The acceptability of SPICE contours was initially determined by visual inspection and the total time to modify them recorded per scan. The Simultaneous Truth and Performance Level Estimation (STAPLE) algorithm created a reference standard from all clinician contours. Clinician, SPICE and modified contours were compared against STAPLE by the Dice similarity coefficient (DSC) and mean/maximum distance to agreement (DTA). For all investigated structures, SPICE contours were less accurate than manual contours. However, for parotid/submandibular glands they were acceptable (median DSC: 0.79/0.80; mean, maximum DTA: 1.5 mm, 14.8 mm/0.6 mm, 5.7 mm). Modified SPICE contours were also less accurate than manual contours. The utilisation of SPICE did not result in time-saving/improve efficiency. Improvements in accuracy of automatic segmentation for head and neck OARs would be worthwhile and are required before its routine clinical implementation.

  2. SU-C-BRA-06: Automatic Brain Tumor Segmentation for Stereotactic Radiosurgery Applications

    DOE Office of Scientific and Technical Information (OSTI.GOV)

    Liu, Y; Stojadinovic, S; Jiang, S

    Purpose: Stereotactic radiosurgery (SRS), which delivers a potent dose of highly conformal radiation to the target in a single fraction, requires accurate tumor delineation for treatment planning. We present an automatic segmentation strategy, that synergizes intensity histogram thresholding, super-voxel clustering, and level-set based contour evolving methods to efficiently and accurately delineate SRS brain tumors on contrast-enhance T1-weighted (T1c) Magnetic Resonance Images (MRI). Methods: The developed auto-segmentation strategy consists of three major steps. Firstly, tumor sites are localized through 2D slice intensity histogram scanning. Then, super voxels are obtained through clustering the corresponding voxels in 3D with reference to the similaritymore » metrics composited from spatial distance and intensity difference. The combination of the above two could generate the initial contour surface. Finally, a localized region active contour model is utilized to evolve the surface to achieve the accurate delineation of the tumors. The developed method was evaluated on numerical phantom data, synthetic BRATS (Multimodal Brain Tumor Image Segmentation challenge) data, and clinical patients’ data. The auto-segmentation results were quantitatively evaluated by comparing to ground truths with both volume and surface similarity metrics. Results: DICE coefficient (DC) was performed as a quantitative metric to evaluate the auto-segmentation in the numerical phantom with 8 tumors. DCs are 0.999±0.001 without noise, 0.969±0.065 with Rician noise and 0.976±0.038 with Gaussian noise. DC, NMI (Normalized Mutual Information), SSIM (Structural Similarity) and Hausdorff distance (HD) were calculated as the metrics for the BRATS and patients’ data. Assessment of BRATS data across 25 tumor segmentation yield DC 0.886±0.078, NMI 0.817±0.108, SSIM 0.997±0.002, and HD 6.483±4.079mm. Evaluation on 8 patients with total 14 tumor sites yield DC 0.872±0.070, NMI 0.824

  3. Highly automatic quantification of myocardial oedema in patients with acute myocardial infarction using bright blood T2-weighted CMR

    PubMed Central

    2013-01-01

    Background T2-weighted cardiovascular magnetic resonance (CMR) is clinically-useful for imaging the ischemic area-at-risk and amount of salvageable myocardium in patients with acute myocardial infarction (MI). However, to date, quantification of oedema is user-defined and potentially subjective. Methods We describe a highly automatic framework for quantifying myocardial oedema from bright blood T2-weighted CMR in patients with acute MI. Our approach retains user input (i.e. clinical judgment) to confirm the presence of oedema on an image which is then subjected to an automatic analysis. The new method was tested on 25 consecutive acute MI patients who had a CMR within 48 hours of hospital admission. Left ventricular wall boundaries were delineated automatically by variational level set methods followed by automatic detection of myocardial oedema by fitting a Rayleigh-Gaussian mixture statistical model. These data were compared with results from manual segmentation of the left ventricular wall and oedema, the current standard approach. Results The mean perpendicular distances between automatically detected left ventricular boundaries and corresponding manual delineated boundaries were in the range of 1-2 mm. Dice similarity coefficients for agreement (0=no agreement, 1=perfect agreement) between manual delineation and automatic segmentation of the left ventricular wall boundaries and oedema regions were 0.86 and 0.74, respectively. Conclusion Compared to standard manual approaches, the new highly automatic method for estimating myocardial oedema is accurate and straightforward. It has potential as a generic software tool for physicians to use in clinical practice. PMID:23548176

  4. A wavelet-based ECG delineation algorithm for 32-bit integer online processing

    PubMed Central

    2011-01-01

    Background Since the first well-known electrocardiogram (ECG) delineator based on Wavelet Transform (WT) presented by Li et al. in 1995, a significant research effort has been devoted to the exploitation of this promising method. Its ability to reliably delineate the major waveform components (mono- or bi-phasic P wave, QRS, and mono- or bi-phasic T wave) would make it a suitable candidate for efficient online processing of ambulatory ECG signals. Unfortunately, previous implementations of this method adopt non-linear operators such as root mean square (RMS) or floating point algebra, which are computationally demanding. Methods This paper presents a 32-bit integer, linear algebra advanced approach to online QRS detection and P-QRS-T waves delineation of a single lead ECG signal, based on WT. Results The QRS detector performance was validated on the MIT-BIH Arrhythmia Database (sensitivity Se = 99.77%, positive predictive value P+ = 99.86%, on 109010 annotated beats) and on the European ST-T Database (Se = 99.81%, P+ = 99.56%, on 788050 annotated beats). The ECG delineator was validated on the QT Database, showing a mean error between manual and automatic annotation below 1.5 samples for all fiducial points: P-onset, P-peak, P-offset, QRS-onset, QRS-offset, T-peak, T-offset, and a mean standard deviation comparable to other established methods. Conclusions The proposed algorithm exhibits reliable QRS detection as well as accurate ECG delineation, in spite of a simple structure built on integer linear algebra. PMID:21457580

  5. Classification of building infrastructure and automatic building footprint delineation using airborne laser swath mapping data

    NASA Astrophysics Data System (ADS)

    Caceres, Jhon

    image analysis for obtaining an initial classification, an automatic approach for delineating accurate building footprints is presented. The physical fact that laser pulses that happen to strike building edges can produce very different 1st and last return elevations has been long recognized. However, in older generation ALSM systems (<50 kHz pulse rates) such points were too few and far between to delineate building footprints precisely. Furthermore, without the robust separation of nearby trees and vegetation from the buildings, simply extracting ALSM shots where the elevation of the first return was much higher than the elevation of the last return, was not a reliable means of identifying building footprints. However, with the advent of ALSM systems with pulse rates in excess of 100 kHz, and by using spin-imaged based segmentation, it is now possible to extract building edges from the point cloud. A refined classification resulting from incorporating "on-edge" information is developed for obtaining quadrangular footprints. The footprint fitting process involves line generalization, least squares-based clustering and dominant points finding for segmenting individual building edges. In addition, an algorithm for fitting complex footprints using the segmented edges and data inside footprints is also proposed.

  6. TU-H-CAMPUS-JeP2-05: Can Automatic Delineation of Cardiac Substructures On Noncontrast CT Be Used for Cardiac Toxicity Analysis?

    DOE Office of Scientific and Technical Information (OSTI.GOV)

    Luo, Y; Liao, Z; Jiang, W

    Purpose: To evaluate the feasibility of using an automatic segmentation tool to delineate cardiac substructures from computed tomography (CT) images for cardiac toxicity analysis for non-small cell lung cancer (NSCLC) patients after radiotherapy. Methods: A multi-atlas segmentation tool developed in-house was used to delineate eleven cardiac substructures including the whole heart, four heart chambers, and six greater vessels automatically from the averaged 4DCT planning images for 49 NSCLC patients. The automatic segmented contours were edited appropriately by two experienced radiation oncologists. The modified contours were compared with the auto-segmented contours using Dice similarity coefficient (DSC) and mean surface distance (MSD)more » to evaluate how much modification was needed. In addition, the dose volume histogram (DVH) of the modified contours were compared with that of the auto-segmented contours to evaluate the dosimetric difference between modified and auto-segmented contours. Results: Of the eleven structures, the averaged DSC values ranged from 0.73 ± 0.08 to 0.95 ± 0.04 and the averaged MSD values ranged from 1.3 ± 0.6 mm to 2.9 ± 5.1mm for the 49 patients. Overall, the modification is small. The pulmonary vein (PV) and the inferior vena cava required the most modifications. The V30 (volume receiving 30 Gy or above) for the whole heart and the mean dose to the whole heart and four heart chambers did not show statistically significant difference between modified and auto-segmented contours. The maximum dose to the greater vessels did not show statistically significant difference except for the PV. Conclusion: The automatic segmentation of the cardiac substructures did not require substantial modification. The dosimetric evaluation showed no statistically significant difference between auto-segmented and modified contours except for the PV, which suggests that auto-segmented contours for the cardiac dose response study are feasible in the

  7. Dentalmaps: Automatic Dental Delineation for Radiotherapy Planning in Head-and-Neck Cancer

    DOE Office of Scientific and Technical Information (OSTI.GOV)

    Thariat, Juliette, E-mail: jthariat@hotmail.com; Ramus, Liliane; INRIA

    Purpose: To propose an automatic atlas-based segmentation framework of the dental structures, called Dentalmaps, and to assess its accuracy and relevance to guide dental care in the context of intensity-modulated radiotherapy. Methods and Materials: A multi-atlas-based segmentation, less sensitive to artifacts than previously published head-and-neck segmentation methods, was used. The manual segmentations of a 21-patient database were first deformed onto the query using nonlinear registrations with the training images and then fused to estimate the consensus segmentation of the query. Results: The framework was evaluated with a leave-one-out protocol. The maximum doses estimated using manual contours were considered as groundmore » truth and compared with the maximum doses estimated using automatic contours. The dose estimation error was within 2-Gy accuracy in 75% of cases (with a median of 0.9 Gy), whereas it was within 2-Gy accuracy in 30% of cases only with the visual estimation method without any contour, which is the routine practice procedure. Conclusions: Dose estimates using this framework were more accurate than visual estimates without dental contour. Dentalmaps represents a useful documentation and communication tool between radiation oncologists and dentists in routine practice. Prospective multicenter assessment is underway on patients extrinsic to the database.« less

  8. DELINEATING SUBTYPES OF SELF-INJURIOUS BEHAVIOR MAINTAINED BY AUTOMATIC REINFORCEMENT

    PubMed Central

    Hagopian, Louis P.; Rooker, Griffin W.; Zarcone, Jennifer R.

    2016-01-01

    Self-injurious behavior (SIB) is maintained by automatic reinforcement in roughly 25% of cases. Automatically reinforced SIB typically has been considered a single functional category, and is less understood than socially reinforced SIB. Subtyping automatically reinforced SIB into functional categories has the potential to guide the development of more targeted interventions and increase our understanding of its biological underpinnings. The current study involved an analysis of 39 individuals with automatically reinforced SIB and a comparison group of 13 individuals with socially reinforced SIB. Automatically reinforced SIB was categorized into 3 subtypes based on patterns of responding in the functional analysis and the presence of self-restraint. These response features were selected as the basis for subtyping on the premise that they could reflect functional properties of SIB unique to each subtype. Analysis of treatment data revealed important differences across subtypes and provides preliminary support to warrant additional research on this proposed subtyping model. PMID:26223959

  9. Validation of automatic segmentation of ribs for NTCP modeling.

    PubMed

    Stam, Barbara; Peulen, Heike; Rossi, Maddalena M G; Belderbos, José S A; Sonke, Jan-Jakob

    2016-03-01

    Determination of a dose-effect relation for rib fractures in a large patient group has been limited by the time consuming manual delineation of ribs. Automatic segmentation could facilitate such an analysis. We determine the accuracy of automatic rib segmentation in the context of normal tissue complication probability modeling (NTCP). Forty-one patients with stage I/II non-small cell lung cancer treated with SBRT to 54 Gy in 3 fractions were selected. Using the 4DCT derived mid-ventilation planning CT, all ribs were manually contoured and automatically segmented. Accuracy of segmentation was assessed using volumetric, shape and dosimetric measures. Manual and automatic dosimetric parameters Dx and EUD were tested for equivalence using the Two One-Sided T-test (TOST), and assessed for agreement using Bland-Altman analysis. NTCP models based on manual and automatic segmentation were compared. Automatic segmentation was comparable with the manual delineation in radial direction, but larger near the costal cartilage and vertebrae. Manual and automatic Dx and EUD were significantly equivalent. The Bland-Altman analysis showed good agreement. The two NTCP models were very similar. Automatic rib segmentation was significantly equivalent to manual delineation and can be used for NTCP modeling in a large patient group. Copyright © 2015 Elsevier Ireland Ltd. All rights reserved.

  10. Automatic knee cartilage delineation using inheritable segmentation

    NASA Astrophysics Data System (ADS)

    Dries, Sebastian P. M.; Pekar, Vladimir; Bystrov, Daniel; Heese, Harald S.; Blaffert, Thomas; Bos, Clemens; van Muiswinkel, Arianne M. C.

    2008-03-01

    We present a fully automatic method for segmentation of knee joint cartilage from fat suppressed MRI. The method first applies 3-D model-based segmentation technology, which allows to reliably segment the femur, patella, and tibia by iterative adaptation of the model according to image gradients. Thin plate spline interpolation is used in the next step to position deformable cartilage models for each of the three bones with reference to the segmented bone models. After initialization, the cartilage models are fine adjusted by automatic iterative adaptation to image data based on gray value gradients. The method has been validated on a collection of 8 (3 left, 5 right) fat suppressed datasets and demonstrated the sensitivity of 83+/-6% compared to manual segmentation on a per voxel basis as primary endpoint. Gross cartilage volume measurement yielded an average error of 9+/-7% as secondary endpoint. For cartilage being a thin structure, already small deviations in distance result in large errors on a per voxel basis, rendering the primary endpoint a hard criterion.

  11. Comparison of manual and automatic MR-CT registration for radiotherapy of prostate cancer.

    PubMed

    Korsager, Anne Sofie; Carl, Jesper; Riis Østergaard, Lasse

    2016-05-08

    In image-guided radiotherapy (IGRT) of prostate cancer, delineation of the clini-cal target volume (CTV) often relies on magnetic resonance (MR) because of its good soft-tissue visualization. Registration of MR and computed tomography (CT) is required in order to add this accurate delineation to the dose planning CT. An automatic approach for local MR-CT registration of the prostate has previously been developed using a voxel property-based registration as an alternative to a manual landmark-based registration. The aim of this study is to compare the two registration approaches and to investigate the clinical potential for replacing the manual registration with the automatic registration. Registrations and analysis were performed for 30 prostate cancer patients treated with IGRT using a Ni-Ti prostate stent as a fiducial marker. The comparison included computing translational and rotational differences between the approaches, visual inspection, and computing the overlap of the CTV. The computed mean translational difference was 1.65, 1.60, and 1.80mm and the computed mean rotational difference was 1.51°, 3.93°, and 2.09° in the superior/inferior, anterior/posterior, and medial/lateral direction, respectively. The sensitivity of overlap was 87%. The results demonstrate that the automatic registration approach performs registrations comparable to the manual registration.

  12. Slic Superpixels for Object Delineation from Uav Data

    NASA Astrophysics Data System (ADS)

    Crommelinck, S.; Bennett, R.; Gerke, M.; Koeva, M. N.; Yang, M. Y.; Vosselman, G.

    2017-08-01

    Unmanned aerial vehicles (UAV) are increasingly investigated with regard to their potential to create and update (cadastral) maps. UAVs provide a flexible and low-cost platform for high-resolution data, from which object outlines can be accurately delineated. This delineation could be automated with image analysis methods to improve existing mapping procedures that are cost, time and labor intensive and of little reproducibility. This study investigates a superpixel approach, namely simple linear iterative clustering (SLIC), in terms of its applicability to UAV data. The approach is investigated in terms of its applicability to high-resolution UAV orthoimages and in terms of its ability to delineate object outlines of roads and roofs. Results show that the approach is applicable to UAV orthoimages of 0.05 m GSD and extents of 100 million and 400 million pixels. Further, the approach delineates the objects with the high accuracy provided by the UAV orthoimages at completeness rates of up to 64 %. The approach is not suitable as a standalone approach for object delineation. However, it shows high potential for a combination with further methods that delineate objects at higher correctness rates in exchange of a lower localization quality. This study provides a basis for future work that will focus on the incorporation of multiple methods for an interactive, comprehensive and accurate object delineation from UAV data. This aims to support numerous application fields such as topographic and cadastral mapping.

  13. Phase II modification of the Water Availability Tool for Environmental Resources (WATER) for Kentucky: The sinkhole-drainage process, point-and-click basin delineation, and results of karst test-basin simulations

    USGS Publications Warehouse

    Taylor, Charles J.; Williamson, Tanja N.; Newson, Jeremy K.; Ulery, Randy L.; Nelson, Hugh L.; Cinotto, Peter J.

    2012-01-01

    This report describes Phase II modifications made to the Water Availability Tool for Environmental Resources (WATER), which applies the process-based TOPMODEL approach to simulate or predict stream discharge in surface basins in the Commonwealth of Kentucky. The previous (Phase I) version of WATER did not provide a means of identifying sinkhole catchments or accounting for the effects of karst (internal) drainage in a TOPMODEL-simulated basin. In the Phase II version of WATER, sinkhole catchments are automatically identified and delineated as internally drained subbasins, and a modified TOPMODEL approach (called the sinkhole drainage process, or SDP-TOPMODEL) is applied that calculates mean daily discharges for the basin based on summed area-weighted contributions from sinkhole drain-age (SD) areas and non-karstic topographically drained (TD) areas. Results obtained using the SDP-TOPMODEL approach were evaluated for 12 karst test basins located in each of the major karst terrains in Kentucky. Visual comparison of simulated hydrographs and flow-duration curves, along with statistical measures applied to the simulated discharge data (bias, correlation, root mean square error, and Nash-Sutcliffe efficiency coefficients), indicate that the SDPOPMODEL approach provides acceptably accurate estimates of discharge for most flow conditions and typically provides more accurate simulation of stream discharge in karstic basins compared to the standard TOPMODEL approach. Additional programming modifications made to the Phase II version of WATER included implementation of a point-and-click graphical user interface (GUI), which fully automates the delineation of simulation-basin boundaries and improves the speed of input-data processing. The Phase II version of WATER enables the user to select a pour point anywhere on a stream reach of interest, and the program will automatically delineate all upstream areas that contribute drainage to that point. This capability enables

  14. An automatic and accurate method of full heart segmentation from CT image based on linear gradient model

    NASA Astrophysics Data System (ADS)

    Yang, Zili

    2017-07-01

    Heart segmentation is an important auxiliary method in the diagnosis of many heart diseases, such as coronary heart disease and atrial fibrillation, and in the planning of tumor radiotherapy. Most of the existing methods for full heart segmentation treat the heart as a whole part and cannot accurately extract the bottom of the heart. In this paper, we propose a new method based on linear gradient model to segment the whole heart from the CT images automatically and accurately. Twelve cases were tested in order to test this method and accurate segmentation results were achieved and identified by clinical experts. The results can provide reliable clinical support.

  15. Interactive contour delineation of organs at risk in radiotherapy: Clinical evaluation on NSCLC patients.

    PubMed

    Dolz, J; Kirişli, H A; Fechter, T; Karnitzki, S; Oehlke, O; Nestle, U; Vermandel, M; Massoptier, L

    2016-05-01

    Accurate delineation of organs at risk (OARs) on computed tomography (CT) image is required for radiation treatment planning (RTP). Manual delineation of OARs being time consuming and prone to high interobserver variability, many (semi-) automatic methods have been proposed. However, most of them are specific to a particular OAR. Here, an interactive computer-assisted system able to segment various OARs required for thoracic radiation therapy is introduced. Segmentation information (foreground and background seeds) is interactively added by the user in any of the three main orthogonal views of the CT volume and is subsequently propagated within the whole volume. The proposed method is based on the combination of watershed transformation and graph-cuts algorithm, which is used as a powerful optimization technique to minimize the energy function. The OARs considered for thoracic radiation therapy are the lungs, spinal cord, trachea, proximal bronchus tree, heart, and esophagus. The method was evaluated on multivendor CT datasets of 30 patients. Two radiation oncologists participated in the study and manual delineations from the original RTP were used as ground truth for evaluation. Delineation of the OARs obtained with the minimally interactive approach was approved to be usable for RTP in nearly 90% of the cases, excluding the esophagus, which segmentation was mostly rejected, thus leading to a gain of time ranging from 50% to 80% in RTP. Considering exclusively accepted cases, overall OARs, a Dice similarity coefficient higher than 0.7 and a Hausdorff distance below 10 mm with respect to the ground truth were achieved. In addition, the interobserver analysis did not highlight any statistically significant difference, at the exception of the segmentation of the heart, in terms of Hausdorff distance and volume difference. An interactive, accurate, fast, and easy-to-use computer-assisted system able to segment various OARs required for thoracic radiation therapy has

  16. Interactive contour delineation of organs at risk in radiotherapy: Clinical evaluation on NSCLC patients

    DOE Office of Scientific and Technical Information (OSTI.GOV)

    Dolz, J., E-mail: jose.dolz.upv@gmail.com; Kirişli, H. A.; Massoptier, L.

    2016-05-15

    Purpose: Accurate delineation of organs at risk (OARs) on computed tomography (CT) image is required for radiation treatment planning (RTP). Manual delineation of OARs being time consuming and prone to high interobserver variability, many (semi-) automatic methods have been proposed. However, most of them are specific to a particular OAR. Here, an interactive computer-assisted system able to segment various OARs required for thoracic radiation therapy is introduced. Methods: Segmentation information (foreground and background seeds) is interactively added by the user in any of the three main orthogonal views of the CT volume and is subsequently propagated within the whole volume.more » The proposed method is based on the combination of watershed transformation and graph-cuts algorithm, which is used as a powerful optimization technique to minimize the energy function. The OARs considered for thoracic radiation therapy are the lungs, spinal cord, trachea, proximal bronchus tree, heart, and esophagus. The method was evaluated on multivendor CT datasets of 30 patients. Two radiation oncologists participated in the study and manual delineations from the original RTP were used as ground truth for evaluation. Results: Delineation of the OARs obtained with the minimally interactive approach was approved to be usable for RTP in nearly 90% of the cases, excluding the esophagus, which segmentation was mostly rejected, thus leading to a gain of time ranging from 50% to 80% in RTP. Considering exclusively accepted cases, overall OARs, a Dice similarity coefficient higher than 0.7 and a Hausdorff distance below 10 mm with respect to the ground truth were achieved. In addition, the interobserver analysis did not highlight any statistically significant difference, at the exception of the segmentation of the heart, in terms of Hausdorff distance and volume difference. Conclusions: An interactive, accurate, fast, and easy-to-use computer-assisted system able to segment various

  17. Comparison of manual and automatic MR‐CT registration for radiotherapy of prostate cancer

    PubMed Central

    Carl, Jesper; Østergaard, Lasse Riis

    2016-01-01

    In image‐guided radiotherapy (IGRT) of prostate cancer, delineation of the clinical target volume (CTV) often relies on magnetic resonance (MR) because of its good soft‐tissue visualization. Registration of MR and computed tomography (CT) is required in order to add this accurate delineation to the dose planning CT. An automatic approach for local MR‐CT registration of the prostate has previously been developed using a voxel property‐based registration as an alternative to a manual landmark‐based registration. The aim of this study is to compare the two registration approaches and to investigate the clinical potential for replacing the manual registration with the automatic registration. Registrations and analysis were performed for 30 prostate cancer patients treated with IGRT using a Ni‐Ti prostate stent as a fiducial marker. The comparison included computing translational and rotational differences between the approaches, visual inspection, and computing the overlap of the CTV. The computed mean translational difference was 1.65, 1.60, and 1.80 mm and the computed mean rotational difference was 1.51°, 3.93°, and 2.09° in the superior/inferior, anterior/posterior, and medial/lateral direction, respectively. The sensitivity of overlap was 87%. The results demonstrate that the automatic registration approach performs registrations comparable to the manual registration. PACS number(s): 87.57.nj, 87.61.‐c, 87.57.Q‐, 87.56.J‐ PMID:27167285

  18. Accurate and consistent automatic seismocardiogram annotation without concurrent ECG.

    PubMed

    Laurin, A; Khosrow-Khavar, F; Blaber, A P; Tavakolian, Kouhyar

    2016-09-01

    Seismocardiography (SCG) is the measurement of vibrations in the sternum caused by the beating of the heart. Precise cardiac mechanical timings that are easily obtained from SCG are critically dependent on accurate identification of fiducial points. So far, SCG annotation has relied on concurrent ECG measurements. An algorithm capable of annotating SCG without the use any other concurrent measurement was designed. We subjected 18 participants to graded lower body negative pressure. We collected ECG and SCG, obtained R peaks from the former, and annotated the latter by hand, using these identified peaks. We also annotated the SCG automatically. We compared the isovolumic moment timings obtained by hand to those obtained using our algorithm. Mean  ±  confidence interval of the percentage of accurately annotated cardiac cycles were [Formula: see text], [Formula: see text], [Formula: see text], [Formula: see text], and [Formula: see text] for levels of negative pressure 0, -20, -30, -40, and  -50 mmHg. LF/HF ratios, the relative power of low-frequency variations to high-frequency variations in heart beat intervals, obtained from isovolumic moments were also compared to those obtained from R peaks. The mean differences  ±  confidence interval were [Formula: see text], [Formula: see text], [Formula: see text], [Formula: see text], and [Formula: see text] for increasing levels of negative pressure. The accuracy and consistency of the algorithm enables the use of SCG as a stand-alone heart monitoring tool in healthy individuals at rest, and could serve as a basis for an eventual application in pathological cases.

  19. pyLIDEM: A Python-Based Tool to Delineate Coastal Watersheds Using LIDAR Data

    NASA Astrophysics Data System (ADS)

    O'Banion, R.; Alameddine, I.; Gronewold, A.; Reckhow, K.

    2008-12-01

    Accurately identifying the boundary of a watershed is one of the most fundamental and important steps in any hydrological assessment. Representative applications include defining a study area, predicting overland flow, estimating groundwater infiltration, modeling pollutant accumulation and wash-off rates, and evaluating effectiveness of pollutant mitigation measures. The United States Environmental Protection Agency (USEPA) Total Maximum Daily Load (TMDL) program, the most comprehensive water quality management program in the United States (US), is just one example of an application in which accurate and efficient watershed delineation tools play a critical role. For example, many impaired water bodies currently being addressed through the TMDL program drain small coastal watersheds with relatively flat terrain, making watershed delineation particularly challenging. Most of these TMDL studies use 30-meter digital elevation models (DEMs) that rarely capture all of the small elevation changes in coastal watersheds, leading to errors not only in watershed boundary delineation, but in subsequent model predictions (such as watershed runoff flow and pollutant deposition rate predictions) for which watershed attributes are key inputs. Manually delineating these low-relief coastal watersheds through the use of expert knowledge of local water flow patterns, often produces relatively accurate (and often more accurate) watershed boundaries as compared to the boundaries generated by the 30-meter DEMs. Yet, manual delineation is a costly and time consuming procedure that is often not opted for. There is a growing need, therefore, particularly to address the ongoing needs of the TMDL program (and similar environmental management programs), for software tools which can utilize high resolution topography data to more accurately delineate coastal watersheds. Here, we address this need by developing pyLIDEM (python LIdar DEM), a python-based tool which processes bare earth high

  20. Semi-automatic delineation of the spino-laminar junction curve on lateral x-ray radiographs of the cervical spine

    NASA Astrophysics Data System (ADS)

    Narang, Benjamin; Phillips, Michael; Knapp, Karen; Appelboam, Andy; Reuben, Adam; Slabaugh, Greg

    2015-03-01

    Assessment of the cervical spine using x-ray radiography is an important task when providing emergency room care to trauma patients suspected of a cervical spine injury. In routine clinical practice, a physician will inspect the alignment of the cervical spine vertebrae by mentally tracing three alignment curves along the anterior and posterior sides of the cervical vertebral bodies, as well as one along the spinolaminar junction. In this paper, we propose an algorithm to semi-automatically delineate the spinolaminar junction curve, given a single reference point and the corners of each vertebral body. From the reference point, our method extracts a region of interest, and performs template matching using normalized cross-correlation to find matching regions along the spinolaminar junction. Matching points are then fit to a third order spline, producing an interpolating curve. Experimental results demonstrate promising results, on average producing a modified Hausdorff distance of 1.8 mm, validated on a dataset consisting of 29 patients including those with degenerative change, retrolisthesis, and fracture.

  1. How large is the Upper Indus Basin? The pitfalls of auto-delineation using DEMs

    NASA Astrophysics Data System (ADS)

    Khan, Asif; Richards, Keith S.; Parker, Geoffrey T.; McRobie, Allan; Mukhopadhyay, Biswajit

    2014-02-01

    Extraction of watershed areas from Digital Elevation Models (DEMs) is increasingly required in a variety of environmental analyses. It is facilitated by the availability of DEMs based on remotely sensed data, and by Geographical Information System (GIS) software. However, accurate delineation depends on the quality of the DEM and the methodology adopted. This paper considers automated and supervised delineation in a case study of the Upper Indus Basin (UIB), Pakistan, for which published estimates of the basin area show significant disagreement, ranging from 166,000 to 266,000 km2. Automated delineation used ArcGIS Archydro and hydrology tools applied to three good quality DEMs (two from SRTM data with 90m resolution, and one from 30m resolution ASTER data). Automatic delineation defined a basin area of c.440,000 km2 for the UIB, but included a large area of internal drainage in the western Tibetan Plateau. It is shown that discrepancies between different estimates reflect differences in the initial extent of the DEM used for watershed delineation, and the unchecked effect of iterative pit-filling of the DEM (going beyond the filling of erroneous pixels to filling entire closed basins). For the UIB we have identified critical points where spurious addition of catchment area has arisen, and use Google Earth to examine the geomorphology adjacent to these points, and also examine the basin boundary data provided by the HydroSHEDS database. We show that the Pangong Tso watershed and some other areas in the western Tibetan plateau are not part of the UIB, but are areas of internal drainage. Our best estimate of the area of the Upper Indus Basin (at Besham Qila) is 164,867 km2 based on the SRTM DEM, and 164,853 km2 using the ASTER DEM). This matches the catchment area measured by WAPDA SWHP. An important lesson from this investigation is that one should not rely on automated delineation, as iterative pit-filling can produce spurious drainage networks and basins, when

  2. Four-chamber heart modeling and automatic segmentation for 3-D cardiac CT volumes using marginal space learning and steerable features.

    PubMed

    Zheng, Yefeng; Barbu, Adrian; Georgescu, Bogdan; Scheuering, Michael; Comaniciu, Dorin

    2008-11-01

    We propose an automatic four-chamber heart segmentation system for the quantitative functional analysis of the heart from cardiac computed tomography (CT) volumes. Two topics are discussed: heart modeling and automatic model fitting to an unseen volume. Heart modeling is a nontrivial task since the heart is a complex nonrigid organ. The model must be anatomically accurate, allow manual editing, and provide sufficient information to guide automatic detection and segmentation. Unlike previous work, we explicitly represent important landmarks (such as the valves and the ventricular septum cusps) among the control points of the model. The control points can be detected reliably to guide the automatic model fitting process. Using this model, we develop an efficient and robust approach for automatic heart chamber segmentation in 3-D CT volumes. We formulate the segmentation as a two-step learning problem: anatomical structure localization and boundary delineation. In both steps, we exploit the recent advances in learning discriminative models. A novel algorithm, marginal space learning (MSL), is introduced to solve the 9-D similarity transformation search problem for localizing the heart chambers. After determining the pose of the heart chambers, we estimate the 3-D shape through learning-based boundary delineation. The proposed method has been extensively tested on the largest dataset (with 323 volumes from 137 patients) ever reported in the literature. To the best of our knowledge, our system is the fastest with a speed of 4.0 s per volume (on a dual-core 3.2-GHz processor) for the automatic segmentation of all four chambers.

  3. 3D automatic anatomy segmentation based on iterative graph-cut-ASM

    DOE Office of Scientific and Technical Information (OSTI.GOV)

    Chen, Xinjian; Bagci, Ulas; Radiology and Imaging Sciences, Clinical Center, National Institutes of Health, Building 10 Room 1C515, Bethesda, Maryland 20892-1182

    2011-08-15

    .22%, and all foot bones for all subjects are 93.75% and 0.28%, respectively. While the delineations for the four organs can be accomplished quite rapidly with average of 78 s, the delineations for the five foot bones can be accomplished with average of 70 s. Conclusions: The experimental results showed the feasibility and efficacy of the proposed automatic anatomy segmentation system: (a) the incorporation of shape priors into the GC framework is feasible in 3D as demonstrated previously for 2D images; (b) our results in 3D confirm the accuracy behavior observed in 2D. The hybrid strategy IGCASM seems to be more robust and accurate than ASM and GC individually; and (c) delineations within body regions and foot bones of clinical importance can be accomplished quite rapidly within 1.5 min.« less

  4. 3D automatic anatomy segmentation based on iterative graph-cut-ASM.

    PubMed

    Chen, Xinjian; Bagci, Ulas

    2011-08-01

    all subjects are 93.75% and 0.28%, respectively. While the delineations for the four organs can be accomplished quite rapidly with average of 78 s, the delineations for the five foot bones can be accomplished with average of 70 s. The experimental results showed the feasibility and efficacy of the proposed automatic anatomy segmentation system: (a) the incorporation of shape priors into the GC framework is feasible in 3D as demonstrated previously for 2D images; (b) our results in 3D confirm the accuracy behavior observed in 2D. The hybrid strategy IGCASM seems to be more robust and accurate than ASM and GC individually; and (c) delineations within body regions and foot bones of clinical importance can be accomplished quite rapidly within 1.5 min.

  5. 3D automatic anatomy segmentation based on iterative graph-cut-ASM

    PubMed Central

    Chen, Xinjian; Bagci, Ulas

    2011-01-01

    foot bones for all subjects are 93.75% and 0.28%, respectively. While the delineations for the four organs can be accomplished quite rapidly with average of 78 s, the delineations for the five foot bones can be accomplished with average of 70 s.Conclusions: The experimental results showed the feasibility and efficacy of the proposed automatic anatomy segmentation system: (a) the incorporation of shape priors into the GC framework is feasible in 3D as demonstrated previously for 2D images; (b) our results in 3D confirm the accuracy behavior observed in 2D. The hybrid strategy IGCASM seems to be more robust and accurate than ASM and GC individually; and (c) delineations within body regions and foot bones of clinical importance can be accomplished quite rapidly within 1.5 min. PMID:21928634

  6. Comparison of [{sup 11}C]choline Positron Emission Tomography With T2- and Diffusion-Weighted Magnetic Resonance Imaging for Delineating Malignant Intraprostatic Lesions

    DOE Office of Scientific and Technical Information (OSTI.GOV)

    Chang, Joe H.; University of Melbourne, Victoria; Lim Joon, Daryl

    2015-06-01

    Purpose: The purpose of this study was to compare the accuracy of [{sup 11}C]choline positron emission tomography (CHOL-PET) with that of the combination of T2-weighted and diffusion-weighted (T2W/DW) magnetic resonance imaging (MRI) for delineating malignant intraprostatic lesions (IPLs) for guiding focal therapies and to investigate factors predicting the accuracy of CHOL-PET. Methods and Materials: This study included 21 patients who underwent CHOL-PET and T2W/DW MRI prior to radical prostatectomy. Two observers manually delineated IPL contours for each scan, and automatic IPL contours were generated on CHOL-PET based on varying proportions of the maximum standardized uptake value (SUV). IPLs identified onmore » prostatectomy specimens defined reference standard contours. The imaging-based contours were compared with the reference standard contours using Dice similarity coefficient (DSC), and sensitivity and specificity values. Factors that could potentially predict the DSC of the best contouring method were analyzed using linear models. Results: The best automatic contouring method, 60% of the maximum SUV (SUV{sub 60}) , had similar correlations (DSC: 0.59) with the manual PET contours (DSC: 0.52, P=.127) and significantly better correlations than the manual MRI contours (DSC: 0.37, P<.001). The sensitivity and specificity values were 72% and 71% for SUV{sub 60}; 53% and 86% for PET manual contouring; and 28% and 92% for MRI manual contouring. The tumor volume and transition zone pattern could independently predict the accuracy of CHOL-PET. Conclusions: CHOL-PET is superior to the combination of T2W/DW MRI for delineating IPLs. The accuracy of CHOL-PET is insufficient for gland-sparing focal therapies but may be accurate enough for focal boost therapies. The transition zone pattern is a new classification that may predict how well CHOL-PET delineates IPLs.« less

  7. Automated basin delineation from digital terrain data

    NASA Technical Reports Server (NTRS)

    Marks, D.; Dozier, J.; Frew, J.

    1983-01-01

    While digital terrain grids are now in wide use, accurate delineation of drainage basins from these data is difficult to efficiently automate. A recursive order N solution to this problem is presented. The algorithm is fast because no point in the basin is checked more than once, and no points outside the basin are considered. Two applications for terrain analysis and one for remote sensing are given to illustrate the method, on a basin with high relief in the Sierra Nevada. This technique for automated basin delineation will enhance the utility of digital terrain analysis for hydrologic modeling and remote sensing.

  8. Automatic Delineation of the Myocardial Wall from CT Images via Shape Segmentation and Variational Region Growing

    PubMed Central

    Zhu, Liangjia; Gao, Yi; Appia, Vikram; Yezzi, Anthony; Arepalli, Chesnal; Faber, Tracy; Stillman, Arthur; Tannenbaum, Allen

    2014-01-01

    Prognosis and diagnosis of cardiac diseases frequently require quantitative evaluation of the ventricle volume, mass, and ejection fraction. The delineation of the myocardial wall is involved in all of these evaluations, which is a challenging task due to large variations in myocardial shapes and image quality. In this work, we present an automatic method for extracting the myocardial wall of the left and right ventricles from cardiac CT images. In the method, the left and right ventricles are located sequentially, in which each ventricle is detected by first identifying the endocardium and then segmenting the epicardium. To this end, the endocardium is localized by utilizing its geometric features obtained on-line from a CT image. After that, a variational region-growing model is employed to extract the epicardium of the ventricles. In particular, the location of the endocardium of the left ventricle is determined via using an active contour model on the blood-pool surface. To localize the right ventricle, the active contour model is applied on a heart surface extracted based on the left ventricle segmentation result. The robustness and accuracy of the proposed approach is demonstrated by experimental results from 33 human and 12 pig CT images. PMID:23744658

  9. Automatic Cell Segmentation in Fluorescence Images of Confluent Cell Monolayers Using Multi-object Geometric Deformable Model.

    PubMed

    Yang, Zhen; Bogovic, John A; Carass, Aaron; Ye, Mao; Searson, Peter C; Prince, Jerry L

    2013-03-13

    With the rapid development of microscopy for cell imaging, there is a strong and growing demand for image analysis software to quantitatively study cell morphology. Automatic cell segmentation is an important step in image analysis. Despite substantial progress, there is still a need to improve the accuracy, efficiency, and adaptability to different cell morphologies. In this paper, we propose a fully automatic method for segmenting cells in fluorescence images of confluent cell monolayers. This method addresses several challenges through a combination of ideas. 1) It realizes a fully automatic segmentation process by first detecting the cell nuclei as initial seeds and then using a multi-object geometric deformable model (MGDM) for final segmentation. 2) To deal with different defects in the fluorescence images, the cell junctions are enhanced by applying an order-statistic filter and principal curvature based image operator. 3) The final segmentation using MGDM promotes robust and accurate segmentation results, and guarantees no overlaps and gaps between neighboring cells. The automatic segmentation results are compared with manually delineated cells, and the average Dice coefficient over all distinguishable cells is 0.88.

  10. 3D automatic anatomy recognition based on iterative graph-cut-ASM

    NASA Astrophysics Data System (ADS)

    Chen, Xinjian; Udupa, Jayaram K.; Bagci, Ulas; Alavi, Abass; Torigian, Drew A.

    2010-02-01

    We call the computerized assistive process of recognizing, delineating, and quantifying organs and tissue regions in medical imaging, occurring automatically during clinical image interpretation, automatic anatomy recognition (AAR). The AAR system we are developing includes five main parts: model building, object recognition, object delineation, pathology detection, and organ system quantification. In this paper, we focus on the delineation part. For the modeling part, we employ the active shape model (ASM) strategy. For recognition and delineation, we integrate several hybrid strategies of combining purely image based methods with ASM. In this paper, an iterative Graph-Cut ASM (IGCASM) method is proposed for object delineation. An algorithm called GC-ASM was presented at this symposium last year for object delineation in 2D images which attempted to combine synergistically ASM and GC. Here, we extend this method to 3D medical image delineation. The IGCASM method effectively combines the rich statistical shape information embodied in ASM with the globally optimal delineation capability of the GC method. We propose a new GC cost function, which effectively integrates the specific image information with the ASM shape model information. The proposed methods are tested on a clinical abdominal CT data set. The preliminary results show that: (a) it is feasible to explicitly bring prior 3D statistical shape information into the GC framework; (b) the 3D IGCASM delineation method improves on ASM and GC and can provide practical operational time on clinical images.

  11. Automatic segmentation and supervised learning-based selection of nuclei in cancer tissue images.

    PubMed

    Nandy, Kaustav; Gudla, Prabhakar R; Amundsen, Ryan; Meaburn, Karen J; Misteli, Tom; Lockett, Stephen J

    2012-09-01

    Analysis of preferential localization of certain genes within the cell nuclei is emerging as a new technique for the diagnosis of breast cancer. Quantitation requires accurate segmentation of 100-200 cell nuclei in each tissue section to draw a statistically significant result. Thus, for large-scale analysis, manual processing is too time consuming and subjective. Fortuitously, acquired images generally contain many more nuclei than are needed for analysis. Therefore, we developed an integrated workflow that selects, following automatic segmentation, a subpopulation of accurately delineated nuclei for positioning of fluorescence in situ hybridization-labeled genes of interest. Segmentation was performed by a multistage watershed-based algorithm and screening by an artificial neural network-based pattern recognition engine. The performance of the workflow was quantified in terms of the fraction of automatically selected nuclei that were visually confirmed as well segmented and by the boundary accuracy of the well-segmented nuclei relative to a 2D dynamic programming-based reference segmentation method. Application of the method was demonstrated for discriminating normal and cancerous breast tissue sections based on the differential positioning of the HES5 gene. Automatic results agreed with manual analysis in 11 out of 14 cancers, all four normal cases, and all five noncancerous breast disease cases, thus showing the accuracy and robustness of the proposed approach. Published 2012 Wiley Periodicals, Inc.

  12. Automatic segmentation of the choroid in enhanced depth imaging optical coherence tomography images.

    PubMed

    Tian, Jing; Marziliano, Pina; Baskaran, Mani; Tun, Tin Aung; Aung, Tin

    2013-03-01

    Enhanced Depth Imaging (EDI) optical coherence tomography (OCT) provides high-definition cross-sectional images of the choroid in vivo, and hence is used in many clinical studies. However, the quantification of the choroid depends on the manual labelings of two boundaries, Bruch's membrane and the choroidal-scleral interface. This labeling process is tedious and subjective of inter-observer differences, hence, automatic segmentation of the choroid layer is highly desirable. In this paper, we present a fast and accurate algorithm that could segment the choroid automatically. Bruch's membrane is detected by searching the pixel with the biggest gradient value above the retinal pigment epithelium (RPE) and the choroidal-scleral interface is delineated by finding the shortest path of the graph formed by valley pixels using Dijkstra's algorithm. The experiments comparing automatic segmentation results with the manual labelings are conducted on 45 EDI-OCT images and the average of Dice's Coefficient is 90.5%, which shows good consistency of the algorithm with the manual labelings. The processing time for each image is about 1.25 seconds.

  13. A multiresolution prostate representation for automatic segmentation in magnetic resonance images.

    PubMed

    Alvarez, Charlens; Martínez, Fabio; Romero, Eduardo

    2017-04-01

    Accurate prostate delineation is necessary in radiotherapy processes for concentrating the dose onto the prostate and reducing side effects in neighboring organs. Currently, manual delineation is performed over magnetic resonance imaging (MRI) taking advantage of its high soft tissue contrast property. Nevertheless, as human intervention is a consuming task with high intra- and interobserver variability rates, (semi)-automatic organ delineation tools have emerged to cope with these challenges, reducing the time spent for these tasks. This work presents a multiresolution representation that defines a novel metric and allows to segment a new prostate by combining a set of most similar prostates in a dataset. The proposed method starts by selecting the set of most similar prostates with respect to a new one using the proposed multiresolution representation. This representation characterizes the prostate through a set of salient points, extracted from a region of interest (ROI) that encloses the organ and refined using structural information, allowing to capture main relevant features of the organ boundary. Afterward, the new prostate is automatically segmented by combining the nonrigidly registered expert delineations associated to the previous selected similar prostates using a weighted patch-based strategy. Finally, the prostate contour is smoothed based on morphological operations. The proposed approach was evaluated with respect to the expert manual segmentation under a leave-one-out scheme using two public datasets, obtaining averaged Dice coefficients of 82% ± 0.07 and 83% ± 0.06, and demonstrating a competitive performance with respect to atlas-based state-of-the-art methods. The proposed multiresolution representation provides a feature space that follows a local salient point criteria and a global rule of the spatial configuration among these points to find out the most similar prostates. This strategy suggests an easy adaptation in the clinical

  14. Validation of Simple Quantification Methods for (18)F-FP-CIT PET Using Automatic Delineation of Volumes of Interest Based on Statistical Probabilistic Anatomical Mapping and Isocontour Margin Setting.

    PubMed

    Kim, Yong-Il; Im, Hyung-Jun; Paeng, Jin Chul; Lee, Jae Sung; Eo, Jae Seon; Kim, Dong Hyun; Kim, Euishin E; Kang, Keon Wook; Chung, June-Key; Lee, Dong Soo

    2012-12-01

    (18)F-FP-CIT positron emission tomography (PET) is an effective imaging for dopamine transporters. In usual clinical practice, (18)F-FP-CIT PET is analyzed visually or quantified using manual delineation of a volume of interest (VOI) for the striatum. In this study, we suggested and validated two simple quantitative methods based on automatic VOI delineation using statistical probabilistic anatomical mapping (SPAM) and isocontour margin setting. Seventy-five (18)F-FP-CIT PET images acquired in routine clinical practice were used for this study. A study-specific image template was made and the subject images were normalized to the template. Afterwards, uptakes in the striatal regions and cerebellum were quantified using probabilistic VOI based on SPAM. A quantitative parameter, QSPAM, was calculated to simulate binding potential. Additionally, the functional volume of each striatal region and its uptake were measured in automatically delineated VOI using isocontour margin setting. Uptake-volume product (QUVP) was calculated for each striatal region. QSPAM and QUVP were compared with visual grading and the influence of cerebral atrophy on the measurements was tested. Image analyses were successful in all the cases. Both the QSPAM and QUVP were significantly different according to visual grading (P < 0.001). The agreements of QUVP or QSPAM with visual grading were slight to fair for the caudate nucleus (κ = 0.421 and 0.291, respectively) and good to perfect to the putamen (κ = 0.663 and 0.607, respectively). Also, QSPAM and QUVP had a significant correlation with each other (P < 0.001). Cerebral atrophy made a significant difference in QSPAM and QUVP of the caudate nuclei regions with decreased (18)F-FP-CIT uptake. Simple quantitative measurements of QSPAM and QUVP showed acceptable agreement with visual grading. Although QSPAM in some group may be influenced by cerebral atrophy, these simple methods are expected to be effective in the quantitative

  15. SPEQTACLE: An automated generalized fuzzy C-means algorithm for tumor delineation in PET

    DOE Office of Scientific and Technical Information (OSTI.GOV)

    Lapuyade-Lahorgue, Jérôme; Visvikis, Dimitris; Hatt, Mathieu, E-mail: hatt@univ-brest.fr

    Purpose: Accurate tumor delineation in positron emission tomography (PET) images is crucial in oncology. Although recent methods achieved good results, there is still room for improvement regarding tumors with complex shapes, low signal-to-noise ratio, and high levels of uptake heterogeneity. Methods: The authors developed and evaluated an original clustering-based method called spatial positron emission quantification of tumor—Automatic Lp-norm estimation (SPEQTACLE), based on the fuzzy C-means (FCM) algorithm with a generalization exploiting a Hilbertian norm to more accurately account for the fuzzy and non-Gaussian distributions of PET images. An automatic and reproducible estimation scheme of the norm on an image-by-image basismore » was developed. Robustness was assessed by studying the consistency of results obtained on multiple acquisitions of the NEMA phantom on three different scanners with varying acquisition parameters. Accuracy was evaluated using classification errors (CEs) on simulated and clinical images. SPEQTACLE was compared to another FCM implementation, fuzzy local information C-means (FLICM) and fuzzy locally adaptive Bayesian (FLAB). Results: SPEQTACLE demonstrated a level of robustness similar to FLAB (variability of 14% ± 9% vs 14% ± 7%, p = 0.15) and higher than FLICM (45% ± 18%, p < 0.0001), and improved accuracy with lower CE (14% ± 11%) over both FLICM (29% ± 29%) and FLAB (22% ± 20%) on simulated images. Improvement was significant for the more challenging cases with CE of 17% ± 11% for SPEQTACLE vs 28% ± 22% for FLAB (p = 0.009) and 40% ± 35% for FLICM (p < 0.0001). For the clinical cases, SPEQTACLE outperformed FLAB and FLICM (15% ± 6% vs 37% ± 14% and 30% ± 17%, p < 0.004). Conclusions: SPEQTACLE benefitted from the fully automatic estimation of the norm on a case-by-case basis. This promising approach will be extended to multimodal images and multiclass estimation in future developments.« less

  16. DoctorEye: A clinically driven multifunctional platform, for accurate processing of tumors in medical images.

    PubMed

    Skounakis, Emmanouil; Farmaki, Christina; Sakkalis, Vangelis; Roniotis, Alexandros; Banitsas, Konstantinos; Graf, Norbert; Marias, Konstantinos

    2010-01-01

    This paper presents a novel, open access interactive platform for 3D medical image analysis, simulation and visualization, focusing in oncology images. The platform was developed through constant interaction and feedback from expert clinicians integrating a thorough analysis of their requirements while having an ultimate goal of assisting in accurately delineating tumors. It allows clinicians not only to work with a large number of 3D tomographic datasets but also to efficiently annotate multiple regions of interest in the same session. Manual and semi-automatic segmentation techniques combined with integrated correction tools assist in the quick and refined delineation of tumors while different users can add different components related to oncology such as tumor growth and simulation algorithms for improving therapy planning. The platform has been tested by different users and over large number of heterogeneous tomographic datasets to ensure stability, usability, extensibility and robustness with promising results. the platform, a manual and tutorial videos are available at: http://biomodeling.ics.forth.gr. it is free to use under the GNU General Public License.

  17. Accurate and automatic extrinsic calibration method for blade measurement system integrated by different optical sensors

    NASA Astrophysics Data System (ADS)

    He, Wantao; Li, Zhongwei; Zhong, Kai; Shi, Yusheng; Zhao, Can; Cheng, Xu

    2014-11-01

    Fast and precise 3D inspection system is in great demand in modern manufacturing processes. At present, the available sensors have their own pros and cons, and hardly exist an omnipotent sensor to handle the complex inspection task in an accurate and effective way. The prevailing solution is integrating multiple sensors and taking advantages of their strengths. For obtaining a holistic 3D profile, the data from different sensors should be registrated into a coherent coordinate system. However, some complex shape objects own thin wall feather such as blades, the ICP registration method would become unstable. Therefore, it is very important to calibrate the extrinsic parameters of each sensor in the integrated measurement system. This paper proposed an accurate and automatic extrinsic parameter calibration method for blade measurement system integrated by different optical sensors. In this system, fringe projection sensor (FPS) and conoscopic holography sensor (CHS) is integrated into a multi-axis motion platform, and the sensors can be optimally move to any desired position at the object's surface. In order to simple the calibration process, a special calibration artifact is designed according to the characteristics of the two sensors. An automatic registration procedure based on correlation and segmentation is used to realize the artifact datasets obtaining by FPS and CHS rough alignment without any manual operation and data pro-processing, and then the Generalized Gauss-Markoff model is used to estimate the optimization transformation parameters. The experiments show the measurement result of a blade, where several sampled patches are merged into one point cloud, and it verifies the performance of the proposed method.

  18. A Fast, Automatic Segmentation Algorithm for Locating and Delineating Touching Cell Boundaries in Imaged Histopathology

    PubMed Central

    Qi, Xin; Xing, Fuyong; Foran, David J.; Yang, Lin

    2013-01-01

    Summary Background Automated analysis of imaged histopathology specimens could potentially provide support for improved reliability in detection and classification in a range of investigative and clinical cancer applications. Automated segmentation of cells in the digitized tissue microarray (TMA) is often the prerequisite for quantitative analysis. However overlapping cells usually bring significant challenges for traditional segmentation algorithms. Objectives In this paper, we propose a novel, automatic algorithm to separate overlapping cells in stained histology specimens acquired using bright-field RGB imaging. Methods It starts by systematically identifying salient regions of interest throughout the image based upon their underlying visual content. The segmentation algorithm subsequently performs a quick, voting based seed detection. Finally, the contour of each cell is obtained using a repulsive level set deformable model using the seeds generated in the previous step. We compared the experimental results with the most current literature, and the pixel wise accuracy between human experts' annotation and those generated using the automatic segmentation algorithm. Results The method is tested with 100 image patches which contain more than 1000 overlapping cells. The overall precision and recall of the developed algorithm is 90% and 78%, respectively. We also implement the algorithm on GPU. The parallel implementation is 22 times faster than its C/C++ sequential implementation. Conclusion The proposed overlapping cell segmentation algorithm can accurately detect the center of each overlapping cell and effectively separate each of the overlapping cells. GPU is proven to be an efficient parallel platform for overlapping cell segmentation. PMID:22526139

  19. Atlas-based automatic segmentation of head and neck organs at risk and nodal target volumes: a clinical validation.

    PubMed

    Daisne, Jean-François; Blumhofer, Andreas

    2013-06-26

    Intensity modulated radiotherapy for head and neck cancer necessitates accurate definition of organs at risk (OAR) and clinical target volumes (CTV). This crucial step is time consuming and prone to inter- and intra-observer variations. Automatic segmentation by atlas deformable registration may help to reduce time and variations. We aim to test a new commercial atlas algorithm for automatic segmentation of OAR and CTV in both ideal and clinical conditions. The updated Brainlab automatic head and neck atlas segmentation was tested on 20 patients: 10 cN0-stages (ideal population) and 10 unselected N-stages (clinical population). Following manual delineation of OAR and CTV, automatic segmentation of the same set of structures was performed and afterwards manually corrected. Dice Similarity Coefficient (DSC), Average Surface Distance (ASD) and Maximal Surface Distance (MSD) were calculated for "manual to automatic" and "manual to corrected" volumes comparisons. In both groups, automatic segmentation saved about 40% of the corresponding manual segmentation time. This effect was more pronounced for OAR than for CTV. The edition of the automatically obtained contours significantly improved DSC, ASD and MSD. Large distortions of normal anatomy or lack of iodine contrast were the limiting factors. The updated Brainlab atlas-based automatic segmentation tool for head and neck Cancer patients is timesaving but still necessitates review and corrections by an expert.

  20. A Modular Low-Complexity ECG Delineation Algorithm for Real-Time Embedded Systems.

    PubMed

    Bote, Jose Manuel; Recas, Joaquin; Rincon, Francisco; Atienza, David; Hermida, Roman

    2018-03-01

    This work presents a new modular and low-complexity algorithm for the delineation of the different ECG waves (QRS, P and T peaks, onsets, and end). Involving a reduced number of operations per second and having a small memory footprint, this algorithm is intended to perform real-time delineation on resource-constrained embedded systems. The modular design allows the algorithm to automatically adjust the delineation quality in runtime to a wide range of modes and sampling rates, from a ultralow-power mode when no arrhythmia is detected, in which the ECG is sampled at low frequency, to a complete high-accuracy delineation mode, in which the ECG is sampled at high frequency and all the ECG fiducial points are detected, in the case of arrhythmia. The delineation algorithm has been adjusted using the QT database, providing very high sensitivity and positive predictivity, and validated with the MIT database. The errors in the delineation of all the fiducial points are below the tolerances given by the Common Standards for Electrocardiography Committee in the high-accuracy mode, except for the P wave onset, for which the algorithm is above the agreed tolerances by only a fraction of the sample duration. The computational load for the ultralow-power 8-MHz TI MSP430 series microcontroller ranges from 0.2% to 8.5% according to the mode used.

  1. Automatic right ventricle (RV) segmentation by propagating a basal spatio-temporal characterization

    NASA Astrophysics Data System (ADS)

    Atehortúa, Angélica; Zuluaga, María. A.; Martínez, Fabio; Romero, Eduardo

    2015-12-01

    An accurate right ventricular (RV) function quantification is important to support the evaluation, diagnosis and prognosis of several cardiac pathologies and to complement the left ventricular function assessment. However, expert RV delineation is a time consuming task with high inter-and-intra observer variability. In this paper we present an automatic segmentation method of the RV in MR-cardiac sequences. Unlike atlas or multi-atlas methods, this approach estimates the RV using exclusively information from the sequence itself. For so doing, a spatio-temporal analysis segments the heart at the basal slice, segmentation that is then propagated to the apex by using a non-rigid-registration strategy. The proposed approach achieves an average Dice Score of 0:79 evaluated with a set of 48 patients.

  2. Automatic 3D liver segmentation based on deep learning and globally optimized surface evolution

    NASA Astrophysics Data System (ADS)

    Hu, Peijun; Wu, Fa; Peng, Jialin; Liang, Ping; Kong, Dexing

    2016-12-01

    The detection and delineation of the liver from abdominal 3D computed tomography (CT) images are fundamental tasks in computer-assisted liver surgery planning. However, automatic and accurate segmentation, especially liver detection, remains challenging due to complex backgrounds, ambiguous boundaries, heterogeneous appearances and highly varied shapes of the liver. To address these difficulties, we propose an automatic segmentation framework based on 3D convolutional neural network (CNN) and globally optimized surface evolution. First, a deep 3D CNN is trained to learn a subject-specific probability map of the liver, which gives the initial surface and acts as a shape prior in the following segmentation step. Then, both global and local appearance information from the prior segmentation are adaptively incorporated into a segmentation model, which is globally optimized in a surface evolution way. The proposed method has been validated on 42 CT images from the public Sliver07 database and local hospitals. On the Sliver07 online testing set, the proposed method can achieve an overall score of 80.3+/- 4.5 , yielding a mean Dice similarity coefficient of 97.25+/- 0.65 % , and an average symmetric surface distance of 0.84+/- 0.25 mm. The quantitative validations and comparisons show that the proposed method is accurate and effective for clinical application.

  3. Automatical and accurate segmentation of cerebral tissues in fMRI dataset with combination of image processing and deep learning

    NASA Astrophysics Data System (ADS)

    Kong, Zhenglun; Luo, Junyi; Xu, Shengpu; Li, Ting

    2018-02-01

    Image segmentation plays an important role in medical science. One application is multimodality imaging, especially the fusion of structural imaging with functional imaging, which includes CT, MRI and new types of imaging technology such as optical imaging to obtain functional images. The fusion process require precisely extracted structural information, in order to register the image to it. Here we used image enhancement, morphometry methods to extract the accurate contours of different tissues such as skull, cerebrospinal fluid (CSF), grey matter (GM) and white matter (WM) on 5 fMRI head image datasets. Then we utilized convolutional neural network to realize automatic segmentation of images in deep learning way. Such approach greatly reduced the processing time compared to manual and semi-automatic segmentation and is of great importance in improving speed and accuracy as more and more samples being learned. The contours of the borders of different tissues on all images were accurately extracted and 3D visualized. This can be used in low-level light therapy and optical simulation software such as MCVM. We obtained a precise three-dimensional distribution of brain, which offered doctors and researchers quantitative volume data and detailed morphological characterization for personal precise medicine of Cerebral atrophy/expansion. We hope this technique can bring convenience to visualization medical and personalized medicine.

  4. Automatic detection of new tumors and tumor burden evaluation in longitudinal liver CT scan studies.

    PubMed

    Vivanti, R; Szeskin, A; Lev-Cohain, N; Sosna, J; Joskowicz, L

    2017-11-01

    Radiological longitudinal follow-up of liver tumors in CT scans is the standard of care for disease progression assessment and for liver tumor therapy. Finding new tumors in the follow-up scan is essential to determine malignancy, to evaluate the total tumor burden, and to determine treatment efficacy. Since new tumors are typically small, they may be missed by examining radiologists. We describe a new method for the automatic detection and segmentation of new tumors in longitudinal liver CT studies and for liver tumors burden quantification. Its inputs are the baseline and follow-up CT scans, the baseline tumors delineation, and a tumor appearance prior model. Its outputs are the new tumors segmentations in the follow-up scan, the tumor burden quantification in both scans, and the tumor burden change. Our method is the first comprehensive method that is explicitly designed to find new liver tumors. It integrates information from the scans, the baseline known tumors delineations, and a tumor appearance prior model in the form of a global convolutional neural network classifier. Unlike other deep learning-based methods, it does not require large tagged training sets. Our experimental results on 246 tumors, of which 97 were new tumors, from 37 longitudinal liver CT studies with radiologist approved ground-truth segmentations, yields a true positive new tumors detection rate of 86 versus 72% with stand-alone detection, and a tumor burden volume overlap error of 16%. New tumors detection and tumor burden volumetry are important for diagnosis and treatment. Our new method enables a simplified radiologist-friendly workflow that is potentially more accurate and reliable than the existing one by automatically and accurately following known tumors and detecting new tumors in the follow-up scan.

  5. MR and CT data with multiobserver delineations of organs in the pelvic area-Part of the Gold Atlas project.

    PubMed

    Nyholm, Tufve; Svensson, Stina; Andersson, Sebastian; Jonsson, Joakim; Sohlin, Maja; Gustafsson, Christian; Kjellén, Elisabeth; Söderström, Karin; Albertsson, Per; Blomqvist, Lennart; Zackrisson, Björn; Olsson, Lars E; Gunnlaugsson, Adalsteinn

    2018-03-01

    We describe a public dataset with MR and CT images of patients performed in the same position with both multiobserver and expert consensus delineations of relevant organs in the male pelvic region. The purpose was to provide means for training and validation of segmentation algorithms and methods to convert MR to CT like data, i.e., so called synthetic CT (sCT). T1- and T2-weighted MR images as well as CT data were collected for 19 patients at three different departments. Five experts delineated nine organs for each patient based on the T2-weighted MR images. An automatic method was used to fuse the delineations. Starting from each fused delineation, a consensus delineation was agreed upon by the five experts for each organ and patient. Segmentation overlap between user delineations with respect to the consensus delineations was measured to describe the spread of the collected data. Finally, an open-source software was used to create deformation vector fields describing the relation between MR and CT images to further increase the usability of the dataset. The dataset has been made publically available to be used for academic purposes, and can be accessed from https://zenodo.org/record/583096. The dataset provides a useful source for training and validation of segmentation algorithms as well as methods to convert MR to CT-like data (sCT). To give some examples: The T2-weighted MR images with their consensus delineations can directly be used as a template in an existing atlas-based segmentation engine; the expert delineations are useful to validate the performance of a segmentation algorithm as they provide a way to measure variability among users which can be compared with the result of an automatic segmentation; and the pairwise deformably registered MR and CT images can be a source for an atlas-based sCT algorithm or for validation of sCT algorithm. © 2018 The Authors. Medical Physics published by Wiley Periodicals, Inc. on behalf of American Association of

  6. Image-based mobile service: automatic text extraction and translation

    NASA Astrophysics Data System (ADS)

    Berclaz, Jérôme; Bhatti, Nina; Simske, Steven J.; Schettino, John C.

    2010-01-01

    We present a new mobile service for the translation of text from images taken by consumer-grade cell-phone cameras. Such capability represents a new paradigm for users where a simple image provides the basis for a service. The ubiquity and ease of use of cell-phone cameras enables acquisition and transmission of images anywhere and at any time a user wishes, delivering rapid and accurate translation over the phone's MMS and SMS facilities. Target text is extracted completely automatically, requiring no bounding box delineation or related user intervention. The service uses localization, binarization, text deskewing, and optical character recognition (OCR) in its analysis. Once the text is translated, an SMS message is sent to the user with the result. Further novelties include that no software installation is required on the handset, any service provider or camera phone can be used, and the entire service is implemented on the server side.

  7. Methods for Data-based Delineation of Spatial Regions

    DOE Office of Scientific and Technical Information (OSTI.GOV)

    Wilson, John E.

    In data analysis, it is often useful to delineate or segregate areas of interest from the general population of data in order to concentrate further analysis efforts on smaller areas. Three methods are presented here for automatically generating polygons around spatial data of interest. Each method addresses a distinct data type. These methods were developed for and implemented in the sample planning tool called Visual Sample Plan (VSP). Method A is used to delineate areas of elevated values in a rectangular grid of data (raster). The data used for this method are spatially related. Although VSP uses data from amore » kriging process for this method, it will work for any type of data that is spatially coherent and appears on a regular grid. Method B is used to surround areas of interest characterized by individual data points that are congregated within a certain distance of each other. Areas where data are “clumped” together spatially will be delineated. Method C is used to recreate the original boundary in a raster of data that separated data values from non-values. This is useful when a rectangular raster of data contains non-values (missing data) that indicate they were outside of some original boundary. If the original boundary is not delivered with the raster, this method will approximate the original boundary.« less

  8. A Low-Cost Approach to Automatically Obtain Accurate 3D Models of Woody Crops.

    PubMed

    Bengochea-Guevara, José M; Andújar, Dionisio; Sanchez-Sardana, Francisco L; Cantuña, Karla; Ribeiro, Angela

    2017-12-24

    Crop monitoring is an essential practice within the field of precision agriculture since it is based on observing, measuring and properly responding to inter- and intra-field variability. In particular, "on ground crop inspection" potentially allows early detection of certain crop problems or precision treatment to be carried out simultaneously with pest detection. "On ground monitoring" is also of great interest for woody crops. This paper explores the development of a low-cost crop monitoring system that can automatically create accurate 3D models (clouds of coloured points) of woody crop rows. The system consists of a mobile platform that allows the easy acquisition of information in the field at an average speed of 3 km/h. The platform, among others, integrates an RGB-D sensor that provides RGB information as well as an array with the distances to the objects closest to the sensor. The RGB-D information plus the geographical positions of relevant points, such as the starting and the ending points of the row, allow the generation of a 3D reconstruction of a woody crop row in which all the points of the cloud have a geographical location as well as the RGB colour values. The proposed approach for the automatic 3D reconstruction is not limited by the size of the sampled space and includes a method for the removal of the drift that appears in the reconstruction of large crop rows.

  9. A Low-Cost Approach to Automatically Obtain Accurate 3D Models of Woody Crops

    PubMed Central

    Andújar, Dionisio; Sanchez-Sardana, Francisco L.; Cantuña, Karla

    2017-01-01

    Crop monitoring is an essential practice within the field of precision agriculture since it is based on observing, measuring and properly responding to inter- and intra-field variability. In particular, “on ground crop inspection” potentially allows early detection of certain crop problems or precision treatment to be carried out simultaneously with pest detection. “On ground monitoring” is also of great interest for woody crops. This paper explores the development of a low-cost crop monitoring system that can automatically create accurate 3D models (clouds of coloured points) of woody crop rows. The system consists of a mobile platform that allows the easy acquisition of information in the field at an average speed of 3 km/h. The platform, among others, integrates an RGB-D sensor that provides RGB information as well as an array with the distances to the objects closest to the sensor. The RGB-D information plus the geographical positions of relevant points, such as the starting and the ending points of the row, allow the generation of a 3D reconstruction of a woody crop row in which all the points of the cloud have a geographical location as well as the RGB colour values. The proposed approach for the automatic 3D reconstruction is not limited by the size of the sampled space and includes a method for the removal of the drift that appears in the reconstruction of large crop rows. PMID:29295536

  10. Automatic metastatic brain tumor segmentation for stereotactic radiosurgery applications.

    PubMed

    Liu, Yan; Stojadinovic, Strahinja; Hrycushko, Brian; Wardak, Zabi; Lu, Weiguo; Yan, Yulong; Jiang, Steve B; Timmerman, Robert; Abdulrahman, Ramzi; Nedzi, Lucien; Gu, Xuejun

    2016-12-21

    The objective of this study is to develop an automatic segmentation strategy for efficient and accurate metastatic brain tumor delineation on contrast-enhanced T1-weighted (T1c) magnetic resonance images (MRI) for stereotactic radiosurgery (SRS) applications. The proposed four-step automatic brain metastases segmentation strategy is comprised of pre-processing, initial contouring, contour evolution, and contour triage. First, T1c brain images are preprocessed to remove the skull. Second, an initial tumor contour is created using a multi-scaled adaptive threshold-based bounding box and a super-voxel clustering technique. Third, the initial contours are evolved to the tumor boundary using a regional active contour technique. Fourth, all detected false-positive contours are removed with geometric characterization. The segmentation process was validated on a realistic virtual phantom containing Gaussian or Rician noise. For each type of noise distribution, five different noise levels were tested. Twenty-one cases from the multimodal brain tumor image segmentation (BRATS) challenge dataset and fifteen clinical metastases cases were also included in validation. Segmentation performance was quantified by the Dice coefficient (DC), normalized mutual information (NMI), structural similarity (SSIM), Hausdorff distance (HD), mean value of surface-to-surface distance (MSSD) and standard deviation of surface-to-surface distance (SDSSD). In the numerical phantom study, the evaluation yielded a DC of 0.98  ±  0.01, an NMI of 0.97  ±  0.01, an SSIM of 0.999  ±  0.001, an HD of 2.2  ±  0.8 mm, an MSSD of 0.1  ±  0.1 mm, and an SDSSD of 0.3  ±  0.1 mm. The validation on the BRATS data resulted in a DC of 0.89  ±  0.08, which outperform the BRATS challenge algorithms. Evaluation on clinical datasets gave a DC of 0.86  ±  0.09, an NMI of 0.80  ±  0.11, an SSIM of 0.999  ±  0.001, an HD of 8

  11. Automatic metastatic brain tumor segmentation for stereotactic radiosurgery applications

    NASA Astrophysics Data System (ADS)

    Liu, Yan; Stojadinovic, Strahinja; Hrycushko, Brian; Wardak, Zabi; Lu, Weiguo; Yan, Yulong; Jiang, Steve B.; Timmerman, Robert; Abdulrahman, Ramzi; Nedzi, Lucien; Gu, Xuejun

    2016-12-01

    The objective of this study is to develop an automatic segmentation strategy for efficient and accurate metastatic brain tumor delineation on contrast-enhanced T1-weighted (T1c) magnetic resonance images (MRI) for stereotactic radiosurgery (SRS) applications. The proposed four-step automatic brain metastases segmentation strategy is comprised of pre-processing, initial contouring, contour evolution, and contour triage. First, T1c brain images are preprocessed to remove the skull. Second, an initial tumor contour is created using a multi-scaled adaptive threshold-based bounding box and a super-voxel clustering technique. Third, the initial contours are evolved to the tumor boundary using a regional active contour technique. Fourth, all detected false-positive contours are removed with geometric characterization. The segmentation process was validated on a realistic virtual phantom containing Gaussian or Rician noise. For each type of noise distribution, five different noise levels were tested. Twenty-one cases from the multimodal brain tumor image segmentation (BRATS) challenge dataset and fifteen clinical metastases cases were also included in validation. Segmentation performance was quantified by the Dice coefficient (DC), normalized mutual information (NMI), structural similarity (SSIM), Hausdorff distance (HD), mean value of surface-to-surface distance (MSSD) and standard deviation of surface-to-surface distance (SDSSD). In the numerical phantom study, the evaluation yielded a DC of 0.98  ±  0.01, an NMI of 0.97  ±  0.01, an SSIM of 0.999  ±  0.001, an HD of 2.2  ±  0.8 mm, an MSSD of 0.1  ±  0.1 mm, and an SDSSD of 0.3  ±  0.1 mm. The validation on the BRATS data resulted in a DC of 0.89  ±  0.08, which outperform the BRATS challenge algorithms. Evaluation on clinical datasets gave a DC of 0.86  ±  0.09, an NMI of 0.80  ±  0.11, an SSIM of 0.999  ±  0.001, an HD of 8

  12. Detection and delineation of underground septic tanks in sandy terrain using ground penetrating radar

    NASA Astrophysics Data System (ADS)

    Omolaiye, Gabriel Efomeh; Ayolabi, Elijah A.

    2010-09-01

    A ground penetrating radar (GPR) survey was conducted on the Lekki Peninsula, Lagos State, Nigeria. The primary target of the survey was the delineation of underground septic tanks (ST). A total of four GPR profiles were acquired on the survey site using Ramac X3M GPR equipment with a 250MHz antenna, chosen based on the depth of interest and resolution. An interpretable depth of penetration of 4.5m below the surface was achieved after processing. The method accurately delineated five underground ST. The tops of the ST were easily identified on the radargram based on the strong-amplitude anomalies, the length and the depths to the base of the ST were estimated with 99 and 73 percent confidence respectively. The continuous vertical profiles provide uninterrupted subsurface data along the lines of traverse, while the non-intrusive nature makes it an ideal tool for the accurate mapping and delineation of underground utilities.

  13. Quantifying the robustness of [18F]FDG-PET/CT radiomic features with respect to tumor delineation in head and neck and pancreatic cancer patients.

    PubMed

    Belli, Maria Luisa; Mori, Martina; Broggi, Sara; Cattaneo, Giovanni Mauro; Bettinardi, Valentino; Dell'Oca, Italo; Fallanca, Federico; Passoni, Paolo; Vanoli, Emilia Giovanna; Calandrino, Riccardo; Di Muzio, Nadia; Picchio, Maria; Fiorino, Claudio

    2018-05-01

    To investigate the robustness of PET radiomic features (RF) against tumour delineation uncertainty in two clinically relevant situations. Twenty-five head-and-neck (HN) and 25 pancreatic cancer patients previously treated with 18 F-Fluorodeoxyglucose (FDG) positron emission tomography/computed tomography (PET/CT)-based planning optimization were considered. Seven FDG-based contours were delineated for tumour (T) and positive lymph nodes (N, for HN patients only) following manual (2 observers), semi-automatic (based on SUV maximum gradient: PET_Edge) and automatic (40%, 50%, 60%, 70% SUV_max thresholds) methods. Seventy-three RF (14 of first order and 59 of higher order) were extracted using the CGITA software (v.1.4). The impact of delineation on volume agreement and RF was assessed by DICE and Intra-class Correlation Coefficients (ICC). A large disagreement between manual and SUV_max method was found for thresholds  ≥50%. Inter-observer variability showed median DICE values between 0.81 (HN-T) and 0.73 (pancreas). Volumes defined by PET_Edge were better consistent with the manual ones compared to SUV40%. Regarding RF, 19%/19%/47% of the features showed ICC < 0.80 between observers for HN-N/HN-T/pancreas, mostly in the Voxel-alignment matrix and in the intensity-size zone matrix families. RFs with ICC < 0.80 against manual delineation (taking the worst value) increased to 44%/36%/61% for PET_Edge and to 69%/53%/75% for SUV40%. About 80%/50% of 72 RF were consistent between observers for HN/pancreas patients. PET_edge was sufficiently robust against manual delineation while SUV40% showed a worse performance. This result suggests the possibility to replace manual with semi-automatic delineation of HN and pancreas tumours in studies including PET radiomic analyses. Copyright © 2018 Associazione Italiana di Fisica Medica. Published by Elsevier Ltd. All rights reserved.

  14. Fully automatic algorithm for segmenting full human diaphragm in non-contrast CT Images

    NASA Astrophysics Data System (ADS)

    Karami, Elham; Gaede, Stewart; Lee, Ting-Yim; Samani, Abbas

    2015-03-01

    The diaphragm is a sheet of muscle which separates the thorax from the abdomen and it acts as the most important muscle of the respiratory system. As such, an accurate segmentation of the diaphragm, not only provides key information for functional analysis of the respiratory system, but also can be used for locating other abdominal organs such as the liver. However, diaphragm segmentation is extremely challenging in non-contrast CT images due to the diaphragm's similar appearance to other abdominal organs. In this paper, we present a fully automatic algorithm for diaphragm segmentation in non-contrast CT images. The method is mainly based on a priori knowledge about the human diaphragm anatomy. The diaphragm domes are in contact with the lungs and the heart while its circumference runs along the lumbar vertebrae of the spine as well as the inferior border of the ribs and sternum. As such, the diaphragm can be delineated by segmentation of these organs followed by connecting relevant parts of their outline properly. More specifically, the bottom surface of the lungs and heart, the spine borders and the ribs are delineated, leading to a set of scattered points which represent the diaphragm's geometry. Next, a B-spline filter is used to find the smoothest surface which pass through these points. This algorithm was tested on a noncontrast CT image of a lung cancer patient. The results indicate that there is an average Hausdorff distance of 2.96 mm between the automatic and manually segmented diaphragms which implies a favourable accuracy.

  15. Is STAPLE algorithm confident to assess segmentation methods in PET imaging?

    NASA Astrophysics Data System (ADS)

    Dewalle-Vignion, Anne-Sophie; Betrouni, Nacim; Baillet, Clio; Vermandel, Maximilien

    2015-12-01

    Accurate tumor segmentation in [18F]-fluorodeoxyglucose positron emission tomography is crucial for tumor response assessment and target volume definition in radiation therapy. Evaluation of segmentation methods from clinical data without ground truth is usually based on physicians’ manual delineations. In this context, the simultaneous truth and performance level estimation (STAPLE) algorithm could be useful to manage the multi-observers variability. In this paper, we evaluated how this algorithm could accurately estimate the ground truth in PET imaging. Complete evaluation study using different criteria was performed on simulated data. The STAPLE algorithm was applied to manual and automatic segmentation results. A specific configuration of the implementation provided by the Computational Radiology Laboratory was used. Consensus obtained by the STAPLE algorithm from manual delineations appeared to be more accurate than manual delineations themselves (80% of overlap). An improvement of the accuracy was also observed when applying the STAPLE algorithm to automatic segmentations results. The STAPLE algorithm, with the configuration used in this paper, is more appropriate than manual delineations alone or automatic segmentations results alone to estimate the ground truth in PET imaging. Therefore, it might be preferred to assess the accuracy of tumor segmentation methods in PET imaging.

  16. Is STAPLE algorithm confident to assess segmentation methods in PET imaging?

    PubMed

    Dewalle-Vignion, Anne-Sophie; Betrouni, Nacim; Baillet, Clio; Vermandel, Maximilien

    2015-12-21

    Accurate tumor segmentation in [18F]-fluorodeoxyglucose positron emission tomography is crucial for tumor response assessment and target volume definition in radiation therapy. Evaluation of segmentation methods from clinical data without ground truth is usually based on physicians' manual delineations. In this context, the simultaneous truth and performance level estimation (STAPLE) algorithm could be useful to manage the multi-observers variability. In this paper, we evaluated how this algorithm could accurately estimate the ground truth in PET imaging. Complete evaluation study using different criteria was performed on simulated data. The STAPLE algorithm was applied to manual and automatic segmentation results. A specific configuration of the implementation provided by the Computational Radiology Laboratory was used. Consensus obtained by the STAPLE algorithm from manual delineations appeared to be more accurate than manual delineations themselves (80% of overlap). An improvement of the accuracy was also observed when applying the STAPLE algorithm to automatic segmentations results. The STAPLE algorithm, with the configuration used in this paper, is more appropriate than manual delineations alone or automatic segmentations results alone to estimate the ground truth in PET imaging. Therefore, it might be preferred to assess the accuracy of tumor segmentation methods in PET imaging.

  17. [A computer tomography assisted method for the automatic detection of region of interest in dynamic kidney images].

    PubMed

    Jing, Xueping; Zheng, Xiujuan; Song, Shaoli; Liu, Kai

    2017-12-01

    Glomerular filtration rate (GFR), which can be estimated by Gates method with dynamic kidney single photon emission computed tomography (SPECT) imaging, is a key indicator of renal function. In this paper, an automatic computer tomography (CT)-assisted detection method of kidney region of interest (ROI) is proposed to achieve the objective and accurate GFR calculation. In this method, the CT coronal projection image and the enhanced SPECT synthetic image are firstly generated and registered together. Then, the kidney ROIs are delineated using a modified level set algorithm. Meanwhile, the background ROIs are also obtained based on the kidney ROIs. Finally, the value of GFR is calculated via Gates method. Comparing with the clinical data, the GFR values estimated by the proposed method were consistent with the clinical reports. This automatic method can improve the accuracy and stability of kidney ROI detection for GFR calculation, especially when the kidney function has been severely damaged.

  18. An automatic method to detect and track the glottal gap from high speed videoendoscopic images.

    PubMed

    Andrade-Miranda, Gustavo; Godino-Llorente, Juan I; Moro-Velázquez, Laureano; Gómez-García, Jorge Andrés

    2015-10-29

    The image-based analysis of the vocal folds vibration plays an important role in the diagnosis of voice disorders. The analysis is based not only on the direct observation of the video sequences, but also in an objective characterization of the phonation process by means of features extracted from the recorded images. However, such analysis is based on a previous accurate identification of the glottal gap, which is the most challenging step for a further automatic assessment of the vocal folds vibration. In this work, a complete framework to automatically segment and track the glottal area (or glottal gap) is proposed. The algorithm identifies a region of interest that is adapted along time, and combine active contours and watershed transform for the final delineation of the glottis and also an automatic procedure for synthesize different videokymograms is proposed. Thanks to the ROI implementation, our technique is robust to the camera shifting and also the objective test proved the effectiveness and performance of the approach in the most challenging scenarios that it is when exist an inappropriate closure of the vocal folds. The novelties of the proposed algorithm relies on the used of temporal information for identify an adaptive ROI and the use of watershed merging combined with active contours for the glottis delimitation. Additionally, an automatic procedure for synthesize multiline VKG by the identification of the glottal main axis is developed.

  19. Individual Tree Crown Delineation Using Multi-Wavelength Titan LIDAR Data

    NASA Astrophysics Data System (ADS)

    Naveed, F.; Hu, B.

    2017-10-01

    The inability to detect the Emerald Ash Borer (EAB) at an early stage has led to the enumerable loss of different species of ash trees. Due to the increasing risk being posed by the EAB, a robust and accurate method is needed for identifying Individual Tree Crowns (ITCs) that are at a risk of being infected or are already diseased. This paper attempts to outline an ITC delineation method that employs airborne multi-spectral Light Detection and Ranging (LiDAR) to accurately delineate tree crowns. The raw LiDAR data were initially pre-processed to generate the Digital Surface Models (DSM) and Digital Elevation Models (DEM) using an iterative progressive TIN (Triangulated Irregular Network) densification method. The DSM and DEM were consequently used for Canopy Height Model (CHM) generation, from which the structural information pertaining to the size and shape of the tree crowns was obtained. The structural information along with the spectral information was used to segment ITCs using a region growing algorithm. The availability of the multi-spectral LiDAR data allows for delineation of crowns that have otherwise homogenous structural characteristics and hence cannot be isolated from the CHM alone. This study exploits the spectral data to derive initial approximations of individual tree tops and consequently grow those regions based on the spectral constraints of the individual trees.

  20. Automatic localization of IASLC-defined mediastinal lymph node stations on CT images using fuzzy models

    NASA Astrophysics Data System (ADS)

    Matsumoto, Monica M. S.; Beig, Niha G.; Udupa, Jayaram K.; Archer, Steven; Torigian, Drew A.

    2014-03-01

    Lung cancer is associated with the highest cancer mortality rates among men and women in the United States. The accurate and precise identification of the lymph node stations on computed tomography (CT) images is important for staging disease and potentially for prognosticating outcome in patients with lung cancer, as well as for pretreatment planning and response assessment purposes. To facilitate a standard means of referring to lymph nodes, the International Association for the Study of Lung Cancer (IASLC) has recently proposed a definition of the different lymph node stations and zones in the thorax. However, nodal station identification is typically performed manually by visual assessment in clinical radiology. This approach leaves room for error due to the subjective and potentially ambiguous nature of visual interpretation, and is labor intensive. We present a method of automatically recognizing the mediastinal IASLC-defined lymph node stations by modifying a hierarchical fuzzy modeling approach previously developed for body-wide automatic anatomy recognition (AAR) in medical imagery. Our AAR-lymph node (AAR-LN) system follows the AAR methodology and consists of two steps. In the first step, the various lymph node stations are manually delineated on a set of CT images following the IASLC definitions. These delineations are then used to build a fuzzy hierarchical model of the nodal stations which are considered as 3D objects. In the second step, the stations are automatically located on any given CT image of the thorax by using the hierarchical fuzzy model and object recognition algorithms. Based on 23 data sets used for model building, 22 independent data sets for testing, and 10 lymph node stations, a mean localization accuracy of within 1-6 voxels has been achieved by the AAR-LN system.

  1. Automatic Segmentation of the Eye in 3D Magnetic Resonance Imaging: A Novel Statistical Shape Model for Treatment Planning of Retinoblastoma.

    PubMed

    Ciller, Carlos; De Zanet, Sandro I; Rüegsegger, Michael B; Pica, Alessia; Sznitman, Raphael; Thiran, Jean-Philippe; Maeder, Philippe; Munier, Francis L; Kowal, Jens H; Cuadra, Meritxell Bach

    2015-07-15

    Proper delineation of ocular anatomy in 3-dimensional (3D) imaging is a big challenge, particularly when developing treatment plans for ocular diseases. Magnetic resonance imaging (MRI) is presently used in clinical practice for diagnosis confirmation and treatment planning for treatment of retinoblastoma in infants, where it serves as a source of information, complementary to the fundus or ultrasonographic imaging. Here we present a framework to fully automatically segment the eye anatomy for MRI based on 3D active shape models (ASM), and we validate the results and present a proof of concept to automatically segment pathological eyes. Manual and automatic segmentation were performed in 24 images of healthy children's eyes (3.29 ± 2.15 years of age). Imaging was performed using a 3-T MRI scanner. The ASM consists of the lens, the vitreous humor, the sclera, and the cornea. The model was fitted by first automatically detecting the position of the eye center, the lens, and the optic nerve, and then aligning the model and fitting it to the patient. We validated our segmentation method by using a leave-one-out cross-validation. The segmentation results were evaluated by measuring the overlap, using the Dice similarity coefficient (DSC) and the mean distance error. We obtained a DSC of 94.90 ± 2.12% for the sclera and the cornea, 94.72 ± 1.89% for the vitreous humor, and 85.16 ± 4.91% for the lens. The mean distance error was 0.26 ± 0.09 mm. The entire process took 14 seconds on average per eye. We provide a reliable and accurate tool that enables clinicians to automatically segment the sclera, the cornea, the vitreous humor, and the lens, using MRI. We additionally present a proof of concept for fully automatically segmenting eye pathology. This tool reduces the time needed for eye shape delineation and thus can help clinicians when planning eye treatment and confirming the extent of the tumor. Copyright © 2015 Elsevier Inc. All rights reserved.

  2. A semiautomatic CT-based ensemble segmentation of lung tumors: comparison with oncologists' delineations and with the surgical specimen.

    PubMed

    Rios Velazquez, Emmanuel; Aerts, Hugo J W L; Gu, Yuhua; Goldgof, Dmitry B; De Ruysscher, Dirk; Dekker, Andre; Korn, René; Gillies, Robert J; Lambin, Philippe

    2012-11-01

    To assess the clinical relevance of a semiautomatic CT-based ensemble segmentation method, by comparing it to pathology and to CT/PET manual delineations by five independent radiation oncologists in non-small cell lung cancer (NSCLC). For 20 NSCLC patients (stages Ib-IIIb) the primary tumor was delineated manually on CT/PET scans by five independent radiation oncologists and segmented using a CT based semi-automatic tool. Tumor volume and overlap fractions between manual and semiautomatic-segmented volumes were compared. All measurements were correlated with the maximal diameter on macroscopic examination of the surgical specimen. Imaging data are available on www.cancerdata.org. High overlap fractions were observed between the semi-automatically segmented volumes and the intersection (92.5±9.0, mean±SD) and union (94.2±6.8) of the manual delineations. No statistically significant differences in tumor volume were observed between the semiautomatic segmentation (71.4±83.2 cm(3), mean±SD) and manual delineations (81.9±94.1 cm(3); p=0.57). The maximal tumor diameter of the semiautomatic-segmented tumor correlated strongly with the macroscopic diameter of the primary tumor (r=0.96). Semiautomatic segmentation of the primary tumor on CT demonstrated high agreement with CT/PET manual delineations and strongly correlated with the macroscopic diameter considered as the "gold standard". This method may be used routinely in clinical practice and could be employed as a starting point for treatment planning, target definition in multi-center clinical trials or for high throughput data mining research. This method is particularly suitable for peripherally located tumors. Copyright © 2012 Elsevier Ireland Ltd. All rights reserved.

  3. Delineation, characterization, and classification of topographic eminences

    NASA Astrophysics Data System (ADS)

    Sinha, Gaurav

    Topographic eminences are defined as upwardly rising, convex shaped topographic landforms that are noticeably distinct in their immediate surroundings. As opposed to everyday objects, the properties of a topographic eminence are dependent not only on how it is conceptualized, but is also intrinsically related to its spatial extent and its relative location in the landscape. In this thesis, a system for automated detection, delineation and characterization of topographic eminences based on an analysis of digital elevation models is proposed. Research has shown that conceptualization of eminences (and other landforms) is linked to the cultural and linguistic backgrounds of people. However, the perception of stimuli from our physical environment is not subject to cultural or linguistic bias. Hence, perceptually salient morphological and spatial properties of the natural landscape can form the basis for generically applicable detection and delineation of topographic eminences. Six principles of cognitive eminence modeling are introduced to develop the philosophical foundation of this research regarding eminence delineation and characterization. The first step in delineating eminences is to automatically detect their presence within digital elevation models. This is achieved by the use of quantitative geomorphometric parameters (e.g., elevation, slope and curvature) and qualitative geomorphometric features (e.g., peaks, passes, pits, ridgelines, and valley lines). The process of eminence delineation follows that of eminence detection. It is posited that eminences may be perceived either as monolithic terrain objects, or as composites of morphological parts (e.g., top, bottom, slope). Individual eminences may also simultaneously be conceived as comprising larger, higher order eminence complexes (e.g., mountain ranges). Multiple algorithms are presented for the delineation of simple and complex eminences, and the morphological parts of eminences. The proposed eminence

  4. Developing an Intelligent Automatic Appendix Extraction Method from Ultrasonography Based on Fuzzy ART and Image Processing.

    PubMed

    Kim, Kwang Baek; Park, Hyun Jun; Song, Doo Heon; Han, Sang-suk

    2015-01-01

    Ultrasound examination (US) does a key role in the diagnosis and management of the patients with clinically suspected appendicitis which is the most common abdominal surgical emergency. Among the various sonographic findings of appendicitis, outer diameter of the appendix is most important. Therefore, clear delineation of the appendix on US images is essential. In this paper, we propose a new intelligent method to extract appendix automatically from abdominal sonographic images as a basic building block of developing such an intelligent tool for medical practitioners. Knowing that the appendix is located at the lower organ area below the bottom fascia line, we conduct a series of image processing techniques to find the fascia line correctly. And then we apply fuzzy ART learning algorithm to the organ area in order to extract appendix accurately. The experiment verifies that the proposed method is highly accurate (successful in 38 out of 40 cases) in extracting appendix.

  5. ATLAAS: an automatic decision tree-based learning algorithm for advanced image segmentation in positron emission tomography.

    PubMed

    Berthon, Beatrice; Marshall, Christopher; Evans, Mererid; Spezi, Emiliano

    2016-07-07

    Accurate and reliable tumour delineation on positron emission tomography (PET) is crucial for radiotherapy treatment planning. PET automatic segmentation (PET-AS) eliminates intra- and interobserver variability, but there is currently no consensus on the optimal method to use, as different algorithms appear to perform better for different types of tumours. This work aimed to develop a predictive segmentation model, trained to automatically select and apply the best PET-AS method, according to the tumour characteristics. ATLAAS, the automatic decision tree-based learning algorithm for advanced segmentation is based on supervised machine learning using decision trees. The model includes nine PET-AS methods and was trained on a 100 PET scans with known true contour. A decision tree was built for each PET-AS algorithm to predict its accuracy, quantified using the Dice similarity coefficient (DSC), according to the tumour volume, tumour peak to background SUV ratio and a regional texture metric. The performance of ATLAAS was evaluated for 85 PET scans obtained from fillable and printed subresolution sandwich phantoms. ATLAAS showed excellent accuracy across a wide range of phantom data and predicted the best or near-best segmentation algorithm in 93% of cases. ATLAAS outperformed all single PET-AS methods on fillable phantom data with a DSC of 0.881, while the DSC for H&N phantom data was 0.819. DSCs higher than 0.650 were achieved in all cases. ATLAAS is an advanced automatic image segmentation algorithm based on decision tree predictive modelling, which can be trained on images with known true contour, to predict the best PET-AS method when the true contour is unknown. ATLAAS provides robust and accurate image segmentation with potential applications to radiation oncology.

  6. ATLAAS: an automatic decision tree-based learning algorithm for advanced image segmentation in positron emission tomography

    NASA Astrophysics Data System (ADS)

    Berthon, Beatrice; Marshall, Christopher; Evans, Mererid; Spezi, Emiliano

    2016-07-01

    Accurate and reliable tumour delineation on positron emission tomography (PET) is crucial for radiotherapy treatment planning. PET automatic segmentation (PET-AS) eliminates intra- and interobserver variability, but there is currently no consensus on the optimal method to use, as different algorithms appear to perform better for different types of tumours. This work aimed to develop a predictive segmentation model, trained to automatically select and apply the best PET-AS method, according to the tumour characteristics. ATLAAS, the automatic decision tree-based learning algorithm for advanced segmentation is based on supervised machine learning using decision trees. The model includes nine PET-AS methods and was trained on a 100 PET scans with known true contour. A decision tree was built for each PET-AS algorithm to predict its accuracy, quantified using the Dice similarity coefficient (DSC), according to the tumour volume, tumour peak to background SUV ratio and a regional texture metric. The performance of ATLAAS was evaluated for 85 PET scans obtained from fillable and printed subresolution sandwich phantoms. ATLAAS showed excellent accuracy across a wide range of phantom data and predicted the best or near-best segmentation algorithm in 93% of cases. ATLAAS outperformed all single PET-AS methods on fillable phantom data with a DSC of 0.881, while the DSC for H&N phantom data was 0.819. DSCs higher than 0.650 were achieved in all cases. ATLAAS is an advanced automatic image segmentation algorithm based on decision tree predictive modelling, which can be trained on images with known true contour, to predict the best PET-AS method when the true contour is unknown. ATLAAS provides robust and accurate image segmentation with potential applications to radiation oncology.

  7. Riparian ecotone: A functional definition and delineation for resource assessment

    Treesearch

    E. S Verry; C. A Dolloff; M. E. Manning

    2004-01-01

    We propose a geomorphic basis for defining riparian areas using the term: riparian ecotone, discuss how past definitions fall short, and illustrate how a linked sequence of definition, delineation, and riparian sampling are used to accurately assess riparian resources on the ground. Our riparian ecotone is based on the width of the valley (its floodprone area width)...

  8. Automatic and semi-automatic approaches for arteriolar-to-venular computation in retinal photographs

    NASA Astrophysics Data System (ADS)

    Mendonça, Ana Maria; Remeseiro, Beatriz; Dashtbozorg, Behdad; Campilho, Aurélio

    2017-03-01

    The Arteriolar-to-Venular Ratio (AVR) is a popular dimensionless measure which allows the assessment of patients' condition for the early diagnosis of different diseases, including hypertension and diabetic retinopathy. This paper presents two new approaches for AVR computation in retinal photographs which include a sequence of automated processing steps: vessel segmentation, caliber measurement, optic disc segmentation, artery/vein classification, region of interest delineation, and AVR calculation. Both approaches have been tested on the INSPIRE-AVR dataset, and compared with a ground-truth provided by two medical specialists. The obtained results demonstrate the reliability of the fully automatic approach which provides AVR ratios very similar to at least one of the observers. Furthermore, the semi-automatic approach, which includes the manual modification of the artery/vein classification if needed, allows to significantly reduce the error to a level below the human error.

  9. SU-E-J-134: Optimizing Technical Parameters for Using Atlas Based Automatic Segmentation for Evaluation of Contour Accuracy Experience with Cardiac Structures From NRG Oncology/RTOG 0617

    DOE Office of Scientific and Technical Information (OSTI.GOV)

    Yu, J; Gong, Y; Bar-Ad, V

    Purpose: Accurate contour delineation is crucial for radiotherapy. Atlas based automatic segmentation tools can be used to increase the efficiency of contour accuracy evaluation. This study aims to optimize technical parameters utilized in the tool by exploring the impact of library size and atlas number on the accuracy of cardiac contour evaluation. Methods: Patient CT DICOMs from RTOG 0617 were used for this study. Five experienced physicians delineated the cardiac structures including pericardium, atria and ventricles following an atlas guideline. The consistency of cardiac structured delineation using the atlas guideline was verified by a study with four observers and seventeenmore » patients. The CT and cardiac structure DICOM files were then used for the ABAS technique.To study the impact of library size (LS) and atlas number (AN) on automatic contour accuracy, automatic contours were generated with varied technique parameters for five randomly selected patients. Three LS (20, 60, and 100) were studied using commercially available software. The AN was four, recommended by the manufacturer. Using the manual contour as the gold standard, Dice Similarity Coefficient (DSC) was calculated between the manual and automatic contours. Five-patient averaged DSCs were calculated for comparison for each cardiac structure.In order to study the impact of AN, the LS was set 100, and AN was tested from one to five. The five-patient averaged DSCs were also calculated for each cardiac structure. Results: DSC values are highest when LS is 100 and AN is four. The DSC is 0.90±0.02 for pericardium, 0.75±0.06 for atria, and 0.86±0.02 for ventricles. Conclusion: By comparing DSC values, the combination AN=4 and LS=100 gives the best performance. This project was supported by NCI grants U24CA12014, U24CA180803, U10CA180868, U10CA180822, PA CURE grant and Bristol-Myers Squibb and Eli Lilly.« less

  10. Using SAR Interferograms and Coherence Images for Object-Based Delineation of Unstable Slopes

    NASA Astrophysics Data System (ADS)

    Friedl, Barbara; Holbling, Daniel

    2015-05-01

    This study uses synthetic aperture radar (SAR) interferometric products for the semi-automated identification and delineation of unstable slopes and active landslides. Single-pair interferograms and coherence images are therefore segmented and classified in an object-based image analysis (OBIA) framework. The rule-based classification approach has been applied to landslide-prone areas located in Taiwan and Southern Germany. The semi-automatically obtained results were validated against landslide polygons derived from manual interpretation.

  11. Automatic design of magazine covers

    NASA Astrophysics Data System (ADS)

    Jahanian, Ali; Liu, Jerry; Tretter, Daniel R.; Lin, Qian; Damera-Venkata, Niranjan; O'Brien-Strain, Eamonn; Lee, Seungyon; Fan, Jian; Allebach, Jan P.

    2012-03-01

    In this paper, we propose a system for automatic design of magazine covers that quantifies a number of concepts from art and aesthetics. Our solution to automatic design of this type of media has been shaped by input from professional designers, magazine art directors and editorial boards, and journalists. Consequently, a number of principles in design and rules in designing magazine covers are delineated. Several techniques are derived and employed in order to quantify and implement these principles and rules in the format of a software framework. At this stage, our framework divides the task of design into three main modules: layout of magazine cover elements, choice of color for masthead and cover lines, and typography of cover lines. Feedback from professional designers on our designs suggests that our results are congruent with their intuition.

  12. Automatic anatomy recognition via multiobject oriented active shape models.

    PubMed

    Chen, Xinjian; Udupa, Jayaram K; Alavi, Abass; Torigian, Drew A

    2010-12-01

    This paper studies the feasibility of developing an automatic anatomy recognition (AAR) system in clinical radiology and demonstrates its operation on clinical 2D images. The anatomy recognition method described here consists of two main components: (a) multiobject generalization of OASM and (b) object recognition strategies. The OASM algorithm is generalized to multiple objects by including a model for each object and assigning a cost structure specific to each object in the spirit of live wire. The delineation of multiobject boundaries is done in MOASM via a three level dynamic programming algorithm, wherein the first level is at pixel level which aims to find optimal oriented boundary segments between successive landmarks, the second level is at landmark level which aims to find optimal location for the landmarks, and the third level is at the object level which aims to find optimal arrangement of object boundaries over all objects. The object recognition strategy attempts to find that pose vector (consisting of translation, rotation, and scale component) for the multiobject model that yields the smallest total boundary cost for all objects. The delineation and recognition accuracies were evaluated separately utilizing routine clinical chest CT, abdominal CT, and foot MRI data sets. The delineation accuracy was evaluated in terms of true and false positive volume fractions (TPVF and FPVF). The recognition accuracy was assessed (1) in terms of the size of the space of the pose vectors for the model assembly that yielded high delineation accuracy, (2) as a function of the number of objects and objects' distribution and size in the model, (3) in terms of the interdependence between delineation and recognition, and (4) in terms of the closeness of the optimum recognition result to the global optimum. When multiple objects are included in the model, the delineation accuracy in terms of TPVF can be improved to 97%-98% with a low FPVF of 0.1%-0.2%. Typically, a

  13. A new automatic blood pressure kit auscultates for accurate reading with a smartphone

    PubMed Central

    Wu, Hongjun; Wang, Bingjian; Zhu, Xinpu; Chu, Guang; Zhang, Zhi

    2016-01-01

    Abstract The widely used oscillometric automated blood pressure (BP) monitor was continuously questioned on its accuracy. A novel BP kit named Accutension which adopted Korotkoff auscultation method was then devised. Accutension worked with a miniature microphone, a pressure sensor, and a smartphone. The BP values were automatically displayed on the smartphone screen through the installed App. Data recorded in the phone could be played back and reconfirmed after measurement. They could also be uploaded and saved to the iCloud. The accuracy and consistency of this novel electronic auscultatory sphygmomanometer was preliminarily verified here. Thirty-two subjects were included and 82 qualified readings were obtained. The mean differences ± SD for systolic and diastolic BP readings between Accutension and mercury sphygmomanometer were 0.87 ± 2.86 and −0.94 ± 2.93 mm Hg. Agreements between Accutension and mercury sphygmomanometer were highly significant for systolic (ICC = 0.993, 95% confidence interval (CI): 0.989–0.995) and diastolic (ICC = 0.987, 95% CI: 0.979–0.991). In conclusion, Accutension worked accurately based on our pilot study data. The difference was acceptable. ICC and Bland–Altman plot charts showed good agreements with manual measurements. Systolic readings of Accutension were slightly higher than those of manual measurement, while diastolic readings were slightly lower. One possible reason was that Accutension captured the first and the last korotkoff sound more sensitively than human ear during manual measurement and avoided sound missing, so that it might be more accurate than traditional mercury sphygmomanometer. By documenting and analyzing of variant tendency of BP values, Accutension helps management of hypertension and therefore contributes to the mobile heath service. PMID:27512876

  14. [Radiotherapy volume delineation based on (18F)-fluorodeoxyglucose positron emission tomography for locally advanced or inoperable oesophageal cancer].

    PubMed

    Encaoua, J; Abgral, R; Leleu, C; El Kabbaj, O; Caradec, P; Bourhis, D; Pradier, O; Schick, U

    2017-06-01

    To study the impact on radiotherapy planning of an automatically segmented target volume delineation based on ( 18 F)-fluorodeoxy-D-glucose (FDG)-hybrid positron emission tomography-computed tomography (PET-CT) compared to a manually delineation based on computed tomography (CT) in oesophageal carcinoma patients. Fifty-eight patients diagnosed with oesophageal cancer between September 2009 and November 2014 were included. The majority had squamous cell carcinoma (84.5 %), and advanced stage (37.9 % were stade IIIA) and 44.8 % had middle oesophageal lesion. Gross tumour volumes were retrospectively defined based either manually on CT or automatically on coregistered PET/CT images using three different threshold methods: standard-uptake value (SUV) of 2.5, 40 % of maximum intensity and signal-to-background ratio. Target volumes were compared in length, volume and using the index of conformality. Radiotherapy plans to the dose of 50Gy and 66Gy using intensity-modulated radiotherapy were generated and compared for both data sets. Planification target volume coverage and doses delivered to organs at risk (heart, lung and spinal cord) were compared. The gross tumour volume based manually on CT was significantly longer than that automatically based on signal-to-background ratio (6.4cm versus 5.3cm; P<0.008). Doses to the lungs (V20, D mean ), heart (V40), and spinal cord (D max ) were significantly lower on plans using the PTV SBR . The PTV SBR coverage was statistically better than the PTV CT coverage on both plans. (50Gy: P<0.0004 and 66Gy: P<0.0006). The automatic PET segmentation algorithm based on the signal-to-background ratio method for the delineation of oesophageal tumours is interesting, and results in better target volume coverage and decreased dose to organs at risk. This may allow dose escalation up to 66Gy to the gross tumour volume. Copyright © 2017 Société française de radiothérapie oncologique (SFRO). Published by Elsevier SAS. All rights

  15. Fully automatic left ventricular myocardial strain estimation in 2D short-axis tagged magnetic resonance imaging

    NASA Astrophysics Data System (ADS)

    Morais, Pedro; Queirós, Sandro; Heyde, Brecht; Engvall, Jan; 'hooge, Jan D.; Vilaça, João L.

    2017-09-01

    Cardiovascular diseases are among the leading causes of death and frequently result in local myocardial dysfunction. Among the numerous imaging modalities available to detect these dysfunctional regions, cardiac deformation imaging through tagged magnetic resonance imaging (t-MRI) has been an attractive approach. Nevertheless, fully automatic analysis of these data sets is still challenging. In this work, we present a fully automatic framework to estimate left ventricular myocardial deformation from t-MRI. This strategy performs automatic myocardial segmentation based on B-spline explicit active surfaces, which are initialized using an annular model. A non-rigid image-registration technique is then used to assess myocardial deformation. Three experiments were set up to validate the proposed framework using a clinical database of 75 patients. First, automatic segmentation accuracy was evaluated by comparing against manual delineations at one specific cardiac phase. The proposed solution showed an average perpendicular distance error of 2.35  ±  1.21 mm and 2.27  ±  1.02 mm for the endo- and epicardium, respectively. Second, starting from either manual or automatic segmentation, myocardial tracking was performed and the resulting strain curves were compared. It is shown that the automatic segmentation adds negligible differences during the strain-estimation stage, corroborating its accuracy. Finally, segmental strain was compared with scar tissue extent determined by delay-enhanced MRI. The results proved that both strain components were able to distinguish between normal and infarct regions. Overall, the proposed framework was shown to be accurate, robust, and attractive for clinical practice, as it overcomes several limitations of a manual analysis.

  16. Method: automatic segmentation of mitochondria utilizing patch classification, contour pair classification, and automatically seeded level sets

    PubMed Central

    2012-01-01

    Background While progress has been made to develop automatic segmentation techniques for mitochondria, there remains a need for more accurate and robust techniques to delineate mitochondria in serial blockface scanning electron microscopic data. Previously developed texture based methods are limited for solving this problem because texture alone is often not sufficient to identify mitochondria. This paper presents a new three-step method, the Cytoseg process, for automated segmentation of mitochondria contained in 3D electron microscopic volumes generated through serial block face scanning electron microscopic imaging. The method consists of three steps. The first is a random forest patch classification step operating directly on 2D image patches. The second step consists of contour-pair classification. At the final step, we introduce a method to automatically seed a level set operation with output from previous steps. Results We report accuracy of the Cytoseg process on three types of tissue and compare it to a previous method based on Radon-Like Features. At step 1, we show that the patch classifier identifies mitochondria texture but creates many false positive pixels. At step 2, our contour processing step produces contours and then filters them with a second classification step, helping to improve overall accuracy. We show that our final level set operation, which is automatically seeded with output from previous steps, helps to smooth the results. Overall, our results show that use of contour pair classification and level set operations improve segmentation accuracy beyond patch classification alone. We show that the Cytoseg process performs well compared to another modern technique based on Radon-Like Features. Conclusions We demonstrated that texture based methods for mitochondria segmentation can be enhanced with multiple steps that form an image processing pipeline. While we used a random-forest based patch classifier to recognize texture, it would be

  17. Optical system for object detection and delineation in space

    NASA Astrophysics Data System (ADS)

    Handelman, Amir; Shwartz, Shoam; Donitza, Liad; Chaplanov, Loran

    2018-01-01

    Object recognition and delineation is an important task in many environments, such as in crime scenes and operating rooms. Marking evidence or surgical tools and attracting the attention of the surrounding staff to the marked objects can affect people's lives. We present an optical system comprising a camera, computer, and small laser projector that can detect and delineate objects in the environment. To prove the optical system's concept, we show that it can operate in a hypothetical crime scene in which a pistol is present and automatically recognize and segment it by various computer-vision algorithms. Based on such segmentation, the laser projector illuminates the actual boundaries of the pistol and thus allows the persons in the scene to comfortably locate and measure the pistol without holding any intermediator device, such as an augmented reality handheld device, glasses, or screens. Using additional optical devices, such as diffraction grating and a cylinder lens, the pistol size can be estimated. The exact location of the pistol in space remains static, even after its removal. Our optical system can be fixed or dynamically moved, making it suitable for various applications that require marking of objects in space.

  18. Automatic Segmentation of the Eye in 3D Magnetic Resonance Imaging: A Novel Statistical Shape Model for Treatment Planning of Retinoblastoma

    DOE Office of Scientific and Technical Information (OSTI.GOV)

    Ciller, Carlos, E-mail: carlos.cillerruiz@unil.ch; Ophthalmic Technology Group, ARTORG Center of the University of Bern, Bern; Centre d’Imagerie BioMédicale, University of Lausanne, Lausanne

    Purpose: Proper delineation of ocular anatomy in 3-dimensional (3D) imaging is a big challenge, particularly when developing treatment plans for ocular diseases. Magnetic resonance imaging (MRI) is presently used in clinical practice for diagnosis confirmation and treatment planning for treatment of retinoblastoma in infants, where it serves as a source of information, complementary to the fundus or ultrasonographic imaging. Here we present a framework to fully automatically segment the eye anatomy for MRI based on 3D active shape models (ASM), and we validate the results and present a proof of concept to automatically segment pathological eyes. Methods and Materials: Manualmore » and automatic segmentation were performed in 24 images of healthy children's eyes (3.29 ± 2.15 years of age). Imaging was performed using a 3-T MRI scanner. The ASM consists of the lens, the vitreous humor, the sclera, and the cornea. The model was fitted by first automatically detecting the position of the eye center, the lens, and the optic nerve, and then aligning the model and fitting it to the patient. We validated our segmentation method by using a leave-one-out cross-validation. The segmentation results were evaluated by measuring the overlap, using the Dice similarity coefficient (DSC) and the mean distance error. Results: We obtained a DSC of 94.90 ± 2.12% for the sclera and the cornea, 94.72 ± 1.89% for the vitreous humor, and 85.16 ± 4.91% for the lens. The mean distance error was 0.26 ± 0.09 mm. The entire process took 14 seconds on average per eye. Conclusion: We provide a reliable and accurate tool that enables clinicians to automatically segment the sclera, the cornea, the vitreous humor, and the lens, using MRI. We additionally present a proof of concept for fully automatically segmenting eye pathology. This tool reduces the time needed for eye shape delineation and thus can help clinicians when planning eye treatment and confirming the extent of the

  19. Automatic CT simulation optimization for radiation therapy: A general strategy.

    PubMed

    Li, Hua; Yu, Lifeng; Anastasio, Mark A; Chen, Hsin-Chen; Tan, Jun; Gay, Hiram; Michalski, Jeff M; Low, Daniel A; Mutic, Sasa

    2014-03-01

    In radiation therapy, x-ray computed tomography (CT) simulation protocol specifications should be driven by the treatment planning requirements in lieu of duplicating diagnostic CT screening protocols. The purpose of this study was to develop a general strategy that allows for automatically, prospectively, and objectively determining the optimal patient-specific CT simulation protocols based on radiation-therapy goals, namely, maintenance of contouring quality and integrity while minimizing patient CT simulation dose. The authors proposed a general prediction strategy that provides automatic optimal CT simulation protocol selection as a function of patient size and treatment planning task. The optimal protocol is the one that delivers the minimum dose required to provide a CT simulation scan that yields accurate contours. Accurate treatment plans depend on accurate contours in order to conform the dose to actual tumor and normal organ positions. An image quality index, defined to characterize how simulation scan quality affects contour delineation, was developed and used to benchmark the contouring accuracy and treatment plan quality within the predication strategy. A clinical workflow was developed to select the optimal CT simulation protocols incorporating patient size, target delineation, and radiation dose efficiency. An experimental study using an anthropomorphic pelvis phantom with added-bolus layers was used to demonstrate how the proposed prediction strategy could be implemented and how the optimal CT simulation protocols could be selected for prostate cancer patients based on patient size and treatment planning task. Clinical IMRT prostate treatment plans for seven CT scans with varied image quality indices were separately optimized and compared to verify the trace of target and organ dosimetry coverage. Based on the phantom study, the optimal image quality index for accurate manual prostate contouring was 4.4. The optimal tube potentials for patient sizes

  20. ARCOCT: Automatic detection of lumen border in intravascular OCT images.

    PubMed

    Cheimariotis, Grigorios-Aris; Chatzizisis, Yiannis S; Koutkias, Vassilis G; Toutouzas, Konstantinos; Giannopoulos, Andreas; Riga, Maria; Chouvarda, Ioanna; Antoniadis, Antonios P; Doulaverakis, Charalambos; Tsamboulatidis, Ioannis; Kompatsiaris, Ioannis; Giannoglou, George D; Maglaveras, Nicos

    2017-11-01

    Intravascular optical coherence tomography (OCT) is an invaluable tool for the detection of pathological features on the arterial wall and the investigation of post-stenting complications. Computational lumen border detection in OCT images is highly advantageous, since it may support rapid morphometric analysis. However, automatic detection is very challenging, since OCT images typically include various artifacts that impact image clarity, including features such as side branches and intraluminal blood presence. This paper presents ARCOCT, a segmentation method for fully-automatic detection of lumen border in OCT images. ARCOCT relies on multiple, consecutive processing steps, accounting for image preparation, contour extraction and refinement. In particular, for contour extraction ARCOCT employs the transformation of OCT images based on physical characteristics such as reflectivity and absorption of the tissue and, for contour refinement, local regression using weighted linear least squares and a 2nd degree polynomial model is employed to achieve artifact and small-branch correction as well as smoothness of the artery mesh. Our major focus was to achieve accurate contour delineation in the various types of OCT images, i.e., even in challenging cases with branches and artifacts. ARCOCT has been assessed in a dataset of 1812 images (308 from stented and 1504 from native segments) obtained from 20 patients. ARCOCT was compared against ground-truth manual segmentation performed by experts on the basis of various geometric features (e.g. area, perimeter, radius, diameter, centroid, etc.) and closed contour matching indicators (the Dice index, the Hausdorff distance and the undirected average distance), using standard statistical analysis methods. The proposed method was proven very efficient and close to the ground-truth, exhibiting non statistically-significant differences for most of the examined metrics. ARCOCT allows accurate and fully-automated lumen border

  1. WE-AB-BRA-05: Fully Automatic Segmentation of Male Pelvic Organs On CT Without Manual Intervention

    DOE Office of Scientific and Technical Information (OSTI.GOV)

    Gao, Y; Lian, J; Chen, R

    Purpose: We aim to develop a fully automatic tool for accurate contouring of major male pelvic organs in CT images for radiotherapy without any manual initialization, yet still achieving superior performance than the existing tools. Methods: A learning-based 3D deformable shape model was developed for automatic contouring. Specifically, we utilized a recent machine learning method, random forest, to jointly learn both image regressor and classifier for each organ. In particular, the image regressor is trained to predict the 3D displacement from each vertex of the 3D shape model towards the organ boundary based on the local image appearance around themore » location of this vertex. The predicted 3D displacements are then used to drive the 3D shape model towards the target organ. Once the shape model is deformed close to the target organ, it is further refined by an organ likelihood map estimated by the learned classifier. As the organ likelihood map provides good guideline for the organ boundary, the precise contouring Result could be achieved, by deforming the 3D shape model locally to fit boundaries in the organ likelihood map. Results: We applied our method to 29 previously-treated prostate cancer patients, each with one planning CT scan. Compared with manually delineated pelvic organs, our method obtains overlap ratios of 85.2%±3.74% for the prostate, 94.9%±1.62% for the bladder, and 84.7%±1.97% for the rectum, respectively. Conclusion: This work demonstrated feasibility of a novel machine-learning based approach for accurate and automatic contouring of major male pelvic organs. It shows the potential to replace the time-consuming and inconsistent manual contouring in the clinic. Also, compared with the existing works, our method is more accurate and also efficient since it does not require any manual intervention, such as manual landmark placement. Moreover, our method obtained very similar contouring results as the clinical experts. Project is partially

  2. Automatic transducer switching provides accurate wide range measurement of pressure differential

    NASA Technical Reports Server (NTRS)

    Yoder, S. K.

    1967-01-01

    Automatic pressure transducer switching network sequentially selects any one of a number of limited-range transducers as gas pressure rises or falls, extending the range of measurement and lessening the chances of damage due to high pressure.

  3. Automatic falx cerebri and tentorium cerebelli segmentation from magnetic resonance images

    NASA Astrophysics Data System (ADS)

    Glaister, Jeffrey; Carass, Aaron; Pham, Dzung L.; Butman, John A.; Prince, Jerry L.

    2017-03-01

    The falx cerebri and tentorium cerebelli are dural structures found in the brain. Due to the roles both structures play in constraining brain motion, the falx and tentorium must be identified and included in finite element models of the head to accurately predict brain dynamics during injury events. To date there has been very little research work on automatically segmenting these two structures, which is understandable given that their 1) thin structure challenges the resolution limits of in vivo 3D imaging, and 2) contrast with respect to surrounding tissue is low in standard magnetic resonance imaging. An automatic segmentation algorithm to find the falx and tentorium which uses the results of a multi-atlas segmentation and cortical reconstruction algorithm is proposed. Gray matter labels are used to find the location of the falx and tentorium. The proposed algorithm is applied to five datasets with manual delineations. 3D visualizations of the final results are provided, and Hausdorff distance (HD) and mean surface distance (MSD) is calculated to quantify the accuracy of the proposed method. For the falx, the mean HD is 43.84 voxels and the mean MSD is 2.78 voxels, with the largest errors occurring at the frontal inferior falx boundary. For the tentorium, the mean HD is 14.50 voxels and mean MSD is 1.38 voxels.

  4. Automatic falx cerebri and tentorium cerebelli segmentation from Magnetic Resonance Images.

    PubMed

    Glaister, Jeffrey; Carass, Aaron; Pham, Dzung L; Butman, John A; Prince, Jerry L

    2017-02-01

    The falx cerebri and tentorium cerebelli are dural structures found in the brain. Due to the roles both structures play in constraining brain motion, the falx and tentorium must be identified and included in finite element models of the head to accurately predict brain dynamics during injury events. To date there has been very little research work on automatically segmenting these two structures, which is understandable given that their 1) thin structure challenges the resolution limits of in vivo 3D imaging, and 2) contrast with respect to surrounding tissue is low in standard magnetic resonance imaging. An automatic segmentation algorithm to find the falx and tentorium which uses the results of a multi-atlas segmentation and cortical reconstruction algorithm is proposed. Gray matter labels are used to find the location of the falx and tentorium. The proposed algorithm is applied to five datasets with manual delineations. 3D visualizations of the final results are provided, and Hausdorff distance (HD) and mean surface distance (MSD) is calculated to quantify the accuracy of the proposed method. For the falx, the mean HD is 43.84 voxels and the mean MSD is 2.78 voxels, with the largest errors occurring at the frontal inferior falx boundary. For the tentorium, the mean HD is 14.50 voxels and mean MSD is 1.38 voxels.

  5. Generic and robust method for automatic segmentation of PET images using an active contour model

    DOE Office of Scientific and Technical Information (OSTI.GOV)

    Zhuang, Mingzan

    Purpose: Although positron emission tomography (PET) images have shown potential to improve the accuracy of targeting in radiation therapy planning and assessment of response to treatment, the boundaries of tumors are not easily distinguishable from surrounding normal tissue owing to the low spatial resolution and inherent noisy characteristics of PET images. The objective of this study is to develop a generic and robust method for automatic delineation of tumor volumes using an active contour model and to evaluate its performance using phantom and clinical studies. Methods: MASAC, a method for automatic segmentation using an active contour model, incorporates the histogrammore » fuzzy C-means clustering, and localized and textural information to constrain the active contour to detect boundaries in an accurate and robust manner. Moreover, the lattice Boltzmann method is used as an alternative approach for solving the level set equation to make it faster and suitable for parallel programming. Twenty simulated phantom studies and 16 clinical studies, including six cases of pharyngolaryngeal squamous cell carcinoma and ten cases of nonsmall cell lung cancer, were included to evaluate its performance. Besides, the proposed method was also compared with the contourlet-based active contour algorithm (CAC) and Schaefer’s thresholding method (ST). The relative volume error (RE), Dice similarity coefficient (DSC), and classification error (CE) metrics were used to analyze the results quantitatively. Results: For the simulated phantom studies (PSs), MASAC and CAC provide similar segmentations of the different lesions, while ST fails to achieve reliable results. For the clinical datasets (2 cases with connected high-uptake regions excluded) (CSs), CAC provides for the lowest mean RE (−8.38% ± 27.49%), while MASAC achieves the best mean DSC (0.71 ± 0.09) and mean CE (53.92% ± 12.65%), respectively. MASAC could reliably quantify different types of lesions assessed in

  6. 3D image fusion of whole-heart dynamic cardiac MR perfusion and late gadolinium enhancement: Intuitive delineation of myocardial hypoperfusion and scar.

    PubMed

    von Spiczak, Jochen; Mannil, Manoj; Kozerke, Sebastian; Alkadhi, Hatem; Manka, Robert

    2018-03-30

    Since patients with myocardial hypoperfusion due to coronary artery disease (CAD) with preserved viability are known to benefit from revascularization, accurate differentiation of hypoperfusion from scar is desirable. To develop a framework for 3D fusion of whole-heart dynamic cardiac MR perfusion and late gadolinium enhancement (LGE) to delineate stress-induced myocardial hypoperfusion and scar. Prospective feasibility study. Sixteen patients (61 ± 14 years, two females) with known/suspected CAD. 1.5T (nine patients); 3.0T (seven patients); whole-heart dynamic 3D cardiac MR perfusion (3D-PERF, under adenosine stress); 3D LGE inversion recovery sequences (3D-SCAR). A software framework was developed for 3D fusion of 3D-PERF and 3D-SCAR. Computation steps included: 1) segmentation of the left ventricle in 3D-PERF and 3D-SCAR; 2) semiautomatic thresholding of perfusion/scar data; 3) automatic calculation of ischemic/scar burden (ie, pathologic relative to total myocardium); 4) projection of perfusion/scar values onto artificial template of the left ventricle; 5) semiautomatic coregistration to an exemplary heart contour easing 3D orientation; and 6) 3D rendering of the combined datasets using automatically defined color tables. All tasks were performed by two independent, blinded readers (J.S. and R.M.). Intraclass correlation coefficients (ICC) for determining interreader agreement. Image acquisition, postprocessing, and 3D fusion were feasible in all cases. In all, 10/16 patients showed stress-induced hypoperfusion in 3D-PERF; 8/16 patients showed LGE in 3D-SCAR. For 3D-PERF, semiautomatic thresholding was possible in all patients. For 3D-SCAR, automatic thresholding was feasible where applicable. Average ischemic burden was 11 ± 7% (J.S.) and 12 ± 7% (R.M.). Average scar burden was 8 ± 5% (J.S.) and 7 ± 4% (R.M.). Interreader agreement was excellent (ICC for 3D-PERF = 0.993, for 3D-SCAR = 0.99). 3D fusion of 3D-PERF and 3D

  7. Improving Thermal Ablation Delineation With Electrode Vibration Elastography Using a Bidirectional Wave Propagation Assumption

    PubMed Central

    DeWall, Ryan J.; Varghese, Tomy

    2013-01-01

    Thermal ablation procedures are commonly used to treat hepatic cancers and accurate ablation representation on shear wave velocity images is crucial to ensure complete treatment of the malignant target. Electrode vibration elastography is a shear wave imaging technique recently developed to monitor thermal ablation extent during treatment procedures. Previous work has shown good lateral boundary delineation of ablated volumes, but axial delineation was more ambiguous, which may have resulted from the assumption of lateral shear wave propagation. In this work, we assume both lateral and axial wave propagation and compare wave velocity images to those assuming only lateral shear wave propagation in finite element simulations, tissue-mimicking phantoms, and bovine liver tissue. Our results show that assuming bidirectional wave propagation minimizes artifacts above and below ablated volumes, yielding a more accurate representation of the ablated region on shear wave velocity images. Area overestimation was reduced from 13.4% to 3.6% in a stiff-inclusion tissue-mimicking phantom and from 9.1% to 0.8% in a radio-frequency ablation in bovine liver tissue. More accurate ablation representation during ablation procedures increases the likelihood of complete treatment of the malignant target, decreasing tumor recurrence. PMID:22293748

  8. Improving thermal ablation delineation with electrode vibration elastography using a bidirectional wave propagation assumption.

    PubMed

    DeWall, Ryan J; Varghese, Tomy

    2012-01-01

    Thermal ablation procedures are commonly used to treat hepatic cancers and accurate ablation representation on shear wave velocity images is crucial to ensure complete treatment of the malignant target. Electrode vibration elastography is a shear wave imaging technique recently developed to monitor thermal ablation extent during treatment procedures. Previous work has shown good lateral boundary delineation of ablated volumes, but axial delineation was more ambiguous, which may have resulted from the assumption of lateral shear wave propagation. In this work, we assume both lateral and axial wave propagation and compare wave velocity images to those assuming only lateral shear wave propagation in finite element simulations, tissue-mimicking phantoms, and bovine liver tissue. Our results show that assuming bidirectional wave propagation minimizes artifacts above and below ablated volumes, yielding a more accurate representation of the ablated region on shear wave velocity images. Area overestimation was reduced from 13.4% to 3.6% in a stiff-inclusion tissue-mimicking phantom and from 9.1% to 0.8% in a radio-frequency ablation in bovine liver tissue. More accurate ablation representation during ablation procedures increases the likelihood of complete treatment of the malignant target, decreasing tumor recurrence. © 2012 IEEE

  9. Automatic readout micrometer

    DOEpatents

    Lauritzen, Ted

    1982-01-01

    A measuring system is disclosed for surveying and very accurately positioning objects with respect to a reference line. A principal use of this surveying system is for accurately aligning the electromagnets which direct a particle beam emitted from a particle accelerator. Prior art surveying systems require highly skilled surveyors. Prior art systems include, for example, optical surveying systems which are susceptible to operator reading errors, and celestial navigation-type surveying systems, with their inherent complexities. The present invention provides an automatic readout micrometer which can very accurately measure distances. The invention has a simplicity of operation which practically eliminates the possibilities of operator optical reading error, owning to the elimination of traditional optical alignments for making measurements. The invention has an extendable arm which carries a laser surveying target. The extendable arm can be continuously positioned over its entire length of travel by either a coarse or fine adjustment without having the fine adjustment outrun the coarse adjustment until a reference laser beam is centered on the target as indicated by a digital readout. The length of the micrometer can then be accurately and automatically read by a computer and compared with a standardized set of alignment measurements. Due to its construction, the micrometer eliminates any errors due to temperature changes when the system is operated within a standard operating temperature range.

  10. Automatic readout micrometer

    DOEpatents

    Lauritzen, T.

    A measuring system is described for surveying and very accurately positioning objects with respect to a reference line. A principle use of this surveying system is for accurately aligning the electromagnets which direct a particle beam emitted from a particle accelerator. Prior art surveying systems require highly skilled surveyors. Prior art systems include, for example, optical surveying systems which are susceptible to operator reading errors, and celestial navigation-type surveying systems, with their inherent complexities. The present invention provides an automatic readout micrometer which can very accurately measure distances. The invention has a simplicity of operation which practically eliminates the possibilities of operator optical reading error, owning to the elimination of traditional optical alignments for making measurements. The invention has an extendable arm which carries a laser surveying target. The extendable arm can be continuously positioned over its entire length of travel by either a coarse of fine adjustment without having the fine adjustment outrun the coarse adjustment until a reference laser beam is centered on the target as indicated by a digital readout. The length of the micrometer can then be accurately and automatically read by a computer and compared with a standardized set of alignment measurements. Due to its construction, the micrometer eliminates any errors due to temperature changes when the system is operated within a standard operating temperature range.

  11. An accurate segmentation method for volumetry of brain tumor in 3D MRI

    NASA Astrophysics Data System (ADS)

    Wang, Jiahui; Li, Qiang; Hirai, Toshinori; Katsuragawa, Shigehiko; Li, Feng; Doi, Kunio

    2008-03-01

    Accurate volumetry of brain tumors in magnetic resonance imaging (MRI) is important for evaluating the interval changes in tumor volumes during and after treatment, and also for planning of radiation therapy. In this study, an automated volumetry method for brain tumors in MRI was developed by use of a new three-dimensional (3-D) image segmentation technique. First, the central location of a tumor was identified by a radiologist, and then a volume of interest (VOI) was determined automatically. To substantially simplify tumor segmentation, we transformed the 3-D image of the tumor into a two-dimensional (2-D) image by use of a "spiral-scanning" technique, in which a radial line originating from the center of the tumor scanned the 3-D image spirally from the "north pole" to the "south pole". The voxels scanned by the radial line provided a transformed 2-D image. We employed dynamic programming to delineate an "optimal" outline of the tumor in the transformed 2-D image. We then transformed the optimal outline back into 3-D image space to determine the volume of the tumor. The volumetry method was trained and evaluated by use of 16 cases with 35 brain tumors. The agreement between tumor volumes provided by computer and a radiologist was employed as a performance metric. Our method provided relatively accurate results with a mean agreement value of 88%.

  12. Fully automatic lesion segmentation in breast MRI using mean-shift and graph-cuts on a region adjacency graph.

    PubMed

    McClymont, Darryl; Mehnert, Andrew; Trakic, Adnan; Kennedy, Dominic; Crozier, Stuart

    2014-04-01

    To present and evaluate a fully automatic method for segmentation (i.e., detection and delineation) of suspicious tissue in breast MRI. The method, based on mean-shift clustering and graph-cuts on a region adjacency graph, was developed and its parameters tuned using multimodal (T1, T2, DCE-MRI) clinical breast MRI data from 35 subjects (training data). It was then tested using two data sets. Test set 1 comprises data for 85 subjects (93 lesions) acquired using the same protocol and scanner system used to acquire the training data. Test set 2 comprises data for eight subjects (nine lesions) acquired using a similar protocol but a different vendor's scanner system. Each lesion was manually delineated in three-dimensions by an experienced breast radiographer to establish segmentation ground truth. The regions of interest identified by the method were compared with the ground truth and the detection and delineation accuracies quantitatively evaluated. One hundred percent of the lesions were detected with a mean of 4.5 ± 1.2 false positives per subject. This false-positive rate is nearly 50% better than previously reported for a fully automatic breast lesion detection system. The median Dice coefficient for Test set 1 was 0.76 (interquartile range, 0.17), and 0.75 (interquartile range, 0.16) for Test set 2. The results demonstrate the efficacy and accuracy of the proposed method as well as its potential for direct application across different MRI systems. It is (to the authors' knowledge) the first fully automatic method for breast lesion detection and delineation in breast MRI.

  13. Delineation of recharge areas for selected wells in the St. Peter-Prairie du Chien-Jordan Aquifer, Rochester, Minnesota

    USGS Publications Warehouse

    Delin, G.N.; Almendinger, James Edward

    1991-01-01

    Hydrogeologic mapping and numerical modeling were used to delineate zones of contribution to wells, defined as all parts of a ground-water-flow system that could supply water to a well. The zones of contribution delineated by use of numerical modeling have similar orientation (parallel to regional flow directions) but significantly different areas than the zones of contribution delineated by use of hydrogeologic mapping. Differences in computed areas of recharge are attributed to the capability of the numerical model to more accurately represent (1) the three-dimensional flow system, (2) hydrologic boundaries like streams, (3) variable recharge, and (4) the influence of nearby pumped wells, compared to the analytical models.

  14. Delineation of recharge areas for selected wells in the St. Peter-Prairie du Chien-Jordan aquifer, Rochester, Minnesota

    USGS Publications Warehouse

    Delin, G.N.; Almendinger, James Edward

    1993-01-01

    Hydrogeologic mapping and numerical modeling were used to delineate zones of contribution to wells, defined as all parts of a ground-water-flow system that could supply water to a well. The zones of contribution delineated by use of numerical modeling have similar orientation (parallel to regional flow directions) but significantly different areas than the zones of contribution delineated by use of hydrogeologic mapping. Differences in computed areas of recharge are attributed to the capability of the numerical model to more accurately represent (1) the three-dimensional flow system, (2) hydrologic boundaries such as streams, (3) variable recharge, and (4) the influence of nearby pumped wells, compared to the analytical models.

  15. A new automatic algorithm for quantification of myocardial infarction imaged by late gadolinium enhancement cardiovascular magnetic resonance: experimental validation and comparison to expert delineations in multi-center, multi-vendor patient data.

    PubMed

    Engblom, Henrik; Tufvesson, Jane; Jablonowski, Robert; Carlsson, Marcus; Aletras, Anthony H; Hoffmann, Pavel; Jacquier, Alexis; Kober, Frank; Metzler, Bernhard; Erlinge, David; Atar, Dan; Arheden, Håkan; Heiberg, Einar

    2016-05-04

    Late gadolinium enhancement (LGE) cardiovascular magnetic resonance (CMR) using magnitude inversion recovery (IR) or phase sensitive inversion recovery (PSIR) has become clinical standard for assessment of myocardial infarction (MI). However, there is no clinical standard for quantification of MI even though multiple methods have been proposed. Simple thresholds have yielded varying results and advanced algorithms have only been validated in single center studies. Therefore, the aim of this study was to develop an automatic algorithm for MI quantification in IR and PSIR LGE images and to validate the new algorithm experimentally and compare it to expert delineations in multi-center, multi-vendor patient data. The new automatic algorithm, EWA (Expectation Maximization, weighted intensity, a priori information), was implemented using an intensity threshold by Expectation Maximization (EM) and a weighted summation to account for partial volume effects. The EWA algorithm was validated in-vivo against triphenyltetrazolium-chloride (TTC) staining (n = 7 pigs with paired IR and PSIR images) and against ex-vivo high resolution T1-weighted images (n = 23 IR and n = 13 PSIR images). The EWA algorithm was also compared to expert delineation in 124 patients from multi-center, multi-vendor clinical trials 2-6 days following first time ST-elevation myocardial infarction (STEMI) treated with percutaneous coronary intervention (PCI) (n = 124 IR and n = 49 PSIR images). Infarct size by the EWA algorithm in vivo in pigs showed a bias to ex-vivo TTC of -1 ± 4%LVM (R = 0.84) in IR and -2 ± 3%LVM (R = 0.92) in PSIR images and a bias to ex-vivo T1-weighted images of 0 ± 4%LVM (R = 0.94) in IR and 0 ± 5%LVM (R = 0.79) in PSIR images. In multi-center patient studies, infarct size by the EWA algorithm showed a bias to expert delineation of -2 ± 6 %LVM (R = 0.81) in IR images (n = 124) and 0 ± 5%LVM (R = 0.89) in

  16. Automaticity in Anxiety Disorders and Major Depressive Disorder

    PubMed Central

    Teachman, Bethany A.; Joormann, Jutta; Steinman, Shari; Gotlib, Ian H.

    2012-01-01

    In this paper we examine the nature of automatic cognitive processing in anxiety disorders and Major Depressive Disorder (MDD). Rather than viewing automaticity as a unitary construct, we follow a social cognition perspective (Bargh, 1994) that argues for four theoretically independent features of automaticity: unconscious (processing of emotional stimuli occurs outside awareness), efficient (processing emotional meaning uses minimal attentional resources), unintentional (no goal is needed to engage in processing emotional meaning), and uncontrollable (limited ability to avoid, alter or terminate processing emotional stimuli). Our review of the literature suggests that most anxiety disorders are characterized by uncontrollable, and likely also unconscious and unintentional, biased processing of threat-relevant information. In contrast, MDD is most clearly typified by uncontrollable, but not unconscious or unintentional, processing of negative information. For the anxiety disorders and for MDD, there is not sufficient evidence to draw firm conclusions about efficiency of processing, though early indications are that neither anxiety disorders nor MDD are characterized by this feature. Clinical and theoretical implications of these findings are discussed and directions for future research are offered. In particular, it is clear that paradigms that more directly delineate the different features of automaticity are required to gain a more comprehensive and systematic understanding of the importance of automatic processing in emotion dysregulation. PMID:22858684

  17. Fitmunk: improving protein structures by accurate, automatic modeling of side-chain conformations.

    PubMed

    Porebski, Przemyslaw Jerzy; Cymborowski, Marcin; Pasenkiewicz-Gierula, Marta; Minor, Wladek

    2016-02-01

    Improvements in crystallographic hardware and software have allowed automated structure-solution pipelines to approach a near-`one-click' experience for the initial determination of macromolecular structures. However, in many cases the resulting initial model requires a laborious, iterative process of refinement and validation. A new method has been developed for the automatic modeling of side-chain conformations that takes advantage of rotamer-prediction methods in a crystallographic context. The algorithm, which is based on deterministic dead-end elimination (DEE) theory, uses new dense conformer libraries and a hybrid energy function derived from experimental data and prior information about rotamer frequencies to find the optimal conformation of each side chain. In contrast to existing methods, which incorporate the electron-density term into protein-modeling frameworks, the proposed algorithm is designed to take advantage of the highly discriminatory nature of electron-density maps. This method has been implemented in the program Fitmunk, which uses extensive conformational sampling. This improves the accuracy of the modeling and makes it a versatile tool for crystallographic model building, refinement and validation. Fitmunk was extensively tested on over 115 new structures, as well as a subset of 1100 structures from the PDB. It is demonstrated that the ability of Fitmunk to model more than 95% of side chains accurately is beneficial for improving the quality of crystallographic protein models, especially at medium and low resolutions. Fitmunk can be used for model validation of existing structures and as a tool to assess whether side chains are modeled optimally or could be better fitted into electron density. Fitmunk is available as a web service at http://kniahini.med.virginia.edu/fitmunk/server/ or at http://fitmunk.bitbucket.org/.

  18. Accurate and Fully Automatic Hippocampus Segmentation Using Subject-Specific 3D Optimal Local Maps Into a Hybrid Active Contour Model

    PubMed Central

    Gkontra, Polyxeni; Daras, Petros; Maglaveras, Nicos

    2014-01-01

    Assessing the structural integrity of the hippocampus (HC) is an essential step toward prevention, diagnosis, and follow-up of various brain disorders due to the implication of the structural changes of the HC in those disorders. In this respect, the development of automatic segmentation methods that can accurately, reliably, and reproducibly segment the HC has attracted considerable attention over the past decades. This paper presents an innovative 3-D fully automatic method to be used on top of the multiatlas concept for the HC segmentation. The method is based on a subject-specific set of 3-D optimal local maps (OLMs) that locally control the influence of each energy term of a hybrid active contour model (ACM). The complete set of the OLMs for a set of training images is defined simultaneously via an optimization scheme. At the same time, the optimal ACM parameters are also calculated. Therefore, heuristic parameter fine-tuning is not required. Training OLMs are subsequently combined, by applying an extended multiatlas concept, to produce the OLMs that are anatomically more suitable to the test image. The proposed algorithm was tested on three different and publicly available data sets. Its accuracy was compared with that of state-of-the-art methods demonstrating the efficacy and robustness of the proposed method. PMID:27170866

  19. Definition and automatic anatomy recognition of lymph node zones in the pelvis on CT images

    NASA Astrophysics Data System (ADS)

    Liu, Yu; Udupa, Jayaram K.; Odhner, Dewey; Tong, Yubing; Guo, Shuxu; Attor, Rosemary; Reinicke, Danica; Torigian, Drew A.

    2016-03-01

    Currently, unlike IALSC-defined thoracic lymph node zones, no explicitly provided definitions for lymph nodes in other body regions are available. Yet, definitions are critical for standardizing the recognition, delineation, quantification, and reporting of lymphadenopathy in other body regions. Continuing from our previous work in the thorax, this paper proposes a standardized definition of the grouping of pelvic lymph nodes into 10 zones. We subsequently employ our earlier Automatic Anatomy Recognition (AAR) framework designed for body-wide organ modeling, recognition, and delineation to actually implement these zonal definitions where the zones are treated as anatomic objects. First, all 10 zones and key anatomic organs used as anchors are manually delineated under expert supervision for constructing fuzzy anatomy models of the assembly of organs together with the zones. Then, optimal hierarchical arrangement of these objects is constructed for the purpose of achieving the best zonal recognition. For actual localization of the objects, two strategies are used -- optimal thresholded search for organs and one-shot method for the zones where the known relationship of the zones to key organs is exploited. Based on 50 computed tomography (CT) image data sets for the pelvic body region and an equal division into training and test subsets, automatic zonal localization within 1-3 voxels is achieved.

  20. Generic method for automatic bladder segmentation on cone beam CT using a patient-specific bladder shape model

    DOE Office of Scientific and Technical Information (OSTI.GOV)

    Schoot, A. J. A. J. van de, E-mail: a.j.schootvande@amc.uva.nl; Schooneveldt, G.; Wognum, S.

    Purpose: The aim of this study is to develop and validate a generic method for automatic bladder segmentation on cone beam computed tomography (CBCT), independent of gender and treatment position (prone or supine), using only pretreatment imaging data. Methods: Data of 20 patients, treated for tumors in the pelvic region with the entire bladder visible on CT and CBCT, were divided into four equally sized groups based on gender and treatment position. The full and empty bladder contour, that can be acquired with pretreatment CT imaging, were used to generate a patient-specific bladder shape model. This model was used tomore » guide the segmentation process on CBCT. To obtain the bladder segmentation, the reference bladder contour was deformed iteratively by maximizing the cross-correlation between directional grey value gradients over the reference and CBCT bladder edge. To overcome incorrect segmentations caused by CBCT image artifacts, automatic adaptations were implemented. Moreover, locally incorrect segmentations could be adapted manually. After each adapted segmentation, the bladder shape model was expanded and new shape patterns were calculated for following segmentations. All available CBCTs were used to validate the segmentation algorithm. The bladder segmentations were validated by comparison with the manual delineations and the segmentation performance was quantified using the Dice similarity coefficient (DSC), surface distance error (SDE) and SD of contour-to-contour distances. Also, bladder volumes obtained by manual delineations and segmentations were compared using a Bland-Altman error analysis. Results: The mean DSC, mean SDE, and mean SD of contour-to-contour distances between segmentations and manual delineations were 0.87, 0.27 cm and 0.22 cm (female, prone), 0.85, 0.28 cm and 0.22 cm (female, supine), 0.89, 0.21 cm and 0.17 cm (male, supine) and 0.88, 0.23 cm and 0.17 cm (male, prone), respectively. Manual local adaptations improved the

  1. Automatic segmentation of the hippocampus for preterm neonates from early-in-life to term-equivalent age.

    PubMed

    Guo, Ting; Winterburn, Julie L; Pipitone, Jon; Duerden, Emma G; Park, Min Tae M; Chau, Vann; Poskitt, Kenneth J; Grunau, Ruth E; Synnes, Anne; Miller, Steven P; Mallar Chakravarty, M

    2015-01-01

    The hippocampus, a medial temporal lobe structure central to learning and memory, is particularly vulnerable in preterm-born neonates. To date, segmentation of the hippocampus for preterm-born neonates has not yet been performed early-in-life (shortly after birth when clinically stable). The present study focuses on the development and validation of an automatic segmentation protocol that is based on the MAGeT-Brain (Multiple Automatically Generated Templates) algorithm to delineate the hippocampi of preterm neonates on their brain MRIs acquired at not only term-equivalent age but also early-in-life. First, we present a three-step manual segmentation protocol to delineate the hippocampus for preterm neonates and apply this protocol on 22 early-in-life and 22 term images. These manual segmentations are considered the gold standard in assessing the automatic segmentations. MAGeT-Brain, automatic hippocampal segmentation pipeline, requires only a small number of input atlases and reduces the registration and resampling errors by employing an intermediate template library. We assess the segmentation accuracy of MAGeT-Brain in three validation studies, evaluate the hippocampal growth from early-in-life to term-equivalent age, and study the effect of preterm birth on the hippocampal volume. The first experiment thoroughly validates MAGeT-Brain segmentation in three sets of 10-fold Monte Carlo cross-validation (MCCV) analyses with 187 different groups of input atlases and templates. The second experiment segments the neonatal hippocampi on 168 early-in-life and 154 term images and evaluates the hippocampal growth rate of 125 infants from early-in-life to term-equivalent age. The third experiment analyzes the effect of gestational age (GA) at birth on the average hippocampal volume at early-in-life and term-equivalent age using linear regression. The final segmentations demonstrate that MAGeT-Brain consistently provides accurate segmentations in comparison to manually

  2. Automatic segmentation of the hippocampus for preterm neonates from early-in-life to term-equivalent age

    PubMed Central

    Guo, Ting; Winterburn, Julie L.; Pipitone, Jon; Duerden, Emma G.; Park, Min Tae M.; Chau, Vann; Poskitt, Kenneth J.; Grunau, Ruth E.; Synnes, Anne; Miller, Steven P.; Mallar Chakravarty, M.

    2015-01-01

    Introduction The hippocampus, a medial temporal lobe structure central to learning and memory, is particularly vulnerable in preterm-born neonates. To date, segmentation of the hippocampus for preterm-born neonates has not yet been performed early-in-life (shortly after birth when clinically stable). The present study focuses on the development and validation of an automatic segmentation protocol that is based on the MAGeT-Brain (Multiple Automatically Generated Templates) algorithm to delineate the hippocampi of preterm neonates on their brain MRIs acquired at not only term-equivalent age but also early-in-life. Methods First, we present a three-step manual segmentation protocol to delineate the hippocampus for preterm neonates and apply this protocol on 22 early-in-life and 22 term images. These manual segmentations are considered the gold standard in assessing the automatic segmentations. MAGeT-Brain, automatic hippocampal segmentation pipeline, requires only a small number of input atlases and reduces the registration and resampling errors by employing an intermediate template library. We assess the segmentation accuracy of MAGeT-Brain in three validation studies, evaluate the hippocampal growth from early-in-life to term-equivalent age, and study the effect of preterm birth on the hippocampal volume. The first experiment thoroughly validates MAGeT-Brain segmentation in three sets of 10-fold Monte Carlo cross-validation (MCCV) analyses with 187 different groups of input atlases and templates. The second experiment segments the neonatal hippocampi on 168 early-in-life and 154 term images and evaluates the hippocampal growth rate of 125 infants from early-in-life to term-equivalent age. The third experiment analyzes the effect of gestational age (GA) at birth on the average hippocampal volume at early-in-life and term-equivalent age using linear regression. Results The final segmentations demonstrate that MAGeT-Brain consistently provides accurate segmentations

  3. Delineating high-density areas in spatial Poisson fields from strip-transect sampling using indicator geostatistics: application to unexploded ordnance removal.

    PubMed

    Saito, Hirotaka; McKenna, Sean A

    2007-07-01

    An approach for delineating high anomaly density areas within a mixture of two or more spatial Poisson fields based on limited sample data collected along strip transects was developed. All sampled anomalies were transformed to anomaly count data and indicator kriging was used to estimate the probability of exceeding a threshold value derived from the cdf of the background homogeneous Poisson field. The threshold value was determined so that the delineation of high-density areas was optimized. Additionally, a low-pass filter was applied to the transect data to enhance such segmentation. Example calculations were completed using a controlled military model site, in which accurate delineation of clusters of unexploded ordnance (UXO) was required for site cleanup.

  4. Automatic tissue segmentation of head and neck MR images for hyperthermia treatment planning

    NASA Astrophysics Data System (ADS)

    Fortunati, Valerio; Verhaart, René F.; Niessen, Wiro J.; Veenland, Jifke F.; Paulides, Margarethus M.; van Walsum, Theo

    2015-08-01

    A hyperthermia treatment requires accurate, patient-specific treatment planning. This planning is based on 3D anatomical models which are generally derived from computed tomography. Because of its superior soft tissue contrast, magnetic resonance imaging (MRI) information can be introduced to improve the quality of these 3D patient models and therefore the treatment planning itself. Thus, we present here an automatic atlas-based segmentation algorithm for MR images of the head and neck. Our method combines multiatlas local weighting fusion with intensity modelling. The accuracy of the method was evaluated using a leave-one-out cross validation experiment over a set of 11 patients for which manual delineation were available. The accuracy of the proposed method was high both in terms of the Dice similarity coefficient (DSC) and the 95th percentile Hausdorff surface distance (HSD) with median DSC higher than 0.8 for all tissues except sclera. For all tissues, except the spine tissues, the accuracy was approaching the interobserver agreement/variability both in terms of DSC and HSD. The positive effect of adding the intensity modelling to the multiatlas fusion decreased when a more accurate atlas fusion method was used. Using the proposed approach we improved the performance of the approach previously presented for H&N hyperthermia treatment planning, making the method suitable for clinical application.

  5. Automatic Quantification of Radiographic Wrist Joint Space Width of Patients With Rheumatoid Arthritis.

    PubMed

    Huo, Yinghe; Vincken, Koen L; van der Heijde, Desiree; de Hair, Maria J H; Lafeber, Floris P; Viergever, Max A

    2017-11-01

    Objective: Wrist joint space narrowing is a main radiographic outcome of rheumatoid arthritis (RA). Yet, automatic radiographic wrist joint space width (JSW) quantification for RA patients has not been widely investigated. The aim of this paper is to present an automatic method to quantify the JSW of three wrist joints that are least affected by bone overlapping and are frequently involved in RA. These joints are located around the scaphoid bone, viz. the multangular-navicular, capitate-navicular-lunate, and radiocarpal joints. Methods: The joint space around the scaphoid bone is detected by using consecutive searches of separate path segments, where each segment location aids in constraining the subsequent one. For joint margin delineation, first the boundary not affected by X-ray projection is extracted, followed by a backtrace process to obtain the actual joint margin. The accuracy of the quantified JSW is evaluated by comparison with the manually obtained ground truth. Results: Two of the 50 radiographs used for evaluation of the method did not yield a correct path through all three wrist joints. The delineated joint margins of the remaining 48 radiographs were used for JSW quantification. It was found that 90% of the joints had a JSW deviating less than 20% from the mean JSW of manual indications, with the mean JSW error less than 10%. Conclusion: The proposed method is able to automatically quantify the JSW of radiographic wrist joints reliably. The proposed method may aid clinical researchers to study the progression of wrist joint damage in RA studies. Objective: Wrist joint space narrowing is a main radiographic outcome of rheumatoid arthritis (RA). Yet, automatic radiographic wrist joint space width (JSW) quantification for RA patients has not been widely investigated. The aim of this paper is to present an automatic method to quantify the JSW of three wrist joints that are least affected by bone overlapping and are frequently involved in RA. These joints

  6. A correlation analysis-based detection and delineation of ECG characteristic events using template waveforms extracted by ensemble averaging of clustered heart cycles.

    PubMed

    Homaeinezhad, M R; Erfanianmoshiri-Nejad, M; Naseri, H

    2014-01-01

    The goal of this study is to introduce a simple, standard and safe procedure to detect and to delineate P and T waves of the electrocardiogram (ECG) signal in real conditions. The proposed method consists of four major steps: (1) a secure QRS detection and delineation algorithm, (2) a pattern recognition algorithm designed for distinguishing various ECG clusters which take place between consecutive R-waves, (3) extracting template of the dominant events of each cluster waveform and (4) application of the correlation analysis in order to delineate automatically the P- and T-waves in noisy conditions. The performance characteristics of the proposed P and T detection-delineation algorithm are evaluated versus various ECG signals whose qualities are altered from the best to the worst cases based on the random-walk noise theory. Also, the method is applied to the MIT-BIH Arrhythmia and the QT databases for comparing some parts of its performance characteristics with a number of P and T detection-delineation algorithms. The conducted evaluations indicate that in a signal with low quality value of about 0.6, the proposed method detects the P and T events with sensitivity Se=85% and positive predictive value of P+=89%, respectively. In addition, at the same quality, the average delineation errors associated with those ECG events are 45 and 63ms, respectively. Stable delineation error, high detection accuracy and high noise tolerance were the most important aspects considered during development of the proposed method. © 2013 Elsevier Ltd. All rights reserved.

  7. Achieving Accurate Automatic Sleep Staging on Manually Pre-processed EEG Data Through Synchronization Feature Extraction and Graph Metrics.

    PubMed

    Chriskos, Panteleimon; Frantzidis, Christos A; Gkivogkli, Polyxeni T; Bamidis, Panagiotis D; Kourtidou-Papadeli, Chrysoula

    2018-01-01

    Sleep staging, the process of assigning labels to epochs of sleep, depending on the stage of sleep they belong, is an arduous, time consuming and error prone process as the initial recordings are quite often polluted by noise from different sources. To properly analyze such data and extract clinical knowledge, noise components must be removed or alleviated. In this paper a pre-processing and subsequent sleep staging pipeline for the sleep analysis of electroencephalographic signals is described. Two novel methods of functional connectivity estimation (Synchronization Likelihood/SL and Relative Wavelet Entropy/RWE) are comparatively investigated for automatic sleep staging through manually pre-processed electroencephalographic recordings. A multi-step process that renders signals suitable for further analysis is initially described. Then, two methods that rely on extracting synchronization features from electroencephalographic recordings to achieve computerized sleep staging are proposed, based on bivariate features which provide a functional overview of the brain network, contrary to most proposed methods that rely on extracting univariate time and frequency features. Annotation of sleep epochs is achieved through the presented feature extraction methods by training classifiers, which are in turn able to accurately classify new epochs. Analysis of data from sleep experiments on a randomized, controlled bed-rest study, which was organized by the European Space Agency and was conducted in the "ENVIHAB" facility of the Institute of Aerospace Medicine at the German Aerospace Center (DLR) in Cologne, Germany attains high accuracy rates, over 90% based on ground truth that resulted from manual sleep staging by two experienced sleep experts. Therefore, it can be concluded that the above feature extraction methods are suitable for semi-automatic sleep staging.

  8. Achieving Accurate Automatic Sleep Staging on Manually Pre-processed EEG Data Through Synchronization Feature Extraction and Graph Metrics

    PubMed Central

    Chriskos, Panteleimon; Frantzidis, Christos A.; Gkivogkli, Polyxeni T.; Bamidis, Panagiotis D.; Kourtidou-Papadeli, Chrysoula

    2018-01-01

    Sleep staging, the process of assigning labels to epochs of sleep, depending on the stage of sleep they belong, is an arduous, time consuming and error prone process as the initial recordings are quite often polluted by noise from different sources. To properly analyze such data and extract clinical knowledge, noise components must be removed or alleviated. In this paper a pre-processing and subsequent sleep staging pipeline for the sleep analysis of electroencephalographic signals is described. Two novel methods of functional connectivity estimation (Synchronization Likelihood/SL and Relative Wavelet Entropy/RWE) are comparatively investigated for automatic sleep staging through manually pre-processed electroencephalographic recordings. A multi-step process that renders signals suitable for further analysis is initially described. Then, two methods that rely on extracting synchronization features from electroencephalographic recordings to achieve computerized sleep staging are proposed, based on bivariate features which provide a functional overview of the brain network, contrary to most proposed methods that rely on extracting univariate time and frequency features. Annotation of sleep epochs is achieved through the presented feature extraction methods by training classifiers, which are in turn able to accurately classify new epochs. Analysis of data from sleep experiments on a randomized, controlled bed-rest study, which was organized by the European Space Agency and was conducted in the “ENVIHAB” facility of the Institute of Aerospace Medicine at the German Aerospace Center (DLR) in Cologne, Germany attains high accuracy rates, over 90% based on ground truth that resulted from manual sleep staging by two experienced sleep experts. Therefore, it can be concluded that the above feature extraction methods are suitable for semi-automatic sleep staging. PMID:29628883

  9. Local Histograms for Per-Pixel Classification

    DTIC Science & Technology

    2012-03-01

    few axioms for such models are presented. These axioms are shown to be satisfied using the convergence of random wavelet expansions. The authors of...pathologists can accurately and consistently identify and delineate tissues and their pathologies , it is an expensive and time-consuming task, therefore...Automatic Identification and Delineation of Tissues and Pathologies in H&E Stained Images. PhD Thesis. Carnegie Mellon University, Pittsburgh, PA (September

  10. Object-based delineation and classification of alluvial fans by application of mean-shift segmentation and support vector machines

    NASA Astrophysics Data System (ADS)

    Pipaud, Isabel; Lehmkuhl, Frank

    2017-09-01

    high segmentation quality. Our analysis further reveals that incorporation of morphometric parameters quantifying specific morphological aspects of a landform is indispensable for developing an accurate classification scheme. Alluvial fans exhibiting accentuated composite morphologies were identified as a major challenge for automatic delineation, as they cannot be fully captured by a single segmentation run. There is, however, a high probability that this shortcoming can be overcome by enhancing the presented approach with a routine merging fan sub-entities based on their spatial relationships.

  11. A segmentation approach for a delineation of terrestrial ecoregions

    NASA Astrophysics Data System (ADS)

    Nowosad, J.; Stepinski, T.

    2017-12-01

    Terrestrial ecoregions are the result of regionalization of land into homogeneous units of similar ecological and physiographic features. Terrestrial Ecoregions of the World (TEW) is a commonly used global ecoregionalization based on expert knowledge and in situ observations. Ecological Land Units (ELUs) is a global classification of 250 meters-sized cells into 4000 types on the basis of the categorical values of four environmental variables. ELUs are automatically calculated and reproducible but they are not a regionalization which makes them impractical for GIS-based spatial analysis and for comparison with TEW. We have regionalized terrestrial ecosystems on the basis of patterns of the same variables (land cover, soils, landform, and bioclimate) previously used in ELUs. Considering patterns of categorical variables makes segmentation and thus regionalization possible. Original raster datasets of the four variables are first transformed into regular grids of square-sized blocks of their cells called eco-sites. Eco-sites are elementary land units containing local patterns of physiographic characteristics and thus assumed to contain a single ecosystem. Next, eco-sites are locally aggregated using a procedure analogous to image segmentation. The procedure optimizes pattern homogeneity of all four environmental variables within each segment. The result is a regionalization of the landmass into land units characterized by uniform pattern of land cover, soils, landforms, climate, and, by inference, by uniform ecosystem. Because several disjoined segments may have very similar characteristics, we cluster the segments to obtain a smaller set of segment types which we identify with ecoregions. Our approach is automatic, reproducible, updatable, and customizable. It yields the first automatic delineation of ecoregions on the global scale. In the resulting vector database each ecoregion/segment is described by numerous attributes which make it a valuable GIS resource for

  12. Automatic intraaortic balloon pump timing using an intrabeat dicrotic notch prediction algorithm.

    PubMed

    Schreuder, Jan J; Castiglioni, Alessandro; Donelli, Andrea; Maisano, Francesco; Jansen, Jos R C; Hanania, Ramzi; Hanlon, Pat; Bovelander, Jan; Alfieri, Ottavio

    2005-03-01

    The efficacy of intraaortic balloon counterpulsation (IABP) during arrhythmic episodes is questionable. A novel algorithm for intrabeat prediction of the dicrotic notch was used for real time IABP inflation timing control. A windkessel model algorithm was used to calculate real-time aortic flow from aortic pressure. The dicrotic notch was predicted using a percentage of calculated peak flow. Automatic inflation timing was set at intrabeat predicted dicrotic notch and was combined with automatic IAB deflation. Prophylactic IABP was applied in 27 patients with low ejection fraction (< 35%) undergoing cardiac surgery. Analysis of IABP at a 1:4 ratio revealed that IAB inflation occurred at a mean of 0.6 +/- 5 ms from the dicrotic notch. In all patients accurate automatic timing at a 1:1 assist ratio was performed. Seventeen patients had episodes of severe arrhythmia, the novel IABP inflation algorithm accurately assisted 318 of 320 arrhythmic beats at a 1:1 ratio. The novel real-time intrabeat IABP inflation timing algorithm performed accurately in all patients during both regular rhythms and severe arrhythmia, allowing fully automatic intrabeat IABP timing.

  13. Automatic FDG-PET-based tumor and metastatic lymph node segmentation in cervical cancer

    NASA Astrophysics Data System (ADS)

    Arbonès, Dídac R.; Jensen, Henrik G.; Loft, Annika; Munck af Rosenschöld, Per; Hansen, Anders Elias; Igel, Christian; Darkner, Sune

    2014-03-01

    Treatment of cervical cancer, one of the three most commonly diagnosed cancers worldwide, often relies on delineations of the tumour and metastases based on PET imaging using the contrast agent 18F-Fluorodeoxyglucose (FDG). We present a robust automatic algorithm for segmenting the gross tumour volume (GTV) and metastatic lymph nodes in such images. As the cervix is located next to the bladder and FDG is washed out through the urine, the PET-positive GTV and the bladder cannot be easily separated. Our processing pipeline starts with a histogram-based region of interest detection followed by level set segmentation. After that, morphological image operations combined with clustering, region growing, and nearest neighbour labelling allow to remove the bladder and to identify the tumour and metastatic lymph nodes. The proposed method was applied to 125 patients and no failure could be detected by visual inspection. We compared our segmentations with results from manual delineations of corresponding MR and CT images, showing that the detected GTV lays at least 97.5% within the MR/CT delineations. We conclude that the algorithm has a very high potential for substituting the tedious manual delineation of PET positive areas.

  14. Automatic and Robust Delineation of the Fiducial Points of the Seismocardiogram Signal for Non-invasive Estimation of Cardiac Time Intervals.

    PubMed

    Khosrow-Khavar, Farzad; Tavakolian, Kouhyar; Blaber, Andrew; Menon, Carlo

    2016-10-12

    The purpose of this research was to design a delineation algorithm that could detect specific fiducial points of the seismocardiogram (SCG) signal with or without using the electrocardiogram (ECG) R-wave as the reference point. The detected fiducial points were used to estimate cardiac time intervals. Due to complexity and sensitivity of the SCG signal, the algorithm was designed to robustly discard the low-quality cardiac cycles, which are the ones that contain unrecognizable fiducial points. The algorithm was trained on a dataset containing 48,318 manually annotated cardiac cycles. It was then applied to three test datasets: 65 young healthy individuals (dataset 1), 15 individuals above 44 years old (dataset 2), and 25 patients with previous heart conditions (dataset 3). The algorithm accomplished high prediction accuracy with the rootmean- square-error of less than 5 ms for all the test datasets. The algorithm overall mean detection rate per individual recordings (DRI) were 74, 68, and 42 percent for the three test datasets when concurrent ECG and SCG were used. For the standalone SCG case, the mean DRI was 32, 14 and 21 percent. When the proposed algorithm applied to concurrent ECG and SCG signals, the desired fiducial points of the SCG signal were successfully estimated with a high detection rate. For the standalone case, however, the algorithm achieved high prediction accuracy and detection rate for only the young individual dataset. The presented algorithm could be used for accurate and non-invasive estimation of cardiac time intervals.

  15. Use of aerial photographs for assessment of soil organic carbon and delineation of agricultural management zones.

    NASA Astrophysics Data System (ADS)

    Bartholomeus, H.; Kooistra, L.

    2012-04-01

    For quantitative estimation of soil properties by means of remote sensing, often hyperspectral data are used. But these data are scarce and expensive, which prohibits wider implementation of the developed techniques in agricultural management. For precision agriculture, observations at a high spatial resolution are required. Colour aerial photographs at this scale are widely available, and can be acquired at no of very low costs. Therefore, we investigated whether publically available aerial photographs can be used to a) automatically delineate management zones and b) estimate levels of organic carbon spatially. We selected three study areas within the Netherlands that cover a large variance in soil type (peat, sand, and clay). For the fields of interest, RGB aerial photographs with a spatial resolution of 50 cm were extracted from a publically available data provider. Further pre-processing exists of geo-referencing only. Since the images originate from different sources and are potentially acquired under unknown illumination conditions, the exact radiometric properties of the data are unknown. Therefore, we used spectral indices to emphasize the differences in reflectance and normalize for differences in radiometry. To delineate management zones we used image segmentation techniques, using the derived indices as input. Comparison with management zone maps as used by the farmers shows that there is good correspondence. Regression analysis between a number of soil properties and the derived indices shows that organic carbon is the major explanatory variable for differences in index values within the fields. However, relations do not hold for large regions, indicating that local models will have to be used, which is a problem that is also still relevant for hyperspectral remote sensing data. With this research, we show that low-cost aerial photographs can be a valuable tool for quantitative analysis of organic carbon and automatic delineation of management zones

  16. In vivo delineation of subdivisions of the human amygdaloid complex in a high-resolution group template

    PubMed Central

    Tyszka, J. Michael; Pauli, Wolfgang M.

    2016-01-01

    The nuclei of the human amygdala remain difficult to distinguish in individual subject structural magnetic resonance images. However, interpretation of the amygdala’s role in whole brain networks requires accurate localization of functional activity to a particular nucleus or subgroup of nuclei. To address this, we constructed high spatial resolution, three-dimensional templates, using joint high accuracy diffeomorphic registration of T1- and T2-weighted structural images from 168 typical adults between 22 and 35 years old released by the Human Connectome Project. Several internuclear boundaries are clearly visible in these templates, which would otherwise be impossible to delineate in individual subject data. A probabilistic atlas of major nuclei and nuclear groups was constructed in this template space and mapped back to individual spaces by inversion of the individual diffeomorphisms. Group level analyses revealed a slight (approximately 2%) bias towards larger total amygdala and nuclear volumes in the right hemisphere. No substantial sex or age differences were found in amygdala volumes normalized to total intracranial volume, or subdivision volumes normalized to amygdala volume. The current delineation provides a finer parcellation of the amygdala with more accurate external boundary definition than current histology-based atlases when used in conjunction with high accuracy registration methods, such as diffeomorphic warping. These templates and delineation are intended to be an open and evolving resource for future functional and structural imaging studies of the human amygdala. PMID:27354150

  17. SU-E-J-07: A Functional MR Protocol for the Pancreatic Tumor Delineation

    DOE Office of Scientific and Technical Information (OSTI.GOV)

    Andreychenko, A; Heerkens, H; Meijer, G

    2014-06-01

    Purpose: Pancreatic cancer is one of the cancers with the poorest survival prognosis. At the time of diagnosis most of pancreatic cancers are unresectable and those patients can be treated by radiotherapy. Radiotherapy for pancreatic cancer is limited due to uncertainties in CT-based delineations. MRI provides an excellent soft tissue contrast. Here, an MR protocol is developed to improve delineations for radiotherapy treatment of pancreatic cancer. In a later stage this protocol can also be used for on-line visualization of the pancreas during MRI guided treatments. Methods: Nine pancreatic cancer patients were included. The MR protocol included T2 weighted(T2w), T1more » weighted(T1w), diffusion weighted(DWI) and dynamic contrast enhanced(DCE) techniques. The tumor was delineated on T2w and T1w MRI by an experienced radiation oncologist. Healthy pancreas or pancreatitis (assigned by the oncologist based on T2w) areas were also delineated. Apparent diffusion coefficient(ADC), and area under the curve(AUC)/time to peak(TTP) maps were obtained from DWI and DCE scans, respectively. Results: A clear demarcation of tumor area was visible on b800 DWI images in 5 patients. ADC maps of those patients characterized tumor as an area with restricted water diffusion. Tumor delineations based on solely DCE were possible in 7 patients. In 6 of those patients AUC maps demonstrated tumor heterogeneity: a hypointense area with a hyperintense ring. TTP values clearly discriminated the tumor and the healthy pancreas but could not distinguish tumor and the pancreatitis accurately. Conclusion: MR imaging results in a more pronounced tumor contrast than contrast enhanced CT. The addition of quantitative, functional MRI provides valuable, additional information to the radiation oncologist on the spatial tumor extent by discriminating tumor from the healthy pancreas(TTP, DWI) and characterizing the tumor(ADC). Our findings indicate that tumor delineation in pancreatic cancer can

  18. Accuracy Assessment of Crown Delineation Methods for the Individual Trees Using LIDAR Data

    NASA Astrophysics Data System (ADS)

    Chang, K. T.; Lin, C.; Lin, Y. C.; Liu, J. K.

    2016-06-01

    Forest canopy density and height are used as variables in a number of environmental applications, including the estimation of biomass, forest extent and condition, and biodiversity. The airborne Light Detection and Ranging (LiDAR) is very useful to estimate forest canopy parameters according to the generated canopy height models (CHMs). The purpose of this work is to introduce an algorithm to delineate crown parameters, e.g. tree height and crown radii based on the generated rasterized CHMs. And accuracy assessment for the extraction of volumetric parameters of a single tree is also performed via manual measurement using corresponding aerial photo pairs. A LiDAR dataset of a golf course acquired by Leica ALS70-HP is used in this study. Two algorithms, i.e. a traditional one with the subtraction of a digital elevation model (DEM) from a digital surface model (DSM), and a pit-free approach are conducted to generate the CHMs firstly. Then two algorithms, a multilevel morphological active-contour (MMAC) and a variable window filter (VWF), are implemented and used in this study for individual tree delineation. Finally, experimental results of two automatic estimation methods for individual trees can be evaluated with manually measured stand-level parameters, i.e. tree height and crown diameter. The resulting CHM generated by a simple subtraction is full of empty pixels (called "pits") that will give vital impact on subsequent analysis for individual tree delineation. The experimental results indicated that if more individual trees can be extracted, tree crown shape will became more completely in the CHM data after the pit-free process.

  19. Automatically high accurate and efficient photomask defects management solution for advanced lithography manufacture

    NASA Astrophysics Data System (ADS)

    Zhu, Jun; Chen, Lijun; Ma, Lantao; Li, Dejian; Jiang, Wei; Pan, Lihong; Shen, Huiting; Jia, Hongmin; Hsiang, Chingyun; Cheng, Guojie; Ling, Li; Chen, Shijie; Wang, Jun; Liao, Wenkui; Zhang, Gary

    2014-04-01

    Defect review is a time consuming job. Human error makes result inconsistent. The defects located on don't care area would not hurt the yield and no need to review them such as defects on dark area. However, critical area defects can impact yield dramatically and need more attention to review them such as defects on clear area. With decrease in integrated circuit dimensions, mask defects are always thousands detected during inspection even more. Traditional manual or simple classification approaches are unable to meet efficient and accuracy requirement. This paper focuses on automatic defect management and classification solution using image output of Lasertec inspection equipment and Anchor pattern centric image process technology. The number of mask defect found during an inspection is always in the range of thousands or even more. This system can handle large number defects with quick and accurate defect classification result. Our experiment includes Die to Die and Single Die modes. The classification accuracy can reach 87.4% and 93.3%. No critical or printable defects are missing in our test cases. The missing classification defects are 0.25% and 0.24% in Die to Die mode and Single Die mode. This kind of missing rate is encouraging and acceptable to apply on production line. The result can be output and reloaded back to inspection machine to have further review. This step helps users to validate some unsure defects with clear and magnification images when captured images can't provide enough information to make judgment. This system effectively reduces expensive inline defect review time. As a fully inline automated defect management solution, the system could be compatible with current inspection approach and integrated with optical simulation even scoring function and guide wafer level defect inspection.

  20. An efficient and accurate framework for calculating lattice thermal conductivity of solids: AFLOW—AAPL Automatic Anharmonic Phonon Library

    NASA Astrophysics Data System (ADS)

    Plata, Jose J.; Nath, Pinku; Usanmaz, Demet; Carrete, Jesús; Toher, Cormac; de Jong, Maarten; Asta, Mark; Fornari, Marco; Nardelli, Marco Buongiorno; Curtarolo, Stefano

    2017-10-01

    One of the most accurate approaches for calculating lattice thermal conductivity, , is solving the Boltzmann transport equation starting from third-order anharmonic force constants. In addition to the underlying approximations of ab-initio parameterization, two main challenges are associated with this path: high computational costs and lack of automation in the frameworks using this methodology, which affect the discovery rate of novel materials with ad-hoc properties. Here, the Automatic Anharmonic Phonon Library (AAPL) is presented. It efficiently computes interatomic force constants by making effective use of crystal symmetry analysis, it solves the Boltzmann transport equation to obtain , and allows a fully integrated operation with minimum user intervention, a rational addition to the current high-throughput accelerated materials development framework AFLOW. An "experiment vs. theory" study of the approach is shown, comparing accuracy and speed with respect to other available packages, and for materials characterized by strong electron localization and correlation. Combining AAPL with the pseudo-hybrid functional ACBN0 is possible to improve accuracy without increasing computational requirements.

  1. Semi-automatic segmentation of myocardium at risk in T2-weighted cardiovascular magnetic resonance.

    PubMed

    Sjögren, Jane; Ubachs, Joey F A; Engblom, Henrik; Carlsson, Marcus; Arheden, Håkan; Heiberg, Einar

    2012-01-31

    T2-weighted cardiovascular magnetic resonance (CMR) has been shown to be a promising technique for determination of ischemic myocardium, referred to as myocardium at risk (MaR), after an acute coronary event. Quantification of MaR in T2-weighted CMR has been proposed to be performed by manual delineation or the threshold methods of two standard deviations from remote (2SD), full width half maximum intensity (FWHM) or Otsu. However, manual delineation is subjective and threshold methods have inherent limitations related to threshold definition and lack of a priori information about cardiac anatomy and physiology. Therefore, the aim of this study was to develop an automatic segmentation algorithm for quantification of MaR using anatomical a priori information. Forty-seven patients with first-time acute ST-elevation myocardial infarction underwent T2-weighted CMR within 1 week after admission. Endocardial and epicardial borders of the left ventricle, as well as the hyper enhanced MaR regions were manually delineated by experienced observers and used as reference method. A new automatic segmentation algorithm, called Segment MaR, defines the MaR region as the continuous region most probable of being MaR, by estimating the intensities of normal myocardium and MaR with an expectation maximization algorithm and restricting the MaR region by an a priori model of the maximal extent for the user defined culprit artery. The segmentation by Segment MaR was compared against inter observer variability of manual delineation and the threshold methods of 2SD, FWHM and Otsu. MaR was 32.9 ± 10.9% of left ventricular mass (LVM) when assessed by the reference observer and 31.0 ± 8.8% of LVM assessed by Segment MaR. The bias and correlation was, -1.9 ± 6.4% of LVM, R = 0.81 (p < 0.001) for Segment MaR, -2.3 ± 4.9%, R = 0.91 (p < 0.001) for inter observer variability of manual delineation, -7.7 ± 11.4%, R = 0.38 (p = 0.008) for 2SD, -21.0 ± 9.9%, R = 0.41 (p = 0.004) for FWHM, and

  2. Development of an Automatic Differentiation Version of the FPX Rotor Code

    NASA Technical Reports Server (NTRS)

    Hu, Hong

    1996-01-01

    The ADIFOR2.0 automatic differentiator is applied to the FPX rotor code along with the grid generator GRGN3. The FPX is an eXtended Full-Potential CFD code for rotor calculations. The automatic differentiation version of the code is obtained, which provides both non-geometry and geometry sensitivity derivatives. The sensitivity derivatives via automatic differentiation are presented and compared with divided difference generated derivatives. The study shows that automatic differentiation method gives accurate derivative values in an efficient manner.

  3. A new automatic blood pressure kit auscultates for accurate reading with a smartphone: A diagnostic accuracy study.

    PubMed

    Wu, Hongjun; Wang, Bingjian; Zhu, Xinpu; Chu, Guang; Zhang, Zhi

    2016-08-01

    The widely used oscillometric automated blood pressure (BP) monitor was continuously questioned on its accuracy. A novel BP kit named Accutension which adopted Korotkoff auscultation method was then devised. Accutension worked with a miniature microphone, a pressure sensor, and a smartphone. The BP values were automatically displayed on the smartphone screen through the installed App. Data recorded in the phone could be played back and reconfirmed after measurement. They could also be uploaded and saved to the iCloud. The accuracy and consistency of this novel electronic auscultatory sphygmomanometer was preliminarily verified here. Thirty-two subjects were included and 82 qualified readings were obtained. The mean differences ± SD for systolic and diastolic BP readings between Accutension and mercury sphygmomanometer were 0.87 ± 2.86 and -0.94 ± 2.93 mm Hg. Agreements between Accutension and mercury sphygmomanometer were highly significant for systolic (ICC = 0.993, 95% confidence interval (CI): 0.989-0.995) and diastolic (ICC = 0.987, 95% CI: 0.979-0.991). In conclusion, Accutension worked accurately based on our pilot study data. The difference was acceptable. ICC and Bland-Altman plot charts showed good agreements with manual measurements. Systolic readings of Accutension were slightly higher than those of manual measurement, while diastolic readings were slightly lower. One possible reason was that Accutension captured the first and the last korotkoff sound more sensitively than human ear during manual measurement and avoided sound missing, so that it might be more accurate than traditional mercury sphygmomanometer. By documenting and analyzing of variant tendency of BP values, Accutension helps management of hypertension and therefore contributes to the mobile heath service.

  4. Comparison of manual and automatic techniques for substriatal segmentation in 11C-raclopride high-resolution PET studies.

    PubMed

    Johansson, Jarkko; Alakurtti, Kati; Joutsa, Juho; Tohka, Jussi; Ruotsalainen, Ulla; Rinne, Juha O

    2016-10-01

    The striatum is the primary target in regional C-raclopride-PET studies, and despite its small volume, it contains several functional and anatomical subregions. The outcome of the quantitative dopamine receptor study using C-raclopride-PET depends heavily on the quality of the region-of-interest (ROI) definition of these subregions. The aim of this study was to evaluate subregional analysis techniques because new approaches have emerged, but have not yet been compared directly. In this paper, we compared manual ROI delineation with several automatic methods. The automatic methods used either direct clustering of the PET image or individualization of chosen brain atlases on the basis of MRI or PET image normalization. State-of-the-art normalization methods and atlases were applied, including those provided in the FreeSurfer, Statistical Parametric Mapping8, and FSL software packages. Evaluation of the automatic methods was based on voxel-wise congruity with the manual delineations and the test-retest variability and reliability of the outcome measures using data from seven healthy male participants who were scanned twice with C-raclopride-PET on the same day. The results show that both manual and automatic methods can be used to define striatal subregions. Although most of the methods performed well with respect to the test-retest variability and reliability of binding potential, the smallest average test-retest variability and SEM were obtained using a connectivity-based atlas and PET normalization (test-retest variability=4.5%, SEM=0.17). The current state-of-the-art automatic ROI methods can be considered good alternatives for subjective and laborious manual segmentation in C-raclopride-PET studies.

  5. Automatic River Network Extraction from LIDAR Data

    NASA Astrophysics Data System (ADS)

    Maderal, E. N.; Valcarcel, N.; Delgado, J.; Sevilla, C.; Ojeda, J. C.

    2016-06-01

    National Geographic Institute of Spain (IGN-ES) has launched a new production system for automatic river network extraction for the Geospatial Reference Information (GRI) within hydrography theme. The goal is to get an accurate and updated river network, automatically extracted as possible. For this, IGN-ES has full LiDAR coverage for the whole Spanish territory with a density of 0.5 points per square meter. To implement this work, it has been validated the technical feasibility, developed a methodology to automate each production phase: hydrological terrain models generation with 2 meter grid size and river network extraction combining hydrographic criteria (topographic network) and hydrological criteria (flow accumulation river network), and finally the production was launched. The key points of this work has been managing a big data environment, more than 160,000 Lidar data files, the infrastructure to store (up to 40 Tb between results and intermediate files), and process; using local virtualization and the Amazon Web Service (AWS), which allowed to obtain this automatic production within 6 months, it also has been important the software stability (TerraScan-TerraSolid, GlobalMapper-Blue Marble , FME-Safe, ArcGIS-Esri) and finally, the human resources managing. The results of this production has been an accurate automatic river network extraction for the whole country with a significant improvement for the altimetric component of the 3D linear vector. This article presents the technical feasibility, the production methodology, the automatic river network extraction production and its advantages over traditional vector extraction systems.

  6. Semiautomatic segmentation of liver metastases on volumetric CT images

    DOE Office of Scientific and Technical Information (OSTI.GOV)

    Yan, Jiayong; Schwartz, Lawrence H.; Zhao, Binsheng, E-mail: bz2166@cumc.columbia.edu

    2015-11-15

    Purpose: Accurate segmentation and quantification of liver metastases on CT images are critical to surgery/radiation treatment planning and therapy response assessment. To date, there are no reliable methods to perform such segmentation automatically. In this work, the authors present a method for semiautomatic delineation of liver metastases on contrast-enhanced volumetric CT images. Methods: The first step is to manually place a seed region-of-interest (ROI) in the lesion on an image. This ROI will (1) serve as an internal marker and (2) assist in automatically identifying an external marker. With these two markers, lesion contour on the image can be accuratelymore » delineated using traditional watershed transformation. Density information will then be extracted from the segmented 2D lesion and help determine the 3D connected object that is a candidate of the lesion volume. The authors have developed a robust strategy to automatically determine internal and external markers for marker-controlled watershed segmentation. By manually placing a seed region-of-interest in the lesion to be delineated on a reference image, the method can automatically determine dual threshold values to approximately separate the lesion from its surrounding structures and refine the thresholds from the segmented lesion for the accurate segmentation of the lesion volume. This method was applied to 69 liver metastases (1.1–10.3 cm in diameter) from a total of 15 patients. An independent radiologist manually delineated all lesions and the resultant lesion volumes served as the “gold standard” for validation of the method’s accuracy. Results: The algorithm received a median overlap, overestimation ratio, and underestimation ratio of 82.3%, 6.0%, and 11.5%, respectively, and a median average boundary distance of 1.2 mm. Conclusions: Preliminary results have shown that volumes of liver metastases on contrast-enhanced CT images can be accurately estimated by a semiautomatic

  7. An accurate method of extracting fat droplets in liver images for quantitative evaluation

    NASA Astrophysics Data System (ADS)

    Ishikawa, Masahiro; Kobayashi, Naoki; Komagata, Hideki; Shinoda, Kazuma; Yamaguchi, Masahiro; Abe, Tokiya; Hashiguchi, Akinori; Sakamoto, Michiie

    2015-03-01

    The steatosis in liver pathological tissue images is a promising indicator of nonalcoholic fatty liver disease (NAFLD) and the possible risk of hepatocellular carcinoma (HCC). The resulting values are also important for ensuring the automatic and accurate classification of HCC images, because the existence of many fat droplets is likely to create errors in quantifying the morphological features used in the process. In this study we propose a method that can automatically detect, and exclude regions with many fat droplets by using the feature values of colors, shapes and the arrangement of cell nuclei. We implement the method and confirm that it can accurately detect fat droplets and quantify the fat droplet ratio of actual images. This investigation also clarifies the effective characteristics that contribute to accurate detection.

  8. Closed circuit TV system automatically guides welding arc

    NASA Technical Reports Server (NTRS)

    Stephans, D. L.; Wall, W. A., Jr.

    1968-01-01

    Closed circuit television /CCTV/ system automatically guides a welding torch to position the welding arc accurately along weld seams. Digital counting and logic techniques incorporated in the control circuitry, ensure performance reliability.

  9. 30 CFR 282.22 - Delineation Plan.

    Code of Federal Regulations, 2011 CFR

    2011-07-01

    ... 30 Mineral Resources 2 2011-07-01 2011-07-01 false Delineation Plan. 282.22 Section 282.22 Mineral Resources BUREAU OF OCEAN ENERGY MANAGEMENT, REGULATION, AND ENFORCEMENT, DEPARTMENT OF THE INTERIOR... a comprehensive Testing or Mining Plan. A Delineation Plan at a minimum shall include the following...

  10. Automatic segmentation of vessels in in-vivo ultrasound scans

    NASA Astrophysics Data System (ADS)

    Tamimi-Sarnikowski, Philip; Brink-Kjær, Andreas; Moshavegh, Ramin; Arendt Jensen, Jørgen

    2017-03-01

    Ultrasound has become highly popular to monitor atherosclerosis, by scanning the carotid artery. The screening involves measuring the thickness of the vessel wall and diameter of the lumen. An automatic segmentation of the vessel lumen, can enable the determination of lumen diameter. This paper presents a fully automatic segmentation algorithm, for robustly segmenting the vessel lumen in longitudinal B-mode ultrasound images. The automatic segmentation is performed using a combination of B-mode and power Doppler images. The proposed algorithm includes a series of preprocessing steps, and performs a vessel segmentation by use of the marker-controlled watershed transform. The ultrasound images used in the study were acquired using the bk3000 ultrasound scanner (BK Ultrasound, Herlev, Denmark) with two transducers "8L2 Linear" and "10L2w Wide Linear" (BK Ultrasound, Herlev, Denmark). The algorithm was evaluated empirically and applied to a dataset of in-vivo 1770 images recorded from 8 healthy subjects. The segmentation results were compared to manual delineation performed by two experienced users. The results showed a sensitivity and specificity of 90.41+/-11.2 % and 97.93+/-5.7% (mean+/-standard deviation), respectively. The amount of overlap of segmentation and manual segmentation, was measured by the Dice similarity coefficient, which was 91.25+/-11.6%. The empirical results demonstrated the feasibility of segmenting the vessel lumen in ultrasound scans using a fully automatic algorithm.

  11. Automatic delineation and 3D visualization of the human ventricular system using probabilistic neural networks

    NASA Astrophysics Data System (ADS)

    Hatfield, Fraser N.; Dehmeshki, Jamshid

    1998-09-01

    Neurosurgery is an extremely specialized area of medical practice, requiring many years of training. It has been suggested that virtual reality models of the complex structures within the brain may aid in the training of neurosurgeons as well as playing an important role in the preparation for surgery. This paper focuses on the application of a probabilistic neural network to the automatic segmentation of the ventricles from magnetic resonance images of the brain, and their three dimensional visualization.

  12. Automatic and accurate reconstruction of distal humerus contours through B-Spline fitting based on control polygon deformation.

    PubMed

    Mostafavi, Kamal; Tutunea-Fatan, O Remus; Bordatchev, Evgueni V; Johnson, James A

    2014-12-01

    The strong advent of computer-assisted technologies experienced by the modern orthopedic surgery prompts for the expansion of computationally efficient techniques to be built on the broad base of computer-aided engineering tools that are readily available. However, one of the common challenges faced during the current developmental phase continues to remain the lack of reliable frameworks to allow a fast and precise conversion of the anatomical information acquired through computer tomography to a format that is acceptable to computer-aided engineering software. To address this, this study proposes an integrated and automatic framework capable to extract and then postprocess the original imaging data to a common planar and closed B-Spline representation. The core of the developed platform relies on the approximation of the discrete computer tomography data by means of an original two-step B-Spline fitting technique based on successive deformations of the control polygon. In addition to its rapidity and robustness, the developed fitting technique was validated to produce accurate representations that do not deviate by more than 0.2 mm with respect to alternate representations of the bone geometry that were obtained through different-contact-based-data acquisition or data processing methods. © IMechE 2014.

  13. Delineation and geometric modeling of road networks

    NASA Astrophysics Data System (ADS)

    Poullis, Charalambos; You, Suya

    In this work we present a novel vision-based system for automatic detection and extraction of complex road networks from various sensor resources such as aerial photographs, satellite images, and LiDAR. Uniquely, the proposed system is an integrated solution that merges the power of perceptual grouping theory (Gabor filtering, tensor voting) and optimized segmentation techniques (global optimization using graph-cuts) into a unified framework to address the challenging problems of geospatial feature detection and classification. Firstly, the local precision of the Gabor filters is combined with the global context of the tensor voting to produce accurate classification of the geospatial features. In addition, the tensorial representation used for the encoding of the data eliminates the need for any thresholds, therefore removing any data dependencies. Secondly, a novel orientation-based segmentation is presented which incorporates the classification of the perceptual grouping, and results in segmentations with better defined boundaries and continuous linear segments. Finally, a set of gaussian-based filters are applied to automatically extract centerline information (magnitude, width and orientation). This information is then used for creating road segments and transforming them to their polygonal representations.

  14. Wetlands delineation by spectral signature analysis and legal implications

    NASA Technical Reports Server (NTRS)

    Anderon, R. R.; Carter, V.

    1972-01-01

    High altitude analysis of wetland resources and the use of such information in an operational mode to address specific problems of wetland preservation at a state level are discussed. Work efforts were directed toward: (1) developing techniques for using large scale color IR photography in state wetlands mapping program, (2) developing methods for obtaining wetlands ecology information from high altitude photography, (3) developing means by which spectral data can be more accurately analyzed visually, and (4) developing spectral data for automatic mapping of wetlands.

  15. Automatic segmentation of time-lapse microscopy images depicting a live Dharma embryo.

    PubMed

    Zacharia, Eleni; Bondesson, Maria; Riu, Anne; Ducharme, Nicole A; Gustafsson, Jan-Åke; Kakadiaris, Ioannis A

    2011-01-01

    Biological inferences about the toxicity of chemicals reached during experiments on the zebrafish Dharma embryo can be greatly affected by the analysis of the time-lapse microscopy images depicting the embryo. Among the stages of image analysis, automatic and accurate segmentation of the Dharma embryo is the most crucial and challenging. In this paper, an accurate and automatic segmentation approach for the segmentation of the Dharma embryo data obtained by fluorescent time-lapse microscopy is proposed. Experiments performed in four stacks of 3D images over time have shown promising results.

  16. High accurate time system of the Low Latitude Meridian Circle.

    NASA Astrophysics Data System (ADS)

    Yang, Jing; Wang, Feng; Li, Zhiming

    In order to obtain the high accurate time signal for the Low Latitude Meridian Circle (LLMC), a new GPS accurate time system is developed which include GPS, 1 MC frequency source and self-made clock system. The second signal of GPS is synchronously used in the clock system and information can be collected by a computer automatically. The difficulty of the cancellation of the time keeper can be overcomed by using this system.

  17. Fast automatic 3D liver segmentation based on a three-level AdaBoost-guided active shape model

    DOE Office of Scientific and Technical Information (OSTI.GOV)

    He, Baochun; Huang, Cheng; Zhou, Shoujun

    Purpose: A robust, automatic, and rapid method for liver delineation is urgently needed for the diagnosis and treatment of liver disorders. Until now, the high variability in liver shape, local image artifacts, and the presence of tumors have complicated the development of automatic 3D liver segmentation. In this study, an automatic three-level AdaBoost-guided active shape model (ASM) is proposed for the segmentation of the liver based on enhanced computed tomography images in a robust and fast manner, with an emphasis on the detection of tumors. Methods: The AdaBoost voxel classifier and AdaBoost profile classifier were used to automatically guide three-levelmore » active shape modeling. In the first level of model initialization, fast automatic liver segmentation by an AdaBoost voxel classifier method is proposed. A shape model is then initialized by registration with the resulting rough segmentation. In the second level of active shape model fitting, a prior model based on the two-class AdaBoost profile classifier is proposed to identify the optimal surface. In the third level, a deformable simplex mesh with profile probability and curvature constraint as the external force is used to refine the shape fitting result. In total, three registration methods—3D similarity registration, probability atlas B-spline, and their proposed deformable closest point registration—are used to establish shape correspondence. Results: The proposed method was evaluated using three public challenge datasets: 3Dircadb1, SLIVER07, and Visceral Anatomy3. The results showed that our approach performs with promising efficiency, with an average of 35 s, and accuracy, with an average Dice similarity coefficient (DSC) of 0.94 ± 0.02, 0.96 ± 0.01, and 0.94 ± 0.02 for the 3Dircadb1, SLIVER07, and Anatomy3 training datasets, respectively. The DSC of the SLIVER07 testing and Anatomy3 unseen testing datasets were 0.964 and 0.933, respectively. Conclusions: The proposed automatic

  18. Fast automatic 3D liver segmentation based on a three-level AdaBoost-guided active shape model.

    PubMed

    He, Baochun; Huang, Cheng; Sharp, Gregory; Zhou, Shoujun; Hu, Qingmao; Fang, Chihua; Fan, Yingfang; Jia, Fucang

    2016-05-01

    A robust, automatic, and rapid method for liver delineation is urgently needed for the diagnosis and treatment of liver disorders. Until now, the high variability in liver shape, local image artifacts, and the presence of tumors have complicated the development of automatic 3D liver segmentation. In this study, an automatic three-level AdaBoost-guided active shape model (ASM) is proposed for the segmentation of the liver based on enhanced computed tomography images in a robust and fast manner, with an emphasis on the detection of tumors. The AdaBoost voxel classifier and AdaBoost profile classifier were used to automatically guide three-level active shape modeling. In the first level of model initialization, fast automatic liver segmentation by an AdaBoost voxel classifier method is proposed. A shape model is then initialized by registration with the resulting rough segmentation. In the second level of active shape model fitting, a prior model based on the two-class AdaBoost profile classifier is proposed to identify the optimal surface. In the third level, a deformable simplex mesh with profile probability and curvature constraint as the external force is used to refine the shape fitting result. In total, three registration methods-3D similarity registration, probability atlas B-spline, and their proposed deformable closest point registration-are used to establish shape correspondence. The proposed method was evaluated using three public challenge datasets: 3Dircadb1, SLIVER07, and Visceral Anatomy3. The results showed that our approach performs with promising efficiency, with an average of 35 s, and accuracy, with an average Dice similarity coefficient (DSC) of 0.94 ± 0.02, 0.96 ± 0.01, and 0.94 ± 0.02 for the 3Dircadb1, SLIVER07, and Anatomy3 training datasets, respectively. The DSC of the SLIVER07 testing and Anatomy3 unseen testing datasets were 0.964 and 0.933, respectively. The proposed automatic approach achieves robust, accurate, and fast liver

  19. Combining registration and active shape models for the automatic segmentation of the lymph node regions in head and neck CT images

    DOE Office of Scientific and Technical Information (OSTI.GOV)

    Chen Antong; Deeley, Matthew A.; Niermann, Kenneth J.

    2010-12-15

    Purpose: Intensity-modulated radiation therapy (IMRT) is the state of the art technique for head and neck cancer treatment. It requires precise delineation of the target to be treated and structures to be spared, which is currently done manually. The process is a time-consuming task of which the delineation of lymph node regions is often the longest step. Atlas-based delineation has been proposed as an alternative, but, in the authors' experience, this approach is not accurate enough for routine clinical use. Here, the authors improve atlas-based segmentation results obtained for level II-IV lymph node regions using an active shape model (ASM)more » approach. Methods: An average image volume was first created from a set of head and neck patient images with minimally enlarged nodes. The average image volume was then registered using affine, global, and local nonrigid transformations to the other volumes to establish a correspondence between surface points in the atlas and surface points in each of the other volumes. Once the correspondence was established, the ASMs were created for each node level. The models were then used to first constrain the results obtained with an atlas-based approach and then to iteratively refine the solution. Results: The method was evaluated through a leave-one-out experiment. The ASM- and atlas-based segmentations were compared to manual delineations via the Dice similarity coefficient (DSC) for volume overlap and the Euclidean distance between manual and automatic 3D surfaces. The mean DSC value obtained with the ASM-based approach is 10.7% higher than with the atlas-based approach; the mean and median surface errors were decreased by 13.6% and 12.0%, respectively. Conclusions: The ASM approach is effective in reducing segmentation errors in areas of low CT contrast where purely atlas-based methods are challenged. Statistical analysis shows that the improvements brought by this approach are significant.« less

  20. Automatic lumbar spine measurement in CT images

    NASA Astrophysics Data System (ADS)

    Mao, Yunxiang; Zheng, Dong; Liao, Shu; Peng, Zhigang; Yan, Ruyi; Liu, Junhua; Dong, Zhongxing; Gong, Liyan; Zhou, Xiang Sean; Zhan, Yiqiang; Fei, Jun

    2017-03-01

    Accurate lumbar spine measurement in CT images provides an essential way for quantitative spinal diseases analysis such as spondylolisthesis and scoliosis. In today's clinical workflow, the measurements are manually performed by radiologists and surgeons, which is time consuming and irreproducible. Therefore, automatic and accurate lumbar spine measurement algorithm becomes highly desirable. In this study, we propose a method to automatically calculate five different lumbar spine measurements in CT images. There are three main stages of the proposed method: First, a learning based spine labeling method, which integrates both the image appearance and spine geometry information, is used to detect lumbar and sacrum vertebrae in CT images. Then, a multiatlases based image segmentation method is used to segment each lumbar vertebra and the sacrum based on the detection result. Finally, measurements are derived from the segmentation result of each vertebra. Our method has been evaluated on 138 spinal CT scans to automatically calculate five widely used clinical spine measurements. Experimental results show that our method can achieve more than 90% success rates across all the measurements. Our method also significantly improves the measurement efficiency compared to manual measurements. Besides benefiting the routine clinical diagnosis of spinal diseases, our method also enables the large scale data analytics for scientific and clinical researches.

  1. Comparison of landmark-based and automatic methods for cortical surface registration

    PubMed Central

    Pantazis, Dimitrios; Joshi, Anand; Jiang, Jintao; Shattuck, David; Bernstein, Lynne E.; Damasio, Hanna; Leahy, Richard M.

    2009-01-01

    Group analysis of structure or function in cerebral cortex typically involves as a first step the alignment of the cortices. A surface based approach to this problem treats the cortex as a convoluted surface and coregisters across subjects so that cortical landmarks or features are aligned. This registration can be performed using curves representing sulcal fundi and gyral crowns to constrain the mapping. Alternatively, registration can be based on the alignment of curvature metrics computed over the entire cortical surface. The former approach typically involves some degree of user interaction in defining the sulcal and gyral landmarks while the latter methods can be completely automated. Here we introduce a cortical delineation protocol consisting of 26 consistent landmarks spanning the entire cortical surface. We then compare the performance of a landmark-based registration method that uses this protocol with that of two automatic methods implemented in the software packages FreeSurfer and BrainVoyager. We compare performance in terms of discrepancy maps between the different methods, the accuracy with which regions of interest are aligned, and the ability of the automated methods to correctly align standard cortical landmarks. Our results show similar performance for ROIs in the perisylvian region for the landmark based method and FreeSurfer. However, the discrepancy maps showed larger variability between methods in occipital and frontal cortex and also that automated methods often produce misalignment of standard cortical landmarks. Consequently, selection of the registration approach should consider the importance of accurate sulcal alignment for the specific task for which coregistration is being performed. When automatic methods are used, the users should ensure that sulci in regions of interest in their studies are adequately aligned before proceeding with subsequent analysis. PMID:19796696

  2. Delineating forested river habitats and riparian floodplain hydrology with LiDAR

    NASA Astrophysics Data System (ADS)

    Vondrasek, Chris

    Rivers and the riparian forest corridor comprise a valuable freshwater ecosystem that has been altered by human activities including timber management, road building, and other land conversions. The habitats of river dependent species in the Pacific Northwest, in particular salmon have often been degraded by these activities. Many salmon runs have become threatened with extinction and have been Endangered Species Act listed. New conservation planning and policies have developed around protecting freshwater habitats and restoring more natural river processes. In WA State, timber landowners, officials from State and Federal agencies, Native tribes, and other stakeholders developed Forest Practice rules and codified a Habitat Conservation Plan with dual goals of providing regulatory surety for timber land owners and helping to recover the threatened salmon runs in forested watersheds. Conserving critical stream ecological functions and potential fish habitats throughout watersheds while managing and regulating timber harvest across the State requires accurate and up-to-date delineation and mapping of channels, tributaries, and off-channel wetlands. Monitoring the effectiveness of protection efforts is necessary but can also be difficult. Agency staff and resources are limited for both day-to-day implementation of Forest Practice rules and adaptive management. The goal of this research has been to develop efficient and accessible methods to delineate wetlands, side-channels, tributaries, and pools and backwaters created by large log jams in forested watersheds. It was also essential to use publicly available LiDAR data and to model these waters at ecologically meaningful flows. I tested a hydraulic model at a 2-year and 50-year flows, and a relative height above river surface model and compared them. I completed two additional remote sensing investigations to correlate channel movement and the locations of off-channel wetlands: an analysis of historical aerial imagery

  3. Delineating wetland catchments and modeling hydrologic ...

    EPA Pesticide Factsheets

    In traditional watershed delineation and topographic modeling, surface depressions are generally treated as spurious features and simply removed from a digital elevation model (DEM) to enforce flow continuity of water across the topographic surface to the watershed outlets. In reality, however, many depressions in the DEM are actual wetland landscape features with seasonal to permanent inundation patterning characterized by nested hierarchical structures and dynamic filling–spilling–merging surface-water hydrological processes. Differentiating and appropriately processing such ecohydrologically meaningful features remains a major technical terrain-processing challenge, particularly as high-resolution spatial data are increasingly used to support modeling and geographic analysis needs. The objectives of this study were to delineate hierarchical wetland catchments and model their hydrologic connectivity using high-resolution lidar data and aerial imagery. The graph-theory-based contour tree method was used to delineate the hierarchical wetland catchments and characterize their geometric and topological properties. Potential hydrologic connectivity between wetlands and streams were simulated using the least-cost-path algorithm. The resulting flow network delineated potential flow paths connecting wetland depressions to each other or to the river network on scales finer than those available through the National Hydrography Dataset. The results demonstrated that

  4. SCAMP: Automatic Astrometric and Photometric Calibration

    NASA Astrophysics Data System (ADS)

    Bertin, Emmanuel

    2010-10-01

    Astrometric and photometric calibrations have remained the most tiresome step in the reduction of large imaging surveys. SCAMP has been written to address this problem. The program efficiently computes accurate astrometric and photometric solutions for any arbitrary sequence of FITS images in a completely automatic way. SCAMP is released under the GNU General Public License.

  5. Automatic segmentation of 4D cardiac MR images for extraction of ventricular chambers using a spatio-temporal approach

    NASA Astrophysics Data System (ADS)

    Atehortúa, Angélica; Zuluaga, Maria A.; Ourselin, Sébastien; Giraldo, Diana; Romero, Eduardo

    2016-03-01

    An accurate ventricular function quantification is important to support evaluation, diagnosis and prognosis of several cardiac pathologies. However, expert heart delineation, specifically for the right ventricle, is a time consuming task with high inter-and-intra observer variability. A fully automatic 3D+time heart segmentation framework is herein proposed for short-axis-cardiac MRI sequences. This approach estimates the heart using exclusively information from the sequence itself without tuning any parameters. The proposed framework uses a coarse-to-fine approach, which starts by localizing the heart via spatio-temporal analysis, followed by a segmentation of the basal heart that is then propagated to the apex by using a non-rigid-registration strategy. The obtained volume is then refined by estimating the ventricular muscle by locally searching a prior endocardium- pericardium intensity pattern. The proposed framework was applied to 48 patients datasets supplied by the organizers of the MICCAI 2012 Right Ventricle segmentation challenge. Results show the robustness, efficiency and competitiveness of the proposed method both in terms of accuracy and computational load.

  6. A Method for Extracting Suspected Parotid Lesions in CT Images using Feature-based Segmentation and Active Contours based on Stationary Wavelet Transform

    NASA Astrophysics Data System (ADS)

    Wu, T. Y.; Lin, S. F.

    2013-10-01

    Automatic suspected lesion extraction is an important application in computer-aided diagnosis (CAD). In this paper, we propose a method to automatically extract the suspected parotid regions for clinical evaluation in head and neck CT images. The suspected lesion tissues in low contrast tissue regions can be localized with feature-based segmentation (FBS) based on local texture features, and can be delineated with accuracy by modified active contour models (ACM). At first, stationary wavelet transform (SWT) is introduced. The derived wavelet coefficients are applied to derive the local features for FBS, and to generate enhanced energy maps for ACM computation. Geometric shape features (GSFs) are proposed to analyze each soft tissue region segmented by FBS; the regions with higher similarity GSFs with the lesions are extracted and the information is also applied as the initial conditions for fine delineation computation. Consequently, the suspected lesions can be automatically localized and accurately delineated for aiding clinical diagnosis. The performance of the proposed method is evaluated by comparing with the results outlined by clinical experts. The experiments on 20 pathological CT data sets show that the true-positive (TP) rate on recognizing parotid lesions is about 94%, and the dimension accuracy of delineation results can also approach over 93%.

  7. Support for Debugging Automatically Parallelized Programs

    NASA Technical Reports Server (NTRS)

    Hood, Robert; Jost, Gabriele

    2001-01-01

    This viewgraph presentation provides information on support sources available for the automatic parallelization of computer program. CAPTools, a support tool developed at the University of Greenwich, transforms, with user guidance, existing sequential Fortran code into parallel message passing code. Comparison routines are then run for debugging purposes, in essence, ensuring that the code transformation was accurate.

  8. Body-wide hierarchical fuzzy modeling, recognition, and delineation of anatomy in medical images.

    PubMed

    Udupa, Jayaram K; Odhner, Dewey; Zhao, Liming; Tong, Yubing; Matsumoto, Monica M S; Ciesielski, Krzysztof C; Falcao, Alexandre X; Vaideeswaran, Pavithra; Ciesielski, Victoria; Saboury, Babak; Mohammadianrasanani, Syedmehrdad; Sin, Sanghun; Arens, Raanan; Torigian, Drew A

    2014-07-01

    To make Quantitative Radiology (QR) a reality in radiological practice, computerized body-wide Automatic Anatomy Recognition (AAR) becomes essential. With the goal of building a general AAR system that is not tied to any specific organ system, body region, or image modality, this paper presents an AAR methodology for localizing and delineating all major organs in different body regions based on fuzzy modeling ideas and a tight integration of fuzzy models with an Iterative Relative Fuzzy Connectedness (IRFC) delineation algorithm. The methodology consists of five main steps: (a) gathering image data for both building models and testing the AAR algorithms from patient image sets existing in our health system; (b) formulating precise definitions of each body region and organ and delineating them following these definitions; (c) building hierarchical fuzzy anatomy models of organs for each body region; (d) recognizing and locating organs in given images by employing the hierarchical models; and (e) delineating the organs following the hierarchy. In Step (c), we explicitly encode object size and positional relationships into the hierarchy and subsequently exploit this information in object recognition in Step (d) and delineation in Step (e). Modality-independent and dependent aspects are carefully separated in model encoding. At the model building stage, a learning process is carried out for rehearsing an optimal threshold-based object recognition method. The recognition process in Step (d) starts from large, well-defined objects and proceeds down the hierarchy in a global to local manner. A fuzzy model-based version of the IRFC algorithm is created by naturally integrating the fuzzy model constraints into the delineation algorithm. The AAR system is tested on three body regions - thorax (on CT), abdomen (on CT and MRI), and neck (on MRI and CT) - involving a total of over 35 organs and 130 data sets (the total used for model building and testing). The training and

  9. Automated delineation of radiotherapy volumes: are we going in the right direction?

    PubMed Central

    Whitfield, G A; Price, P; Price, G J; Moore, C J

    2013-01-01

    ABSTRACT. Rapid and accurate delineation of target volumes and multiple organs at risk, within the enduring International Commission on Radiation Units and Measurement framework, is now hugely important in radiotherapy, owing to the rapid proliferation of intensity-modulated radiotherapy and the advent of four-dimensional image-guided adaption. Nevertheless, delineation is still generally clinically performed with little if any machine assistance, even though it is both time-consuming and prone to interobserver variation. Currently available segmentation tools include those based on image greyscale interrogation, statistical shape modelling and body atlas-based methods. However, all too often these are not able to match the accuracy of the expert clinician, which remains the universally acknowledged gold standard. In this article we suggest that current methods are fundamentally limited by their lack of ability to incorporate essential human clinical decision-making into the underlying models. Hybrid techniques that utilise prior knowledge, make sophisticated use of greyscale information and allow clinical expertise to be integrated are needed. This may require a change in focus from automated segmentation to machine-assisted delineation. Similarly, new metrics of image quality reflecting fitness for purpose would be extremely valuable. We conclude that methods need to be developed to take account of the clinician's expertise and honed visual processing capabilities as much as the underlying, clinically meaningful information content of the image data being interrogated. We illustrate our observations and suggestions through our own experiences with two software tools developed as part of research council-funded projects. PMID:23239689

  10. 12 CFR 228.41 - Assessment area delineation.

    Code of Federal Regulations, 2010 CFR

    2010-01-01

    ... does not evaluate the bank's delineation of its assessment area(s) as a separate performance criterion..., such as those consumer loans on which the bank elects to have its performance assessed). (d... area(s) delineated by a bank in its evaluation of the bank's CRA performance unless the Board...

  11. Real-time automatic registration in optical surgical navigation

    NASA Astrophysics Data System (ADS)

    Lin, Qinyong; Yang, Rongqian; Cai, Ken; Si, Xuan; Chen, Xiuwen; Wu, Xiaoming

    2016-05-01

    An image-guided surgical navigation system requires the improvement of the patient-to-image registration time to enhance the convenience of the registration procedure. A critical step in achieving this aim is performing a fully automatic patient-to-image registration. This study reports on a design of custom fiducial markers and the performance of a real-time automatic patient-to-image registration method using these markers on the basis of an optical tracking system for rigid anatomy. The custom fiducial markers are designed to be automatically localized in both patient and image spaces. An automatic localization method is performed by registering a point cloud sampled from the three dimensional (3D) pedestal model surface of a fiducial marker to each pedestal of fiducial markers searched in image space. A head phantom is constructed to estimate the performance of the real-time automatic registration method under four fiducial configurations. The head phantom experimental results demonstrate that the real-time automatic registration method is more convenient, rapid, and accurate than the manual method. The time required for each registration is approximately 0.1 s. The automatic localization method precisely localizes the fiducial markers in image space. The averaged target registration error for the four configurations is approximately 0.7 mm. The automatic registration performance is independent of the positions relative to the tracking system and the movement of the patient during the operation.

  12. Estimating spatial travel times using automatic vehicle identification data

    DOT National Transportation Integrated Search

    2001-01-01

    Prepared ca. 2001. The paper describes an algorithm that was developed for estimating reliable and accurate average roadway link travel times using Automatic Vehicle Identification (AVI) data. The algorithm presented is unique in two aspects. First, ...

  13. 12 CFR 345.41 - Assessment area delineation.

    Code of Federal Regulations, 2010 CFR

    2010-01-01

    ... the bank's delineation of its assessment area(s) as a separate performance criterion, but the FDIC..., such as those consumer loans on which the bank elects to have its performance assessed). (d... area(s) delineated by a bank in its evaluation of the bank's CRA performance unless the FDIC determines...

  14. Automatic anatomical segmentation of the liver by separation planes

    NASA Astrophysics Data System (ADS)

    Boltcheva, Dobrina; Passat, Nicolas; Agnus, Vincent; Jacob-Da, Marie-Andrée, , Col; Ronse, Christian; Soler, Luc

    2006-03-01

    Surgical planning in oncological liver surgery is based on the location of the 8 anatomical segments according to Couinaud's definition and tumors inside these structures. The detection of the boundaries between the segments is then the first step of the preoperative planning. The proposed method, devoted to binary images of livers segmented from CT-scans, has been designed to delineate these segments. It automatically detects a set of landmarks using a priori anatomical knowledge and differential geometry criteria. These landmarks are then used to position the Couinaud's segments. Validations performed on 7 clinical cases tend to prove that the method is reliable for most of these separation planes.

  15. 12 CFR 25.41 - Assessment area delineation.

    Code of Federal Regulations, 2010 CFR

    2010-01-01

    ... performance assessed). (d) Adjustments to geographic area(s). A bank may adjust the boundaries of its... uses the assessment area(s) delineated by a bank in its evaluation of the bank's CRA performance unless... 12 Banks and Banking 1 2010-01-01 2010-01-01 false Assessment area delineation. 25.41 Section 25...

  16. Semi-automatic knee cartilage segmentation

    NASA Astrophysics Data System (ADS)

    Dam, Erik B.; Folkesson, Jenny; Pettersen, Paola C.; Christiansen, Claus

    2006-03-01

    Osteo-Arthritis (OA) is a very common age-related cause of pain and reduced range of motion. A central effect of OA is wear-down of the articular cartilage that otherwise ensures smooth joint motion. Quantification of the cartilage breakdown is central in monitoring disease progression and therefore cartilage segmentation is required. Recent advances allow automatic cartilage segmentation with high accuracy in most cases. However, the automatic methods still fail in some problematic cases. For clinical studies, even if a few failing cases will be averaged out in the overall results, this reduces the mean accuracy and precision and thereby necessitates larger/longer studies. Since the severe OA cases are often most problematic for the automatic methods, there is even a risk that the quantification will introduce a bias in the results. Therefore, interactive inspection and correction of these problematic cases is desirable. For diagnosis on individuals, this is even more crucial since the diagnosis will otherwise simply fail. We introduce and evaluate a semi-automatic cartilage segmentation method combining an automatic pre-segmentation with an interactive step that allows inspection and correction. The automatic step consists of voxel classification based on supervised learning. The interactive step combines a watershed transformation of the original scan with the posterior probability map from the classification step at sub-voxel precision. We evaluate the method for the task of segmenting the tibial cartilage sheet from low-field magnetic resonance imaging (MRI) of knees. The evaluation shows that the combined method allows accurate and highly reproducible correction of the segmentation of even the worst cases in approximately ten minutes of interaction.

  17. Automatic tracking of labeled red blood cells in microchannels.

    PubMed

    Pinho, Diana; Lima, Rui; Pereira, Ana I; Gayubo, Fernando

    2013-09-01

    The current study proposes an automatic method for the segmentation and tracking of red blood cells flowing through a 100- μm glass capillary. The original images were obtained by means of a confocal system and then processed in MATLAB using the Image Processing Toolbox. The measurements obtained with the proposed automatic method were compared with the results determined by a manual tracking method. The comparison was performed by using both linear regressions and Bland-Altman analysis. The results have shown a good agreement between the two methods. Therefore, the proposed automatic method is a powerful way to provide rapid and accurate measurements for in vitro blood experiments in microchannels. Copyright © 2012 John Wiley & Sons, Ltd.

  18. Prosody's Contribution to Fluency: An Examination of the Theory of Automatic Information Processing

    ERIC Educational Resources Information Center

    Schrauben, Julie E.

    2010-01-01

    LaBerge and Samuels' (1974) theory of automatic information processing in reading offers a model that explains how and where the processing of information occurs and the degree to which processing of information occurs. These processes are dependent upon two criteria: accurate word decoding and automatic word recognition. However, LaBerge and…

  19. Computer aided weld defect delineation using statistical parametric active contours in radiographic inspection.

    PubMed

    Goumeidane, Aicha Baya; Nacereddine, Nafaa; Khamadja, Mohammed

    2015-01-01

    A perfect knowledge of a defect shape is determinant for the analysis step in automatic radiographic inspection. Image segmentation is carried out on radiographic images and extract defects indications. This paper deals with weld defect delineation in radiographic images. The proposed method is based on a new statistics-based explicit active contour. An association of local and global modeling of the image pixels intensities is used to push the model to the desired boundaries. Furthermore, other strategies are proposed to accelerate its evolution and make the convergence speed depending only on the defect size as selecting a band around the active contour curve. The experimental results are very promising, since experiments on synthetic and radiographic images show the ability of the proposed model to extract a piece-wise homogenous object from very inhomogeneous background, even in a bad quality image.

  20. Computer-based radiological longitudinal evaluation of meningiomas following stereotactic radiosurgery.

    PubMed

    Shimol, Eli Ben; Joskowicz, Leo; Eliahou, Ruth; Shoshan, Yigal

    2018-02-01

    Stereotactic radiosurgery (SRS) is a common treatment for intracranial meningiomas. SRS is planned on a pre-therapy gadolinium-enhanced T1-weighted MRI scan (Gd-T1w MRI) in which the meningioma contours have been delineated. Post-SRS therapy serial Gd-T1w MRI scans are then acquired for longitudinal treatment evaluation. Accurate tumor volume change quantification is required for treatment efficacy evaluation and for treatment continuation. We present a new algorithm for the automatic segmentation and volumetric assessment of meningioma in post-therapy Gd-T1w MRI scans. The inputs are the pre- and post-therapy Gd-T1w MRI scans and the meningioma delineation in the pre-therapy scan. The output is the meningioma delineations and volumes in the post-therapy scan. The algorithm uses the pre-therapy scan and its meningioma delineation to initialize an extended Chan-Vese active contour method and as a strong patient-specific intensity and shape prior for the post-therapy scan meningioma segmentation. The algorithm is automatic, obviates the need for independent tumor localization and segmentation initialization, and incorporates the same tumor delineation criteria in both the pre- and post-therapy scans. Our experimental results on retrospective pre- and post-therapy scans with a total of 32 meningiomas with volume ranges 0.4-26.5 cm[Formula: see text] yield a Dice coefficient of [Formula: see text]% with respect to ground-truth delineations in post-therapy scans created by two clinicians. These results indicate a high correspondence to the ground-truth delineations. Our algorithm yields more reliable and accurate tumor volume change measurements than other stand-alone segmentation methods. It may be a useful tool for quantitative meningioma prognosis evaluation after SRS.

  1. Automatic assessment of volume asymmetries applied to hip abductor muscles in patients with hip arthroplasty

    NASA Astrophysics Data System (ADS)

    Klemt, Christian; Modat, Marc; Pichat, Jonas; Cardoso, M. J.; Henckel, Joahnn; Hart, Alister; Ourselin, Sebastien

    2015-03-01

    Metal-on-metal (MoM) hip arthroplasties have been utilised over the last 15 years to restore hip function for 1.5 million patients worldwide. Althoug widely used, this hip arthroplasty releases metal wear debris which lead to muscle atrophy. The degree of muscle wastage differs across patients ranging from mild to severe. The longterm outcomes for patients with MoM hip arthroplasty are reduced for increasing degrees of muscle atrophy, highlighting the need to automatically segment pathological muscles. The automated segmentation of pathological soft tissues is challenging as these lack distinct boundaries and morphologically differ across subjects. As a result, there is no method reported in the literature which has been successfully applied to automatically segment pathological muscles. We propose the first automated framework to delineate severely atrophied muscles by applying a novel automated segmentation propagation framework to patients with MoM hip arthroplasty. The proposed algorithm was used to automatically quantify muscle wastage in these patients.

  2. Experimental use of temporary traffic delineators : final report.

    DOT National Transportation Integrated Search

    1988-12-01

    During construction, there is a safety need to provide highly visible channelizing delineation for temporary protection and direction of traffic. "Super Duck" Surface Mounted Delineator (SDR-328, Rounded) by Carsonite is marked to meet the safety nee...

  3. A novel automatic segmentation workflow of axial breast DCE-MRI

    NASA Astrophysics Data System (ADS)

    Besbes, Feten; Gargouri, Norhene; Damak, Alima; Sellami, Dorra

    2018-04-01

    In this paper we propose a novel process of a fully automatic breast tissue segmentation which is independent from expert calibration and contrast. The proposed algorithm is composed by two major steps. The first step consists in the detection of breast boundaries. It is based on image content analysis and Moore-Neighbour tracing algorithm. As a processing step, Otsu thresholding and neighbors algorithm are applied. Then, the external area of breast is removed to get an approximated breast region. The second preprocessing step is the delineation of the chest wall which is considered as the lowest cost path linking three key points; These points are located automatically at the breast. They are respectively, the left and right boundary points and the middle upper point placed at the sternum region using statistical method. For the minimum cost path search problem, we resolve it through Dijkstra algorithm. Evaluation results reveal the robustness of our process face to different breast densities, complex forms and challenging cases. In fact, the mean overlap between manual segmentation and automatic segmentation through our method is 96.5%. A comparative study shows that our proposed process is competitive and faster than existing methods. The segmentation of 120 slices with our method is achieved at least in 20.57+/-5.2s.

  4. Towards an Automatic Framework for Urban Settlement Mapping from Satellite Images: Applications of Geo-referenced Social Media and One Class Classification

    NASA Astrophysics Data System (ADS)

    Miao, Zelang

    2017-04-01

    Currently, urban dwellers comprise more than half of the world's population and this percentage is still dramatically increasing. The explosive urban growth over the next two decades poses long-term profound impact on people as well as the environment. Accurate and up-to-date delineation of urban settlements plays a fundamental role in defining planning strategies and in supporting sustainable development of urban settlements. In order to provide adequate data about urban extents and land covers, classifying satellite data has become a common practice, usually with accurate enough results. Indeed, a number of supervised learning methods have proven effective in urban area classification, but they usually depend on a large amount of training samples, whose collection is a time and labor expensive task. This issue becomes particularly serious when classifying large areas at the regional/global level. As an alternative to manual ground truth collection, in this work we use geo-referenced social media data. Cities and densely populated areas are an extremely fertile land for the production of individual geo-referenced data (such as GPS and social network data). Training samples derived from geo-referenced social media have several advantages: they are easy to collect, usually they are freely exploitable; and, finally, data from social media are spatially available in many locations, and with no doubt in most urban areas around the world. Despite these advantages, the selection of training samples from social media meets two challenges: 1) there are many duplicated points; 2) method is required to automatically label them as "urban/non-urban". The objective of this research is to validate automatic sample selection from geo-referenced social media and its applicability in one class classification for urban extent mapping from satellite images. The findings in this study shed new light on social media applications in the field of remote sensing.

  5. Improved business driveway delineation in urban work zones.

    DOT National Transportation Integrated Search

    2015-04-01

    This report documents the efforts and results of a two-year research project aimed at improving driveway : delineation in work zones. The first year of the project included a closed-course study to identify the most : promising driveway delineation a...

  6. Automatic segmentation of the left ventricle in a cardiac MR short axis image using blind morphological operation

    NASA Astrophysics Data System (ADS)

    Irshad, Mehreen; Muhammad, Nazeer; Sharif, Muhammad; Yasmeen, Mussarat

    2018-04-01

    Conventionally, cardiac MR image analysis is done manually. Automatic examination for analyzing images can replace the monotonous tasks of massive amounts of data to analyze the global and regional functions of the cardiac left ventricle (LV). This task is performed using MR images to calculate the analytic cardiac parameter like end-systolic volume, end-diastolic volume, ejection fraction, and myocardial mass, respectively. These analytic parameters depend upon genuine delineation of epicardial, endocardial, papillary muscle, and trabeculations contours. In this paper, we propose an automatic segmentation method using the sum of absolute differences technique to localize the left ventricle. Blind morphological operations are proposed to segment and detect the LV contours of the epicardium and endocardium, automatically. We test the benchmark Sunny Brook dataset for evaluation of the proposed work. Contours of epicardium and endocardium are compared quantitatively to determine contour's accuracy and observe high matching values. Similarity or overlapping of an automatic examination to the given ground truth analysis by an expert are observed with high accuracy as with an index value of 91.30% . The proposed method for automatic segmentation gives better performance relative to existing techniques in terms of accuracy.

  7. Semi-automated stand delineation in Mediterranean Pinus sylvestris plantations through segmentation of LiDAR data: The influence of pulse density

    NASA Astrophysics Data System (ADS)

    Varo-Martínez, Mª Ángeles; Navarro-Cerrillo, Rafael M.; Hernández-Clemente, Rocío; Duque-Lazo, Joaquín

    2017-04-01

    Traditionally, forest-stand delineation has been assessed based on orthophotography. The application of LiDAR has improved forest management by providing high-spatial-resolution data on the vertical structure of the forest. The aim of this study was to develop and test a semi-automated algorithm for stands delineation in a plantation of Pinus sylvestris L. using LiDAR data. Three specific objectives were evaluated, i) to assess two complementary LiDAR metrics, Assmann dominant height and basal area, for the characterization of the structure of P. sylvestris Mediterranean forests based on object-oriented segmentation, ii) to evaluate the influence of the LiDAR pulse density on forest-stand delineation accuracy, and iii) to investigate the algorithmś effectiveness in the delineation of P. sylvestris stands for map prediction of Assmann dominant height and basal area. Our results show that it is possible to generate accurate P. sylvestris forest-stand segmentations using multiresolution or mean shift segmentation methods, even with low-pulse-density LiDAR - which is an important economic advantage for forest management. However, eCognition multiresolution methods provided better results than the OTB (Orfeo Tool Box) for stand delineation based on dominant height and basal area estimations. Furthermore, the influence of pulse density on the results was not statistically significant in the basal area calculations. However, there was a significant effect of pulse density on Assmann dominant height [F2,9595 = 5.69, p = 0.003].for low pulse density. We propose that the approach shown here should be considered for stand delineation in other large Pinus plantations in Mediterranean regions with similar characteristics.

  8. Rat brain digital stereotaxic white matter atlas with fine tract delineation in Paxinos space and its automated applications in DTI data analysis.

    PubMed

    Liang, Shengxiang; Wu, Shang; Huang, Qi; Duan, Shaofeng; Liu, Hua; Li, Yuxiao; Zhao, Shujun; Nie, Binbin; Shan, Baoci

    2017-11-01

    To automatically analyze diffusion tensor images of the rat brain via both voxel-based and ROI-based approaches, we constructed a new white matter atlas of the rat brain with fine tracts delineation in the Paxinos and Watson space. Unlike in previous studies, we constructed a digital atlas image from the latest edition of the Paxinos and Watson. This atlas contains 111 carefully delineated white matter fibers. A white matter network of rat brain based on anatomy was constructed by locating the intersection of all these tracts and recording the nuclei on the pathway of each white matter tract. Moreover, a compatible rat brain template from DTI images was created and standardized into the atlas space. To evaluate the automated application of the atlas in DTI data analysis, a group of rats with right-side middle cerebral artery occlusion (MCAO) and those without were enrolled in this study. The voxel-based analysis result shows that the brain region showing significant declines in signal in the MCAO rats was consistent with the occlusion position. We constructed a stereotaxic white matter atlas of the rat brain with fine tract delineation and a compatible template for the data analysis of DTI images of the rat brain. Copyright © 2017 Elsevier Inc. All rights reserved.

  9. Automatic aneurysm neck detection using surface Voronoi diagrams.

    PubMed

    Cárdenes, Rubén; Pozo, José María; Bogunovic, Hrvoje; Larrabide, Ignacio; Frangi, Alejandro F

    2011-10-01

    A new automatic approach for saccular intracranial aneurysm isolation is proposed in this work. Due to the inter- and intra-observer variability in manual delineation of the aneurysm neck, a definition based on a minimum cost path around the aneurysm sac is proposed that copes with this variability and is able to make consistent measurements along different data sets, as well as to automate and speedup the analysis of cerebral aneurysms. The method is based on the computation of a minimal path along a scalar field obtained on the vessel surface, to find the aneurysm neck in a robust and fast manner. The computation of the scalar field on the surface is obtained using a fast marching approach with a speed function based on the exponential of the distance from the centerline bifurcation between the aneurysm dome and the parent vessels. In order to assure a correct topology of the aneurysm sac, the neck computation is constrained to a region defined by a surface Voronoi diagram obtained from the branches of the vessel centerline. We validate this method comparing our results in 26 real cases with manual aneurysm isolation obtained using a cut-plane, and also with results obtained using manual delineations from three different observers by comparing typical morphological measures. © 2011 IEEE

  10. Automatic analysis for neuron by confocal laser scanning microscope

    NASA Astrophysics Data System (ADS)

    Satou, Kouhei; Aoki, Yoshimitsu; Mataga, Nobuko; Hensh, Takao K.; Taki, Katuhiko

    2005-12-01

    The aim of this study is to develop a system that recognizes both the macro- and microscopic configurations of nerve cells and automatically performs the necessary 3-D measurements and functional classification of spines. The acquisition of 3-D images of cranial nerves has been enabled by the use of a confocal laser scanning microscope, although the highly accurate 3-D measurements of the microscopic structures of cranial nerves and their classification based on their configurations have not yet been accomplished. In this study, in order to obtain highly accurate measurements of the microscopic structures of cranial nerves, existing positions of spines were predicted by the 2-D image processing of tomographic images. Next, based on the positions that were predicted on the 2-D images, the positions and configurations of the spines were determined more accurately by 3-D image processing of the volume data. We report the successful construction of an automatic analysis system that uses a coarse-to-fine technique to analyze the microscopic structures of cranial nerves with high speed and accuracy by combining 2-D and 3-D image analyses.

  11. The transition to increased automaticity during finger sequence learning in adult males who stutter.

    PubMed

    Smits-Bandstra, Sarah; De Nil, Luc; Rochon, Elizabeth

    2006-01-01

    The present study compared the automaticity levels of persons who stutter (PWS) and persons who do not stutter (PNS) on a practiced finger sequencing task under dual task conditions. Automaticity was defined as the amount of attention required for task performance. Twelve PWS and 12 control subjects practiced finger tapping sequences under single and then dual task conditions. Control subjects performed the sequencing task significantly faster and less variably under single versus dual task conditions while PWS' performance was consistently slow and variable (comparable to the dual task performance of control subjects) under both conditions. Control subjects were significantly more accurate on a colour recognition distracter task than PWS under dual task conditions. These results suggested that control subjects transitioned to quick, accurate and increasingly automatic performance on the sequencing task after practice, while PWS did not. Because most stuttering treatment programs for adults include practice and automatization of new motor speech skills, findings of this finger sequencing study and future studies of speech sequence learning may have important implications for how to maximize stuttering treatment effectiveness. As a result of this activity, the participant will be able to: (1) Define automaticity and explain the importance of dual task paradigms to investigate automaticity; (2) Relate the proposed relationship between motor learning and automaticity as stated by the authors; (3) Summarize the reviewed literature concerning the performance of PWS on dual tasks; and (4) Explain why the ability to transition to automaticity during motor learning may have important clinical implications for stuttering treatment effectiveness.

  12. Automatic systems and the low-level wind hazard

    NASA Technical Reports Server (NTRS)

    Schaeffer, Dwight R.

    1987-01-01

    Automatic flight control systems provide means for significantly enhancing survivability in severe wind hazards. The technology required to produce the necessary control algorithms is available and has been made technically feasible by the advent of digital flight control systems and accurate, low-noise sensors, especially strap-down inertial sensors. The application of this technology and these means has not generally been enabled except for automatic landing systems, and even then the potential has not been fully exploited. To fully exploit the potential of automatic systems for enhancing safety in wind hazards requires providing incentives, creating demand, inspiring competition, education, and eliminating prejudicial disincentitives to overcome the economic penalties associated with the extensive and riskly development and certification of these systems. If these changes will come about at all, it will likely be through changes in the regulations provided by the certifying agencies.

  13. Current Trends in Intraoperative Optical Imaging for Functional Brain Mapping and Delineation of Lesions of Language Cortex

    PubMed Central

    Prakash, Neal; Uhleman, Falk; Sheth, Sameer A.; Bookheimer, Susan; Martin, Neil; Toga, Arthur W.

    2009-01-01

    Resection of a cerebral arteriovenous malformation (AVM), epileptic focus, or glioma, ideally has a prerequisite of microscopic delineation of the lesion borders in relation to the normal gray and white matter that mediate critical functions. Currently, Wada testing and functional magnetic resonance imaging (fMRI) are used for preoperative mapping of critical function, whereas electrical stimulation mapping (ESM) is used for intraoperative mapping. For lesion delineation, MRI and positron emission tomography (PET) are used preoperatively, whereas microscopy and histological sectioning are used intraoperatively. However, for lesions near eloquent cortex, these imaging techniques may lack sufficient resolution to define the relationship between the lesion and language function, and thus not accurately determine which patients will benefit from neurosurgical resection of the lesion without iatrogenic aphasia. Optical techniques such as intraoperative optical imaging of intrinsic signals (iOIS) show great promise for the precise functional mapping of cortices, as well as delineation of the borders of AVMs, epileptic foci, and gliomas. Here we first review the physiology of neuroimaging, and then progress towards the validation and justification of using intraoperative optical techniques, especially in relation to neurosurgical planning of resection AVMs, epileptic foci, and gliomas near or in eloquent cortex. We conclude with a short description of potential novel intraoperative optical techniques. PMID:18786643

  14. CAD-based Automatic Modeling Method for Geant4 geometry model Through MCAM

    NASA Astrophysics Data System (ADS)

    Wang, Dong; Nie, Fanzhi; Wang, Guozhong; Long, Pengcheng; LV, Zhongliang; LV, Zhongliang

    2014-06-01

    Geant4 is a widely used Monte Carlo transport simulation package. Before calculating using Geant4, the calculation model need be established which could be described by using Geometry Description Markup Language (GDML) or C++ language. However, it is time-consuming and error-prone to manually describe the models by GDML. Automatic modeling methods have been developed recently, but there are some problem existed in most of present modeling programs, specially some of them were not accurate or adapted to specifically CAD format. To convert the GDML format models to CAD format accurately, a Geant4 Computer Aided Design (CAD) based modeling method was developed for automatically converting complex CAD geometry model into GDML geometry model. The essence of this method was dealing with CAD model represented with boundary representation (B-REP) and GDML model represented with constructive solid geometry (CSG). At first, CAD model was decomposed to several simple solids which had only one close shell. And then the simple solid was decomposed to convex shell set. Then corresponding GDML convex basic solids were generated by the boundary surfaces getting from the topological characteristic of a convex shell. After the generation of these solids, GDML model was accomplished with series boolean operations. This method was adopted in CAD/Image-based Automatic Modeling Program for Neutronics & Radiation Transport (MCAM), and tested with several models including the examples in Geant4 install package. The results showed that this method could convert standard CAD model accurately, and can be used for Geant4 automatic modeling.

  15. Automatic Detection of Storm Damages Using High-Altitude Photogrammetric Imaging

    NASA Astrophysics Data System (ADS)

    Litkey, P.; Nurminen, K.; Honkavaara, E.

    2013-05-01

    The risks of storms that cause damage in forests are increasing due to climate change. Quickly detecting fallen trees, assessing the amount of fallen trees and efficiently collecting them are of great importance for economic and environmental reasons. Visually detecting and delineating storm damage is a laborious and error-prone process; thus, it is important to develop cost-efficient and highly automated methods. Objective of our research project is to investigate and develop a reliable and efficient method for automatic storm damage detection, which is based on airborne imagery that is collected after a storm. The requirements for the method are the before-storm and after-storm surface models. A difference surface is calculated using two DSMs and the locations where significant changes have appeared are automatically detected. In our previous research we used four-year old airborne laser scanning surface model as the before-storm surface. The after-storm DSM was provided from the photogrammetric images using the Next Generation Automatic Terrain Extraction (NGATE) algorithm of Socet Set software. We obtained 100% accuracy in detection of major storm damages. In this investigation we will further evaluate the sensitivity of the storm-damage detection process. We will investigate the potential of national airborne photography, that is collected at no-leaf season, to automatically produce a before-storm DSM using image matching. We will also compare impact of the terrain extraction algorithm to the results. Our results will also promote the potential of national open source data sets in the management of natural disasters.

  16. Automatic Astrometric and Photometric Calibration with SCAMP

    NASA Astrophysics Data System (ADS)

    Bertin, E.

    2006-07-01

    Astrometric and photometric calibrations have remained the most tiresome step in the reduction of large imaging surveys. I present a new software package, SCAMP which has been written to address this problem. SCAMP efficiently computes accurate astrometric and photometric solutions for any arbitrary sequence of FITS images in a completely automatic way. SCAMP is released under the GNU General Public Licence.

  17. An automatic multi-atlas prostate segmentation in MRI using a multiscale representation and a label fusion strategy

    NASA Astrophysics Data System (ADS)

    Álvarez, Charlens; Martínez, Fabio; Romero, Eduardo

    2015-01-01

    The pelvic magnetic Resonance images (MRI) are used in Prostate cancer radiotherapy (RT), a process which is part of the radiation planning. Modern protocols require a manual delineation, a tedious and variable activity that may take about 20 minutes per patient, even for trained experts. That considerable time is an important work ow burden in most radiological services. Automatic or semi-automatic methods might improve the efficiency by decreasing the measure times while conserving the required accuracy. This work presents a fully automatic atlas- based segmentation strategy that selects the more similar templates for a new MRI using a robust multi-scale SURF analysis. Then a new segmentation is achieved by a linear combination of the selected templates, which are previously non-rigidly registered towards the new image. The proposed method shows reliable segmentations, obtaining an average DICE Coefficient of 79%, when comparing with the expert manual segmentation, under a leave-one-out scheme with the training database.

  18. Syndromology: an updated conceptual overview. III. Syndrome delineation.

    PubMed

    Cohen, M M

    1989-10-01

    In Part III, the process of syndrome delineation is unfolded and its significance discussed. Unknown genesis syndrome categories include provisionally unique pattern syndromes and recurrent pattern syndromes. As more information becomes available, it is often possible to establish a Mendelian, chromosomal, teratogenic, or biochemical basis for the disorder in question. It has been estimated that newly recognized syndromes are being described at the rate of one or more per week. The process of syndrome delineation is not an academic exercise but actually fosters good patient care. As an unknown syndrome becomes delineated, its phenotypic spectrum, its natural history, and its recurrence risk become known, allowing for better patient care and family counseling.

  19. Automatic abdominal multi-organ segmentation using deep convolutional neural network and time-implicit level sets.

    PubMed

    Hu, Peijun; Wu, Fa; Peng, Jialin; Bao, Yuanyuan; Chen, Feng; Kong, Dexing

    2017-03-01

    Multi-organ segmentation from CT images is an essential step for computer-aided diagnosis and surgery planning. However, manual delineation of the organs by radiologists is tedious, time-consuming and poorly reproducible. Therefore, we propose a fully automatic method for the segmentation of multiple organs from three-dimensional abdominal CT images. The proposed method employs deep fully convolutional neural networks (CNNs) for organ detection and segmentation, which is further refined by a time-implicit multi-phase evolution method. Firstly, a 3D CNN is trained to automatically localize and delineate the organs of interest with a probability prediction map. The learned probability map provides both subject-specific spatial priors and initialization for subsequent fine segmentation. Then, for the refinement of the multi-organ segmentation, image intensity models, probability priors as well as a disjoint region constraint are incorporated into an unified energy functional. Finally, a novel time-implicit multi-phase level-set algorithm is utilized to efficiently optimize the proposed energy functional model. Our method has been evaluated on 140 abdominal CT scans for the segmentation of four organs (liver, spleen and both kidneys). With respect to the ground truth, average Dice overlap ratios for the liver, spleen and both kidneys are 96.0, 94.2 and 95.4%, respectively, and average symmetric surface distance is less than 1.3 mm for all the segmented organs. The computation time for a CT volume is 125 s in average. The achieved accuracy compares well to state-of-the-art methods with much higher efficiency. A fully automatic method for multi-organ segmentation from abdominal CT images was developed and evaluated. The results demonstrated its potential in clinical usage with high effectiveness, robustness and efficiency.

  20. Automatic segmentation of equine larynx for diagnosis of laryngeal hemiplegia

    NASA Astrophysics Data System (ADS)

    Salehin, Md. Musfequs; Zheng, Lihong; Gao, Junbin

    2013-10-01

    This paper presents an automatic segmentation method for delineation of the clinically significant contours of the equine larynx from an endoscopic image. These contours are used to diagnose the most common disease of horse larynx laryngeal hemiplegia. In this study, hierarchal structured contour map is obtained by the state-of-the-art segmentation algorithm, gPb-OWT-UCM. The conic-shaped outer boundary of equine larynx is extracted based on Pascal's theorem. Lastly, Hough Transformation method is applied to detect lines related to the edges of vocal folds. The experimental results show that the proposed approach has better performance in extracting the targeted contours of equine larynx than the results of using only the gPb-OWT-UCM method.

  1. Automatic iterative segmentation of multiple sclerosis lesions using Student's t mixture models and probabilistic anatomical atlases in FLAIR images.

    PubMed

    Freire, Paulo G L; Ferrari, Ricardo J

    2016-06-01

    Multiple sclerosis (MS) is a demyelinating autoimmune disease that attacks the central nervous system (CNS) and affects more than 2 million people worldwide. The segmentation of MS lesions in magnetic resonance imaging (MRI) is a very important task to assess how a patient is responding to treatment and how the disease is progressing. Computational approaches have been proposed over the years to segment MS lesions and reduce the amount of time spent on manual delineation and inter- and intra-rater variability and bias. However, fully-automatic segmentation of MS lesions still remains an open problem. In this work, we propose an iterative approach using Student's t mixture models and probabilistic anatomical atlases to automatically segment MS lesions in Fluid Attenuated Inversion Recovery (FLAIR) images. Our technique resembles a refinement approach by iteratively segmenting brain tissues into smaller classes until MS lesions are grouped as the most hyperintense one. To validate our technique we used 21 clinical images from the 2015 Longitudinal Multiple Sclerosis Lesion Segmentation Challenge dataset. Evaluation using Dice Similarity Coefficient (DSC), True Positive Ratio (TPR), False Positive Ratio (FPR), Volume Difference (VD) and Pearson's r coefficient shows that our technique has a good spatial and volumetric agreement with raters' manual delineations. Also, a comparison between our proposal and the state-of-the-art shows that our technique is comparable and, in some cases, better than some approaches, thus being a viable alternative for automatic MS lesion segmentation in MRI. Copyright © 2016 Elsevier Ltd. All rights reserved.

  2. Impact of tumor size and tracer uptake heterogeneity in (18)F-FDG PET and CT non-small cell lung cancer tumor delineation.

    PubMed

    Hatt, Mathieu; Cheze-le Rest, Catherine; van Baardwijk, Angela; Lambin, Philippe; Pradier, Olivier; Visvikis, Dimitris

    2011-11-01

    large heterogeneous NSCLC, as these methods tend to largely underestimate the spatial extent of the functional tumor in such cases. For an accurate delineation of PET volumes in NSCLC, advanced image segmentation algorithms able to deal with tracer uptake heterogeneity should be preferred.

  3. SU-F-J-113: Multi-Atlas Based Automatic Organ Segmentation for Lung Radiotherapy Planning

    DOE Office of Scientific and Technical Information (OSTI.GOV)

    Kim, J; Han, J; Ailawadi, S

    Purpose: Normal organ segmentation is one time-consuming and labor-intensive step for lung radiotherapy treatment planning. The aim of this study is to evaluate the performance of a multi-atlas based segmentation approach for automatic organs at risk (OAR) delineation. Methods: Fifteen Lung stereotactic body radiation therapy patients were randomly selected. Planning CT images and OAR contours of the heart - HT, aorta - AO, vena cava - VC, pulmonary trunk - PT, and esophagus – ES were exported and used as reference and atlas sets. For automatic organ delineation for a given target CT, 1) all atlas sets were deformably warpedmore » to the target CT, 2) the deformed sets were accumulated and normalized to produce organ probability density (OPD) maps, and 3) the OPD maps were converted to contours via image thresholding. Optimal threshold for each organ was empirically determined by comparing the auto-segmented contours against their respective reference contours. The delineated results were evaluated by measuring contour similarity metrics: DICE, mean distance (MD), and true detection rate (TD), where DICE=(intersection volume/sum of two volumes) and TD = {1.0 - (false positive + false negative)/2.0}. Diffeomorphic Demons algorithm was employed for CT-CT deformable image registrations. Results: Optimal thresholds were determined to be 0.53 for HT, 0.38 for AO, 0.28 for PT, 0.43 for VC, and 0.31 for ES. The mean similarity metrics (DICE[%], MD[mm], TD[%]) were (88, 3.2, 89) for HT, (79, 3.2, 82) for AO, (75, 2.7, 77) for PT, (68, 3.4, 73) for VC, and (51,2.7, 60) for ES. Conclusion: The investigated multi-atlas based approach produced reliable segmentations for the organs with large and relatively clear boundaries (HT and AO). However, the detection of small and narrow organs with diffused boundaries (ES) were challenging. Sophisticated atlas selection and multi-atlas fusion algorithms may further improve the quality of segmentations.« less

  4. Description and application of capture zone delineation for a wellfield at Hilton Head Island, South Carolina

    USGS Publications Warehouse

    Landmeyer, J.E.

    1994-01-01

    Ground-water capture zone boundaries for individual pumped wells in a confined aquffer were delineated by using groundwater models. Both analytical and numerical (semi-analytical) models that more accurately represent the $round-water-flow system were used. All models delineated 2-dimensional boundaries (capture zones) that represent the areal extent of groundwater contribution to a pumped well. The resultant capture zones were evaluated on the basis of the ability of each model to realistically rapresent the part of the ground-water-flow system that contributed water to the pumped wells. Analytical models used were based on a fixed radius approach, and induded; an arbitrary radius model, a calculated fixed radius model based on the volumetric-flow equation with a time-of-travel criterion, and a calculated fixed radius model derived from modification of the Theis model with a drawdown criterion. Numerical models used induded the 2-dimensional, finite-difference models RESSQC and MWCAP. The arbitrary radius and Theis analytical models delineated capture zone boundaries that compared least favorably with capture zones delineated using the volumetric-flow analytical model and both numerical models. The numerical models produced more hydrologically reasonable capture zones (that were oriented parallel to the regional flow direction) than the volumetric-flow equation. The RESSQC numerical model computed more hydrologically realistic capture zones than the MWCAP numerical model by accounting for changes in the shape of capture zones caused by multiple-well interference. The capture zone boundaries generated by using both analytical and numerical models indicated that the curnmtly used 100-foot radius of protection around a wellhead in South Carolina is an underestimate of the extent of ground-water capture for pumped wetis in this particular wellfield in the Upper Floridan aquifer. The arbitrary fixed radius of 100 feet was shown to underestimate the upgradient

  5. Automatic segmentation of the lateral geniculate nucleus: Application to control and glaucoma patients.

    PubMed

    Wang, Jieqiong; Miao, Wen; Li, Jing; Li, Meng; Zhen, Zonglei; Sabel, Bernhard; Xian, Junfang; He, Huiguang

    2015-11-30

    The lateral geniculate nucleus (LGN) is a key relay center of the visual system. Because the LGN morphology is affected by different diseases, it is of interest to analyze its morphology by segmentation. However, existing LGN segmentation methods are non-automatic, inefficient and prone to experimenters' bias. To address these problems, we proposed an automatic LGN segmentation algorithm based on T1-weighted imaging. First, the prior information of LGN was used to create a prior mask. Then region growing was applied to delineate LGN. We evaluated this automatic LGN segmentation method by (1) comparison with manually segmented LGN, (2) anatomically locating LGN in the visual system via LGN-based tractography, (3) application to control and glaucoma patients. The similarity coefficients of automatic segmented LGN and manually segmented one are 0.72 (0.06) for the left LGN and 0.77 (0.07) for the right LGN. LGN-based tractography shows the subcortical pathway seeding from LGN passes the optic tract and also reaches V1 through the optic radiation, which is consistent with the LGN location in the visual system. In addition, LGN asymmetry as well as LGN atrophy along with age is observed in normal controls. The investigation of glaucoma effects on LGN volumes demonstrates that the bilateral LGN volumes shrink in patients. The automatic LGN segmentation is objective, efficient, valid and applicable. Experiment results proved the validity and applicability of the algorithm. Our method will speed up the research on visual system and greatly enhance studies of different vision-related diseases. Copyright © 2015 Elsevier B.V. All rights reserved.

  6. Development of delineator testing standard.

    DOT National Transportation Integrated Search

    2015-02-01

    The objective of this project was to develop a new test method for evaluating the impact performance : of delineators for given applications. The researchers focused on developing a test method that was : reproducible and attempted to reproduce failu...

  7. Delineation of gravel-bed clusters via factorial kriging

    NASA Astrophysics Data System (ADS)

    Wu, Fu-Chun; Wang, Chi-Kuei; Huang, Guo-Hao

    2018-05-01

    Gravel-bed clusters are the most prevalent microforms that affect local flows and sediment transport. A growing consensus is that the practice of cluster delineation should be based primarily on bed topography rather than grain sizes. Here we present a novel approach for cluster delineation using patch-scale high-resolution digital elevation models (DEMs). We use a geostatistical interpolation method, i.e., factorial kriging, to decompose the short- and long-range (grain- and microform-scale) DEMs. The required parameters are determined directly from the scales of the nested variograms. The short-range DEM exhibits a flat bed topography, yet individual grains are sharply outlined, making the short-range DEM a useful aid for grain segmentation. The long-range DEM exhibits a smoother topography than the original full DEM, yet groupings of particles emerge as small-scale bedforms, making the contour percentile levels of the long-range DEM a useful tool for cluster identification. Individual clusters are delineated using the segmented grains and identified clusters via a range of contour percentile levels. Our results reveal that the density and total area of delineated clusters decrease with increasing contour percentile level, while the mean grain size of clusters and average size of anchor clast (i.e., the largest particle in a cluster) increase with the contour percentile level. These results support the interpretation that larger particles group as clusters and protrude higher above the bed than other smaller grains. A striking feature of the delineated clusters is that anchor clasts are invariably greater than the D90 of the grain sizes even though a threshold anchor size was not adopted herein. The average areal fractal dimensions (Hausdorff-Besicovich dimensions of the projected areas) of individual clusters, however, demonstrate that clusters delineated with different contour percentile levels exhibit similar planform morphologies. Comparisons with a

  8. Automatic segmentation of coronary arteries from computed tomography angiography data cloud using optimal thresholding

    NASA Astrophysics Data System (ADS)

    Ansari, Muhammad Ahsan; Zai, Sammer; Moon, Young Shik

    2017-01-01

    Manual analysis of the bulk data generated by computed tomography angiography (CTA) is time consuming, and interpretation of such data requires previous knowledge and expertise of the radiologist. Therefore, an automatic method that can isolate the coronary arteries from a given CTA dataset is required. We present an automatic yet effective segmentation method to delineate the coronary arteries from a three-dimensional CTA data cloud. Instead of a region growing process, which is usually time consuming and prone to leakages, the method is based on the optimal thresholding, which is applied globally on the Hessian-based vesselness measure in a localized way (slice by slice) to track the coronaries carefully to their distal ends. Moreover, to make the process automatic, we detect the aorta using the Hough transform technique. The proposed segmentation method is independent of the starting point to initiate its process and is fast in the sense that coronary arteries are obtained without any preprocessing or postprocessing steps. We used 12 real clinical datasets to show the efficiency and accuracy of the presented method. Experimental results reveal that the proposed method achieves 95% average accuracy.

  9. Geomorphic Flood Area (GFA): a QGIS tool for a cost-effective delineation of the floodplains

    NASA Astrophysics Data System (ADS)

    Samela, Caterina; Albano, Raffaele; Sole, Aurelia; Manfreda, Salvatore

    2017-04-01

    The importance of delineating flood hazard and risk areas at a global scale has been highlighted for many years. However, its complete achievement regularly encounters practical difficulties, above all the lack of data and implementation costs. In conditions of scarce data availability (e.g. ungauged basins, large-scale analyses), a fast and cost-effective floodplain delineation can be carried out using geomorphic methods (e.g., Manfreda et al., 2011; 2014). In particular, an automatic DEM-based procedure has been implemented in an open-source QGIS plugin named Geomorphic Flood Area - tool (GFA - tool). This tool performs a linear binary classification based on the recently proposed Geomorphic Flood Index (GFI), which exhibited high classification accuracy and reliability in several test sites located in Europe, United States and Africa (Manfreda et al., 2015; Samela et al., 2016, 2017; Samela, 2016). The GFA - tool is designed to make available to all users the proposed procedure, that includes a number of operations requiring good geomorphic and GIS competences. It allows computing the GFI through terrain analysis, turning it into a binary classifier, and training it on the base of a standard inundation map derived for a portion of the river basin (a minimum of 2% of the river basin's area is suggested) using detailed methods of analysis (e.g. flood hazard maps produced by emergency management agencies or river basin authorities). Finally, GFA - tool allows to extend the classification outside the calibration area to delineate the flood-prone areas across the entire river basin. The full analysis has been implemented in this plugin with a user-friendly interface that should make it easy to all user to apply the approach and produce the desired results. Keywords: flood susceptibility; data scarce environments; geomorphic flood index; linear binary classification; Digital elevation models (DEMs). References Manfreda, S., Di Leo, M., Sole, A., (2011). Detection of

  10. Automatic portion estimation and visual refinement in mobile dietary assessment

    PubMed Central

    Woo, Insoo; Otsmo, Karl; Kim, SungYe; Ebert, David S.; Delp, Edward J.; Boushey, Carol J.

    2011-01-01

    As concern for obesity grows, the need for automated and accurate methods to monitor nutrient intake becomes essential as dietary intake provides a valuable basis for managing dietary imbalance. Moreover, as mobile devices with built-in cameras have become ubiquitous, one potential means of monitoring dietary intake is photographing meals using mobile devices and having an automatic estimate of the nutrient contents returned. One of the challenging problems of the image-based dietary assessment is the accurate estimation of food portion size from a photograph taken with a mobile digital camera. In this work, we describe a method to automatically calculate portion size of a variety of foods through volume estimation using an image. These “portion volumes” utilize camera parameter estimation and model reconstruction to determine the volume of food items, from which nutritional content is then extrapolated. In this paper, we describe our initial results of accuracy evaluation using real and simulated meal images and demonstrate the potential of our approach. PMID:22242198

  11. Automatic portion estimation and visual refinement in mobile dietary assessment

    NASA Astrophysics Data System (ADS)

    Woo, Insoo; Otsmo, Karl; Kim, SungYe; Ebert, David S.; Delp, Edward J.; Boushey, Carol J.

    2010-01-01

    As concern for obesity grows, the need for automated and accurate methods to monitor nutrient intake becomes essential as dietary intake provides a valuable basis for managing dietary imbalance. Moreover, as mobile devices with built-in cameras have become ubiquitous, one potential means of monitoring dietary intake is photographing meals using mobile devices and having an automatic estimate of the nutrient contents returned. One of the challenging problems of the image-based dietary assessment is the accurate estimation of food portion size from a photograph taken with a mobile digital camera. In this work, we describe a method to automatically calculate portion size of a variety of foods through volume estimation using an image. These "portion volumes" utilize camera parameter estimation and model reconstruction to determine the volume of food items, from which nutritional content is then extrapolated. In this paper, we describe our initial results of accuracy evaluation using real and simulated meal images and demonstrate the potential of our approach.

  12. Automatic orbital GTAW welding: Highest quality welds for tomorrow's high-performance systems

    NASA Technical Reports Server (NTRS)

    Henon, B. K.

    1985-01-01

    Automatic orbital gas tungsten arc welding (GTAW) or TIG welding is certain to play an increasingly prominent role in tomorrow's technology. The welds are of the highest quality and the repeatability of automatic weldings is vastly superior to that of manual welding. Since less heat is applied to the weld during automatic welding than manual welding, there is less change in the metallurgical properties of the parent material. The possibility of accurate control and the cleanliness of the automatic GTAW welding process make it highly suitable to the welding of the more exotic and expensive materials which are now widely used in the aerospace and hydrospace industries. Titanium, stainless steel, Inconel, and Incoloy, as well as, aluminum can all be welded to the highest quality specifications automatically. Automatic orbital GTAW equipment is available for the fusion butt welding of tube-to-tube, as well as, tube to autobuttweld fittings. The same equipment can also be used for the fusion butt welding of up to 6 inch pipe with a wall thickness of up to 0.154 inches.

  13. Comparison of Eh and H2 measurements for delineating redox processes in a contaminated aquifer

    USGS Publications Warehouse

    Chapelle, Francis H.; Haack, Sheridan K.; Adriaens, Peter; Henry, Mark A.; Bradley, Paul M.

    1996-01-01

    Measurements of oxidation-reduction potential (Eh) and concentrations of dissolved hydrogen (H2) were made in a shallow groundwater system contaminated with solvents and jet fuel to delineate the zonation of redox processes. Eh measurements ranged from +69 to -158 mV in a cross section of the contaminated plume and accurately delineated oxic from anoxic groundwater. Plotting measured Eh and pH values on an equilibrium stability diagram indicated that Fe(III) reduction was the predominant redox process in the anoxic zone and did not indicate the presence of methanogenesis and sulfate reduction. In contrast, measurements of H2concentrations indicated that methanogenesis predominated in heavily contaminated sediments near the water table surface (H2 ∼ 7.0 nM) and that the methanogenic zone was surrounded by distinct sulfate-reducing (H2 ∼ 1-4 nM) and Fe(III)-reducing (H2 ∼ 0.1-0.8 nM) zones. The presence of methanogenesis, sulfate reduction, and Fe(III) reduction was confirmed by the distribution of dissolved oxygen, sulfate, Fe(II), and methane in groundwater. These results show that H2 concentrations were more useful for identifying anoxic redox processes than Ehmeasurements in this groundwater system. However, H2-based redox zone delineations are more reliable when H2 concentrations are interpreted in the context of electron-acceptor (oxygen, nitrate, sulfate) availability and the presence of final products [Fe(II), sulfide, methane] of microbial metabolism.

  14. An Approximate Approach to Automatic Kernel Selection.

    PubMed

    Ding, Lizhong; Liao, Shizhong

    2016-02-02

    Kernel selection is a fundamental problem of kernel-based learning algorithms. In this paper, we propose an approximate approach to automatic kernel selection for regression from the perspective of kernel matrix approximation. We first introduce multilevel circulant matrices into automatic kernel selection, and develop two approximate kernel selection algorithms by exploiting the computational virtues of multilevel circulant matrices. The complexity of the proposed algorithms is quasi-linear in the number of data points. Then, we prove an approximation error bound to measure the effect of the approximation in kernel matrices by multilevel circulant matrices on the hypothesis and further show that the approximate hypothesis produced with multilevel circulant matrices converges to the accurate hypothesis produced with kernel matrices. Experimental evaluations on benchmark datasets demonstrate the effectiveness of approximate kernel selection.

  15. Interactive semiautomatic contour delineation using statistical conditional random fields framework.

    PubMed

    Hu, Yu-Chi; Grossberg, Michael D; Wu, Abraham; Riaz, Nadeem; Perez, Carmen; Mageras, Gig S

    2012-07-01

    Contouring a normal anatomical structure during radiation treatment planning requires significant time and effort. The authors present a fast and accurate semiautomatic contour delineation method to reduce the time and effort required of expert users. Following an initial segmentation on one CT slice, the user marks the target organ and nontarget pixels with a few simple brush strokes. The algorithm calculates statistics from this information that, in turn, determines the parameters of an energy function containing both boundary and regional components. The method uses a conditional random field graphical model to define the energy function to be minimized for obtaining an estimated optimal segmentation, and a graph partition algorithm to efficiently solve the energy function minimization. Organ boundary statistics are estimated from the segmentation and propagated to subsequent images; regional statistics are estimated from the simple brush strokes that are either propagated or redrawn as needed on subsequent images. This greatly reduces the user input needed and speeds up segmentations. The proposed method can be further accelerated with graph-based interpolation of alternating slices in place of user-guided segmentation. CT images from phantom and patients were used to evaluate this method. The authors determined the sensitivity and specificity of organ segmentations using physician-drawn contours as ground truth, as well as the predicted-to-ground truth surface distances. Finally, three physicians evaluated the contours for subjective acceptability. Interobserver and intraobserver analysis was also performed and Bland-Altman plots were used to evaluate agreement. Liver and kidney segmentations in patient volumetric CT images show that boundary samples provided on a single CT slice can be reused through the entire 3D stack of images to obtain accurate segmentation. In liver, our method has better sensitivity and specificity (0.925 and 0.995) than region growing

  16. Drinking behavior in nursery pigs: Determining the accuracy between an automatic water meter versus human observers

    USDA-ARS?s Scientific Manuscript database

    Assimilating accurate behavioral events over a long period can be labor intensive and relatively expensive. If an automatic device could accurately record the duration and frequency for a given behavioral event, it would be a valuable alternative to the traditional use of human observers for behavio...

  17. A novel algorithm for delineating wetland depressions and ...

    EPA Pesticide Factsheets

    In traditional watershed delineation and topographic modeling, surface depressions are generally treated as spurious features and simply removed from a digital elevation model (DEM) to enforce flow continuity of water across the topographic surface to the watershed outlets. In reality, however, many depressions in the DEM are actual wetland landscape features that are seldom fully filled with water. For instance, wetland depressions in the Prairie Pothole Region (PPR) are seasonally to permanently flooded wetlands characterized by nested hierarchical structures with dynamic filling- spilling-merging surface-water hydrological processes. The objectives of this study were to delineate hierarchical wetland catchments and model their hydrologic connectivity using high-resolution LiDAR data and aerial imagery. We proposed a novel algorithm delineate the hierarchical wetland catchments and characterize their geometric and topological properties. Potential hydrologic connectivity between wetlands and streams were simulated using the least-cost path algorithm. The resulting flow network delineated putative temporary or seasonal flow paths connecting wetland depressions to each other or to the river network at scales finer than available through the National Hydrography Dataset. The results demonstrated that our proposed framework is promising for improving overland flow modeling and hydrologic connectivity analysis. Presentation at AWRA Spring Specialty Conference in Sn

  18. Reevaluation of pollen quantitation by an automatic pollen counter.

    PubMed

    Muradil, Mutarifu; Okamoto, Yoshitaka; Yonekura, Syuji; Chazono, Hideaki; Hisamitsu, Minako; Horiguchi, Shigetoshi; Hanazawa, Toyoyuki; Takahashi, Yukie; Yokota, Kunihiko; Okumura, Satoshi

    2010-01-01

    Accurate and detailed pollen monitoring is useful for selection of medication and for allergen avoidance in patients with allergic rhinitis. Burkard and Durham pollen samplers are commonly used, but are labor and time intensive. In contrast, automatic pollen counters allow simple real-time pollen counting; however, these instruments have difficulty in distinguishing pollen from small nonpollen airborne particles. Misidentification and underestimation rates for an automatic pollen counter were examined to improve the accuracy of the pollen count. The characteristics of the automatic pollen counter were determined in a chamber study with exposure to cedar pollens or soil grains. The cedar pollen counts were monitored in 2006 and 2007, and compared with those from a Durham sampler. The pollen counts from the automatic counter showed a good correlation (r > 0.7) with those from the Durham sampler when pollen dispersal was high, but a poor correlation (r < 0.5) when pollen dispersal was low. The new correction method, which took into account the misidentification and underestimation, improved this correlation to r > 0.7 during the pollen season. The accuracy of automatic pollen counting can be improved using a correction to include rates of underestimation and misidentification in a particular geographical area.

  19. A novel fully automatic scheme for fiducial marker-based alignment in electron tomography.

    PubMed

    Han, Renmin; Wang, Liansan; Liu, Zhiyong; Sun, Fei; Zhang, Fa

    2015-12-01

    Although the topic of fiducial marker-based alignment in electron tomography (ET) has been widely discussed for decades, alignment without human intervention remains a difficult problem. Specifically, the emergence of subtomogram averaging has increased the demand for batch processing during tomographic reconstruction; fully automatic fiducial marker-based alignment is the main technique in this process. However, the lack of an accurate method for detecting and tracking fiducial markers precludes fully automatic alignment. In this paper, we present a novel, fully automatic alignment scheme for ET. Our scheme has two main contributions: First, we present a series of algorithms to ensure a high recognition rate and precise localization during the detection of fiducial markers. Our proposed solution reduces fiducial marker detection to a sampling and classification problem and further introduces an algorithm to solve the parameter dependence of marker diameter and marker number. Second, we propose a novel algorithm to solve the tracking of fiducial markers by reducing the tracking problem to an incomplete point set registration problem. Because a global optimization of a point set registration occurs, the result of our tracking is independent of the initial image position in the tilt series, allowing for the robust tracking of fiducial markers without pre-alignment. The experimental results indicate that our method can achieve an accurate tracking, almost identical to the current best one in IMOD with half automatic scheme. Furthermore, our scheme is fully automatic, depends on fewer parameters (only requires a gross value of the marker diameter) and does not require any manual interaction, providing the possibility of automatic batch processing of electron tomographic reconstruction. Copyright © 2015 Elsevier Inc. All rights reserved.

  20. Ranking of predictor variables based on effect size criterion provides an accurate means of automatically classifying opinion column articles

    NASA Astrophysics Data System (ADS)

    Legara, Erika Fille; Monterola, Christopher; Abundo, Cheryl

    2011-01-01

    We demonstrate an accurate procedure based on linear discriminant analysis that allows automatic authorship classification of opinion column articles. First, we extract the following stylometric features of 157 column articles from four authors: statistics on high frequency words, number of words per sentence, and number of sentences per paragraph. Then, by systematically ranking these features based on an effect size criterion, we show that we can achieve an average classification accuracy of 93% for the test set. In comparison, frequency size based ranking has an average accuracy of 80%. The highest possible average classification accuracy of our data merely relying on chance is ∼31%. By carrying out sensitivity analysis, we show that the effect size criterion is superior than frequency ranking because there exist low frequency words that significantly contribute to successful author discrimination. Consistent results are seen when the procedure is applied in classifying the undisputed Federalist papers of Alexander Hamilton and James Madison. To the best of our knowledge, the work is the first attempt in classifying opinion column articles, that by virtue of being shorter in length (as compared to novels or short stories), are more prone to over-fitting issues. The near perfect classification for the longer papers supports this claim. Our results provide an important insight on authorship attribution that has been overlooked in previous studies: that ranking discriminant variables based on word frequency counts is not necessarily an optimal procedure.

  1. A hierarchical network-based algorithm for multi-scale watershed delineation

    NASA Astrophysics Data System (ADS)

    Castronova, Anthony M.; Goodall, Jonathan L.

    2014-11-01

    Watershed delineation is a process for defining a land area that contributes surface water flow to a single outlet point. It is a commonly used in water resources analysis to define the domain in which hydrologic process calculations are applied. There has been a growing effort over the past decade to improve surface elevation measurements in the U.S., which has had a significant impact on the accuracy of hydrologic calculations. Traditional watershed processing on these elevation rasters, however, becomes more burdensome as data resolution increases. As a result, processing of these datasets can be troublesome on standard desktop computers. This challenge has resulted in numerous works that aim to provide high performance computing solutions to large data, high resolution data, or both. This work proposes an efficient watershed delineation algorithm for use in desktop computing environments that leverages existing data, U.S. Geological Survey (USGS) National Hydrography Dataset Plus (NHD+), and open source software tools to construct watershed boundaries. This approach makes use of U.S. national-level hydrography data that has been precomputed using raster processing algorithms coupled with quality control routines. Our approach uses carefully arranged data and mathematical graph theory to traverse river networks and identify catchment boundaries. We demonstrate this new watershed delineation technique, compare its accuracy with traditional algorithms that derive watershed solely from digital elevation models, and then extend our approach to address subwatershed delineation. Our findings suggest that the open-source hierarchical network-based delineation procedure presented in the work is a promising approach to watershed delineation that can be used summarize publicly available datasets for hydrologic model input pre-processing. Through our analysis, we explore the benefits of reusing the NHD+ datasets for watershed delineation, and find that the our technique

  2. Barcoding against a paradox? Combined molecular species delineations reveal multiple cryptic lineages in elusive meiofaunal sea slugs

    PubMed Central

    2012-01-01

    Background Many marine meiofaunal species are reported to have wide distributions, which creates a paradox considering their hypothesized low dispersal abilities. Correlated with this paradox is an especially high taxonomic deficit for meiofauna, partly related to a lower taxonomic effort and partly to a high number of putative cryptic species. Molecular-based species delineation and barcoding approaches have been advocated for meiofaunal biodiversity assessments to speed up description processes and uncover cryptic lineages. However, these approaches show sensitivity to sampling coverage (taxonomic and geographic) and the success rate has never been explored on mesopsammic Mollusca. Results We collected the meiofaunal sea-slug Pontohedyle (Acochlidia, Heterobranchia) from 28 localities worldwide. With a traditional morphological approach, all specimens fall into two morphospecies. However, with a multi-marker genetic approach, we reveal multiple lineages that are reciprocally monophyletic on single and concatenated gene trees in phylogenetic analyses. These lineages are largely concordant with geographical and oceanographic parameters, leading to our primary species hypothesis (PSH). In parallel, we apply four independent methods of molecular based species delineation: General Mixed Yule Coalescent model (GMYC), statistical parsimony, Bayesian Species Delineation (BPP) and Automatic Barcode Gap Discovery (ABGD). The secondary species hypothesis (SSH) is gained by relying only on uncontradicted results of the different approaches (‘minimum consensus approach’), resulting in the discovery of a radiation of (at least) 12 mainly cryptic species, 9 of them new to science, some sympatric and some allopatric with respect to ocean boundaries. However, the meiofaunal paradox still persists in some Pontohedyle species identified here with wide coastal and trans-archipelago distributions. Conclusions Our study confirms extensive, morphologically cryptic diversity among

  3. Delineating Concealed Faults within Cogdell Oil Field via Earthquake Detection

    NASA Astrophysics Data System (ADS)

    Aiken, C.; Walter, J. I.; Brudzinski, M.; Skoumal, R.; Savvaidis, A.; Frohlich, C.; Borgfeldt, T.; Dotray, P.

    2016-12-01

    Cogdell oil field, located within the Permian Basin of western Texas, has experienced several earthquakes ranging from magnitude 1.7 to 4.6, most of which were recorded since 2006. Using the Earthscope USArray, Gan and Frohlich [2013] relocated some of these events and found a positive correlation in the timing of increased earthquake activity and increased CO2 injection volume. However, focal depths of these earthquakes are unknown due to 70 km station spacing of the USArray. Accurate focal depths as well as new detections can delineate subsurface faults and establish whether earthquakes are occurring in the shallow sediments or in the deeper basement. To delineate subsurface fault(s) in this region, we first detect earthquakes not currently listed in the USGS catalog by applying continuous waveform-template matching algorithms to multiple seismic data sets. We utilize seismic data spanning the time frame of 2006 to 2016 - which includes data from the U.S. Geological Survey Global Seismographic Network, the USArray, and the Sweetwater, TX broadband and nodal array located 20-40 km away. The catalog of earthquakes enhanced by template matching reveals events that were well recorded by the large-N Sweetwater array, so we are experimenting with strategies for optimizing template matching using different configurations of many stations. Since earthquake activity in the Cogdell oil field is on-going (a magnitude 2.6 occurred on May 29, 2016), a temporary deployment of TexNet seismometers has been planned for the immediate vicinity of Cogdell oil field in August 2016. Results on focal depths and detection of small magnitude events are pending this small local network deployment.

  4. Semi-automatic assessment of pediatric hydronephrosis severity in 3D ultrasound

    NASA Astrophysics Data System (ADS)

    Cerrolaza, Juan J.; Otero, Hansel; Yao, Peter; Biggs, Elijah; Mansoor, Awais; Ardon, Roberto; Jago, James; Peters, Craig A.; Linguraru, Marius George

    2016-03-01

    Hydronephrosis is the most common abnormal finding in pediatric urology. Thanks to its non-ionizing nature, ultrasound (US) imaging is the preferred diagnostic modality for the evaluation of the kidney and the urinary track. However, due to the lack of correlation of US with renal function, further invasive and/or ionizing studies might be required (e.g., diuretic renograms). This paper presents a computer-aided diagnosis (CAD) tool for the accurate and objective assessment of pediatric hydronephrosis based on morphological analysis of kidney from 3DUS scans. The integration of specific segmentation tools in the system, allows to delineate the relevant renal structures from 3DUS scans of the patients with minimal user interaction, and the automatic computation of 90 anatomical features. Using the washout half time (T1/2) as indicative of renal obstruction, an optimal subset of predictive features is selected to differentiate, with maximum sensitivity, those severe cases where further attention is required (e.g., in the form of diuretic renograms), from the noncritical ones. The performance of this new 3DUS-based CAD system is studied for two clinically relevant T1/2 thresholds, 20 and 30 min. Using a dataset of 20 hydronephrotic cases, pilot experiments show how the system outperforms previous 2D implementations by successfully identifying all the critical cases (100% of sensitivity), and detecting up to 100% (T1/2 = 20 min) and 67% (T1/2 = 30 min) of non-critical ones for T1/2 thresholds of 20 and 30 min, respectively.

  5. MRI target delineation may reduce long-term toxicity after prostate radiotherapy.

    PubMed

    Sander, Lotte; Langkilde, Niels Christian; Holmberg, Mats; Carl, Jesper

    2014-06-01

    Aiming for minimal toxicity after radical prostate cancer (PC) radiotherapy (RT), magnetic resonance imaging (MRI) target delineation could be a possible benefit knowing that clinical target volumes (CTV) are up to 30% smaller, when CTV delineation on MRI is compared to standard computed tomography (CT). This study compares long-term toxicity using CT or MRI delineation before PC RT. Urinary and rectal toxicity assessments 36 months after image-guided RT (78 Gy) using CTC-AE scores in two groups of PC patients. Peak symptom score values were registered. One group of patients (n=72) had standard CT target delineation and gold markers as fiducials. Another group of patients (n=73) had MRI target delineation and a nickel-titanium stent as fiducial. At 36 months no difference in overall survival (92% in both groups, p=0.29) or in PSA-relapse free survival was found between the groups (MRI=89% and CT=94%, p=0.67). A significantly smaller CTV was found in the MRI group (p=0.02). Urinary retention and frequency were significantly reduced in the MRI group (p=0.03 in the matter of both). The overall urinary and rectal toxicity did not differ between the two groups. MRI delineation leads to a significantly reduced CTV. Significantly lower urinary frequency and urinary retention toxicity scores were observed following MRI delineation. The study did not find significant differences in overall urinary or rectal toxicity between the two groups. PSA-relapse survival did not differ between the two groups at 36 months.

  6. Automatic anatomy recognition using neural network learning of object relationships via virtual landmarks

    NASA Astrophysics Data System (ADS)

    Yan, Fengxia; Udupa, Jayaram K.; Tong, Yubing; Xu, Guoping; Odhner, Dewey; Torigian, Drew A.

    2018-03-01

    The recently developed body-wide Automatic Anatomy Recognition (AAR) methodology depends on fuzzy modeling of individual objects, hierarchically arranging objects, constructing an anatomy ensemble of these models, and a dichotomous object recognition-delineation process. The parent-to-offspring spatial relationship in the object hierarchy is crucial in the AAR method. We have found this relationship to be quite complex, and as such any improvement in capturing this relationship information in the anatomy model will improve the process of recognition itself. Currently, the method encodes this relationship based on the layout of the geometric centers of the objects. Motivated by the concept of virtual landmarks (VLs), this paper presents a new one-shot AAR recognition method that utilizes the VLs to learn object relationships by training a neural network to predict the pose and the VLs of an offspring object given the VLs of the parent object in the hierarchy. We set up two neural networks for each parent-offspring object pair in a body region, one for predicting the VLs and another for predicting the pose parameters. The VL-based learning/prediction method is evaluated on two object hierarchies involving 14 objects. We utilize 54 computed tomography (CT) image data sets of head and neck cancer patients and the associated object contours drawn by dosimetrists for routine radiation therapy treatment planning. The VL neural network method is found to yield more accurate object localization than the currently used simple AAR method.

  7. Automatic AVHRR image navigation software

    NASA Technical Reports Server (NTRS)

    Baldwin, Dan; Emery, William

    1992-01-01

    This is the final report describing the work done on the project entitled Automatic AVHRR Image Navigation Software funded through NASA-Washington, award NAGW-3224, Account 153-7529. At the onset of this project, we had developed image navigation software capable of producing geo-registered images from AVHRR data. The registrations were highly accurate but required a priori knowledge of the spacecraft's axes alignment deviations, commonly known as attitude. The three angles needed to describe the attitude are called roll, pitch, and yaw, and are the components of the deviations in the along scan, along track and about center directions. The inclusion of the attitude corrections in the navigation software results in highly accurate georegistrations, however, the computation of the angles is very tedious and involves human interpretation for several steps. The technique also requires easily identifiable ground features which may not be available due to cloud cover or for ocean data. The current project was motivated by the need for a navigation system which was automatic and did not require human intervention or ground control points. The first step in creating such a system must be the ability to parameterize the spacecraft's attitude. The immediate goal of this project was to study the attitude fluctuations and determine if they displayed any systematic behavior which could be modeled or parameterized. We chose a period in 1991-1992 to study the attitude of the NOAA 11 spacecraft using data from the Tiros receiving station at the Colorado Center for Astrodynamic Research (CCAR) at the University of Colorado.

  8. Design of automatic leveling and centering system of theodolite

    NASA Astrophysics Data System (ADS)

    Liu, Chun-tong; He, Zhen-Xin; Huang, Xian-xiang; Zhan, Ying

    2012-09-01

    To realize the theodolite automation and improve the azimuth Angle measurement instrument, the theodolite automatic leveling and centering system with the function of leveling error compensation is designed, which includes the system solution, key components selection, the mechanical structure of leveling and centering, and system software solution. The redesigned leveling feet are driven by the DC servo motor; and the electronic control center device is installed. Using high precision of tilt sensors as horizontal skew detection sensors ensures the effectiveness of the leveling error compensation. Aiming round mark center is located using digital image processing through surface array CCD; and leveling measurement precision can reach the pixel level, which makes the theodolite accurate centering possible. Finally, experiments are conducted using the automatic leveling and centering system of the theodolite. The results show the leveling and centering system can realize automatic operation with high centering accuracy of 0.04mm.The measurement precision of the orientation angle after leveling error compensation is improved, compared with that of in the traditional method. Automatic leveling and centering system of theodolite can satisfy the requirements of the measuring precision and its automation.

  9. Neural networks: Alternatives to conventional techniques for automatic docking

    NASA Technical Reports Server (NTRS)

    Vinz, Bradley L.

    1994-01-01

    Automatic docking of orbiting spacecraft is a crucial operation involving the identification of vehicle orientation as well as complex approach dynamics. The chaser spacecraft must be able to recognize the target spacecraft within a scene and achieve accurate closing maneuvers. In a video-based system, a target scene must be captured and transformed into a pattern of pixels. Successful recognition lies in the interpretation of this pattern. Due to their powerful pattern recognition capabilities, artificial neural networks offer a potential role in interpretation and automatic docking processes. Neural networks can reduce the computational time required by existing image processing and control software. In addition, neural networks are capable of recognizing and adapting to changes in their dynamic environment, enabling enhanced performance, redundancy, and fault tolerance. Most neural networks are robust to failure, capable of continued operation with a slight degradation in performance after minor failures. This paper discusses the particular automatic docking tasks neural networks can perform as viable alternatives to conventional techniques.

  10. 43 CFR 3922.40 - Tract delineation.

    Code of Federal Regulations, 2011 CFR

    2011-10-01

    ..., DEPARTMENT OF THE INTERIOR RANGE MANAGEMENT (4000) OIL SHALE LEASING Application Processing § 3922.40 Tract... the oil shale resource. (b) The BLM may delineate more or less lands than were covered by an...

  11. 43 CFR 3922.40 - Tract delineation.

    Code of Federal Regulations, 2012 CFR

    2012-10-01

    ..., DEPARTMENT OF THE INTERIOR MINERALS MANAGEMENT (3000) OIL SHALE LEASING Application Processing § 3922.40... development of the oil shale resource. (b) The BLM may delineate more or less lands than were covered by an...

  12. 43 CFR 3922.40 - Tract delineation.

    Code of Federal Regulations, 2013 CFR

    2013-10-01

    ..., DEPARTMENT OF THE INTERIOR MINERALS MANAGEMENT (3000) OIL SHALE LEASING Application Processing § 3922.40... development of the oil shale resource. (b) The BLM may delineate more or less lands than were covered by an...

  13. 43 CFR 3922.40 - Tract delineation.

    Code of Federal Regulations, 2014 CFR

    2014-10-01

    ..., DEPARTMENT OF THE INTERIOR MINERALS MANAGEMENT (3000) OIL SHALE LEASING Application Processing § 3922.40... development of the oil shale resource. (b) The BLM may delineate more or less lands than were covered by an...

  14. A semi-automatic annotation tool for cooking video

    NASA Astrophysics Data System (ADS)

    Bianco, Simone; Ciocca, Gianluigi; Napoletano, Paolo; Schettini, Raimondo; Margherita, Roberto; Marini, Gianluca; Gianforme, Giorgio; Pantaleo, Giuseppe

    2013-03-01

    In order to create a cooking assistant application to guide the users in the preparation of the dishes relevant to their profile diets and food preferences, it is necessary to accurately annotate the video recipes, identifying and tracking the foods of the cook. These videos present particular annotation challenges such as frequent occlusions, food appearance changes, etc. Manually annotate the videos is a time-consuming, tedious and error-prone task. Fully automatic tools that integrate computer vision algorithms to extract and identify the elements of interest are not error free, and false positive and false negative detections need to be corrected in a post-processing stage. We present an interactive, semi-automatic tool for the annotation of cooking videos that integrates computer vision techniques under the supervision of the user. The annotation accuracy is increased with respect to completely automatic tools and the human effort is reduced with respect to completely manual ones. The performance and usability of the proposed tool are evaluated on the basis of the time and effort required to annotate the same video sequences.

  15. Strategies for automatic processing of large aftershock sequences

    NASA Astrophysics Data System (ADS)

    Kvaerna, T.; Gibbons, S. J.

    2017-12-01

    Aftershock sequences following major earthquakes present great challenges to seismic bulletin generation. The analyst resources needed to locate events increase with increased event numbers as the quality of underlying, fully automatic, event lists deteriorates. While current pipelines, designed a generation ago, are usually limited to single passes over the raw data, modern systems also allow multiple passes. Processing the raw data from each station currently generates parametric data streams that are later subject to phase-association algorithms which form event hypotheses. We consider a major earthquake scenario and propose to define a region of likely aftershock activity in which we will detect and accurately locate events using a separate, specially targeted, semi-automatic process. This effort may use either pattern detectors or more general algorithms that cover wider source regions without requiring waveform similarity. An iterative procedure to generate automatic bulletins would incorporate all the aftershock event hypotheses generated by the auxiliary process, and filter all phases from these events from the original detection lists prior to a new iteration of the global phase-association algorithm.

  16. Application of image recognition-based automatic hyphae detection in fungal keratitis.

    PubMed

    Wu, Xuelian; Tao, Yuan; Qiu, Qingchen; Wu, Xinyi

    2018-03-01

    the conventional artificial identification of confocal microscope corneal images, of being accurate, stable and does not rely on human expertise. It was the most useful to the medical experts who are not familiar with fungal keratitis. The technology of automatic hyphae detection based on image recognition can quantify the hyphae density and grade this property. Being noninvasive, it can provide an evaluation criterion to fungal keratitis in a timely, accurate, objective and quantitative manner.

  17. Interobserver delineation variation in lung tumour stereotactic body radiotherapy

    PubMed Central

    Persson, G F; Nygaard, D E; Hollensen, C; Munck af Rosenschöld, P; Mouritsen, L S; Due, A K; Berthelsen, A K; Nyman, J; Markova, E; Roed, A P; Roed, H; Korreman, S; Specht, L

    2012-01-01

    Objectives In radiotherapy, delineation uncertainties are important as they contribute to systematic errors and can lead to geographical miss of the target. For margin computation, standard deviations (SDs) of all uncertainties must be included as SDs. The aim of this study was to quantify the interobserver delineation variation for stereotactic body radiotherapy (SBRT) of peripheral lung tumours using a cross-sectional study design. Methods 22 consecutive patients with 26 tumours were included. Positron emission tomography/CT scans were acquired for planning of SBRT. Three oncologists and three radiologists independently delineated the gross tumour volume. The interobserver variation was calculated as a mean of multiple SDs of distances to a reference contour, and calculated for the transversal plane (SDtrans) and craniocaudal (CC) direction (SDcc) separately. Concordance indexes and volume deviations were also calculated. Results Median tumour volume was 13.0 cm3, ranging from 0.3 to 60.4 cm3. The mean SDtrans was 0.15 cm (SD 0.08 cm) and the overall mean SDcc was 0.26 cm (SD 0.15 cm). Tumours with pleural contact had a significantly larger SDtrans than tumours surrounded by lung tissue. Conclusions The interobserver delineation variation was very small in this systematic cross-sectional analysis, although significantly larger in the CC direction than in the transversal plane, stressing that anisotropic margins should be applied. This study is the first to make a systematic cross-sectional analysis of delineation variation for peripheral lung tumours referred for SBRT, establishing the evidence that interobserver variation is very small for these tumours. PMID:22919015

  18. Automatic classification of blank substrate defects

    NASA Astrophysics Data System (ADS)

    Boettiger, Tom; Buck, Peter; Paninjath, Sankaranarayanan; Pereira, Mark; Ronald, Rob; Rost, Dan; Samir, Bhamidipati

    2014-10-01

    Mask preparation stages are crucial in mask manufacturing, since this mask is to later act as a template for considerable number of dies on wafer. Defects on the initial blank substrate, and subsequent cleaned and coated substrates, can have a profound impact on the usability of the finished mask. This emphasizes the need for early and accurate identification of blank substrate defects and the risk they pose to the patterned reticle. While Automatic Defect Classification (ADC) is a well-developed technology for inspection and analysis of defects on patterned wafers and masks in the semiconductors industry, ADC for mask blanks is still in the early stages of adoption and development. Calibre ADC is a powerful analysis tool for fast, accurate, consistent and automatic classification of defects on mask blanks. Accurate, automated classification of mask blanks leads to better usability of blanks by enabling defect avoidance technologies during mask writing. Detailed information on blank defects can help to select appropriate job-decks to be written on the mask by defect avoidance tools [1][4][5]. Smart algorithms separate critical defects from the potentially large number of non-critical defects or false defects detected at various stages during mask blank preparation. Mechanisms used by Calibre ADC to identify and characterize defects include defect location and size, signal polarity (dark, bright) in both transmitted and reflected review images, distinguishing defect signals from background noise in defect images. The Calibre ADC engine then uses a decision tree to translate this information into a defect classification code. Using this automated process improves classification accuracy, repeatability and speed, while avoiding the subjectivity of human judgment compared to the alternative of manual defect classification by trained personnel [2]. This paper focuses on the results from the evaluation of Automatic Defect Classification (ADC) product at MP Mask

  19. Delineation of contaminant plume for an inorganic contaminated site using electrical resistivity tomography: comparison with direct-push technique.

    PubMed

    Liao, Qing; Deng, Yaping; Shi, Xiaoqing; Sun, Yuanyuan; Duan, Weidong; Wu, Jichun

    2018-03-03

    Precise delineation of contaminant plume distribution is essential for effective remediation of contaminated sites. Traditional in situ investigation methods like direct-push (DP) sampling are accurate, but are usually intrusive and costly. Electrical resistivity tomography (ERT) method, as a non-invasive geophysical technique to map spatiotemporal changes in resistivity of the subsurface, is becoming increasingly popular in environmental science. However, the resolution of ERT for delineation of contaminant plumes still remains controversial. In this study, ERT and DP technique were both conducted at a real inorganic contaminated site. The reliability of the ERT method was validated by the direct comparisons of their investigation results that the resistivity acquired by ERT method is in accordance with the total dissolved solid concentration in groundwater and the overall variation of the total iron content in soil obtained by DP technique. After testifying the applicability of ERT method for contaminant identification, the extension of contaminant plume at the study site was revealed by supplementary ERT surveys conducted subsequently in the surrounding area of the contaminant source zone.

  20. Evaluation of an automatic MR-based gold fiducial marker localisation method for MR-only prostate radiotherapy

    NASA Astrophysics Data System (ADS)

    Maspero, Matteo; van den Berg, Cornelis A. T.; Zijlstra, Frank; Sikkes, Gonda G.; de Boer, Hans C. J.; Meijer, Gert J.; Kerkmeijer, Linda G. W.; Viergever, Max A.; Lagendijk, Jan J. W.; Seevinck, Peter R.

    2017-10-01

    An MR-only radiotherapy planning (RTP) workflow would reduce the cost, radiation exposure and uncertainties introduced by CT-MRI registrations. In the case of prostate treatment, one of the remaining challenges currently holding back the implementation of an RTP workflow is the MR-based localisation of intraprostatic gold fiducial markers (FMs), which is crucial for accurate patient positioning. Currently, MR-based FM localisation is clinically performed manually. This is sub-optimal, as manual interaction increases the workload. Attempts to perform automatic FM detection often rely on being able to detect signal voids induced by the FMs in magnitude images. However, signal voids may not always be sufficiently specific, hampering accurate and robust automatic FM localisation. Here, we present an approach that aims at automatic MR-based FM localisation. This method is based on template matching using a library of simulated complex-valued templates, and exploiting the behaviour of the complex MR signal in the vicinity of the FM. Clinical evaluation was performed on seventeen prostate cancer patients undergoing external beam radiotherapy treatment. Automatic MR-based FM localisation was compared to manual MR-based and semi-automatic CT-based localisation (the current gold standard) in terms of detection rate and the spatial accuracy and precision of localisation. The proposed method correctly detected all three FMs in 15/17 patients. The spatial accuracy (mean) and precision (STD) were 0.9 mm and 0.5 mm respectively, which is below the voxel size of 1.1 × 1.1 × 1.2 mm3 and comparable to MR-based manual localisation. FM localisation failed (3/51 FMs) in the presence of bleeding or calcifications in the direct vicinity of the FM. The method was found to be spatially accurate and precise, which is essential for clinical use. To overcome any missed detection, we envision the use of the proposed method along with verification by an observer. This will result in a

  1. Delineating Personal Distance and Territoriality

    ERIC Educational Resources Information Center

    Becker, Franklin D.; Mayo, Clara

    1971-01-01

    Two concepts describing human special behavior are delineated. A field experiment was designed to test whether personal belongings left as markers in public areas (i.e. cafeteria setting) function to protect a territory or to maintain a comfortable social distance. It is suggested the term territory" be restricted to situations in which the space…

  2. [Development of automatic urine monitoring system].

    PubMed

    Wei, Liang; Li, Yongqin; Chen, Bihua

    2014-03-01

    An automatic urine monitoring system is presented to replace manual operation. The system is composed of the flow sensor, MSP430f149 single chip microcomputer, human-computer interaction module, LCD module, clock module and memory module. The signal of urine volume is captured when the urine flows through the flow sensor and then displayed on the LCD after data processing. The experiment results suggest that the design of the monitor provides a high stability, accurate measurement and good real-time, and meets the demand of the clinical application.

  3. [Development of a Compared Software for Automatically Generated DVH in Eclipse TPS].

    PubMed

    Xie, Zhao; Luo, Kelin; Zou, Lian; Hu, Jinyou

    2016-03-01

    This study is to automatically calculate the dose volume histogram(DVH) for the treatment plan, then to compare it with requirements of doctor's prescriptions. The scripting language Autohotkey and programming language C# were used to develop a compared software for automatically generated DVH in Eclipse TPS. This software is named Show Dose Volume Histogram (ShowDVH), which is composed of prescription documents generation, operation functions of DVH, software visualization and DVH compared report generation. Ten cases in different cancers have been separately selected, in Eclipse TPS 11.0 ShowDVH could not only automatically generate DVH reports but also accurately determine whether treatment plans meet the requirements of doctor’s prescriptions, then reports gave direction for setting optimization parameters of intensity modulated radiated therapy. The ShowDVH is an user-friendly and powerful software, and can automatically generated compared DVH reports fast in Eclipse TPS 11.0. With the help of ShowDVH, it greatly saves plan designing time and improves working efficiency of radiation therapy physicists.

  4. Automatic grade classification of Barretts Esophagus through feature enhancement

    NASA Astrophysics Data System (ADS)

    Ghatwary, Noha; Ahmed, Amr; Ye, Xujiong; Jalab, Hamid

    2017-03-01

    Barretts Esophagus (BE) is a precancerous condition that affects the esophagus tube and has the risk of developing esophageal adenocarcinoma. BE is the process of developing metaplastic intestinal epithelium and replacing the normal cells in the esophageal area. The detection of BE is considered difficult due to its appearance and properties. The diagnosis is usually done through both endoscopy and biopsy. Recently, Computer Aided Diagnosis systems have been developed to support physicians opinion when facing difficulty in detection/classification in different types of diseases. In this paper, an automatic classification of Barretts Esophagus condition is introduced. The presented method enhances the internal features of a Confocal Laser Endomicroscopy (CLE) image by utilizing a proposed enhancement filter. This filter depends on fractional differentiation and integration that improve the features in the discrete wavelet transform of an image. Later on, various features are extracted from each enhanced image on different levels for the multi-classification process. Our approach is validated on a dataset that consists of a group of 32 patients with 262 images with different histology grades. The experimental results demonstrated the efficiency of the proposed technique. Our method helps clinicians for more accurate classification. This potentially helps to reduce the need for biopsies needed for diagnosis, facilitate the regular monitoring of treatment/development of the patients case and can help train doctors with the new endoscopy technology. The accurate automatic classification is particularly important for the Intestinal Metaplasia (IM) type, which could turn into deadly cancerous. Hence, this work contributes to automatic classification that facilitates early intervention/treatment and decreasing biopsy samples needed.

  5. Automated segmentation of myocardial scar in late enhancement MRI using combined intensity and spatial information.

    PubMed

    Tao, Qian; Milles, Julien; Zeppenfeld, Katja; Lamb, Hildo J; Bax, Jeroen J; Reiber, Johan H C; van der Geest, Rob J

    2010-08-01

    Accurate assessment of the size and distribution of a myocardial infarction (MI) from late gadolinium enhancement (LGE) MRI is of significant prognostic value for postinfarction patients. In this paper, an automatic MI identification method combining both intensity and spatial information is presented in a clear framework of (i) initialization, (ii) false acceptance removal, and (iii) false rejection removal. The method was validated on LGE MR images of 20 chronic postinfarction patients, using manually traced MI contours from two independent observers as reference. Good agreement was observed between automatic and manual MI identification. Validation results showed that the average Dice indices, which describe the percentage of overlap between two regions, were 0.83 +/- 0.07 and 0.79 +/- 0.08 between the automatic identification and the manual tracing from observer 1 and observer 2, and the errors in estimated infarct percentage were 0.0 +/- 1.9% and 3.8 +/- 4.7% compared with observer 1 and observer 2. The difference between the automatic method and manual tracing is in the order of interobserver variation. In conclusion, the developed automatic method is accurate and robust in MI delineation, providing an objective tool for quantitative assessment of MI in LGE MR imaging.

  6. Enhancements to TauDEM to support Rapid Watershed Delineation Services

    NASA Astrophysics Data System (ADS)

    Sazib, N. S.; Tarboton, D. G.

    2015-12-01

    Watersheds are widely recognized as the basic functional unit for water resources management studies and are important for a variety of problems in hydrology, ecology, and geomorphology. Nevertheless, delineating a watershed spread across a large region is still cumbersome due to the processing burden of working with large Digital Elevation Model. Terrain Analysis Using Digital Elevation Models (TauDEM) software supports the delineation of watersheds and stream networks from within desktop Geographic Information Systems. A rich set of watershed and stream network attributes are computed. However limitations of the TauDEM desktop tools are (1) it supports only one type of raster (tiff format) data (2) requires installation of software for parallel processing, and (3) data have to be in projected coordinate system. This paper presents enhancements to TauDEM that have been developed to extend its generality and support web based watershed delineation services. The enhancements of TauDEM include (1) reading and writing raster data with the open-source geospatial data abstraction library (GDAL) not limited to the tiff data format and (2) support for both geographic and projected coordinates. To support web services for rapid watershed delineation a procedure has been developed for sub setting the domain based on sub-catchments, with preprocessed data prepared for each catchment stored. This allows the watershed delineation to function locally, while extending to the full extent of watersheds using preprocessed information. Additional capabilities of this program includes computation of average watershed properties and geomorphic and channel network variables such as drainage density, shape factor, relief ratio and stream ordering. The updated version of TauDEM increases the practical applicability of it in terms of raster data type, size and coordinate system. The watershed delineation web service functionality is useful for web based software as service deployments

  7. Neural Signatures of Controlled and Automatic Retrieval Processes in Memory-based Decision-making.

    PubMed

    Khader, Patrick H; Pachur, Thorsten; Weber, Lilian A E; Jost, Kerstin

    2016-01-01

    Decision-making often requires retrieval from memory. Drawing on the neural ACT-R theory [Anderson, J. R., Fincham, J. M., Qin, Y., & Stocco, A. A central circuit of the mind. Trends in Cognitive Sciences, 12, 136-143, 2008] and other neural models of memory, we delineated the neural signatures of two fundamental retrieval aspects during decision-making: automatic and controlled activation of memory representations. To disentangle these processes, we combined a paradigm developed to examine neural correlates of selective and sequential memory retrieval in decision-making with a manipulation of associative fan (i.e., the decision options were associated with one, two, or three attributes). The results show that both the automatic activation of all attributes associated with a decision option and the controlled sequential retrieval of specific attributes can be traced in material-specific brain areas. Moreover, the two facets of memory retrieval were associated with distinct activation patterns within the frontoparietal network: The dorsolateral prefrontal cortex was found to reflect increasing retrieval effort during both automatic and controlled activation of attributes. In contrast, the superior parietal cortex only responded to controlled retrieval, arguably reflecting the sequential updating of attribute information in working memory. This dissociation in activation pattern is consistent with ACT-R and constitutes an important step toward a neural model of the retrieval dynamics involved in memory-based decision-making.

  8. Automatic Classification of Medical Text: The Influence of Publication Form1

    PubMed Central

    Cole, William G.; Michael, Patricia A.; Stewart, James G.; Blois, Marsden S.

    1988-01-01

    Previous research has shown that within the domain of medical journal abstracts the statistical distribution of words is neither random nor uniform, but is highly characteristic. Many words are used mainly or solely by one medical specialty or when writing about one particular level of description. Due to this regularity of usage, automatic classification within journal abstracts has proved quite successful. The present research asks two further questions. It investigates whether this statistical regularity and automatic classification success can also be achieved in medical textbook chapters. It then goes on to see whether the statistical distribution found in textbooks is sufficiently similar to that found in abstracts to permit accurate classification of abstracts based solely on previous knowledge of textbooks. 14 textbook chapters and 45 MEDLINE abstracts were submitted to an automatic classification program that had been trained only on chapters drawn from a standard textbook series. Statistical analysis of the properties of abstracts vs. chapters revealed important differences in word use. Automatic classification performance was good for chapters, but poor for abstracts.

  9. Automatic labeling of MR brain images through extensible learning and atlas forests.

    PubMed

    Xu, Lijun; Liu, Hong; Song, Enmin; Yan, Meng; Jin, Renchao; Hung, Chih-Cheng

    2017-12-01

    Multiatlas-based method is extensively used in MR brain images segmentation because of its simplicity and robustness. This method provides excellent accuracy although it is time consuming and limited in terms of obtaining information about new atlases. In this study, an automatic labeling of MR brain images through extensible learning and atlas forest is presented to address these limitations. We propose an extensible learning model which allows the multiatlas-based framework capable of managing the datasets with numerous atlases or dynamic atlas datasets and simultaneously ensure the accuracy of automatic labeling. Two new strategies are used to reduce the time and space complexity and improve the efficiency of the automatic labeling of brain MR images. First, atlases are encoded to atlas forests through random forest technology to reduce the time consumed for cross-registration between atlases and target image, and a scatter spatial vector is designed to eliminate errors caused by inaccurate registration. Second, an atlas selection method based on the extensible learning model is used to select atlases for target image without traversing the entire dataset and then obtain the accurate labeling. The labeling results of the proposed method were evaluated in three public datasets, namely, IBSR, LONI LPBA40, and ADNI. With the proposed method, the dice coefficient metric values on the three datasets were 84.17 ± 4.61%, 83.25 ± 4.29%, and 81.88 ± 4.53% which were 5% higher than those of the conventional method, respectively. The efficiency of the extensible learning model was evaluated by state-of-the-art methods for labeling of MR brain images. Experimental results showed that the proposed method could achieve accurate labeling for MR brain images without traversing the entire datasets. In the proposed multiatlas-based method, extensible learning and atlas forests were applied to control the automatic labeling of brain anatomies on large atlas datasets or dynamic

  10. Automatic lumbar vertebrae detection based on feature fusion deep learning for partial occluded C-arm X-ray images.

    PubMed

    Yang Li; Wei Liang; Yinlong Zhang; Haibo An; Jindong Tan

    2016-08-01

    Automatic and accurate lumbar vertebrae detection is an essential step of image-guided minimally invasive spine surgery (IG-MISS). However, traditional methods still require human intervention due to the similarity of vertebrae, abnormal pathological conditions and uncertain imaging angle. In this paper, we present a novel convolutional neural network (CNN) model to automatically detect lumbar vertebrae for C-arm X-ray images. Training data is augmented by DRR and automatic segmentation of ROI is able to reduce the computational complexity. Furthermore, a feature fusion deep learning (FFDL) model is introduced to combine two types of features of lumbar vertebrae X-ray images, which uses sobel kernel and Gabor kernel to obtain the contour and texture of lumbar vertebrae, respectively. Comprehensive qualitative and quantitative experiments demonstrate that our proposed model performs more accurate in abnormal cases with pathologies and surgical implants in multi-angle views.

  11. On the use of airborne LiDAR for braided river monitoring and water surface delineation

    NASA Astrophysics Data System (ADS)

    Vetter, M.; Höfle, B.; Pfeifer, N.; Rutzinger, M.; Stötter, J.

    2009-04-01

    Airborne LiDAR is an established technology for Earth surface surveying. With LiDAR data sets it is possible to derive maps with different land use classes, which are important for hydraulic simulations. We present a 3D point cloud based method for automatic water surface delineation using single as well as multitemporal LiDAR data sets. With the developed method it is possible to detect the location of the water surface with high planimetric accuracy. The multitemporal analysis of different LiDAR data sets makes it possible to visualize, monitor and quantify the changes of the flow path of braided rivers as well as derived water surface land use classes. The reflection properties from laser beams (1064 nm wavelength) on water surfaces are characterized by strong absorption or specular reflection resulting in a dominance of low signal amplitude values and a high number of laser shot dropouts (i.e. non-recorded laser echoes). The occurrence of dropouts is driven by (i) the incidence angle, (ii) the surface reflectance and (iii) the roughness of the water body. The input data of the presented delineation method are the modeled dropouts and the point cloud attributes of geometry and signal amplitude. A terrestrial orthophoto is used to explore the point cloud in order to find proper information about the geometry and amplitude attributes that are characteristic for water surfaces. The delineation method is divided into five major steps. (a) We compute calibrated amplitude values by reducing the atmospheric, topographic influences and the scan geometry for each laser echo. (b) Then, the dropouts are modeled by using the information from the time stamps, the pulse repetition frequency, the inertial measurement unit and the GPS information of the laser shots and the airplane. The next step is to calculate the standard deviation of the heights for all reflections and all modeled dropouts (c) in a specific radius around the points. (d) We compute the amplitude ratio

  12. Automatic cardiac LV segmentation in MRI using modified graph cuts with smoothness and interslice constraints.

    PubMed

    Albà, Xènia; Figueras I Ventura, Rosa M; Lekadir, Karim; Tobon-Gomez, Catalina; Hoogendoorn, Corné; Frangi, Alejandro F

    2014-12-01

    Magnetic resonance imaging (MRI), specifically late-enhanced MRI, is the standard clinical imaging protocol to assess cardiac viability. Segmentation of myocardial walls is a prerequisite for this assessment. Automatic and robust multisequence segmentation is required to support processing massive quantities of data. A generic rule-based framework to automatically segment the left ventricle myocardium is presented here. We use intensity information, and include shape and interslice smoothness constraints, providing robustness to subject- and study-specific changes. Our automatic initialization considers the geometrical and appearance properties of the left ventricle, as well as interslice information. The segmentation algorithm uses a decoupled, modified graph cut approach with control points, providing a good balance between flexibility and robustness. The method was evaluated on late-enhanced MRI images from a 20-patient in-house database, and on cine-MRI images from a 15-patient open access database, both using as reference manually delineated contours. Segmentation agreement, measured using the Dice coefficient, was 0.81±0.05 and 0.92±0.04 for late-enhanced MRI and cine-MRI, respectively. The method was also compared favorably to a three-dimensional Active Shape Model approach. The experimental validation with two magnetic resonance sequences demonstrates increased accuracy and versatility. © 2013 Wiley Periodicals, Inc.

  13. Towards automatic music transcription: note extraction based on independent subspace analysis

    NASA Astrophysics Data System (ADS)

    Wellhausen, Jens; Hoynck, Michael

    2005-01-01

    Due to the increasing amount of music available electronically the need of automatic search, retrieval and classification systems for music becomes more and more important. In this paper an algorithm for automatic transcription of polyphonic piano music into MIDI data is presented, which is a very interesting basis for database applications, music analysis and music classification. The first part of the algorithm performs a note accurate temporal audio segmentation. In the second part, the resulting segments are examined using Independent Subspace Analysis to extract sounding notes. Finally, the results are used to build a MIDI file as a new representation of the piece of music which is examined.

  14. Towards automatic music transcription: note extraction based on independent subspace analysis

    NASA Astrophysics Data System (ADS)

    Wellhausen, Jens; Höynck, Michael

    2004-12-01

    Due to the increasing amount of music available electronically the need of automatic search, retrieval and classification systems for music becomes more and more important. In this paper an algorithm for automatic transcription of polyphonic piano music into MIDI data is presented, which is a very interesting basis for database applications, music analysis and music classification. The first part of the algorithm performs a note accurate temporal audio segmentation. In the second part, the resulting segments are examined using Independent Subspace Analysis to extract sounding notes. Finally, the results are used to build a MIDI file as a new representation of the piece of music which is examined.

  15. AISLE: an automatic volumetric segmentation method for the study of lung allometry.

    PubMed

    Ren, Hongliang; Kazanzides, Peter

    2011-01-01

    We developed a fully automatic segmentation method for volumetric CT (computer tomography) datasets to support construction of a statistical atlas for the study of allometric laws of the lung. The proposed segmentation method, AISLE (Automated ITK-Snap based on Level-set), is based on the level-set implementation from an existing semi-automatic segmentation program, ITK-Snap. AISLE can segment the lung field without human interaction and provide intermediate graphical results as desired. The preliminary experimental results show that the proposed method can achieve accurate segmentation, in terms of volumetric overlap metric, by comparing with the ground-truth segmentation performed by a radiologist.

  16. Delineating baseflow contribution areas for streams - A model and methods comparison

    NASA Astrophysics Data System (ADS)

    Chow, Reynold; Frind, Michael E.; Frind, Emil O.; Jones, Jon P.; Sousa, Marcelo R.; Rudolph, David L.; Molson, John W.; Nowak, Wolfgang

    2016-12-01

    This study addresses the delineation of areas that contribute baseflow to a stream reach, also known as stream capture zones. Such areas can be delineated using standard well capture zone delineation methods, with three important differences: (1) natural gradients are smaller compared to those produced by supply wells and are therefore subject to greater numerical errors, (2) stream discharge varies seasonally, and (3) stream discharge varies spatially. This study focuses on model-related uncertainties due to model characteristics, discretization schemes, delineation methods, and particle tracking algorithms. The methodology is applied to the Alder Creek watershed in southwestern Ontario. Four different model codes are compared: HydroGeoSphere, WATFLOW, MODFLOW, and FEFLOW. In addition, two delineation methods are compared: reverse particle tracking and reverse transport, where the latter considers local-scale parameter uncertainty by using a macrodispersion term to produce a capture probability plume. The results from this study indicate that different models can calibrate acceptably well to the same data and produce very similar distributions of hydraulic head, but can produce different capture zones. The stream capture zone is found to be highly sensitive to the particle tracking algorithm. It was also found that particle tracking by itself, if applied to complex systems such as the Alder Creek watershed, would require considerable subjective judgement in the delineation of stream capture zones. Reverse transport is an alternative and more reliable approach that provides probability intervals for the baseflow contribution areas, taking uncertainty into account. The two approaches can be used together to enhance the confidence in the final outcome.

  17. Automatic Evidence Retrieval for Systematic Reviews

    PubMed Central

    Choong, Miew Keen; Galgani, Filippo; Dunn, Adam G

    2014-01-01

    Background Snowballing involves recursively pursuing relevant references cited in the retrieved literature and adding them to the search results. Snowballing is an alternative approach to discover additional evidence that was not retrieved through conventional search. Snowballing’s effectiveness makes it best practice in systematic reviews despite being time-consuming and tedious. Objective Our goal was to evaluate an automatic method for citation snowballing’s capacity to identify and retrieve the full text and/or abstracts of cited articles. Methods Using 20 review articles that contained 949 citations to journal or conference articles, we manually searched Microsoft Academic Search (MAS) and identified 78.0% (740/949) of the cited articles that were present in the database. We compared the performance of the automatic citation snowballing method against the results of this manual search, measuring precision, recall, and F1 score. Results The automatic method was able to correctly identify 633 (as proportion of included citations: recall=66.7%, F1 score=79.3%; as proportion of citations in MAS: recall=85.5%, F1 score=91.2%) of citations with high precision (97.7%), and retrieved the full text or abstract for 490 (recall=82.9%, precision=92.1%, F1 score=87.3%) of the 633 correctly retrieved citations. Conclusions The proposed method for automatic citation snowballing is accurate and is capable of obtaining the full texts or abstracts for a substantial proportion of the scholarly citations in review articles. By automating the process of citation snowballing, it may be possible to reduce the time and effort of common evidence surveillance tasks such as keeping trial registries up to date and conducting systematic reviews. PMID:25274020

  18. Automatic evidence retrieval for systematic reviews.

    PubMed

    Choong, Miew Keen; Galgani, Filippo; Dunn, Adam G; Tsafnat, Guy

    2014-10-01

    Snowballing involves recursively pursuing relevant references cited in the retrieved literature and adding them to the search results. Snowballing is an alternative approach to discover additional evidence that was not retrieved through conventional search. Snowballing's effectiveness makes it best practice in systematic reviews despite being time-consuming and tedious. Our goal was to evaluate an automatic method for citation snowballing's capacity to identify and retrieve the full text and/or abstracts of cited articles. Using 20 review articles that contained 949 citations to journal or conference articles, we manually searched Microsoft Academic Search (MAS) and identified 78.0% (740/949) of the cited articles that were present in the database. We compared the performance of the automatic citation snowballing method against the results of this manual search, measuring precision, recall, and F1 score. The automatic method was able to correctly identify 633 (as proportion of included citations: recall=66.7%, F1 score=79.3%; as proportion of citations in MAS: recall=85.5%, F1 score=91.2%) of citations with high precision (97.7%), and retrieved the full text or abstract for 490 (recall=82.9%, precision=92.1%, F1 score=87.3%) of the 633 correctly retrieved citations. The proposed method for automatic citation snowballing is accurate and is capable of obtaining the full texts or abstracts for a substantial proportion of the scholarly citations in review articles. By automating the process of citation snowballing, it may be possible to reduce the time and effort of common evidence surveillance tasks such as keeping trial registries up to date and conducting systematic reviews.

  19. Procedures for Delineating and Characterizing Watersheds for Stream and River Monitoring Programs (Final Report)

    EPA Science Inventory

    EPA has released the document, Procedures for Delineating and Characterizing Watersheds for Stream and River Monitoring Programs (EPA/600/R-17/448F). This manual describes how states and tribes can delineate and characterize watersheds. It explains how to delineate water...

  20. Automatic and hierarchical segmentation of the human skeleton in CT images.

    PubMed

    Fu, Yabo; Liu, Shi; Li, Harold; Yang, Deshan

    2017-04-07

    Accurate segmentation of each bone of the human skeleton is useful in many medical disciplines. The results of bone segmentation could facilitate bone disease diagnosis and post-treatment assessment, and support planning and image guidance for many treatment modalities including surgery and radiation therapy. As a medium level medical image processing task, accurate bone segmentation can facilitate automatic internal organ segmentation by providing stable structural reference for inter- or intra-patient registration and internal organ localization. Even though bones in CT images can be visually observed with minimal difficulty due to the high image contrast between the bony structures and surrounding soft tissues, automatic and precise segmentation of individual bones is still challenging due to the many limitations of the CT images. The common limitations include low signal-to-noise ratio, insufficient spatial resolution, and indistinguishable image intensity between spongy bones and soft tissues. In this study, a novel and automatic method is proposed to segment all the major individual bones of the human skeleton above the upper legs in CT images based on an articulated skeleton atlas. The reported method is capable of automatically segmenting 62 major bones, including 24 vertebrae and 24 ribs, by traversing a hierarchical anatomical tree and by using both rigid and deformable image registration. The degrees of freedom of femora and humeri are modeled to support patients in different body and limb postures. The segmentation results are evaluated using the Dice coefficient and point-to-surface error (PSE) against manual segmentation results as the ground-truth. The results suggest that the reported method can automatically segment and label the human skeleton into detailed individual bones with high accuracy. The overall average Dice coefficient is 0.90. The average PSEs are 0.41 mm for the mandible, 0.62 mm for cervical vertebrae, 0.92 mm for thoracic

  1. Automatic and hierarchical segmentation of the human skeleton in CT images

    NASA Astrophysics Data System (ADS)

    Fu, Yabo; Liu, Shi; Li, H. Harold; Yang, Deshan

    2017-04-01

    Accurate segmentation of each bone of the human skeleton is useful in many medical disciplines. The results of bone segmentation could facilitate bone disease diagnosis and post-treatment assessment, and support planning and image guidance for many treatment modalities including surgery and radiation therapy. As a medium level medical image processing task, accurate bone segmentation can facilitate automatic internal organ segmentation by providing stable structural reference for inter- or intra-patient registration and internal organ localization. Even though bones in CT images can be visually observed with minimal difficulty due to the high image contrast between the bony structures and surrounding soft tissues, automatic and precise segmentation of individual bones is still challenging due to the many limitations of the CT images. The common limitations include low signal-to-noise ratio, insufficient spatial resolution, and indistinguishable image intensity between spongy bones and soft tissues. In this study, a novel and automatic method is proposed to segment all the major individual bones of the human skeleton above the upper legs in CT images based on an articulated skeleton atlas. The reported method is capable of automatically segmenting 62 major bones, including 24 vertebrae and 24 ribs, by traversing a hierarchical anatomical tree and by using both rigid and deformable image registration. The degrees of freedom of femora and humeri are modeled to support patients in different body and limb postures. The segmentation results are evaluated using the Dice coefficient and point-to-surface error (PSE) against manual segmentation results as the ground-truth. The results suggest that the reported method can automatically segment and label the human skeleton into detailed individual bones with high accuracy. The overall average Dice coefficient is 0.90. The average PSEs are 0.41 mm for the mandible, 0.62 mm for cervical vertebrae, 0.92 mm for thoracic

  2. Contour propagation for lung tumor delineation in 4D-CT using tensor-product surface of uniform and non-uniform closed cubic B-splines

    NASA Astrophysics Data System (ADS)

    Jin, Renchao; Liu, Yongchuan; Chen, Mi; Zhang, Sheng; Song, Enmin

    2018-01-01

    -spline method and the diffeomorphic demons method. The proposed method is useful for helping physicians delineate target volumes efficiently and accurately.

  3. Quality assurance using outlier detection on an automatic segmentation method for the cerebellar peduncles

    NASA Astrophysics Data System (ADS)

    Li, Ke; Ye, Chuyang; Yang, Zhen; Carass, Aaron; Ying, Sarah H.; Prince, Jerry L.

    2016-03-01

    Cerebellar peduncles (CPs) are white matter tracts connecting the cerebellum to other brain regions. Automatic segmentation methods of the CPs have been proposed for studying their structure and function. Usually the performance of these methods is evaluated by comparing segmentation results with manual delineations (ground truth). However, when a segmentation method is run on new data (for which no ground truth exists) it is highly desirable to efficiently detect and assess algorithm failures so that these cases can be excluded from scientific analysis. In this work, two outlier detection methods aimed to assess the performance of an automatic CP segmentation algorithm are presented. The first one is a univariate non-parametric method using a box-whisker plot. We first categorize automatic segmentation results of a dataset of diffusion tensor imaging (DTI) scans from 48 subjects as either a success or a failure. We then design three groups of features from the image data of nine categorized failures for failure detection. Results show that most of these features can efficiently detect the true failures. The second method—supervised classification—was employed on a larger DTI dataset of 249 manually categorized subjects. Four classifiers—linear discriminant analysis (LDA), logistic regression (LR), support vector machine (SVM), and random forest classification (RFC)—were trained using the designed features and evaluated using a leave-one-out cross validation. Results show that the LR performs worst among the four classifiers and the other three perform comparably, which demonstrates the feasibility of automatically detecting segmentation failures using classification methods.

  4. Automatic draft reading based on image processing

    NASA Astrophysics Data System (ADS)

    Tsujii, Takahiro; Yoshida, Hiromi; Iiguni, Youji

    2016-10-01

    In marine transportation, a draft survey is a means to determine the quantity of bulk cargo. Automatic draft reading based on computer image processing has been proposed. However, the conventional draft mark segmentation may fail when the video sequence has many other regions than draft marks and a hull, and the estimated waterline is inherently higher than the true one. To solve these problems, we propose an automatic draft reading method that uses morphological operations to detect draft marks and estimate the waterline for every frame with Canny edge detection and a robust estimation. Moreover, we emulate surveyors' draft reading process for getting the understanding of a shipper and a receiver. In an experiment in a towing tank, the draft reading error of the proposed method was <1 cm, showing the advantage of the proposed method. It is also shown that accurate draft reading has been achieved in a real-world scene.

  5. Automatic x-ray image contrast enhancement based on parameter auto-optimization.

    PubMed

    Qiu, Jianfeng; Harold Li, H; Zhang, Tiezhi; Ma, Fangfang; Yang, Deshan

    2017-11-01

    Insufficient image contrast associated with radiation therapy daily setup x-ray images could negatively affect accurate patient treatment setup. We developed a method to perform automatic and user-independent contrast enhancement on 2D kilo voltage (kV) and megavoltage (MV) x-ray images. The goal was to provide tissue contrast optimized for each treatment site in order to support accurate patient daily treatment setup and the subsequent offline review. The proposed method processes the 2D x-ray images with an optimized image processing filter chain, which consists of a noise reduction filter and a high-pass filter followed by a contrast limited adaptive histogram equalization (CLAHE) filter. The most important innovation is to optimize the image processing parameters automatically to determine the required image contrast settings per disease site and imaging modality. Three major parameters controlling the image processing chain, i.e., the Gaussian smoothing weighting factor for the high-pass filter, the block size, and the clip limiting parameter for the CLAHE filter, were determined automatically using an interior-point constrained optimization algorithm. Fifty-two kV and MV x-ray images were included in this study. The results were manually evaluated and ranked with scores from 1 (worst, unacceptable) to 5 (significantly better than adequate and visually praise worthy) by physicians and physicists. The average scores for the images processed by the proposed method, the CLAHE, and the best window-level adjustment were 3.92, 2.83, and 2.27, respectively. The percentage of the processed images received a score of 5 were 48, 29, and 18%, respectively. The proposed method is able to outperform the standard image contrast adjustment procedures that are currently used in the commercial clinical systems. When the proposed method is implemented in the clinical systems as an automatic image processing filter, it could be useful for allowing quicker and potentially more

  6. Comparison Of Semi-Automatic And Automatic Slick Detection Algorithms For Jiyeh Power Station Oil Spill, Lebanon

    NASA Astrophysics Data System (ADS)

    Osmanoglu, B.; Ozkan, C.; Sunar, F.

    2013-10-01

    After air strikes on July 14 and 15, 2006 the Jiyeh Power Station started leaking oil into the eastern Mediterranean Sea. The power station is located about 30 km south of Beirut and the slick covered about 170 km of coastline threatening the neighboring countries Turkey and Cyprus. Due to the ongoing conflict between Israel and Lebanon, cleaning efforts could not start immediately resulting in 12 000 to 15 000 tons of fuel oil leaking into the sea. In this paper we compare results from automatic and semi-automatic slick detection algorithms. The automatic detection method combines the probabilities calculated for each pixel from each image to obtain a joint probability, minimizing the adverse effects of atmosphere on oil spill detection. The method can readily utilize X-, C- and L-band data where available. Furthermore wind and wave speed observations can be used for a more accurate analysis. For this study, we utilize Envisat ASAR ScanSAR data. A probability map is generated based on the radar backscatter, effect of wind and dampening value. The semi-automatic algorithm is based on supervised classification. As a classifier, Artificial Neural Network Multilayer Perceptron (ANN MLP) classifier is used since it is more flexible and efficient than conventional maximum likelihood classifier for multisource and multi-temporal data. The learning algorithm for ANN MLP is chosen as the Levenberg-Marquardt (LM). Training and test data for supervised classification are composed from the textural information created from SAR images. This approach is semiautomatic because tuning the parameters of classifier and composing training data need a human interaction. We point out the similarities and differences between the two methods and their results as well as underlining their advantages and disadvantages. Due to the lack of ground truth data, we compare obtained results to each other, as well as other published oil slick area assessments.

  7. Automatic creation of three-dimensional avatars

    NASA Astrophysics Data System (ADS)

    Villa-Uriol, Maria-Cruz; Sainz, Miguel; Kuester, Falko; Bagherzadeh, Nader

    2003-01-01

    Highly accurate avatars of humans promise a new level of realism in engineering and entertainment applications, including areas such as computer animated movies, computer game development interactive virtual environments and tele-presence. In order to provide high-quality avatars, new techniques for the automatic acquisition and creation are required. A framework for the capture and construction of arbitrary avatars from image data is presented in this paper. Avatars are automatically reconstructed from multiple static images of a human subject by utilizing image information to reshape a synthetic three-dimensional articulated reference model. A pipeline is presented that combines a set of hardware-accelerated stages into one seamless system. Primary stages in this pipeline include pose estimation, skeleton fitting, body part segmentation, geometry construction and coloring, leading to avatars that can be animated and included into interactive environments. The presented system removes traditional constraints in the initial pose of the captured subject by using silhouette-based modification techniques in combination with a reference model. Results can be obtained in near-real time with very limited user intervention.

  8. Delineating baseflow contribution areas for streams - A model and methods comparison.

    PubMed

    Chow, Reynold; Frind, Michael E; Frind, Emil O; Jones, Jon P; Sousa, Marcelo R; Rudolph, David L; Molson, John W; Nowak, Wolfgang

    2016-12-01

    This study addresses the delineation of areas that contribute baseflow to a stream reach, also known as stream capture zones. Such areas can be delineated using standard well capture zone delineation methods, with three important differences: (1) natural gradients are smaller compared to those produced by supply wells and are therefore subject to greater numerical errors, (2) stream discharge varies seasonally, and (3) stream discharge varies spatially. This study focuses on model-related uncertainties due to model characteristics, discretization schemes, delineation methods, and particle tracking algorithms. The methodology is applied to the Alder Creek watershed in southwestern Ontario. Four different model codes are compared: HydroGeoSphere, WATFLOW, MODFLOW, and FEFLOW. In addition, two delineation methods are compared: reverse particle tracking and reverse transport, where the latter considers local-scale parameter uncertainty by using a macrodispersion term to produce a capture probability plume. The results from this study indicate that different models can calibrate acceptably well to the same data and produce very similar distributions of hydraulic head, but can produce different capture zones. The stream capture zone is found to be highly sensitive to the particle tracking algorithm. It was also found that particle tracking by itself, if applied to complex systems such as the Alder Creek watershed, would require considerable subjective judgement in the delineation of stream capture zones. Reverse transport is an alternative and more reliable approach that provides probability intervals for the baseflow contribution areas, taking uncertainty into account. The two approaches can be used together to enhance the confidence in the final outcome. Copyright © 2016 The Authors. Published by Elsevier B.V. All rights reserved.

  9. Using analytic element models to delineate drinking water source protection areas.

    PubMed

    Raymond, Heather A; Bondoc, Michael; McGinnis, John; Metropulos, Kathy; Heider, Pat; Reed, Allison; Saines, Steve

    2006-01-01

    Since 1999, Ohio EPA hydrogeologists have used two analytic element models (AEMs), the proprietary software GFLOW and U.S. EPA's WhAEM, to delineate protection areas for 535 public water systems. Both models now use the GFLOW2001 solution engine, integrate well with Geographic Information System (GIS) technology, have a user-friendly graphical interface, are capable of simulating a variety of complex hydrogeologic settings, and do not rely upon a model grid. These features simplify the modeling process and enable AEMs to bridge the gap between existing simplistic delineation methods and more complex numerical models. Ohio EPA hydrogeologists demonstrated that WhAEM2000 and GFLOW2000 were capable of producing capture zones similar to more widely accepted models by applying the AEMs to eight sites that had been previously delineated using other methods. After the Ohio EPA delineated protection areas using AEMs, more simplistic delineation methods used by other states (volumetric equation and arbitrary fixed radii) were applied to the same water systems to compare the differences between various methods. GIS software and two-tailed paired t-tests were used to quantify the differences in protection areas and analyze the data. The results of this analysis demonstrate that AEMs typically produce significantly different protection areas than the most simplistic delineation methods, in terms of total area and shape. If the volumetric equation had been used instead of AEMs, Ohio would not have protected 265 km2 of critical upgradient area and would have overprotected 269 km2 of primarily downgradient land. Since an increasing number of land-use restrictions are being tied to drinking water protection areas, this analysis has broad policy implications.

  10. 30 CFR 582.22 - Delineation Plan.

    Code of Federal Regulations, 2014 CFR

    2014-07-01

    ... 30 Mineral Resources 2 2014-07-01 2014-07-01 false Delineation Plan. 582.22 Section 582.22 Mineral Resources BUREAU OF OCEAN ENERGY MANAGEMENT, DEPARTMENT OF THE INTERIOR OFFSHORE OPERATIONS IN THE OUTER... minerals, and generate other information needed for the development of a comprehensive Testing or Mining...

  11. 30 CFR 582.22 - Delineation Plan.

    Code of Federal Regulations, 2012 CFR

    2012-07-01

    ... 30 Mineral Resources 2 2012-07-01 2012-07-01 false Delineation Plan. 582.22 Section 582.22 Mineral Resources BUREAU OF OCEAN ENERGY MANAGEMENT, DEPARTMENT OF THE INTERIOR OFFSHORE OPERATIONS IN THE OUTER... minerals, and generate other information needed for the development of a comprehensive Testing or Mining...

  12. 30 CFR 582.22 - Delineation Plan.

    Code of Federal Regulations, 2013 CFR

    2013-07-01

    ... 30 Mineral Resources 2 2013-07-01 2013-07-01 false Delineation Plan. 582.22 Section 582.22 Mineral Resources BUREAU OF OCEAN ENERGY MANAGEMENT, DEPARTMENT OF THE INTERIOR OFFSHORE OPERATIONS IN THE OUTER... minerals, and generate other information needed for the development of a comprehensive Testing or Mining...

  13. Automatic Imitation

    ERIC Educational Resources Information Center

    Heyes, Cecilia

    2011-01-01

    "Automatic imitation" is a type of stimulus-response compatibility effect in which the topographical features of task-irrelevant action stimuli facilitate similar, and interfere with dissimilar, responses. This article reviews behavioral, neurophysiological, and neuroimaging research on automatic imitation, asking in what sense it is "automatic"…

  14. Application of a Novel Semi-Automatic Technique for Determining the Bilateral Symmetry Plane of the Facial Skeleton of Normal Adult Males.

    PubMed

    Roumeliotis, Grayson; Willing, Ryan; Neuert, Mark; Ahluwalia, Romy; Jenkyn, Thomas; Yazdani, Arjang

    2015-09-01

    The accurate assessment of symmetry in the craniofacial skeleton is important for cosmetic and reconstructive craniofacial surgery. Although there have been several published attempts to develop an accurate system for determining the correct plane of symmetry, all are inaccurate and time consuming. Here, the authors applied a novel semi-automatic method for the calculation of craniofacial symmetry, based on principal component analysis and iterative corrective point computation, to a large sample of normal adult male facial computerized tomography scans obtained clinically (n = 32). The authors hypothesized that this method would generate planes of symmetry that would result in less error when one side of the face was compared to the other than a symmetry plane generated using a plane defined by cephalometric landmarks. When a three-dimensional model of one side of the face was reflected across the semi-automatic plane of symmetry there was less error than when reflected across the cephalometric plane. The semi-automatic plane was also more accurate when the locations of bilateral cephalometric landmarks (eg, frontozygomatic sutures) were compared across the face. The authors conclude that this method allows for accurate and fast measurements of craniofacial symmetry. This has important implications for studying the development of the facial skeleton, and clinical application for reconstruction.

  15. Atlas-based segmentation technique incorporating inter-observer delineation uncertainty for whole breast

    NASA Astrophysics Data System (ADS)

    Bell, L. R.; Dowling, J. A.; Pogson, E. M.; Metcalfe, P.; Holloway, L.

    2017-01-01

    Accurate, efficient auto-segmentation methods are essential for the clinical efficacy of adaptive radiotherapy delivered with highly conformal techniques. Current atlas based auto-segmentation techniques are adequate in this respect, however fail to account for inter-observer variation. An atlas-based segmentation method that incorporates inter-observer variation is proposed. This method is validated for a whole breast radiotherapy cohort containing 28 CT datasets with CTVs delineated by eight observers. To optimise atlas accuracy, the cohort was divided into categories by mean body mass index and laterality, with atlas’ generated for each in a leave-one-out approach. Observer CTVs were merged and thresholded to generate an auto-segmentation model representing both inter-observer and inter-patient differences. For each category, the atlas was registered to the left-out dataset to enable propagation of the auto-segmentation from atlas space. Auto-segmentation time was recorded. The segmentation was compared to the gold-standard contour using the dice similarity coefficient (DSC) and mean absolute surface distance (MASD). Comparison with the smallest and largest CTV was also made. This atlas-based auto-segmentation method incorporating inter-observer variation was shown to be efficient (<4min) and accurate for whole breast radiotherapy, with good agreement (DSC>0.7, MASD <9.3mm) between the auto-segmented contours and CTV volumes.

  16. Handheld optical coherence tomography-reflectance confocal microscopy probe for detection of basal cell carcinoma and delineation of margins

    NASA Astrophysics Data System (ADS)

    Iftimia, Nicusor; Yélamos, Oriol; Chen, Chih-Shan J.; Maguluri, Gopi; Cordova, Miguel A.; Sahu, Aditi; Park, Jesung; Fox, William; Alessi-Fox, Christi; Rajadhyaksha, Milind

    2017-07-01

    We present a hand-held implementation and preliminary evaluation of a combined optical coherence tomography (OCT) and reflectance confocal microscopy (RCM) probe for detecting and delineating the margins of basal cell carcinomas (BCCs) in human skin in vivo. A standard OCT approach (spectrometer-based) with a central wavelength of 1310 nm and 0.11 numerical aperture (NA) was combined with a standard RCM approach (830-nm wavelength and 0.9 NA) into a common path hand-held probe. Cross-sectional OCT images and enface RCM images are simultaneously displayed, allowing for three-dimensional microscopic assessment of tumor morphology in real time. Depending on the subtype and depth of the BCC tumor and surrounding skin conditions, OCT and RCM imaging are able to complement each other, the strengths of each helping overcome the limitations of the other. Four representative cases are summarized, out of the 15 investigated in a preliminary pilot study, demonstrating how OCT and RCM imaging may be synergistically combined to more accurately detect BCCs and more completely delineate margins. Our preliminary results highlight the potential benefits of combining the two technologies within a single probe to potentially guide diagnosis as well as treatment of BCCs.

  17. Automatic detection of confusion in elderly users of a web-based health instruction video.

    PubMed

    Postma-Nilsenová, Marie; Postma, Eric; Tates, Kiek

    2015-06-01

    Because of cognitive limitations and lower health literacy, many elderly patients have difficulty understanding verbal medical instructions. Automatic detection of facial movements provides a nonintrusive basis for building technological tools supporting confusion detection in healthcare delivery applications on the Internet. Twenty-four elderly participants (70-90 years old) were recorded while watching Web-based health instruction videos involving easy and complex medical terminology. Relevant fragments of the participants' facial expressions were rated by 40 medical students for perceived level of confusion and analyzed with automatic software for facial movement recognition. A computer classification of the automatically detected facial features performed more accurately and with a higher sensitivity than the human observers (automatic detection and classification, 64% accuracy, 0.64 sensitivity; human observers, 41% accuracy, 0.43 sensitivity). A drill-down analysis of cues to confusion indicated the importance of the eye and eyebrow region. Confusion caused by misunderstanding of medical terminology is signaled by facial cues that can be automatically detected with currently available facial expression detection technology. The findings are relevant for the development of Web-based services for healthcare consumers.

  18. Accurate and Standardized Coronary Wave Intensity Analysis.

    PubMed

    Rivolo, Simone; Patterson, Tiffany; Asrress, Kaleab N; Marber, Michael; Redwood, Simon; Smith, Nicolas P; Lee, Jack

    2017-05-01

    Coronary wave intensity analysis (cWIA) has increasingly been applied in the clinical research setting to distinguish between the proximal and distal mechanical influences on coronary blood flow. Recently, a cWIA-derived clinical index demonstrated prognostic value in predicting functional recovery postmyocardial infarction. Nevertheless, the known operator dependence of the cWIA metrics currently hampers its routine application in clinical practice. Specifically, it was recently demonstrated that the cWIA metrics are highly dependent on the chosen Savitzky-Golay filter parameters used to smooth the acquired traces. Therefore, a novel method to make cWIA standardized and automatic was proposed and evaluated in vivo. The novel approach combines an adaptive Savitzky-Golay filter with high-order central finite differencing after ensemble-averaging the acquired waveforms. Its accuracy was assessed using in vivo human data. The proposed approach was then modified to automatically perform beat wise cWIA. Finally, the feasibility (accuracy and robustness) of the method was evaluated. The automatic cWIA algorithm provided satisfactory accuracy under a wide range of noise scenarios (≤10% and ≤20% error in the estimation of wave areas and peaks, respectively). These results were confirmed when beat-by-beat cWIA was performed. An accurate, standardized, and automated cWIA was developed. Moreover, the feasibility of beat wise cWIA was demonstrated for the first time. The proposed algorithm provides practitioners with a standardized technique that could broaden the application of cWIA in the clinical practice as enabling multicenter trials. Furthermore, the demonstrated potential of beatwise cWIA opens the possibility investigating the coronary physiology in real time.

  19. Driver response to delineation treatments on horizontal curves on two-lane roads.

    DOT National Transportation Integrated Search

    2009-05-01

    The delineation of horizontal curves on two-lane rural roads is an important component of safety : improvements to reduce run-off-road and head-on crashes. This project assessed four types of vertical : delineation in conjunction with edgeline markin...

  20. Evaluation of tomotherapy MVCT image enhancement program for tumor volume delineation

    PubMed Central

    Martin, Spencer; Rodrigues, George; Chen, Quan; Pavamani, Simon; Read, Nancy; Ahmad, Belal; Hammond, J. Alex; Venkatesan, Varagur; Renaud, James

    2011-01-01

    The aims of this study were to investigate the variability between physicians in delineation of head and neck tumors on original tomotherapy megavoltage CT (MVCT) studies and corresponding software enhanced MVCT images, and to establish an optimal approach for evaluation of image improvement. Five physicians contoured the gross tumor volume (GTV) for three head and neck cancer patients on 34 original and enhanced MVCT studies. Variation between original and enhanced MVCT studies was quantified by DICE coefficient and the coefficient of variance. Based on volume of agreement between physicians, higher correlation in terms of average DICE coefficients was observed in GTV delineation for enhanced MVCT for patients 1, 2, and 3 by 15%, 3%, and 7%, respectively, while delineation variance among physicians was reduced using enhanced MVCT for 12 of 17 weekly image studies. Enhanced MVCT provides advantages in reduction of variance among physicians in delineation of the GTV. Agreement on contouring by the same physician on both original and enhanced MVCT was equally high. PACS numbers: 87.57.N‐, 87.57.np, 87.57.nt

  1. Markov random field based automatic image alignment for electron tomography.

    PubMed

    Amat, Fernando; Moussavi, Farshid; Comolli, Luis R; Elidan, Gal; Downing, Kenneth H; Horowitz, Mark

    2008-03-01

    We present a method for automatic full-precision alignment of the images in a tomographic tilt series. Full-precision automatic alignment of cryo electron microscopy images has remained a difficult challenge to date, due to the limited electron dose and low image contrast. These facts lead to poor signal to noise ratio (SNR) in the images, which causes automatic feature trackers to generate errors, even with high contrast gold particles as fiducial features. To enable fully automatic alignment for full-precision reconstructions, we frame the problem probabilistically as finding the most likely particle tracks given a set of noisy images, using contextual information to make the solution more robust to the noise in each image. To solve this maximum likelihood problem, we use Markov Random Fields (MRF) to establish the correspondence of features in alignment and robust optimization for projection model estimation. The resulting algorithm, called Robust Alignment and Projection Estimation for Tomographic Reconstruction, or RAPTOR, has not needed any manual intervention for the difficult datasets we have tried, and has provided sub-pixel alignment that is as good as the manual approach by an expert user. We are able to automatically map complete and partial marker trajectories and thus obtain highly accurate image alignment. Our method has been applied to challenging cryo electron tomographic datasets with low SNR from intact bacterial cells, as well as several plastic section and X-ray datasets.

  2. Comparing Models and Methods for the Delineation of Stream Baseflow Contribution Areas

    NASA Astrophysics Data System (ADS)

    Chow, R.; Frind, M.; Frind, E. O.; Jones, J. P.; Sousa, M.; Rudolph, D. L.; Nowak, W.

    2016-12-01

    This study addresses the delineation of areas that contribute baseflow to a stream reach, also known as stream capture zones. Such areas can be delineated using standard well capture zone delineation methods, with three important differences: (1) natural gradients are smaller compared to those produced by supply wells and are therefore subject to greater numerical errors, (2) stream discharge varies seasonally, and (3) stream discharge varies spatially. This study focuses on model-related uncertainties due to parameter non-uniqueness, discretization schemes, and particle tracking algorithms. The methodology is applied to the Alder Creek watershed in southwestern Ontario. Four different model codes are compared: HydroGeoSphere, WATFLOW, MODFLOW, and FEFLOW. In addition, two delineation methods are compared: reverse particle tracking and reverse transport, where the latter considers local-scale parameter uncertainty by using a macrodispersion term to produce a capture probability plume. The results from this study indicate that different models can calibrate acceptably well to the same data and produce very similar distributions of hydraulic head, but can produce different capture zones. The stream capture zone is found to be highly sensitive to the particle tracking algorithm. It was also found that particle tracking by itself, if applied to complex systems such as the Alder Creek watershed, would require considerable subjective judgement in the delineation of stream capture zones. Reverse transport is an alternate approach that provides probability intervals for the baseflow contribution areas. In situations where the two approaches agree, the confidence in the delineation is reinforced.

  3. Automatic quality assessment and peak identification of auditory brainstem responses with fitted parametric peaks.

    PubMed

    Valderrama, Joaquin T; de la Torre, Angel; Alvarez, Isaac; Segura, Jose Carlos; Thornton, A Roger D; Sainz, Manuel; Vargas, Jose Luis

    2014-05-01

    The recording of the auditory brainstem response (ABR) is used worldwide for hearing screening purposes. In this process, a precise estimation of the most relevant components is essential for an accurate interpretation of these signals. This evaluation is usually carried out subjectively by an audiologist. However, the use of automatic methods for this purpose is being encouraged nowadays in order to reduce human evaluation biases and ensure uniformity among test conditions, patients, and screening personnel. This article describes a new method that performs automatic quality assessment and identification of the peaks, the fitted parametric peaks (FPP). This method is based on the use of synthesized peaks that are adjusted to the ABR response. The FPP is validated, on one hand, by an analysis of amplitudes and latencies measured manually by an audiologist and automatically by the FPP method in ABR signals recorded at different stimulation rates; and on the other hand, contrasting the performance of the FPP method with the automatic evaluation techniques based on the correlation coefficient, FSP, and cross correlation with a predefined template waveform by comparing the automatic evaluations of the quality of these methods with subjective evaluations provided by five experienced evaluators on a set of ABR signals of different quality. The results of this study suggest (a) that the FPP method can be used to provide an accurate parameterization of the peaks in terms of amplitude, latency, and width, and (b) that the FPP remains as the method that best approaches the averaged subjective quality evaluation, as well as provides the best results in terms of sensitivity and specificity in ABR signals validation. The significance of these findings and the clinical value of the FPP method are highlighted on this paper. Copyright © 2014 Elsevier Ireland Ltd. All rights reserved.

  4. Assessing the Agreement Between Eo-Based Semi-Automated Landslide Maps with Fuzzy Manual Landslide Delineation

    NASA Astrophysics Data System (ADS)

    Albrecht, F.; Hölbling, D.; Friedl, B.

    2017-09-01

    Landslide mapping benefits from the ever increasing availability of Earth Observation (EO) data resulting from programmes like the Copernicus Sentinel missions and improved infrastructure for data access. However, there arises the need for improved automated landslide information extraction processes from EO data while the dominant method is still manual delineation. Object-based image analysis (OBIA) provides the means for the fast and efficient extraction of landslide information. To prove its quality, automated results are often compared to manually delineated landslide maps. Although there is awareness of the uncertainties inherent in manual delineations, there is a lack of understanding how they affect the levels of agreement in a direct comparison of OBIA-derived landslide maps and manually derived landslide maps. In order to provide an improved reference, we present a fuzzy approach for the manual delineation of landslides on optical satellite images, thereby making the inherent uncertainties of the delineation explicit. The fuzzy manual delineation and the OBIA classification are compared by accuracy metrics accepted in the remote sensing community. We have tested this approach for high resolution (HR) satellite images of three large landslides in Austria and Italy. We were able to show that the deviation of the OBIA result from the manual delineation can mainly be attributed to the uncertainty inherent in the manual delineation process, a relevant issue for the design of validation processes for OBIA-derived landslide maps.

  5. A locally adaptive kernel regression method for facies delineation

    NASA Astrophysics Data System (ADS)

    Fernàndez-Garcia, D.; Barahona-Palomo, M.; Henri, C. V.; Sanchez-Vila, X.

    2015-12-01

    Facies delineation is defined as the separation of geological units with distinct intrinsic characteristics (grain size, hydraulic conductivity, mineralogical composition). A major challenge in this area stems from the fact that only a few scattered pieces of hydrogeological information are available to delineate geological facies. Several methods to delineate facies are available in the literature, ranging from those based only on existing hard data, to those including secondary data or external knowledge about sedimentological patterns. This paper describes a methodology to use kernel regression methods as an effective tool for facies delineation. The method uses both the spatial and the actual sampled values to produce, for each individual hard data point, a locally adaptive steering kernel function, self-adjusting the principal directions of the local anisotropic kernels to the direction of highest local spatial correlation. The method is shown to outperform the nearest neighbor classification method in a number of synthetic aquifers whenever the available number of hard data is small and randomly distributed in space. In the case of exhaustive sampling, the steering kernel regression method converges to the true solution. Simulations ran in a suite of synthetic examples are used to explore the selection of kernel parameters in typical field settings. It is shown that, in practice, a rule of thumb can be used to obtain suboptimal results. The performance of the method is demonstrated to significantly improve when external information regarding facies proportions is incorporated. Remarkably, the method allows for a reasonable reconstruction of the facies connectivity patterns, shown in terms of breakthrough curves performance.

  6. A Compressive Sensing Approach for Glioma Margin Delineation Using Mass Spectrometry

    PubMed Central

    Gholami, Behnood; Agar, Nathalie Y. R.; Jolesz, Ferenc A.; Haddad, Wassim M.; Tannenbaum, Allen R.

    2013-01-01

    Surgery, and specifically, tumor resection, is the primary treatment for most patients suffering from brain tumors. Medical imaging techniques, and in particular, magnetic resonance imaging are currently used in diagnosis as well as image-guided surgery procedures. However, studies show that computed tomography and magnetic resonance imaging fail to accurately identify the full extent of malignant brain tumors and their microscopic infiltration. Mass spectrometry is a well-known analytical technique used to identify molecules in a given sample based on their mass. In a recent study, it is proposed to use mass spectrometry as an intraoperative tool for discriminating tumor and non-tumor tissue. Integration of mass spectrometry with the resection module allows for tumor resection and immediate molecular analysis. In this paper, we propose a framework for tumor margin delineation using compressive sensing. Specifically, we show that the spatial distribution of tumor cell concentration can be efficiently reconstructed and updated using mass spectrometry information from the resected tissue. In addition, our proposed framework is model-free, and hence, requires no prior information of spatial distribution of the tumor cell concentration. PMID:22255629

  7. Flood-plain delineation for Accotink Creek Basin, Fairfax County, Virginia

    USGS Publications Warehouse

    Soule, Pat L.

    1977-01-01

    Water-surface profiles of the 25-year and 100-year floods maps on which the 25-, 50-, and 100-year flood limits are delineated for streams in the Accotink Creek basin are presented in this report. Excluded are segments of Accotink Creek within the Fort Belvoir Military Reservation. The techniques used in the computation of the flood profiles and delineation of flood limits are presented, and specific hydraulic problems encountered within the study area are also included.

  8. Experimental pavement delineation treatments

    NASA Astrophysics Data System (ADS)

    Bryden, J. E.; Lorini, R. A.

    1981-06-01

    Visibility and durability of materials used to delineate shoulders and medians adjacent to asphalt pavements were evaluated. Materials evaluated were polysulfide and coal tar epoxies, one and two component polyesters, portland cement, acrylic paints, modified-alkyd traffic paint, preformed plastic tape, and thermoplastic markings. Neat applications, sand mortars, and surface treatments were installed in several geometric patterns including cross hatches, solid median treatments, and various widths of edge lines. Thermoplastic pavement markings generally performed very well, providing good visibility under adverse viewing conditions for at least 4 years. Thermoplastic 4 in. wide edge lines appear to provide adequate visibility for most conditions.

  9. Experimental Study for Automatic Colony Counting System Based Onimage Processing

    NASA Astrophysics Data System (ADS)

    Fang, Junlong; Li, Wenzhe; Wang, Guoxin

    Colony counting in many colony experiments is detected by manual method at present, therefore it is difficult for man to execute the method quickly and accurately .A new automatic colony counting system was developed. Making use of image-processing technology, a study was made on the feasibility of distinguishing objectively white bacterial colonies from clear plates according to the RGB color theory. An optimal chromatic value was obtained based upon a lot of experiments on the distribution of the chromatic value. It has been proved that the method greatly improves the accuracy and efficiency of the colony counting and the counting result is not affected by using inoculation, shape or size of the colony. It is revealed that automatic detection of colony quantity using image-processing technology could be an effective way.

  10. Mining Software Usage with the Automatic Library Tracking Database (ALTD)

    DOE Office of Scientific and Technical Information (OSTI.GOV)

    Hadri, Bilel; Fahey, Mark R

    2013-01-01

    Tracking software usage is important for HPC centers, computer vendors, code developers and funding agencies to provide more efficient and targeted software support, and to forecast needs and guide HPC software effort towards the Exascale era. However, accurately tracking software usage on HPC systems has been a challenging task. In this paper, we present a tool called Automatic Library Tracking Database (ALTD) that has been developed and put in production on several Cray systems. The ALTD infrastructure prototype automatically and transparently stores information about libraries linked into an application at compilation time and also the executables launched in a batchmore » job. We will illustrate the usage of libraries, compilers and third party software applications on a system managed by the National Institute for Computational Sciences.« less

  11. Automatic system for 3D reconstruction of the chick eye based on digital photographs.

    PubMed

    Wong, Alexander; Genest, Reno; Chandrashekar, Naveen; Choh, Vivian; Irving, Elizabeth L

    2012-01-01

    The geometry of anatomical specimens is very complex and accurate 3D reconstruction is important for morphological studies, finite element analysis (FEA) and rapid prototyping. Although magnetic resonance imaging, computed tomography and laser scanners can be used for reconstructing biological structures, the cost of the equipment is fairly high and specialised technicians are required to operate the equipment, making such approaches limiting in terms of accessibility. In this paper, a novel automatic system for 3D surface reconstruction of the chick eye from digital photographs of a serially sectioned specimen is presented as a potential cost-effective and practical alternative. The system is designed to allow for automatic detection of the external surface of the chick eye. Automatic alignment of the photographs is performed using a combination of coloured markers and an algorithm based on complex phase order likelihood that is robust to noise and illumination variations. Automatic segmentation of the external boundaries of the eye from the aligned photographs is performed using a novel level-set segmentation approach based on a complex phase order energy functional. The extracted boundaries are sampled to construct a 3D point cloud, and a combination of Delaunay triangulation and subdivision surfaces is employed to construct the final triangular mesh. Experimental results using digital photographs of the chick eye show that the proposed system is capable of producing accurate 3D reconstructions of the external surface of the eye. The 3D model geometry is similar to a real chick eye and could be used for morphological studies and FEA.

  12. Delineation of fault zones using imaging radar

    NASA Technical Reports Server (NTRS)

    Toksoz, M. N.; Gulen, L.; Prange, M.; Matarese, J.; Pettengill, G. H.; Ford, P. G.

    1986-01-01

    The assessment of earthquake hazards and mineral and oil potential of a given region requires a detailed knowledge of geological structure, including the configuration of faults. Delineation of faults is traditionally based on three types of data: (1) seismicity data, which shows the location and magnitude of earthquake activity; (2) field mapping, which in remote areas is typically incomplete and of insufficient accuracy; and (3) remote sensing, including LANDSAT images and high altitude photography. Recently, high resolution radar images of tectonically active regions have been obtained by SEASAT and Shuttle Imaging Radar (SIR-A and SIR-B) systems. These radar images are sensitive to terrain slope variations and emphasize the topographic signatures of fault zones. Techniques were developed for using the radar data in conjunction with the traditional types of data to delineate major faults in well-known test sites, and to extend interpretation techniques to remote areas.

  13. Automatization of hydrodynamic modelling in a Floreon+ system

    NASA Astrophysics Data System (ADS)

    Ronovsky, Ales; Kuchar, Stepan; Podhoranyi, Michal; Vojtek, David

    2017-07-01

    The paper describes fully automatized hydrodynamic modelling as a part of the Floreon+ system. The main purpose of hydrodynamic modelling in the disaster management is to provide an accurate overview of the hydrological situation in a given river catchment. Automatization of the process as a web service could provide us with immediate data based on extreme weather conditions, such as heavy rainfall, without the intervention of an expert. Such a service can be used by non scientific users such as fire-fighter operators or representatives of a military service organizing evacuation during floods or river dam breaks. The paper describes the whole process beginning with a definition of a schematization necessary for hydrodynamic model, gathering of necessary data and its processing for a simulation, the model itself and post processing of a result and visualization on a web service. The process is demonstrated on a real data collected during floods in our Moravian-Silesian region in 2010.

  14. Wetland delineation with IKONOS high-resolution satellite imagery, Fort Custer Training Center, Battle Creek, Michigan, 2005

    USGS Publications Warehouse

    Fuller, L.M.; Morgan, T.R.; Aichele, Stephen S.

    2006-01-01

    The Michigan Army National Guard’s Fort Custer Training Center (FCTC) in Battle Creek, Mich., has the responsibility to protect wetland resources on the training grounds while providing training opportunities, and for future development planning at the facility. The National Wetlands Inventory (NWI) data have been the primary wetland-boundary resource, but a check on scale and accuracy of the wetland boundary information for the Fort Custer Training Center was needed. In cooperation with the FCTC, the U.S. Geological Survey (USGS) used an early spring IKONOS pan-sharpened satellite image to delineate the wetlands and create a more accurate wetland map for the FCTC. The USGS tested automated approaches (supervised and unsupervised classifications) to identify the wetland areas from the IKONOS satellite image, but the automated approaches alone did not yield accurate results. To ensure accurate wetland boundaries, the final wetland map was manually digitized on the basis of the automated supervised and unsupervised classifications, in combination with NWI data, field verifications, and visual interpretation of the IKONOS satellite image. The final wetland areas digitized from the IKONOS satellite imagery were similar to those in NWI; however, the wetland boundaries differed in some areas, a few wetlands mapped on the NWI were determined not to be wetlands from the IKONOS image and field verification, and additional previously unmapped wetlands not recognized by the NWI were identified from the IKONOS image.

  15. Automatic 3d Building Model Generations with Airborne LiDAR Data

    NASA Astrophysics Data System (ADS)

    Yastikli, N.; Cetin, Z.

    2017-11-01

    LiDAR systems become more and more popular because of the potential use for obtaining the point clouds of vegetation and man-made objects on the earth surface in an accurate and quick way. Nowadays, these airborne systems have been frequently used in wide range of applications such as DEM/DSM generation, topographic mapping, object extraction, vegetation mapping, 3 dimensional (3D) modelling and simulation, change detection, engineering works, revision of maps, coastal management and bathymetry. The 3D building model generation is the one of the most prominent applications of LiDAR system, which has the major importance for urban planning, illegal construction monitoring, 3D city modelling, environmental simulation, tourism, security, telecommunication and mobile navigation etc. The manual or semi-automatic 3D building model generation is costly and very time-consuming process for these applications. Thus, an approach for automatic 3D building model generation is needed in a simple and quick way for many studies which includes building modelling. In this study, automatic 3D building models generation is aimed with airborne LiDAR data. An approach is proposed for automatic 3D building models generation including the automatic point based classification of raw LiDAR point cloud. The proposed point based classification includes the hierarchical rules, for the automatic production of 3D building models. The detailed analyses for the parameters which used in hierarchical rules have been performed to improve classification results using different test areas identified in the study area. The proposed approach have been tested in the study area which has partly open areas, forest areas and many types of the buildings, in Zekeriyakoy, Istanbul using the TerraScan module of TerraSolid. The 3D building model was generated automatically using the results of the automatic point based classification. The obtained results of this research on study area verified that automatic 3D

  16. DNA Probe Pooling for Rapid Delineation of Chromosomal Breakpoints

    DOE Office of Scientific and Technical Information (OSTI.GOV)

    Lu, Chun-Mei; Kwan, Johnson; Baumgartner, Adolf

    2009-01-30

    Structural chromosome aberrations are hallmarks of many human genetic diseases. The precise mapping of translocation breakpoints in tumors is important for identification of genes with altered levels of expression, prediction of tumor progression, therapy response, or length of disease-free survival as well as the preparation of probes for detection of tumor cells in peripheral blood. Similarly, in vitro fertilization (IVF) and preimplantation genetic diagnosis (PGD) for carriers of balanced, reciprocal translocations benefit from accurate breakpoint maps in the preparation of patient-specific DNA probes followed by a selection of normal or balanced oocytes or embryos. We expedited the process of breakpointmore » mapping and preparation of case-specific probes by utilizing physically mapped bacterial artificial chromosome (BAC) clones. Historically, breakpoint mapping is based on the definition of the smallest interval between proximal and distal probes. Thus, many of the DNA probes prepared for multi-clone and multi-color mapping experiments do not generate additional information. Our pooling protocol described here with examples from thyroid cancer research and PGD accelerates the delineation of translocation breakpoints without sacrificing resolution. The turnaround time from clone selection to mapping results using tumor or IVF patient samples can be as short as three to four days.« less

  17. A novel scheme for automatic nonrigid image registration using deformation invariant feature and geometric constraint

    NASA Astrophysics Data System (ADS)

    Deng, Zhipeng; Lei, Lin; Zhou, Shilin

    2015-10-01

    Automatic image registration is a vital yet challenging task, particularly for non-rigid deformation images which are more complicated and common in remote sensing images, such as distorted UAV (unmanned aerial vehicle) images or scanning imaging images caused by flutter. Traditional non-rigid image registration methods are based on the correctly matched corresponding landmarks, which usually needs artificial markers. It is a rather challenging task to locate the accurate position of the points and get accurate homonymy point sets. In this paper, we proposed an automatic non-rigid image registration algorithm which mainly consists of three steps: To begin with, we introduce an automatic feature point extraction method based on non-linear scale space and uniform distribution strategy to extract the points which are uniform distributed along the edge of the image. Next, we propose a hybrid point matching algorithm using DaLI (Deformation and Light Invariant) descriptor and local affine invariant geometric constraint based on triangulation which is constructed by K-nearest neighbor algorithm. Based on the accurate homonymy point sets, the two images are registrated by the model of TPS (Thin Plate Spline). Our method is demonstrated by three deliberately designed experiments. The first two experiments are designed to evaluate the distribution of point set and the correctly matching rate on synthetic data and real data respectively. The last experiment is designed on the non-rigid deformation remote sensing images and the three experimental results demonstrate the accuracy, robustness, and efficiency of the proposed algorithm compared with other traditional methods.

  18. Pancreas and cyst segmentation

    NASA Astrophysics Data System (ADS)

    Dmitriev, Konstantin; Gutenko, Ievgeniia; Nadeem, Saad; Kaufman, Arie

    2016-03-01

    Accurate segmentation of abdominal organs from medical images is an essential part of surgical planning and computer-aided disease diagnosis. Many existing algorithms are specialized for the segmentation of healthy organs. Cystic pancreas segmentation is especially challenging due to its low contrast boundaries, variability in shape, location and the stage of the pancreatic cancer. We present a semi-automatic segmentation algorithm for pancreata with cysts. In contrast to existing automatic segmentation approaches for healthy pancreas segmentation which are amenable to atlas/statistical shape approaches, a pancreas with cysts can have even higher variability with respect to the shape of the pancreas due to the size and shape of the cyst(s). Hence, fine results are better attained with semi-automatic steerable approaches. We use a novel combination of random walker and region growing approaches to delineate the boundaries of the pancreas and cysts with respective best Dice coefficients of 85.1% and 86.7%, and respective best volumetric overlap errors of 26.0% and 23.5%. Results show that the proposed algorithm for pancreas and pancreatic cyst segmentation is accurate and stable.

  19. MRI to delineate the gross tumor volume of nasopharyngeal cancers: which sequences and planes should be used?

    PubMed

    Popovtzer, Aron; Ibrahim, Mohannad; Tatro, Daniel; Feng, Felix Y; Ten Haken, Randall K; Eisbruch, Avraham

    2014-09-01

    Magnetic resonance imaging (MRI) has been found to be better than computed tomography for defining the extent of primary gross tumor volume (GTV) in advanced nasopharyngeal cancer. It is routinely applied for target delineation in planning radiotherapy. However, the specific MRI sequences/planes that should be used are unknown. Twelve patients with nasopharyngeal cancer underwent primary GTV evaluation with gadolinium-enhanced axial T1 weighted image (T1) and T2 weighted image (T2), coronal T1, and sagittal T1 sequences. Each sequence was registered with the planning computed tomography scans. Planning target volumes (PTVs) were derived by uniform expansions of the GTVs. The volumes encompassed by the various sequences/planes, and the volumes common to all sequences/planes, were compared quantitatively and anatomically to the volume delineated by the commonly used axial T1-based dataset. Addition of the axial T2 sequence increased the axial T1-based GTV by 12% on average (p = 0.004), and composite evaluations that included the coronal T1 and sagittal T1 planes increased the axial T1-based GTVs by 30% on average (p = 0.003). The axial T1-based PTVs were increased by 20% by the additional sequences (p = 0.04). Each sequence/plane added unique volume extensions. The GTVs common to all the T1 planes accounted for 38% of the total volumes of all the T1 planes. Anatomically, addition of the coronal and sagittal-based GTVs extended the axial T1-based GTV caudally and cranially, notably to the base of the skull. Adding MRI planes and sequences to the traditional axial T1 sequence yields significant quantitative and anatomically important extensions of the GTVs and PTVs. For accurate target delineation in nasopharyngeal cancer, we recommend that GTVs be outlined in all MRI sequences/planes and registered with the planning computed tomography scans.

  20. SU-F-I-51: CT/MR Image Deformation: The Clinical Assessment QA in Target Delineation

    DOE Office of Scientific and Technical Information (OSTI.GOV)

    Yang, C; Chen, Y

    Purpose: To study the deformation effects in CT/MR image registration of head and neck (HN) cancers. We present a clinical indication in guiding and simplifying registration procedures of this process while CT images possessed artifacts. Methods: CT/MR image fusion provides better soft tissue contrast in intracranial GTV definition with artifacts. However, whether the fusion process should include the deformation process is questionable and not recommended. We performed CT/MR image registration of a HN patient with tonsil GTV and nodes delineation on Varian Velocity™ system. Both rigid transformation and deformable registration of the same CT/MR imaging data were processed separately. Physician’smore » selection of target delineation was implemented to identify the variations. Transformation matrix was shown with visual identification, as well as the deformation QA numbers and figures were assessed. Results: The deformable CT/MR images were traced with the calculated matrix, both translation and rotational parameters were summarized. In deformable quality QA, the calculated Jacobian matrix was analyzed, which the min/mean/max of 0.73/0/99/1.37, respectively. Jacobian matrix of right neck node was 0.84/1.13/1.41, which present dis-similarity of the nodal area. If Jacobian = 1, the deformation is at the optimum situation. In this case, the deformation results have shown better target delineation for CT/MR deformation than rigid transformation. Though the root-mean-square vector difference is 1.48 mm, with similar rotational components, the cord and vertebrae position were aligned much better in the deformable MR images than the rigid transformation. Conclusion: CT/MR with/without image deformation presents similar image registration matrix; there were significant differentiate the anatomical structures in the region of interest by deformable process. Though vendor suggested only rigid transformation between CT/MR assuming the geometry remain similar, our

  1. Enhanced delineation of degradation in aortic walls through OCT

    NASA Astrophysics Data System (ADS)

    Real, Eusebio; Val-Bernal, José Fernando; Revuelta, José M.; Pontón, Alejandro; Calvo Díez, Marta; Mayorga, Marta; López-Higuera, José M.; Conde, Olga M.

    2015-03-01

    Degradation of the wall of human ascending thoracic aorta has been assessed through Optical Coherence Tomography (OCT). OCT images of the media layer of the aortic wall exhibit micro-structure degradation in case of diseased aortas from aneurysmal vessels or in aortas prone to aortic dissections. The degeneration in vessel walls appears as low-reflectivity areas due to the invasive appearance of acidic polysaccharides and mucopolysaccharides within a typical ordered microstructure of parallel lamellae of smooth muscle cells, elastin and collagen fibers. An OCT indicator of wall degradation can be generated upon the spatial quantification of the extension of degraded areas in a similar way as conventional histopathology. This proposed OCT marker offers a real-time clinical insight of the vessel status to help cardiovascular surgeons in vessel repair interventions. However, the delineation of degraded areas on the B-scan image from OCT is sometimes difficult due to presence of speckle noise, variable SNR conditions on the measurement process, etc. Degraded areas could be outlined by basic thresholding techniques taking advantage of disorders evidences in B-scan images, but this delineation is not always optimum and requires complex additional processing stages. This work proposes an optimized delineation of degraded spots in vessel walls, robust to noisy environments, based on the analysis of the second order variation of image intensity of backreflection to determine the type of local structure. Results improve the delineation of wall anomalies providing a deeper physiological perception of the vessel wall conditions. Achievements could be also transferred to other clinical scenarios: carotid arteries, aorto-iliac or ilio-femoral sections, intracranial, etc.

  2. Evaluating automatic attentional capture by self-relevant information.

    PubMed

    Ocampo, Brenda; Kahan, Todd A

    2016-01-01

    Our everyday decisions and memories are inadvertently influenced by self-relevant information. For example, we are faster and more accurate at making perceptual judgments about stimuli associated with ourselves, such as our own face or name, as compared with familiar non-self-relevant stimuli. Humphreys and Sui propose a "self-attention network" to account for these effects, wherein self-relevant stimuli automatically capture our attention and subsequently enhance the perceptual processing of self-relevant information. We propose that the masked priming paradigm and continuous flash suppression represent two ways to experimentally examine these controversial claims.

  3. Example based lesion segmentation

    NASA Astrophysics Data System (ADS)

    Roy, Snehashis; He, Qing; Carass, Aaron; Jog, Amod; Cuzzocreo, Jennifer L.; Reich, Daniel S.; Prince, Jerry; Pham, Dzung

    2014-03-01

    Automatic and accurate detection of white matter lesions is a significant step toward understanding the progression of many diseases, like Alzheimer's disease or multiple sclerosis. Multi-modal MR images are often used to segment T2 white matter lesions that can represent regions of demyelination or ischemia. Some automated lesion segmentation methods describe the lesion intensities using generative models, and then classify the lesions with some combination of heuristics and cost minimization. In contrast, we propose a patch-based method, in which lesions are found using examples from an atlas containing multi-modal MR images and corresponding manual delineations of lesions. Patches from subject MR images are matched to patches from the atlas and lesion memberships are found based on patch similarity weights. We experiment on 43 subjects with MS, whose scans show various levels of lesion-load. We demonstrate significant improvement in Dice coefficient and total lesion volume compared to a state of the art model-based lesion segmentation method, indicating more accurate delineation of lesions.

  4. Evaluating road delineation practices in Michigan : final report.

    DOT National Transportation Integrated Search

    2017-03-31

    This research was conducted to assist the Michigan Department of Transportation in assessing the States : current delineation program. Opus International Consultants (Opus) and Western Michigan University : (WMU) were retained to undertake this re...

  5. Accurate high-speed liquid handling of very small biological samples.

    PubMed

    Schober, A; Günther, R; Schwienhorst, A; Döring, M; Lindemann, B F

    1993-08-01

    Molecular biology techniques require the accurate pipetting of buffers and solutions with volumes in the microliter range. Traditionally, hand-held pipetting devices are used to fulfill these requirements, but many laboratories have also introduced robotic workstations for the handling of liquids. Piston-operated pumps are commonly used in manually as well as automatically operated pipettors. These devices cannot meet the demands for extremely accurate pipetting of very small volumes at the high speed that would be necessary for certain applications (e.g., in sequencing projects with high throughput). In this paper we describe a technique for the accurate microdispensation of biochemically relevant solutions and suspensions with the aid of a piezoelectric transducer. It is suitable for liquids of a viscosity between 0.5 and 500 milliPascals. The obtainable drop sizes range from 5 picoliters to a few nanoliters with up to 10,000 drops per second. Liquids can be dispensed in single or accumulated drops to handle a wide volume range. The system proved to be excellently suitable for the handling of biological samples. It did not show any detectable negative impact on the biological function of dissolved or suspended molecules or particles.

  6. Automatic Extraction of Urban Built-Up Area Based on Object-Oriented Method and Remote Sensing Data

    NASA Astrophysics Data System (ADS)

    Li, L.; Zhou, H.; Wen, Q.; Chen, T.; Guan, F.; Ren, B.; Yu, H.; Wang, Z.

    2018-04-01

    Built-up area marks the use of city construction land in the different periods of the development, the accurate extraction is the key to the studies of the changes of urban expansion. This paper studies the technology of automatic extraction of urban built-up area based on object-oriented method and remote sensing data, and realizes the automatic extraction of the main built-up area of the city, which saves the manpower cost greatly. First, the extraction of construction land based on object-oriented method, the main technical steps include: (1) Multi-resolution segmentation; (2) Feature Construction and Selection; (3) Information Extraction of Construction Land Based on Rule Set, The characteristic parameters used in the rule set mainly include the mean of the red band (Mean R), Normalized Difference Vegetation Index (NDVI), Ratio of residential index (RRI), Blue band mean (Mean B), Through the combination of the above characteristic parameters, the construction site information can be extracted. Based on the degree of adaptability, distance and area of the object domain, the urban built-up area can be quickly and accurately defined from the construction land information without depending on other data and expert knowledge to achieve the automatic extraction of the urban built-up area. In this paper, Beijing city as an experimental area for the technical methods of the experiment, the results show that: the city built-up area to achieve automatic extraction, boundary accuracy of 2359.65 m to meet the requirements. The automatic extraction of urban built-up area has strong practicality and can be applied to the monitoring of the change of the main built-up area of city.

  7. Stratiform/convective rain delineation for TRMM microwave imager

    NASA Astrophysics Data System (ADS)

    Islam, Tanvir; Srivastava, Prashant K.; Dai, Qiang; Gupta, Manika; Wan Jaafar, Wan Zurina

    2015-10-01

    This article investigates the potential for using machine learning algorithms to delineate stratiform/convective (S/C) rain regimes for passive microwave imager taking calibrated brightness temperatures as only spectral parameters. The algorithms have been implemented for the Tropical Rainfall Measuring Mission (TRMM) microwave imager (TMI), and calibrated as well as validated taking the Precipitation Radar (PR) S/C information as the target class variables. Two different algorithms are particularly explored for the delineation. The first one is metaheuristic adaptive boosting algorithm that includes the real, gentle, and modest versions of the AdaBoost. The second one is the classical linear discriminant analysis that includes the Fisher's and penalized versions of the linear discriminant analysis. Furthermore, prior to the development of the delineation algorithms, a feature selection analysis has been conducted for a total of 85 features, which contains the combinations of brightness temperatures from 10 GHz to 85 GHz and some derived indexes, such as scattering index, polarization corrected temperature, and polarization difference with the help of mutual information aided minimal redundancy maximal relevance criterion (mRMR). It has been found that the polarization corrected temperature at 85 GHz and the features derived from the "addition" operator associated with the 85 GHz channels have good statistical dependency to the S/C target class variables. Further, it has been shown how the mRMR feature selection technique helps to reduce the number of features without deteriorating the results when applying through the machine learning algorithms. The proposed scheme is able to delineate the S/C rain regimes with reasonable accuracy. Based on the statistical validation experience from the validation period, the Matthews correlation coefficients are in the range of 0.60-0.70. Since, the proposed method does not rely on any a priori information, this makes it very

  8. Automated delineation and characterization of drumlins using a localized contour tree approach

    NASA Astrophysics Data System (ADS)

    Wang, Shujie; Wu, Qiusheng; Ward, Dylan

    2017-10-01

    Drumlins are ubiquitous landforms in previously glaciated regions, formed through a series of complex subglacial processes operating underneath the paleo-ice sheets. Accurate delineation and characterization of drumlins are essential for understanding the formation mechanism of drumlins as well as the flow behaviors and basal conditions of paleo-ice sheets. Automated mapping of drumlins is particularly important for examining the distribution patterns of drumlins across large spatial scales. This paper presents an automated vector-based approach to mapping drumlins from high-resolution light detection and ranging (LiDAR) data. The rationale is to extract a set of concentric contours by building localized contour trees and establishing topological relationships. This automated method can overcome the shortcomings of previously manual and automated methods for mapping drumlins, for instance, the azimuthal biases during the generation of shaded relief images. A case study was carried out over a portion of the New York Drumlin Field. Overall 1181 drumlins were identified from the LiDAR-derived DEM across the study region, which had been underestimated in previous literature. The delineation results were visually and statistically compared to the manual digitization results. The morphology of drumlins was characterized by quantifying the length, width, elongation ratio, height, area, and volume. Statistical and spatial analyses were conducted to examine the distribution pattern and spatial variability of drumlin size and form. The drumlins and the morphologic characteristics exhibit significant spatial clustering rather than randomly distributed patterns. The form of drumlins varies from ovoid to spindle shapes towards the downstream direction of paleo ice flows, along with the decrease in width, area, and volume. This observation is in line with previous studies, which may be explained by the variations in sediment thickness and/or the velocity increases of ice flows

  9. A novel imaging technique for fusion of high-quality immobilised MR images of the head and neck with CT scans for radiotherapy target delineation.

    PubMed

    Webster, G J; Kilgallon, J E; Ho, K F; Rowbottom, C G; Slevin, N J; Mackay, R I

    2009-06-01

    Uncertainty and inconsistency are observed in target volume delineation in the head and neck for radiotherapy treatment planning based only on CT imaging. Alternative modalities such as MRI have previously been incorporated into the delineation process to provide additional anatomical information. This work aims to improve on previous studies by combining good image quality with precise patient immobilisation in order to maintain patient position between scans. MR images were acquired using quadrature coils placed over the head and neck while the patient was immobilised in the treatment position using a five-point thermoplastic shell. The MR image and CT images were automatically fused in the Pinnacle treatment planning system using Syntegra software. Image quality, distortion and accuracy of the image registration using patient anatomy were evaluated. Image quality was found to be superior to that acquired using the body coil, while distortion was < 1.0 mm to a radius of 8.7 cm from the scan centre. Image registration accuracy was found to be 2.2 mm (+/- 0.9 mm) and < 3.0 degrees (n = 6). A novel MRI technique that combines good image quality with patient immobilization has been developed and is now in clinical use. The scan duration of approximately 15 min has been well tolerated by all patients.

  10. Automatic detection and agronomic characterization of olive groves using high-resolution imagery and LIDAR data

    NASA Astrophysics Data System (ADS)

    Caruso, T.; Rühl, J.; Sciortino, R.; Marra, F. P.; La Scalia, G.

    2014-10-01

    The Common Agricultural Policy of the European Union grants subsidies for olive production. Areas of intensified olive farming will be of major importance for the increasing demand for oil production of the next decades, and countries with a high ratio of intensively and super-intensively managed olive groves will be more competitive than others, since they are able to reduce production costs. It can be estimated that about 25-40% of the Sicilian oliviculture must be defined as "marginal". Modern olive cultivation systems, which permit the mechanization of pruning and harvest operations, are limited. Agronomists, landscape planners, policy decision-makers and other professionals have a growing need for accurate and cost-effective information on land use in general and agronomic parameters in the particular. The availability of high spatial resolution imagery has enabled researchers to propose analysis tools on agricultural parcel and tree level. In our study, we test the performance of WorldView-2 imagery relative to the detection of olive groves and the delineation of olive tree crowns, using an object-oriented approach of image classification in combined use with LIDAR data. We selected two sites, which differ in their environmental conditions and in their agronomic parameters of olive grove cultivation. The main advantage of the proposed methodology is the low necessary quantity of data input and its automatibility. However, it should be applied in other study areas to test if the good results of accuracy assessment can be confirmed. Data extracted by the proposed methodology can be used as input data for decision-making support systems for olive grove management.

  11. Delineating Concept Meanings: The Case of Terrorism.

    ERIC Educational Resources Information Center

    Kleg, Milton; Mahlios, Marc

    1990-01-01

    Presents a teacher-initiated model for reaching class consensus on the meaning of confusing or interchangeable concepts in social studies classrooms. Illustrates the model by delineating terrorism. Shows procedural steps that involve students in self and small group interviews where definitions are clarified until consensus is reached. Suggests…

  12. Polymer delineation system. [Patent application: traffic lane lines

    DOEpatents

    Woolman, S.; Steinberg, M.

    1975-06-24

    A delineation system (traffic lane lines) for highways is described in which polymerizable substances are applied to existing or newly prepared highway pavements. The substances would contain a suitable pigment and may incorporate reflective elements.

  13. Automatic joint alignment measurements in pre- and post-operative long leg standing radiographs.

    PubMed

    Goossen, A; Weber, G M; Dries, S P M

    2012-01-01

    For diagnosis or treatment assessment of knee joint osteoarthritis it is required to measure bone morphometry from radiographic images. We propose a method for automatic measurement of joint alignment from pre-operative as well as post-operative radiographs. In a two step approach we first detect and segment any implants or other artificial objects within the image. We exploit physical characteristics and avoid prior shape information to cope with the vast amount of implant types. Subsequently, we exploit the implant delineations to adapt the initialization and adaptation phase of a dedicated bone segmentation scheme using deformable template models. Implant and bone contours are fused to derive the final joint segmentation and thus the alignment measurements. We evaluated our method on clinical long leg radiographs and compared both the initialization rate, corresponding to the number of images successfully processed by the proposed algorithm, and the accuracy of the alignment measurement. Ground truth has been generated by an experienced orthopedic surgeon. For comparison a second reader reevaluated the measurements. Experiments on two sets of 70 and 120 digital radiographs show that 92% of the joints could be processed automatically and the derived measurements of the automatic method are comparable to a human reader for pre-operative as well as post-operative images with a typical error of 0.7° and correlations of r = 0.82 to r = 0.99 with the ground truth. The proposed method allows deriving objective measures of joint alignment from clinical radiographs. Its accuracy and precision are on par with a human reader for all evaluated measurements.

  14. Automated particle correspondence and accurate tilt-axis detection in tilted-image pairs

    DOE PAGES

    Shatsky, Maxim; Arbelaez, Pablo; Han, Bong-Gyoon; ...

    2014-07-01

    Tilted electron microscope images are routinely collected for an ab initio structure reconstruction as a part of the Random Conical Tilt (RCT) or Orthogonal Tilt Reconstruction (OTR) methods, as well as for various applications using the "free-hand" procedure. These procedures all require identification of particle pairs in two corresponding images as well as accurate estimation of the tilt-axis used to rotate the electron microscope (EM) grid. Here we present a computational approach, PCT (particle correspondence from tilted pairs), based on tilt-invariant context and projection matching that addresses both problems. The method benefits from treating the two problems as a singlemore » optimization task. It automatically finds corresponding particle pairs and accurately computes tilt-axis direction even in the cases when EM grid is not perfectly planar.« less

  15. Statistical Validation of Automatic Methods for Hippocampus Segmentation in MR Images of Epileptic Patients

    PubMed Central

    Hosseini, Mohammad-Parsa; Nazem-Zadeh, Mohammad R.; Pompili, Dario; Soltanian-Zadeh, Hamid

    2015-01-01

    Hippocampus segmentation is a key step in the evaluation of mesial Temporal Lobe Epilepsy (mTLE) by MR images. Several automated segmentation methods have been introduced for medical image segmentation. Because of multiple edges, missing boundaries, and shape changing along its longitudinal axis, manual outlining still remains the benchmark for hippocampus segmentation, which however, is impractical for large datasets due to time constraints. In this study, four automatic methods, namely FreeSurfer, Hammer, Automatic Brain Structure Segmentation (ABSS), and LocalInfo segmentation, are evaluated to find the most accurate and applicable method that resembles the bench-mark of hippocampus. Results from these four methods are compared against those obtained using manual segmentation for T1-weighted images of 157 symptomatic mTLE patients. For performance evaluation of automatic segmentation, Dice coefficient, Hausdorff distance, Precision, and Root Mean Square (RMS) distance are extracted and compared. Among these four automated methods, ABSS generates the most accurate results and the reproducibility is more similar to expert manual outlining by statistical validation. By considering p-value<0.05, the results of performance measurement for ABSS reveal that, Dice is 4%, 13%, and 17% higher, Hausdorff is 23%, 87%, and 70% lower, precision is 5%, -5%, and 12% higher, and RMS is 19%, 62%, and 65% lower compared to LocalInfo, FreeSurfer, and Hammer, respectively. PMID:25571043

  16. Automatic detection and severity measurement of eczema using image processing.

    PubMed

    Alam, Md Nafiul; Munia, Tamanna Tabassum Khan; Tavakolian, Kouhyar; Vasefi, Fartash; MacKinnon, Nick; Fazel-Rezai, Reza

    2016-08-01

    Chronic skin diseases like eczema may lead to severe health and financial consequences for patients if not detected and controlled early. Early measurement of disease severity, combined with a recommendation for skin protection and use of appropriate medication can prevent the disease from worsening. Current diagnosis can be costly and time-consuming. In this paper, an automatic eczema detection and severity measurement model are presented using modern image processing and computer algorithm. The system can successfully detect regions of eczema and classify the identified region as mild or severe based on image color and texture feature. Then the model automatically measures skin parameters used in the most common assessment tool called "Eczema Area and Severity Index (EASI)," by computing eczema affected area score, eczema intensity score, and body region score of eczema allowing both patients and physicians to accurately assess the affected skin.

  17. Use of Data Layering to Address Changes in Nitrogen Management Zone Delineation

    Treesearch

    D.W. Franzen; T. Nanna

    2006-01-01

    Use of N management zones appears to be a practical method of revealing dominant patterns of residual soil nitrate in North Dakota crop fields, where fall soil nitrate sampling is a common N management tool. Delineation of zone boundaries to date has been a largely subjective process. A weighted, classified method of delineating nitrogen management zones is presented....

  18. Appearance Constrained Semi-Automatic Segmentation from DCE-MRI is Reproducible and Feasible for Breast Cancer Radiomics: A Feasibility Study.

    PubMed

    Veeraraghavan, Harini; Dashevsky, Brittany Z; Onishi, Natsuko; Sadinski, Meredith; Morris, Elizabeth; Deasy, Joseph O; Sutton, Elizabeth J

    2018-03-19

    We present a segmentation approach that combines GrowCut (GC) with cancer-specific multi-parametric Gaussian Mixture Model (GCGMM) to produce accurate and reproducible segmentations. We evaluated GCGMM using a retrospectively collected 75 invasive ductal carcinoma with ERPR+ HER2- (n = 15), triple negative (TN) (n = 9), and ER-HER2+ (n = 57) cancers with variable presentation (mass and non-mass enhancement) and background parenchymal enhancement (mild and marked). Expert delineated manual contours were used to assess the segmentation performance using Dice coefficient (DSC), mean surface distance (mSD), Hausdorff distance, and volume ratio (VR). GCGMM segmentations were significantly more accurate than GrowCut (GC) and fuzzy c-means clustering (FCM). GCGMM's segmentations and the texture features computed from those segmentations were the most reproducible compared with manual delineations and other analyzed segmentation methods. Finally, random forest (RF) classifier trained with leave-one-out cross-validation using features extracted from GCGMM segmentation resulted in the best accuracy for ER-HER2+ vs. ERPR+/TN (GCGMM 0.95, expert 0.95, GC 0.90, FCM 0.92) and for ERPR + HER2- vs. TN (GCGMM 0.92, expert 0.91, GC 0.77, FCM 0.83).

  19. A Noise-Assisted Data Analysis Method for Automatic EOG-Based Sleep Stage Classification Using Ensemble Learning.

    PubMed

    Olesen, Alexander Neergaard; Christensen, Julie A E; Sorensen, Helge B D; Jennum, Poul J

    2016-08-01

    Reducing the number of recording modalities for sleep staging research can benefit both researchers and patients, under the condition that they provide as accurate results as conventional systems. This paper investigates the possibility of exploiting the multisource nature of the electrooculography (EOG) signals by presenting a method for automatic sleep staging using the complete ensemble empirical mode decomposition with adaptive noise algorithm, and a random forest classifier. It achieves a high overall accuracy of 82% and a Cohen's kappa of 0.74 indicating substantial agreement between automatic and manual scoring.

  20. Automatic physical inference with information maximizing neural networks

    NASA Astrophysics Data System (ADS)

    Charnock, Tom; Lavaux, Guilhem; Wandelt, Benjamin D.

    2018-04-01

    Compressing large data sets to a manageable number of summaries that are informative about the underlying parameters vastly simplifies both frequentist and Bayesian inference. When only simulations are available, these summaries are typically chosen heuristically, so they may inadvertently miss important information. We introduce a simulation-based machine learning technique that trains artificial neural networks to find nonlinear functionals of data that maximize Fisher information: information maximizing neural networks (IMNNs). In test cases where the posterior can be derived exactly, likelihood-free inference based on automatically derived IMNN summaries produces nearly exact posteriors, showing that these summaries are good approximations to sufficient statistics. In a series of numerical examples of increasing complexity and astrophysical relevance we show that IMNNs are robustly capable of automatically finding optimal, nonlinear summaries of the data even in cases where linear compression fails: inferring the variance of Gaussian signal in the presence of noise, inferring cosmological parameters from mock simulations of the Lyman-α forest in quasar spectra, and inferring frequency-domain parameters from LISA-like detections of gravitational waveforms. In this final case, the IMNN summary outperforms linear data compression by avoiding the introduction of spurious likelihood maxima. We anticipate that the automatic physical inference method described in this paper will be essential to obtain both accurate and precise cosmological parameter estimates from complex and large astronomical data sets, including those from LSST and Euclid.

  1. Fully automatic segmentation of white matter hyperintensities in MR images of the elderly.

    PubMed

    Admiraal-Behloul, F; van den Heuvel, D M J; Olofsen, H; van Osch, M J P; van der Grond, J; van Buchem, M A; Reiber, J H C

    2005-11-15

    The role of quantitative image analysis in large clinical trials is continuously increasing. Several methods are available for performing white matter hyperintensity (WMH) volume quantification. They vary in the amount of the human interaction involved. In this paper, we describe a fully automatic segmentation that was used to quantify WMHs in a large clinical trial on elderly subjects. Our segmentation method combines information from 3 different MR images: proton density (PD), T2-weighted and fluid-attenuated inversion recovery (FLAIR) images; our method uses an established artificial intelligent technique (fuzzy inference system) and does not require extensive computations. The reproducibility of the segmentation was evaluated in 9 patients who underwent scan-rescan with repositioning; an inter-class correlation coefficient (ICC) of 0.91 was obtained. The effect of differences in image resolution was tested in 44 patients, scanned with 6- and 3-mm slice thickness FLAIR images; we obtained an ICC value of 0.99. The accuracy of the segmentation was evaluated on 100 patients for whom manual delineation of WMHs was available; the obtained ICC was 0.98 and the similarity index was 0.75. Besides the fact that the approach demonstrated very high volumetric and spatial agreement with expert delineation, the software did not require more than 2 min per patient (from loading the images to saving the results) on a Pentium-4 processor (512 MB RAM).

  2. Automatic segmentation of white matter hyperintensities robust to multicentre acquisition and pathological variability

    NASA Astrophysics Data System (ADS)

    Samaille, T.; Colliot, O.; Cuingnet, R.; Jouvent, E.; Chabriat, H.; Dormont, D.; Chupin, M.

    2012-02-01

    White matter hyperintensities (WMH), commonly seen on FLAIR images in elderly people, are a risk factor for dementia onset and have been associated with motor and cognitive deficits. We present here a method to fully automatically segment WMH from T1 and FLAIR images. Iterative steps of non linear diffusion followed by watershed segmentation were applied on FLAIR images until convergence. Diffusivity function and associated contrast parameter were carefully designed to adapt to WMH segmentation. It resulted in piecewise constant images with enhanced contrast between lesions and surrounding tissues. Selection of WMH areas was based on two characteristics: 1) a threshold automatically computed for intensity selection, 2) main location of areas in white matter. False positive areas were finally removed based on their proximity with cerebrospinal fluid/grey matter interface. Evaluation was performed on 67 patients: 24 with amnestic mild cognitive impairment (MCI), from five different centres, and 43 with Cerebral Autosomal Dominant Arteriopathy with Subcortical Infarcts and Leukoaraiosis (CADASIL) acquired in a single centre. Results showed excellent volume agreement with manual delineation (Pearson coefficient: r=0.97, p<0.001) and substantial spatial correspondence (Similarity Index: 72%+/-16%). Our method appeared robust to acquisition differences across the centres as well as to pathological variability.

  3. Automatic segmentation of stereoelectroencephalography (SEEG) electrodes post-implantation considering bending.

    PubMed

    Granados, Alejandro; Vakharia, Vejay; Rodionov, Roman; Schweiger, Martin; Vos, Sjoerd B; O'Keeffe, Aidan G; Li, Kuo; Wu, Chengyuan; Miserocchi, Anna; McEvoy, Andrew W; Clarkson, Matthew J; Duncan, John S; Sparks, Rachel; Ourselin, Sébastien

    2018-06-01

    The accurate and automatic localisation of SEEG electrodes is crucial for determining the location of epileptic seizure onset. We propose an algorithm for the automatic segmentation of electrode bolts and contacts that accounts for electrode bending in relation to regional brain anatomy. Co-registered post-implantation CT, pre-implantation MRI, and brain parcellation images are used to create regions of interest to automatically segment bolts and contacts. Contact search strategy is based on the direction of the bolt with distance and angle constraints, in addition to post-processing steps that assign remaining contacts and predict contact position. We measured the accuracy of contact position, bolt angle, and anatomical region at the tip of the electrode in 23 post-SEEG cases comprising two different surgical approaches when placing a guiding stylet close to and far from target point. Local and global bending are computed when modelling electrodes as elastic rods. Our approach executed on average in 36.17 s with a sensitivity of 98.81% and a positive predictive value (PPV) of 95.01%. Compared to manual segmentation, the position of contacts had a mean absolute error of 0.38 mm and the mean bolt angle difference of [Formula: see text] resulted in a mean displacement error of 0.68 mm at the tip of the electrode. Anatomical regions at the tip of the electrode were in strong concordance with those selected manually by neurosurgeons, [Formula: see text], with average distance between regions of 0.82 mm when in disagreement. Our approach performed equally in two surgical approaches regardless of the amount of electrode bending. We present a method robust to electrode bending that can accurately segment contact positions and bolt orientation. The techniques presented in this paper will allow further characterisation of bending within different brain regions.

  4. Identification of mycobacterium tuberculosis in sputum smear slide using automatic scanning microscope

    NASA Astrophysics Data System (ADS)

    Rulaningtyas, Riries; Suksmono, Andriyan B.; Mengko, Tati L. R.; Saptawati, Putri

    2015-04-01

    Sputum smear observation has an important role in tuberculosis (TB) disease diagnosis, because it needs accurate identification to avoid high errors diagnosis. In development countries, sputum smear slide observation is commonly done with conventional light microscope from Ziehl-Neelsen stained tissue and it doesn't need high cost to maintain the microscope. The clinicians do manual screening process for sputum smear slide which is time consuming and needs highly training to detect the presence of TB bacilli (mycobacterium tuberculosis) accurately, especially for negative slide and slide with less number of TB bacilli. For helping the clinicians, we propose automatic scanning microscope with automatic identification of TB bacilli. The designed system modified the field movement of light microscope with stepper motor which was controlled by microcontroller. Every sputum smear field was captured by camera. After that some image processing techniques were done for the sputum smear images. The color threshold was used for background subtraction with hue canal in HSV color space. Sobel edge detection algorithm was used for TB bacilli image segmentation. We used feature extraction based on shape for bacilli analyzing and then neural network classified TB bacilli or not. The results indicated identification of TB bacilli that we have done worked well and detected TB bacilli accurately in sputum smear slide with normal staining, but not worked well in over staining and less staining tissue slide. However, overall the designed system can help the clinicians in sputum smear observation becomes more easily.

  5. Planning Evaluation of C-Arm Cone Beam CT Angiography for Target Delineation in Stereotactic Radiation Surgery of Brain Arteriovenous Malformations

    DOE Office of Scientific and Technical Information (OSTI.GOV)

    Kang, Jun; Department of Radiation Oncology and Molecular Radiation Sciences, Johns Hopkins University School of Medicine, Baltimore, Maryland; Huang, Judy

    Purpose: Stereotactic radiation surgery (SRS) is one of the therapeutic modalities currently available to treat cerebral arteriovenous malformations (AVM). Conventionally, magnetic resonance imaging (MRI) and MR angiography (MRA) and digital subtraction angiography (DSA) are used in combination to identify the target volume for SRS treatment. The purpose of this study was to evaluate the use of C-arm cone beam computed tomography (CBCT) in the treatment planning of SRS for cerebral AVMs. Methods and Materials: Sixteen consecutive patients treated for brain AVMs at our institution were included in this retrospective study. Prior to treatment, all patients underwent MRA, DSA, and C-arm CBCT.more » All images were coregistered using the GammaPlan planning system. AVM regions were delineated independently by 2 physicians using either C-arm CBCT or MRA, resulting in 2 volumes: a CBCT volume (VCBCT) and an MRA volume (V{sub MRA}). SRS plans were generated based on the delineated regions. Results: The average volume of treatment targets delineated using C-arm CBCT and MRA were similar, 6.40 cm{sup 3} and 6.98 cm{sup 3}, respectively (P=.82). However, significant regions of nonoverlap existed. On average, the overlap of the MRA with the C-arm CBCT was only 52.8% of the total volume. In most cases, radiation plans based on V{sub MRA} did not provide adequate dose to the region identified on C-arm CBCT; the mean minimum dose to V{sub CBCT} was 29.5%, whereas the intended goal was 45% (P<.001). The mean volume of normal brain receiving 12 Gy or more in C-arm CBCT-based plans was not greater than in the MRA-based plans. Conclusions: Use of C-arm CBCT images significantly alters the delineated regions of AVMs for SRS planning, compared to that of MRA/MRI images. CT-based planning can be accomplished without increasing the dose to normal brain and may represent a more accurate definition of the nidus, increasing the chances for successful obliteration.« less

  6. Segmentation of Nerve Bundles and Ganglia in Spine MRI Using Particle Filters

    PubMed Central

    Dalca, Adrian; Danagoulian, Giovanna; Kikinis, Ron; Schmidt, Ehud; Golland, Polina

    2011-01-01

    Automatic segmentation of spinal nerve bundles that originate within the dural sac and exit the spinal canal is important for diagnosis and surgical planning. The variability in intensity, contrast, shape and direction of nerves seen in high resolution myelographic MR images makes segmentation a challenging task. In this paper, we present an automatic tracking method for nerve segmentation based on particle filters. We develop a novel approach to particle representation and dynamics, based on Bézier splines. Moreover, we introduce a robust image likelihood model that enables delineation of nerve bundles and ganglia from the surrounding anatomical structures. We demonstrate accurate and fast nerve tracking and compare it to expert manual segmentation. PMID:22003741

  7. Segmentation of nerve bundles and ganglia in spine MRI using particle filters.

    PubMed

    Dalca, Adrian; Danagoulian, Giovanna; Kikinis, Ron; Schmidt, Ehud; Golland, Polina

    2011-01-01

    Automatic segmentation of spinal nerve bundles that originate within the dural sac and exit the spinal canal is important for diagnosis and surgical planning. The variability in intensity, contrast, shape and direction of nerves seen in high resolution myelographic MR images makes segmentation a challenging task. In this paper, we present an automatic tracking method for nerve segmentation based on particle filters. We develop a novel approach to particle representation and dynamics, based on Bézier splines. Moreover, we introduce a robust image likelihood model that enables delineation of nerve bundles and ganglia from the surrounding anatomical structures. We demonstrate accurate and fast nerve tracking and compare it to expert manual segmentation.

  8. Automatic quantitative analysis of in-stent restenosis using FD-OCT in vivo intra-arterial imaging.

    PubMed

    Mandelias, Kostas; Tsantis, Stavros; Spiliopoulos, Stavros; Katsakiori, Paraskevi F; Karnabatidis, Dimitris; Nikiforidis, George C; Kagadis, George C

    2013-06-01

    A new segmentation technique is implemented for automatic lumen area extraction and stent strut detection in intravascular optical coherence tomography (OCT) images for the purpose of quantitative analysis of in-stent restenosis (ISR). In addition, a user-friendly graphical user interface (GUI) is developed based on the employed algorithm toward clinical use. Four clinical datasets of frequency-domain OCT scans of the human femoral artery were analyzed. First, a segmentation method based on fuzzy C means (FCM) clustering and wavelet transform (WT) was applied toward inner luminal contour extraction. Subsequently, stent strut positions were detected by utilizing metrics derived from the local maxima of the wavelet transform into the FCM membership function. The inner lumen contour and the position of stent strut were extracted with high precision. Compared to manual segmentation by an expert physician, the automatic lumen contour delineation had an average overlap value of 0.917 ± 0.065 for all OCT images included in the study. The strut detection procedure achieved an overall accuracy of 93.80% and successfully identified 9.57 ± 0.5 struts for every OCT image. Processing time was confined to approximately 2.5 s per OCT frame. A new fast and robust automatic segmentation technique combining FCM and WT for lumen border extraction and strut detection in intravascular OCT images was designed and implemented. The proposed algorithm integrated in a GUI represents a step forward toward the employment of automated quantitative analysis of ISR in clinical practice.

  9. A direct morphometric comparison of five labeling protocols for multi-atlas driven automatic segmentation of the hippocampus in Alzheimer's disease.

    PubMed

    Nestor, Sean M; Gibson, Erin; Gao, Fu-Qiang; Kiss, Alex; Black, Sandra E

    2013-02-01

    Hippocampal volumetry derived from structural MRI is increasingly used to delineate regions of interest for functional measurements, assess efficacy in therapeutic trials of Alzheimer's disease (AD) and has been endorsed by the new AD diagnostic guidelines as a radiological marker of disease progression. Unfortunately, morphological heterogeneity in AD can prevent accurate demarcation of the hippocampus. Recent developments in automated volumetry commonly use multi-template fusion driven by expert manual labels, enabling highly accurate and reproducible segmentation in disease and healthy subjects. However, there are several protocols to define the hippocampus anatomically in vivo, and the method used to generate atlases may impact automatic accuracy and sensitivity - particularly in pathologically heterogeneous samples. Here we report a fully automated segmentation technique that provides a robust platform to directly evaluate both technical and biomarker performance in AD among anatomically unique labeling protocols. For the first time we test head-to-head the performance of five common hippocampal labeling protocols for multi-atlas based segmentation, using both the Sunnybrook Longitudinal Dementia Study and the entire Alzheimer's Disease Neuroimaging Initiative 1 (ADNI-1) baseline and 24-month dataset. We based these atlas libraries on the protocols of (Haller et al., 1997; Killiany et al., 1993; Malykhin et al., 2007; Pantel et al., 2000; Pruessner et al., 2000), and a single operator performed all manual tracings to generate de facto "ground truth" labels. All methods distinguished between normal elders, mild cognitive impairment (MCI), and AD in the expected directions, and showed comparable correlations with measures of episodic memory performance. Only more inclusive protocols distinguished between stable MCI and MCI-to-AD converters, and had slightly better associations with episodic memory. Moreover, we demonstrate that protocols including more posterior

  10. Improving work-zone delineation on limited access highways.

    DOT National Transportation Integrated Search

    1989-01-01

    The purpose of this study was to investigate vehicle guidance through work zones by evaluating the effectiveness of two primary components of traffic control relative to delineation. First, a comparison of the steady-burn lights presently used on top...

  11. Validation of semi-automatic segmentation of the left atrium

    NASA Astrophysics Data System (ADS)

    Rettmann, M. E.; Holmes, D. R., III; Camp, J. J.; Packer, D. L.; Robb, R. A.

    2008-03-01

    and between raters, and accurate when compared to the truth model. Finally, we generated a visualization to assess the spatial variability in the segmentation errors between the semi-automatic approach and the truth model. The visualization demonstrates the highest errors occur at the boundaries between the left atium and pulmonary veins as well as the left atrium and left atrial appendage. In conclusion, we describe a semi-automatic approach for left atrial segmentation that demonstrates repeatability and accuracy, with the advantage of significant time reduction in user interaction time.

  12. Evaluation of curve delineation signs on rural highways.

    DOT National Transportation Integrated Search

    1983-01-01

    The three post-mounted delineator systems currently used in the state of Virginia were tested at five sites for their effectiveness in controlling run-off-the-road accidents. The changes in speed and lateral placement noted with the systems in place ...

  13. SU-E-J-129: Atlas Development for Cardiac Automatic Contouring Using Multi-Atlas Segmentation

    DOE Office of Scientific and Technical Information (OSTI.GOV)

    Zhou, R; Yang, J; Pan, T

    Purpose: To develop a set of atlases for automatic contouring of cardiac structures to determine heart radiation dose and the associated toxicity. Methods: Six thoracic cancer patients with both contrast and non-contrast CT images were acquired for this study. Eight radiation oncologists manually and independently delineated cardiac contours on the non-contrast CT by referring to the fused contrast CT and following the RTOG 1106 atlas contouring guideline. Fifteen regions of interest (ROIs) were delineated, including heart, four chambers, four coronary arteries, pulmonary artery and vein, inferior and superior vena cava, and ascending and descending aorta. Individual expert contours were fusedmore » using the simultaneous truth and performance level estimation (STAPLE) algorithm for each ROI and each patient. The fused contours became atlases for an in-house multi-atlas segmentation. Using leave-one-out test, we generated auto-segmented contours for each ROI and each patient. The auto-segmented contours were compared with the fused contours using the Dice similarity coefficient (DSC) and the mean surface distance (MSD). Results: Inter-observer variability was not obvious for heart, chambers, and aorta but was large for other structures that were not clearly distinguishable on CT image. The average DSC between individual expert contours and the fused contours were less than 50% for coronary arteries and pulmonary vein, and the average MSD were greater than 4.0 mm. The largest MSD of expert contours deviating from the fused contours was 2.5 cm. The mean DSC and MSD of auto-segmented contours were within one standard deviation of expert contouring variability except the right coronary artery. The coronary arteries, vena cava, and pulmonary vein had DSC<70% and MSD>3.0 mm. Conclusion: A set of cardiac atlases was created for cardiac automatic contouring, the accuracy of which was comparable to the variability in expert contouring. However, substantial modification

  14. Automatic acquisition of motion trajectories: tracking hockey players

    NASA Astrophysics Data System (ADS)

    Okuma, Kenji; Little, James J.; Lowe, David

    2003-12-01

    Computer systems that have the capability of analyzing complex and dynamic scenes play an essential role in video annotation. Scenes can be complex in such a way that there are many cluttered objects with different colors, shapes and sizes, and can be dynamic with multiple interacting moving objects and a constantly changing background. In reality, there are many scenes that are complex, dynamic, and challenging enough for computers to describe. These scenes include games of sports, air traffic, car traffic, street intersections, and cloud transformations. Our research is about the challenge of inventing a descriptive computer system that analyzes scenes of hockey games where multiple moving players interact with each other on a constantly moving background due to camera motions. Ultimately, such a computer system should be able to acquire reliable data by extracting the players" motion as their trajectories, querying them by analyzing the descriptive information of data, and predict the motions of some hockey players based on the result of the query. Among these three major aspects of the system, we primarily focus on visual information of the scenes, that is, how to automatically acquire motion trajectories of hockey players from video. More accurately, we automatically analyze the hockey scenes by estimating parameters (i.e., pan, tilt, and zoom) of the broadcast cameras, tracking hockey players in those scenes, and constructing a visual description of the data by displaying trajectories of those players. Many technical problems in vision such as fast and unpredictable players' motions and rapid camera motions make our challenge worth tackling. To the best of our knowledge, there have not been any automatic video annotation systems for hockey developed in the past. Although there are many obstacles to overcome, our efforts and accomplishments would hopefully establish the infrastructure of the automatic hockey annotation system and become a milestone for

  15. Reduction of observer variation using matched CT-PET for lung cancer delineation: A three-dimensional analysis

    DOE Office of Scientific and Technical Information (OSTI.GOV)

    Steenbakkers, Roel; Duppen, Joop C.; Fitton, Isabelle

    2006-02-01

    Purpose: Target delineation using only CT information introduces large geometric uncertainties in radiotherapy for lung cancer. Therefore, a reduction of the delineation variability is needed. The impact of including a matched CT scan with 2-[{sup 18}F]fluoro-2-deoxy-D-glucose positron emission tomography (FDG-PET) and adaptation of the delineation protocol and software on target delineation in lung cancer was evaluated in an extensive multi-institutional setting and compared with the delineations using CT only. Methods and Materials: The study was separated into two phases. For the first phase, 11 radiation oncologists (observers) delineated the gross tumor volume (GTV), including the pathologic lymph nodes of 22more » lung cancer patients (Stages I-IIIB) on CT only. For the second phase (1 year later), the same radiation oncologists delineated the GTV of the same 22 patients on a matched CT-FDG-PET scan using an adapted delineation protocol and software (according to the results of the first phase). All delineated volumes were analyzed in detail. The observer variation was computed in three dimensions by measuring the distance between the median GTV surface and each individual GTV. The variation in distance of all radiation oncologists was expressed as a standard deviation. The observer variation was evaluated for anatomic regions (lung, mediastinum, chest wall, atelectasis, and lymph nodes) and interpretation regions (agreement and disagreement; i.e., >80% vs. <80% of the radiation oncologists delineated the same structure, respectively). All radiation oncologist-computer interactions were recorded and analyzed with a tool called 'Big Brother.' Results: The overall three-dimensional observer variation was reduced from 1.0 cm (SD) for the first phase (CT only) to 0.4 cm (SD) for the second phase (matched CT-FDG-PET). The largest reduction in the observer variation was seen in the atelectasis region (SD 1.9 cm reduced to 0.5 cm). The mean ratio between the

  16. A new accurate pill recognition system using imprint information

    NASA Astrophysics Data System (ADS)

    Chen, Zhiyuan; Kamata, Sei-ichiro

    2013-12-01

    Great achievements in modern medicine benefit human beings. Also, it has brought about an explosive growth of pharmaceuticals that current in the market. In daily life, pharmaceuticals sometimes confuse people when they are found unlabeled. In this paper, we propose an automatic pill recognition technique to solve this problem. It functions mainly based on the imprint feature of the pills, which is extracted by proposed MSWT (modified stroke width transform) and described by WSC (weighted shape context). Experiments show that our proposed pill recognition method can reach an accurate rate up to 92.03% within top 5 ranks when trying to classify more than 10 thousand query pill images into around 2000 categories.

  17. A new class of accurate, mesh-free hydrodynamic simulation methods

    NASA Astrophysics Data System (ADS)

    Hopkins, Philip F.

    2015-06-01

    We present two new Lagrangian methods for hydrodynamics, in a systematic comparison with moving-mesh, smoothed particle hydrodynamics (SPH), and stationary (non-moving) grid methods. The new methods are designed to simultaneously capture advantages of both SPH and grid-based/adaptive mesh refinement (AMR) schemes. They are based on a kernel discretization of the volume coupled to a high-order matrix gradient estimator and a Riemann solver acting over the volume `overlap'. We implement and test a parallel, second-order version of the method with self-gravity and cosmological integration, in the code GIZMO:1 this maintains exact mass, energy and momentum conservation; exhibits superior angular momentum conservation compared to all other methods we study; does not require `artificial diffusion' terms; and allows the fluid elements to move with the flow, so resolution is automatically adaptive. We consider a large suite of test problems, and find that on all problems the new methods appear competitive with moving-mesh schemes, with some advantages (particularly in angular momentum conservation), at the cost of enhanced noise. The new methods have many advantages versus SPH: proper convergence, good capturing of fluid-mixing instabilities, dramatically reduced `particle noise' and numerical viscosity, more accurate sub-sonic flow evolution, and sharp shock-capturing. Advantages versus non-moving meshes include: automatic adaptivity, dramatically reduced advection errors and numerical overmixing, velocity-independent errors, accurate coupling to gravity, good angular momentum conservation and elimination of `grid alignment' effects. We can, for example, follow hundreds of orbits of gaseous discs, while AMR and SPH methods break down in a few orbits. However, fixed meshes minimize `grid noise'. These differences are important for a range of astrophysical problems.

  18. Using aquatic invertebrates to delineate seasonal and temporary wetlands in the Prairie Pothole Region of North America

    USGS Publications Warehouse

    Euliss, Ned H.; Mushet, David M.; Johnson, Douglas H.

    2002-01-01

    Tillage can destroy or greatly disturb indicators of hydric soils and hydrophytic vegetation, making delineation of tilled wetlands difficult. The remains of aquatic invertebrates (e.g., shells, drought-resistant eggs, and trichopteran cases) are easily identifiable and persist in wetland substrates even when wetlands are dry. Additionally, these remains are not easily destroyed by mechanical tillage. To test the feasibility of using invertebrate remains to delineate wetlands, we used two methods to identify the wetland edge of ten seasonal and ten temporary wetlands, evenly divided between grassland and cropland landscapes. First, we identified the wetland edge using hydric soil and vegetation indicators along six evenly spaced transects in each wetland (our “standard” delineation). We then identified the wetland edge along the same transects using aquatic invertebrate remains as our indicator. In grassland landscapes, delineations of the wetland edge made using invertebrate remains were consistently at the same location or closer to the wetland center as the standard delineations for both seasonal and temporary wetlands. In cropland landscapes, however, many of our invertebrate delineations of seasonal and temporary wetlands were on the upland side of our standard delineations. We attribute the differences to movement of remains during tillage, increased maximum pool levels in cropland wetlands, and disturbance of hydric soils and plants. We found that the elevations of the wetland edge indicated by invertebrate remains were more consistent within a wetland than elevations determined by standard delineations. Aquatic invertebrate remains can be useful in delineating wetlands when other indicators have been destroyed or severely disturbed by tillage.

  19. Machine Beats Experts: Automatic Discovery of Skill Models for Data-Driven Online Course Refinement

    ERIC Educational Resources Information Center

    Matsuda, Noboru; Furukawa, Tadanobu; Bier, Norman; Faloutsos, Christos

    2015-01-01

    How can we automatically determine which skills must be mastered for the successful completion of an online course? Large-scale online courses (e.g., MOOCs) often contain a broad range of contents frequently intended to be a semester's worth of materials; this breadth often makes it difficult to articulate an accurate set of skills and knowledge…

  20. Full automatic fiducial marker detection on coil arrays for accurate instrumentation placement during MRI guided breast interventions

    NASA Astrophysics Data System (ADS)

    Filippatos, Konstantinos; Boehler, Tobias; Geisler, Benjamin; Zachmann, Harald; Twellmann, Thorsten

    2010-02-01

    With its high sensitivity, dynamic contrast-enhanced MR imaging (DCE-MRI) of the breast is today one of the first-line tools for early detection and diagnosis of breast cancer, particularly in the dense breast of young women. However, many relevant findings are very small or occult on targeted ultrasound images or mammography, so that MRI guided biopsy is the only option for a precise histological work-up [1]. State-of-the-art software tools for computer-aided diagnosis of breast cancer in DCE-MRI data offer also means for image-based planning of biopsy interventions. One step in the MRI guided biopsy workflow is the alignment of the patient position with the preoperative MR images. In these images, the location and orientation of the coil localization unit can be inferred from a number of fiducial markers, which for this purpose have to be manually or semi-automatically detected by the user. In this study, we propose a method for precise, full-automatic localization of fiducial markers, on which basis a virtual localization unit can be subsequently placed in the image volume for the purpose of determining the parameters for needle navigation. The method is based on adaptive thresholding for separating breast tissue from background followed by rigid registration of marker templates. In an evaluation of 25 clinical cases comprising 4 different commercial coil array models and 3 different MR imaging protocols, the method yielded a sensitivity of 0.96 at a false positive rate of 0.44 markers per case. The mean distance deviation between detected fiducial centers and ground truth information that was appointed from a radiologist was 0.94mm.

  1. A Comparative Review of North American Tundra Delineations

    NASA Technical Reports Server (NTRS)

    Silver, Kirk C.; Carroll, Mark

    2013-01-01

    Recent profound changes have been observed in the Arctic environment, including record low sea ice extents and high latitude greening. Studying the Arctic and how it is changing is an important element of climate change science. The Tundra, an ecoregion of the Arctic, is directly related to climate change due to its effects on the snow ice feedback mechanism and greenhouse gas cycling. Like all ecoregions, the Tundra border is shifting, yet studies and policies require clear delineation of boundaries. There are many options for ecoregion classification systems, as well as resources for creating custom maps. To help decision makers identify the best classification system possible, we present a review of North American Tundra ecoregion delineations and further explore the methodologies, purposes, limitations, and physical properties of five common ecoregion classification systems. We quantitatively compare the corresponding maps by area using a geographic information system.

  2. Magnetic Transfer Contrast Accurately Localizes Substantia Nigra Confirmed by Histology

    PubMed Central

    Bolding, Mark S.; Reid, Meredith A.; Avsar, Kathy B.; Roberts, Rosalinda C.; Gamlin, Paul D.; Gawne, Timothy J.; White, David M.; den Hollander, Jan A.; Lahti, Adrienne C.

    2012-01-01

    Background Magnetic Resonance Imaging (MRI) has multiple contrast mechanisms. Like various staining techniques in histology, each contrast type reveals different information about the structure of the brain. However, it is not always clear how structures visible in MRI correspond to structures previously identified by histology. The purpose of this study was to determine if magnetic transfer contrast (MTC) or T2 contrast MRI was better at delineating the substantia nigra. Methods MRI scans were acquired in-vivo from two non-human primates (NHPs). The NHPs were subsequently euthanized, perfused, and their brains sectioned for histological analyses. Each slice was photographed prior to sectioning. Each brain was sectioned into approximately 500, 40-micron sections, encompassing most of the cortex, midbrain, and dorsal parts of the hindbrain. Levels corresponding to anatomical MRI images were selected. From these, adjacent sections were stained using Kluver Barrera (myelin and cell bodies) or tyrosine hydroxylase (TH) (dopaminergic neurons) immunohistochemistry. The resulting images were coregistered to the block-face images using a moving least squares algorithm with similarity transformations. MR images were similarly coregistered to the block-face images, allowing the structures in the MRI to be identified with structures in the histological images. Results We found that hyperintense (light) areas in MTC images were coextensive with the SN as delineated histologically. The hypointense (dark) areas in T2-weighted images were not coextensive with the SN, but extended partially into the SN and partially into the cerebral peduncles. Conclusions MTC is a more accurate contrast mechanism than T2-weighting for localizing the SN in vivo. PMID:22981657

  3. Evaluation of catchment delineation methods for the medium-resolution National Hydrography Dataset

    USGS Publications Warehouse

    Johnston, Craig M.; Dewald, Thomas G.; Bondelid, Timothy R.; Worstell, Bruce B.; McKay, Lucinda D.; Rea, Alan; Moore, Richard B.; Goodall, Jonathan L.

    2009-01-01

    Different methods for determining catchments (incremental drainage areas) for stream segments of the medium-resolution (1:100,000-scale) National Hydrography Dataset (NHD) were evaluated by the U.S. Geological Survey (USGS), in cooperation with the U.S. Environmental Protection Agency (USEPA). The NHD is a comprehensive set of digital spatial data that contains information about surface-water features (such as lakes, ponds, streams, and rivers) of the United States. The need for NHD catchments was driven primarily by the goal to estimate NHD streamflow and velocity to support water-quality modeling. The application of catchments for this purpose also demonstrates the broader value of NHD catchments for supporting landscape characterization and analysis. Five catchment delineation methods were evaluated. Four of the methods use topographic information for the delineation of the NHD catchments. These methods include the Raster Seeding Method; two variants of a method first used in a USGS New England study-one used the Watershed Boundary Dataset (WBD) and the other did not-termed the 'New England Methods'; and the Outlet Matching Method. For these topographically based methods, the elevation data source was the 30-meter (m) resolution National Elevation Dataset (NED), as this was the highest resolution available for the conterminous United States and Hawaii. The fifth method evaluated, the Thiessen Polygon Method, uses distance to the nearest NHD stream segments to determine catchment boundaries. Catchments were generated using each method for NHD stream segments within six hydrologically and geographically distinct Subbasins to evaluate the applicability of the method across the United States. The five methods were evaluated by comparing the resulting catchments with the boundaries and the computed area measurements available from several verification datasets that were developed independently using manual methods. The results of the evaluation indicated that the two

  4. An approach for delineating drinking water wellhead protection areas at the Nile Delta, Egypt.

    PubMed

    Fadlelmawla, Amr A; Dawoud, Mohamed A

    2006-04-01

    In Egypt, production has a high priority. To this end protecting the quality of the groundwater, specifically when used for drinking water, and delineating protection areas around the drinking water wellheads for strict landuse restrictions is essential. The delineation methods are numerous; nonetheless, the uniqueness of the hydrogeological, institutional as well as social conditions in the Nile Delta region dictate a customized approach. The analysis of the hydrological conditions and land ownership at the Nile Delta indicates the need for an accurate methodology. On the other hand, attempting to calculate the wellhead protected areas around each of the drinking wells (more than 1500) requires data, human resources, and time that exceed the capabilities of the groundwater management agency. Accordingly, a combination of two methods (simplified variable shapes and numerical modeling) was adopted. Sensitivity analyses carried out using hypothetical modeling conditions have identified the pumping rate, clay thickness, hydraulic gradient, vertical conductivity of the clay, and the hydraulic conductivity as the most significant parameters in determining the dimensions of the wellhead protection areas (WHPAs). Tables of sets of WHPAs dimensions were calculated using synthetic modeling conditions representing the most common ranges of the significant parameters. Specific WHPA dimensions can be calculated by interpolation, utilizing the produced tables along with the operational and hydrogeological conditions for the well under consideration. In order to simplify the interpolation of the appropriate dimensions of the WHPAs from the calculated tables, an interactive computer program was written. The program accepts the real time data of the significant parameters as its input, and gives the appropriate WHPAs dimensions as its output.

  5. Automatic fluid dispenser

    NASA Technical Reports Server (NTRS)

    Sakellaris, P. C. (Inventor)

    1977-01-01

    Fluid automatically flows to individual dispensing units at predetermined times from a fluid supply and is available only for a predetermined interval of time after which an automatic control causes the fluid to drain from the individual dispensing units. Fluid deprivation continues until the beginning of a new cycle when the fluid is once again automatically made available at the individual dispensing units.

  6. On the feasibility of automatically selecting similar patients in highly individualized radiotherapy dose reconstruction for historic data of pediatric cancer survivors.

    PubMed

    Virgolin, Marco; van Dijk, Irma W E M; Wiersma, Jan; Ronckers, Cécile M; Witteveen, Cees; Bel, Arjan; Alderliesten, Tanja; Bosman, Peter A N

    2018-04-01

    The aim of this study is to establish the first step toward a novel and highly individualized three-dimensional (3D) dose distribution reconstruction method, based on CT scans and organ delineations of recently treated patients. Specifically, the feasibility of automatically selecting the CT scan of a recently treated childhood cancer patient who is similar to a given historically treated child who suffered from Wilms' tumor is assessed. A cohort of 37 recently treated children between 2- and 6-yr old are considered. Five potential notions of ground-truth similarity are proposed, each focusing on different anatomical aspects. These notions are automatically computed from CT scans of the abdomen and 3D organ delineations (liver, spleen, spinal cord, external body contour). The first is based on deformable image registration, the second on the Dice similarity coefficient, the third on the Hausdorff distance, the fourth on pairwise organ distances, and the last is computed by means of the overlap volume histogram. The relationship between typically available features of historically treated patients and the proposed ground-truth notions of similarity is studied by adopting state-of-the-art machine learning techniques, including random forest. Also, the feasibility of automatically selecting the most similar patient is assessed by comparing ground-truth rankings of similarity with predicted rankings. Similarities (mainly) based on the external abdomen shape and on the pairwise organ distances are highly correlated (Pearson r p ≥ 0.70) and are successfully modeled with random forests based on historically recorded features (pseudo-R 2 ≥ 0.69). In contrast, similarities based on the shape of internal organs cannot be modeled. For the similarities that random forest can reliably model, an estimation of feature relevance indicates that abdominal diameters and weight are the most important. Experiments on automatically selecting similar patients lead to coarse, yet

  7. A Direct Morphometric Comparison of Five Labeling Protocols for Multi-Atlas Driven Automatic Segmentation of the Hippocampus in Alzheimer’s Disease

    PubMed Central

    Nestor, Sean M.; Gibson, Erin; Gao, Fu-Qiang; Kiss, Alex; Black, Sandra E.

    2012-01-01

    Hippocampal volumetry derived from structural MRI is increasingly used to delineate regions of interest for functional measurements, assess efficacy in therapeutic trials of Alzheimer’s disease (AD) and has been endorsed by the new AD diagnostic guidelines as a radiological marker of disease progression. Unfortunately, morphological heterogeneity in AD can prevent accurate demarcation of the hippocampus. Recent developments in automated volumetry commonly use multitemplate fusion driven by expert manual labels, enabling highly accurate and reproducible segmentation in disease and healthy subjects. However, there are several protocols to define the hippocampus anatomically in vivo, and the method used to generate atlases may impact automatic accuracy and sensitivity – particularly in pathologically heterogeneous samples. Here we report a fully automated segmentation technique that provides a robust platform to directly evaluate both technical and biomarker performance in AD among anatomically unique labeling protocols. For the first time we test head-to-head the performance of five common hippocampal labeling protocols for multi-atlas based segmentation, using both the Sunnybrook Longitudinal Dementia Study and the entire Alzheimer’s Disease Neuroimaging Initiative 1 (ADNI-1) baseline and 24-month dataset. We based these atlas libraries on the protocols of (Haller et al., 1997; Killiany et al., 1993; Malykhin et al., 2007; Pantel et al., 2000; Pruessner et al., 2000), and a single operator performed all manual tracings to generate de facto “ground truth” labels. All methods distinguished between normal elders, mild cognitive impairment (MCI), and AD in the expected directions, and showed comparable correlations with measures of episodic memory performance. Only more inclusive protocols distinguished between stable MCI and MCI-to-AD converters, and had slightly better associations with episodic memory. Moreover, we demonstrate that protocols including more

  8. Automatic pelvis segmentation from x-ray images of a mouse model

    NASA Astrophysics Data System (ADS)

    Al Okashi, Omar M.; Du, Hongbo; Al-Assam, Hisham

    2017-05-01

    The automatic detection and quantification of skeletal structures has a variety of different applications for biological research. Accurate segmentation of the pelvis from X-ray images of mice in a high-throughput project such as the Mouse Genomes Project not only saves time and cost but also helps achieving an unbiased quantitative analysis within the phenotyping pipeline. This paper proposes an automatic solution for pelvis segmentation based on structural and orientation properties of the pelvis in X-ray images. The solution consists of three stages including pre-processing image to extract pelvis area, initial pelvis mask preparation and final pelvis segmentation. Experimental results on a set of 100 X-ray images showed consistent performance of the algorithm. The automated solution overcomes the weaknesses of a manual annotation procedure where intra- and inter-observer variations cannot be avoided.

  9. Anatomy-based algorithm for automatic segmentation of human diaphragm in noncontrast computed tomography images

    PubMed Central

    Karami, Elham; Wang, Yong; Gaede, Stewart; Lee, Ting-Yim; Samani, Abbas

    2016-01-01

    Abstract. In-depth understanding of the diaphragm’s anatomy and physiology has been of great interest to the medical community, as it is the most important muscle of the respiratory system. While noncontrast four-dimensional (4-D) computed tomography (CT) imaging provides an interesting opportunity for effective acquisition of anatomical and/or functional information from a single modality, segmenting the diaphragm in such images is very challenging not only because of the diaphragm’s lack of image contrast with its surrounding organs but also because of respiration-induced motion artifacts in 4-D CT images. To account for such limitations, we present an automatic segmentation algorithm, which is based on a priori knowledge of diaphragm anatomy. The novelty of the algorithm lies in using the diaphragm’s easy-to-segment contacting organs—including the lungs, heart, aorta, and ribcage—to guide the diaphragm’s segmentation. Obtained results indicate that average mean distance to the closest point between diaphragms segmented using the proposed technique and corresponding manual segmentation is 2.55±0.39  mm, which is favorable. An important feature of the proposed technique is that it is the first algorithm to delineate the entire diaphragm. Such delineation facilitates applications, where the diaphragm boundary conditions are required such as biomechanical modeling for in-depth understanding of the diaphragm physiology. PMID:27921072

  10. Evaluation of a risk-based environmental hot spot delineation algorithm.

    PubMed

    Sinha, Parikhit; Lambert, Michael B; Schew, William A

    2007-10-22

    Following remedial investigations of hazardous waste sites, remedial strategies may be developed that target the removal of "hot spots," localized areas of elevated contamination. For a given exposure area, a hot spot may be defined as a sub-area that causes risks for the whole exposure area to be unacceptable. The converse of this statement may also apply: when a hot spot is removed from within an exposure area, risks for the exposure area may drop below unacceptable thresholds. The latter is the motivation for a risk-based approach to hot spot delineation, which was evaluated using Monte Carlo simulation. Random samples taken from a virtual site ("true site") were used to create an interpolated site. The latter was gridded and concentrations from the center of each grid box were used to calculate 95% upper confidence limits on the mean site contaminant concentration and corresponding hazard quotients for a potential receptor. Grid cells with the highest concentrations were removed and hazard quotients were recalculated until the site hazard quotient dropped below the threshold of 1. The grid cells removed in this way define the spatial extent of the hot spot. For each of the 100,000 Monte Carlo iterations, the delineated hot spot was compared to the hot spot in the "true site." On average, the algorithm was able to delineate hot spots that were collocated with and equal to or greater in size than the "true hot spot." When delineated hot spots were mapped onto the "true site," setting contaminant concentrations in the mapped area to zero, the hazard quotients for these "remediated true sites" were on average within 5% of the acceptable threshold of 1.

  11. Delineation and segmentation of cerebral tumors by mapping blood-brain barrier disruption with dynamic contrast-enhanced CT and tracer kinetics modeling-a feasibility study.

    PubMed

    Bisdas, S; Yang, X; Lim, C C T; Vogl, T J; Koh, T S

    2008-01-01

    Dynamic contrast-enhanced (DCE) imaging is a promising approach for in vivo assessment of tissue microcirculation. Twenty patients with clinical and routine computed tomography (CT) evidence of intracerebral neoplasm were examined with DCE-CT imaging. Using a distributed-parameter model for tracer kinetics modeling of DCE-CT data, voxel-level maps of cerebral blood flow (F), intravascular blood volume (vi) and intravascular mean transit time (t1) were generated. Permeability-surface area product (PS), extravascular extracellular blood volume (ve) and extraction ratio (E) maps were also calculated to reveal pathologic locations of tracer extravasation, which are indicative of disruptions in the blood-brain barrier (BBB). All maps were visually assessed for quality of tumor delineation and measurement of tumor extent by two radiologists. Kappa (kappa) coefficients and their 95% confidence intervals (CI) were calculated to determine the interobserver agreement for each DCE-CT map. There was a substantial agreement for the tumor delineation quality in the F, ve and t1 maps. The agreement for the quality of the tumor delineation was excellent for the vi, PS and E maps. Concerning the measurement of tumor extent, excellent and nearly excellent agreement was achieved only for E and PS maps, respectively. According to these results, we performed a segmentation of the cerebral tumors on the base of the E maps. The interobserver agreement for the tumor extent quantification based on manual segmentation of tumor in the E maps vs. the computer-assisted segmentation was excellent (kappa = 0.96, CI: 0.93-0.99). The interobserver agreement for the tumor extent quantification based on computer segmentation in the mean images and the E maps was substantial (kappa = 0.52, CI: 0.42-0.59). This study illustrates the diagnostic usefulness of parametric maps associated with BBB disruption on a physiology-based approach and highlights the feasibility for automatic segmentation of

  12. An accurate automated technique for quasi-optics measurement of the microwave diagnostics for fusion plasma

    NASA Astrophysics Data System (ADS)

    Hu, Jianqiang; Liu, Ahdi; Zhou, Chu; Zhang, Xiaohui; Wang, Mingyuan; Zhang, Jin; Feng, Xi; Li, Hong; Xie, Jinlin; Liu, Wandong; Yu, Changxuan

    2017-08-01

    A new integrated technique for fast and accurate measurement of the quasi-optics, especially for the microwave/millimeter wave diagnostic systems of fusion plasma, has been developed. Using the LabVIEW-based comprehensive scanning system, we can realize not only automatic but also fast and accurate measurement, which will help to eliminate the effects of temperature drift and standing wave/multi-reflection. With the Matlab-based asymmetric two-dimensional Gaussian fitting method, all the desired parameters of the microwave beam can be obtained. This technique can be used in the design and testing of microwave diagnostic systems such as reflectometers and the electron cyclotron emission imaging diagnostic systems of the Experimental Advanced Superconducting Tokamak.

  13. Automatic Welding System

    NASA Astrophysics Data System (ADS)

    1982-01-01

    Robotic welding has been of interest to industrial firms because it offers higher productivity at lower cost than manual welding. There are some systems with automated arc guidance available, but they have disadvantages, such as limitations on types of materials or types of seams that can be welded; susceptibility to stray electrical signals; restricted field of view; or tendency to contaminate the weld seam. Wanting to overcome these disadvantages, Marshall Space Flight Center, aided by Hayes International Corporation, developed system that uses closed-circuit TV signals for automatic guidance of the welding torch. NASA granted license to Combined Technologies, Inc. for commercial application of the technology. They developed a refined and improved arc guidance system. CTI in turn, licensed the Merrick Corporation, also of Nashville, for marketing and manufacturing of the new system, called the CT2 Optical Trucker. CT2 is a non-contracting system that offers adaptability to broader range of welding jobs and provides greater reliability in high speed operation. It is extremely accurate and can travel at high speed of up to 150 inches per minute.

  14. A real-time digital computer program for the simulation of automatic spacecraft reentries

    NASA Technical Reports Server (NTRS)

    Kaylor, J. T.; Powell, L. F.; Powell, R. W.

    1977-01-01

    The automatic reentry flight dynamics simulator, a nonlinear, six-degree-of-freedom simulation, digital computer program, has been developed. The program includes a rotating, oblate earth model for accurate navigation calculations and contains adjustable gains on the aerodynamic stability and control parameters. This program uses a real-time simulation system and is designed to examine entries of vehicles which have constant mass properties whose attitudes are controlled by both aerodynamic surfaces and reaction control thrusters, and which have automatic guidance and control systems. The program has been used to study the space shuttle orbiter entry. This report includes descriptions of the equations of motion used, the control and guidance schemes that were implemented, the program flow and operation, and the hardware involved.

  15. Automatic corpus callosum segmentation for standardized MR brain scanning

    NASA Astrophysics Data System (ADS)

    Xu, Qing; Chen, Hong; Zhang, Li; Novak, Carol L.

    2007-03-01

    Magnetic Resonance (MR) brain scanning is often planned manually with the goal of aligning the imaging plane with key anatomic landmarks. The planning is time-consuming and subject to inter- and intra- operator variability. An automatic and standardized planning of brain scans is highly useful for clinical applications, and for maximum utility should work on patients of all ages. In this study, we propose a method for fully automatic planning that utilizes the landmarks from two orthogonal images to define the geometry of the third scanning plane. The corpus callosum (CC) is segmented in sagittal images by an active shape model (ASM), and the result is further improved by weighting the boundary movement with confidence scores and incorporating region based refinement. Based on the extracted contour of the CC, several important landmarks are located and then combined with landmarks from the coronal or transverse plane to define the geometry of the third plane. Our automatic method is tested on 54 MR images from 24 patients and 3 healthy volunteers, with ages ranging from 4 months to 70 years old. The average accuracy with respect to two manually labeled points on the CC is 3.54 mm and 4.19 mm, and differed by an average of 2.48 degrees from the orientation of the line connecting them, demonstrating that our method is sufficiently accurate for clinical use.

  16. Automatic anatomy recognition in whole-body PET/CT images

    DOE Office of Scientific and Technical Information (OSTI.GOV)

    Wang, Huiqian; Udupa, Jayaram K., E-mail: jay@mail.med.upenn.edu; Odhner, Dewey

    Purpose: Whole-body positron emission tomography/computed tomography (PET/CT) has become a standard method of imaging patients with various disease conditions, especially cancer. Body-wide accurate quantification of disease burden in PET/CT images is important for characterizing lesions, staging disease, prognosticating patient outcome, planning treatment, and evaluating disease response to therapeutic interventions. However, body-wide anatomy recognition in PET/CT is a critical first step for accurately and automatically quantifying disease body-wide, body-region-wise, and organwise. This latter process, however, has remained a challenge due to the lower quality of the anatomic information portrayed in the CT component of this imaging modality and the paucity ofmore » anatomic details in the PET component. In this paper, the authors demonstrate the adaptation of a recently developed automatic anatomy recognition (AAR) methodology [Udupa et al., “Body-wide hierarchical fuzzy modeling, recognition, and delineation of anatomy in medical images,” Med. Image Anal. 18, 752–771 (2014)] to PET/CT images. Their goal was to test what level of object localization accuracy can be achieved on PET/CT compared to that achieved on diagnostic CT images. Methods: The authors advance the AAR approach in this work in three fronts: (i) from body-region-wise treatment in the work of Udupa et al. to whole body; (ii) from the use of image intensity in optimal object recognition in the work of Udupa et al. to intensity plus object-specific texture properties, and (iii) from the intramodality model-building-recognition strategy to the intermodality approach. The whole-body approach allows consideration of relationships among objects in different body regions, which was previously not possible. Consideration of object texture allows generalizing the previous optimal threshold-based fuzzy model recognition method from intensity images to any derived fuzzy membership image, and in the

  17. A Complete System for Automatic Extraction of Left Ventricular Myocardium From CT Images Using Shape Segmentation and Contour Evolution

    PubMed Central

    Zhu, Liangjia; Gao, Yi; Appia, Vikram; Yezzi, Anthony; Arepalli, Chesnal; Faber, Tracy; Stillman, Arthur; Tannenbaum, Allen

    2014-01-01

    The left ventricular myocardium plays a key role in the entire circulation system and an automatic delineation of the myocardium is a prerequisite for most of the subsequent functional analysis. In this paper, we present a complete system for an automatic segmentation of the left ventricular myocardium from cardiac computed tomography (CT) images using the shape information from images to be segmented. The system follows a coarse-to-fine strategy by first localizing the left ventricle and then deforming the myocardial surfaces of the left ventricle to refine the segmentation. In particular, the blood pool of a CT image is extracted and represented as a triangulated surface. Then, the left ventricle is localized as a salient component on this surface using geometric and anatomical characteristics. After that, the myocardial surfaces are initialized from the localization result and evolved by applying forces from the image intensities with a constraint based on the initial myocardial surface locations. The proposed framework has been validated on 34-human and 12-pig CT images, and the robustness and accuracy are demonstrated. PMID:24723531

  18. Fully automatic segmentation of femurs with medullary canal definition in high and in low resolution CT scans.

    PubMed

    Almeida, Diogo F; Ruben, Rui B; Folgado, João; Fernandes, Paulo R; Audenaert, Emmanuel; Verhegghe, Benedict; De Beule, Matthieu

    2016-12-01

    Femur segmentation can be an important tool in orthopedic surgical planning. However, in order to overcome the need of an experienced user with extensive knowledge on the techniques, segmentation should be fully automatic. In this paper a new fully automatic femur segmentation method for CT images is presented. This method is also able to define automatically the medullary canal and performs well even in low resolution CT scans. Fully automatic femoral segmentation was performed adapting a template mesh of the femoral volume to medical images. In order to achieve this, an adaptation of the active shape model (ASM) technique based on the statistical shape model (SSM) and local appearance model (LAM) of the femur with a novel initialization method was used, to drive the template mesh deformation in order to fit the in-image femoral shape in a time effective approach. With the proposed method a 98% convergence rate was achieved. For high resolution CT images group the average error is less than 1mm. For the low resolution image group the results are also accurate and the average error is less than 1.5mm. The proposed segmentation pipeline is accurate, robust and completely user free. The method is robust to patient orientation, image artifacts and poorly defined edges. The results excelled even in CT images with a significant slice thickness, i.e., above 5mm. Medullary canal segmentation increases the geometric information that can be used in orthopedic surgical planning or in finite element analysis. Copyright © 2016 IPEM. Published by Elsevier Ltd. All rights reserved.

  19. Hypochromic vitiligo: delineation of a new entity.

    PubMed

    Ezzedine, K; Mahé, A; van Geel, N; Cardot-Leccia, N; Gauthier, Y; Descamps, V; Al Issa, A; Ly, F; Chosidow, O; Taïeb, A; Passeron, T

    2015-03-01

    Hypochromic vitiligo is a rare entity that has been reported only twice under the term 'vitiligo minor', with an absence of clear delineation. To delineate hypochromic vitiligo through a case series of patients with typical bilateral hypopigmented lesions affecting the face and trunk. This is a retrospective multicentric evaluation study conducted in eight departments of dermatology in France, Belgium, Senegal and Saudi Arabia. Twenty-four cases of hypochromic vitiligo were identified. Fourteen were men and 10 women. The mean age at diagnosis was 35·4 years (range 8-66). Strikingly, all patients were dark skinned, with skin types V and VI. The pattern of distribution was highly similar in most of the patients (18 of 24), with involvement of the face and neck area predominating on seborrhoeic areas associated with multiple isolated hypopigmented macules involving predominantly the scalp. The retrospective nature of this study is its main limitation. Hypochromic vitiligo is not yet part of a conventional classification. The disease seems to be limited to individuals with dark skin types. Hypopigmented seborrhoeic face and neck involvement associated with hypopigmented macules of the trunk and scalp is the hallmark of the disease. © 2014 British Association of Dermatologists.

  20. "Performance Of A Wafer Stepper With Automatic Intra-Die Registration Correction."

    NASA Astrophysics Data System (ADS)

    van den Brink, M. A.; Wittekoek, S.; Linders, H. F. D.; van Hout, F. J.; George, R. A.

    1987-01-01

    An evaluation of a wafer stepper with the new improved Philips/ASM-L phase grating alignment system is reported. It is shown that an accurate alignment system needs an accurate X-Y-0 wafer stage and an accurate reticle Z stage to realize optimum overlay accuracy. This follows from a discussion of the overlay budget and an alignment procedure model. The accurate wafer stage permits high overlay accuracy using global alignment only, thus eliminating the throughput penalty of align-by-field schemes. The accurate reticle Z stage enables an intra-die magnification control with respect to the wafer scale. Various overlay data are reported, which have been measured with the automatic metrology program of the stepper. It is demonstrated that the new dual alignment system (with the external spatial filter) has improved the ability to align to weakly reflecting layers. The results are supported by a Fourier analysis of the alignment signal. Resolution data are given for the PAS 2500 projection lenses, which show that the high overlay accuracy of the system is properly matched with submicron linewidth control. The results of a recently introduced 20mm i-line lens with a numerical aperture of 0.4 (Zeiss 10-78-58) are included.

  1. Attention to Automatic Movements in Parkinson's Disease: Modified Automatic Mode in the Striatum

    PubMed Central

    Wu, Tao; Liu, Jun; Zhang, Hejia; Hallett, Mark; Zheng, Zheng; Chan, Piu

    2015-01-01

    We investigated neural correlates when attending to a movement that could be made automatically in healthy subjects and Parkinson's disease (PD) patients. Subjects practiced a visuomotor association task until they could perform it automatically, and then directed their attention back to the automated task. Functional MRI was obtained during the early-learning, automatic stage, and when re-attending. In controls, attention to automatic movement induced more activation in the dorsolateral prefrontal cortex (DLPFC), anterior cingulate cortex, and rostral supplementary motor area. The motor cortex received more influence from the cortical motor association regions. In contrast, the pattern of the activity and connectivity of the striatum remained at the level of the automatic stage. In PD patients, attention enhanced activity in the DLPFC, premotor cortex, and cerebellum, but the connectivity from the putamen to the motor cortex decreased. Our findings demonstrate that, in controls, when a movement achieves the automatic stage, attention can influence the attentional networks and cortical motor association areas, but has no apparent effect on the striatum. In PD patients, attention induces a shift from the automatic mode back to the controlled pattern within the striatum. The shifting between controlled and automatic behaviors relies in part on striatal function. PMID:24925772

  2. [Wearable Automatic External Defibrillators].

    PubMed

    Luo, Huajie; Luo, Zhangyuan; Jin, Xun; Zhang, Leilei; Wang, Changjin; Zhang, Wenzan; Tu, Quan

    2015-11-01

    Defibrillation is the most effective method of treating ventricular fibrillation(VF), this paper introduces wearable automatic external defibrillators based on embedded system which includes EGG measurements, bioelectrical impedance measurement, discharge defibrillation module, which can automatic identify VF signal, biphasic exponential waveform defibrillation discharge. After verified by animal tests, the device can realize EGG acquisition and automatic identification. After identifying the ventricular fibrillation signal, it can automatic defibrillate to abort ventricular fibrillation and to realize the cardiac electrical cardioversion.

  3. Automatic generation of user material subroutines for biomechanical growth analysis.

    PubMed

    Young, Jonathan M; Yao, Jiang; Ramasubramanian, Ashok; Taber, Larry A; Perucchio, Renato

    2010-10-01

    The analysis of the biomechanics of growth and remodeling in soft tissues requires the formulation of specialized pseudoelastic constitutive relations. The nonlinear finite element analysis package ABAQUS allows the user to implement such specialized material responses through the coding of a user material subroutine called UMAT. However, hand coding UMAT subroutines is a challenge even for simple pseudoelastic materials and requires substantial time to debug and test the code. To resolve this issue, we develop an automatic UMAT code generation procedure for pseudoelastic materials using the symbolic mathematics package MATHEMATICA and extend the UMAT generator to include continuum growth. The performance of the automatically coded UMAT is tested by simulating the stress-stretch response of a material defined by a Fung-orthotropic strain energy function, subject to uniaxial stretching, equibiaxial stretching, and simple shear in ABAQUS. The MATHEMATICA UMAT generator is then extended to include continuum growth by adding a growth subroutine to the automatically generated UMAT. The MATHEMATICA UMAT generator correctly derives the variables required in the UMAT code, quickly providing a ready-to-use UMAT. In turn, the UMAT accurately simulates the pseudoelastic response. In order to test the growth UMAT, we simulate the growth-based bending of a bilayered bar with differing fiber directions in a nongrowing passive layer. The anisotropic passive layer, being topologically tied to the growing isotropic layer, causes the bending bar to twist laterally. The results of simulations demonstrate the validity of the automatically coded UMAT, used in both standardized tests of hyperelastic materials and for a biomechanical growth analysis.

  4. A deep-learning based automatic pulmonary nodule detection system

    NASA Astrophysics Data System (ADS)

    Zhao, Yiyuan; Zhao, Liang; Yan, Zhennan; Wolf, Matthias; Zhan, Yiqiang

    2018-02-01

    Lung cancer is the deadliest cancer worldwide. Early detection of lung cancer is a promising way to lower the risk of dying. Accurate pulmonary nodule detection in computed tomography (CT) images is crucial for early diagnosis of lung cancer. The development of computer-aided detection (CAD) system of pulmonary nodules contributes to making the CT analysis more accurate and with more efficiency. Recent studies from other groups have been focusing on lung cancer diagnosis CAD system by detecting medium to large nodules. However, to fully investigate the relevance between nodule features and cancer diagnosis, a CAD that is capable of detecting nodules with all sizes is needed. In this paper, we present a deep-learning based automatic all size pulmonary nodule detection system by cascading two artificial neural networks. We firstly use a U-net like 3D network to generate nodule candidates from CT images. Then, we use another 3D neural network to refine the locations of the nodule candidates generated from the previous subsystem. With the second sub-system, we bring the nodule candidates closer to the center of the ground truth nodule locations. We evaluate our system on a public CT dataset provided by the Lung Nodule Analysis (LUNA) 2016 grand challenge. The performance on the testing dataset shows that our system achieves 90% sensitivity with an average of 4 false positives per scan. This indicates that our system can be an aid for automatic nodule detection, which is beneficial for lung cancer diagnosis.

  5. Validation of Computerized Automatic Calculation of the Sequential Organ Failure Assessment Score

    PubMed Central

    Harrison, Andrew M.; Pickering, Brian W.; Herasevich, Vitaly

    2013-01-01

    Purpose. To validate the use of a computer program for the automatic calculation of the sequential organ failure assessment (SOFA) score, as compared to the gold standard of manual chart review. Materials and Methods. Adult admissions (age > 18 years) to the medical ICU with a length of stay greater than 24 hours were studied in the setting of an academic tertiary referral center. A retrospective cross-sectional analysis was performed using a derivation cohort to compare automatic calculation of the SOFA score to the gold standard of manual chart review. After critical appraisal of sources of disagreement, another analysis was performed using an independent validation cohort. Then, a prospective observational analysis was performed using an implementation of this computer program in AWARE Dashboard, which is an existing real-time patient EMR system for use in the ICU. Results. Good agreement between the manual and automatic SOFA calculations was observed for both the derivation (N=94) and validation (N=268) cohorts: 0.02 ± 2.33 and 0.29 ± 1.75 points, respectively. These results were validated in AWARE (N=60). Conclusion. This EMR-based automatic tool accurately calculates SOFA scores and can facilitate ICU decisions without the need for manual data collection. This tool can also be employed in a real-time electronic environment. PMID:23936639

  6. Using airborne LiDAR in geoarchaeological contexts: Assessment of an automatic tool for the detection and the morphometric analysis of grazing archaeological structures (French Massif Central).

    NASA Astrophysics Data System (ADS)

    Roussel, Erwan; Toumazet, Jean-Pierre; Florez, Marta; Vautier, Franck; Dousteyssier, Bertrand

    2014-05-01

    Airborne laser scanning (ALS) of archaeological regions of interest is nowadays a widely used and established method for accurate topographic and microtopographic survey. The penetration of the vegetation cover by the laser beam allows the reconstruction of reliable digital terrain models (DTM) of forested areas where traditional prospection methods are inefficient, time-consuming and non-exhaustive. The ALS technology provides the opportunity to discover new archaeological features hidden by vegetation and provides a comprehensive survey of cultural heritage sites within their environmental context. However, the post-processing of LiDAR points clouds produces a huge quantity of data in which relevant archaeological features are not easily detectable with common visualizing and analysing tools. Undoubtedly, there is an urgent need for automation of structures detection and morphometric extraction techniques, especially for the "archaeological desert" in densely forested areas. This presentation deals with the development of automatic detection procedures applied to archaeological structures located in the French Massif Central, in the western forested part of the Puy-de-Dôme volcano between 950 and 1100 m a.s.l.. These unknown archaeological sites were discovered by the March 2011 ALS mission and display a high density of subcircular depressions with a corridor access. The spatial organization of these depressions vary from isolated to aggregated or aligned features. Functionally, they appear to be former grazing constructions built from the medieval to the modern period. Similar grazing structures are known in other locations of the French Massif Central (Sancy, Artense, Cézallier) where the ground is vegetation-free. In order to develop a reliable process of automatic detection and mapping of these archaeological structures, a learning zone has been delineated within the ALS surveyed area. The grazing features were mapped and typical morphometric attributes

  7. Automatic thoracic anatomy segmentation on CT images using hierarchical fuzzy models and registration

    NASA Astrophysics Data System (ADS)

    Sun, Kaioqiong; Udupa, Jayaram K.; Odhner, Dewey; Tong, Yubing; Torigian, Drew A.

    2014-03-01

    This paper proposes a thoracic anatomy segmentation method based on hierarchical recognition and delineation guided by a built fuzzy model. Labeled binary samples for each organ are registered and aligned into a 3D fuzzy set representing the fuzzy shape model for the organ. The gray intensity distributions of the corresponding regions of the organ in the original image are recorded in the model. The hierarchical relation and mean location relation between different organs are also captured in the model. Following the hierarchical structure and location relation, the fuzzy shape model of different organs is registered to the given target image to achieve object recognition. A fuzzy connected delineation method is then used to obtain the final segmentation result of organs with seed points provided by recognition. The hierarchical structure and location relation integrated in the model provide the initial parameters for registration and make the recognition efficient and robust. The 3D fuzzy model combined with hierarchical affine registration ensures that accurate recognition can be obtained for both non-sparse and sparse organs. The results on real images are presented and shown to be better than a recently reported fuzzy model-based anatomy recognition strategy.

  8. Iterative Otsu's method for OCT improved delineation in the aorta wall

    NASA Astrophysics Data System (ADS)

    Alonso, Daniel; Real, Eusebio; Val-Bernal, José F.; Revuelta, José M.; Pontón, Alejandro; Calvo Díez, Marta; Mayorga, Marta; López-Higuera, José M.; Conde, Olga M.

    2015-07-01

    Degradation of human ascending thoracic aorta has been visualized with Optical Coherence Tomography (OCT). OCT images of the vessel wall exhibit structural degradation in the media layer of the artery, being this disorder the final trigger of the pathology. The degeneration in the vessel wall appears as low-reflectivity areas due to different optical properties of acidic polysaccharides and mucopolysaccharides in contrast with typical ordered structure of smooth muscle cells, elastin and collagen fibers. An OCT dimension indicator of wall degradation can be generated upon the spatial quantification of the extension of degraded areas in a similar way as conventional histopathology. This proposed OCT marker can offer in the future a real-time clinical perception of the vessel status to help cardiovascular surgeons in vessel repair interventions. However, the delineation of degraded areas on the B-scan image from OCT is sometimes difficult due to presence of speckle noise, variable signal to noise ratio (SNR) conditions on the measurement process, etc. Degraded areas can be delimited by basic thresholding techniques taking advantage of disorders evidences in B-scan images, but this delineation is not optimum in the aorta samples and requires complex additional processing stages. This work proposes an optimized delineation of degraded areas within the aorta wall, robust to noisy environments, based on the iterative application of Otsu's thresholding method. Results improve the delineation of wall anomalies compared with the simple application of the algorithm. Achievements could be also transferred to other clinical scenarios: carotid arteries, aorto-iliac or ilio-femoral sections, intracranial, etc.

  9. Automatic detection of Parkinson's disease in running speech spoken in three different languages.

    PubMed

    Orozco-Arroyave, J R; Hönig, F; Arias-Londoño, J D; Vargas-Bonilla, J F; Daqrouq, K; Skodda, S; Rusz, J; Nöth, E

    2016-01-01

    The aim of this study is the analysis of continuous speech signals of people with Parkinson's disease (PD) considering recordings in different languages (Spanish, German, and Czech). A method for the characterization of the speech signals, based on the automatic segmentation of utterances into voiced and unvoiced frames, is addressed here. The energy content of the unvoiced sounds is modeled using 12 Mel-frequency cepstral coefficients and 25 bands scaled according to the Bark scale. Four speech tasks comprising isolated words, rapid repetition of the syllables /pa/-/ta/-/ka/, sentences, and read texts are evaluated. The method proves to be more accurate than classical approaches in the automatic classification of speech of people with PD and healthy controls. The accuracies range from 85% to 99% depending on the language and the speech task. Cross-language experiments are also performed confirming the robustness and generalization capability of the method, with accuracies ranging from 60% to 99%. This work comprises a step forward for the development of computer aided tools for the automatic assessment of dysarthric speech signals in multiple languages.

  10. Automatic limb identification and sleeping parameters assessment for pressure ulcer prevention.

    PubMed

    Baran Pouyan, Maziyar; Birjandtalab, Javad; Nourani, Mehrdad; Matthew Pompeo, M D

    2016-08-01

    Pressure ulcers (PUs) are common among vulnerable patients such as elderly, bedridden and diabetic. PUs are very painful for patients and costly for hospitals and nursing homes. Assessment of sleeping parameters on at-risk limbs is critical for ulcer prevention. An effective assessment depends on automatic identification and tracking of at-risk limbs. An accurate limb identification can be used to analyze the pressure distribution and assess risk for each limb. In this paper, we propose a graph-based clustering approach to extract the body limbs from the pressure data collected by a commercial pressure map system. A robust signature-based technique is employed to automatically label each limb. Finally, an assessment technique is applied to evaluate the experienced stress by each limb over time. The experimental results indicate high performance and more than 94% average accuracy of the proposed approach. Copyright © 2016 Elsevier Ltd. All rights reserved.

  11. Vessel extraction in retinal images using automatic thresholding and Gabor Wavelet.

    PubMed

    Ali, Aziah; Hussain, Aini; Wan Zaki, Wan Mimi Diyana

    2017-07-01

    Retinal image analysis has been widely used for early detection and diagnosis of multiple systemic diseases. Accurate vessel extraction in retinal image is a crucial step towards a fully automated diagnosis system. This work affords an efficient unsupervised method for extracting blood vessels from retinal images by combining existing Gabor Wavelet (GW) method with automatic thresholding. Green channel image is extracted from color retinal image and used to produce Gabor feature image using GW. Both green channel image and Gabor feature image undergo vessel-enhancement step in order to highlight blood vessels. Next, the two vessel-enhanced images are transformed to binary images using automatic thresholding before combined to produce the final vessel output. Combining the images results in significant improvement of blood vessel extraction performance compared to using individual image. Effectiveness of the proposed method was proven via comparative analysis with existing methods validated using publicly available database, DRIVE.

  12. Temporal Delineation and Quantification of Short Term Clustered Mining Seismicity

    NASA Astrophysics Data System (ADS)

    Woodward, Kyle; Wesseloo, Johan; Potvin, Yves

    2017-07-01

    The assessment of the temporal characteristics of seismicity is fundamental to understanding and quantifying the seismic hazard associated with mining, the effectiveness of strategies and tactics used to manage seismic hazard, and the relationship between seismicity and changes to the mining environment. This article aims to improve the accuracy and precision in which the temporal dimension of seismic responses can be quantified and delineated. We present a review and discussion on the occurrence of time-dependent mining seismicity with a specific focus on temporal modelling and the modified Omori law (MOL). This forms the basis for the development of a simple weighted metric that allows for the consistent temporal delineation and quantification of a seismic response. The optimisation of this metric allows for the selection of the most appropriate modelling interval given the temporal attributes of time-dependent mining seismicity. We evaluate the performance weighted metric for the modelling of a synthetic seismic dataset. This assessment shows that seismic responses can be quantified and delineated by the MOL, with reasonable accuracy and precision, when the modelling is optimised by evaluating the weighted MLE metric. Furthermore, this assessment highlights that decreased weighted MLE metric performance can be expected if there is a lack of contrast between the temporal characteristics of events associated with different processes.

  13. Impact of the accuracy of automatic segmentation of cell nuclei clusters on classification of thyroid follicular lesions.

    PubMed

    Jung, Chanho; Kim, Changick

    2014-08-01

    Automatic segmentation of cell nuclei clusters is a key building block in systems for quantitative analysis of microscopy cell images. For that reason, it has received a great attention over the last decade, and diverse automatic approaches to segment clustered nuclei with varying levels of performance under different test conditions have been proposed in literature. To the best of our knowledge, however, so far there is no comparative study on the methods. This study is a first attempt to fill this research gap. More precisely, the purpose of this study is to present an objective performance comparison of existing state-of-the-art segmentation methods. Particularly, the impact of their accuracy on classification of thyroid follicular lesions is also investigated "quantitatively" under the same experimental condition, to evaluate the applicability of the methods. Thirteen different segmentation approaches are compared in terms of not only errors in nuclei segmentation and delineation, but also their impact on the performance of system to classify thyroid follicular lesions using different metrics (e.g., diagnostic accuracy, sensitivity, specificity, etc.). Extensive experiments have been conducted on a total of 204 digitized thyroid biopsy specimens. Our study demonstrates that significant diagnostic errors can be avoided using more advanced segmentation approaches. We believe that this comprehensive comparative study serves as a reference point and guide for developers and practitioners in choosing an appropriate automatic segmentation technique adopted for building automated systems for specifically classifying follicular thyroid lesions. © 2014 International Society for Advancement of Cytometry.

  14. An ultra low-power CMOS automatic action potential detector.

    PubMed

    Gosselin, Benoit; Sawan, Mohamad

    2009-08-01

    We present a low-power complementary metal-oxide semiconductor (CMOS) analog integrated biopotential detector intended for neural recording in wireless multichannel implants. The proposed detector can achieve accurate automatic discrimination of action potential (APs) from the background activity by means of an energy-based preprocessor and a linear delay element. This strategy improves detected waveforms integrity and prompts for better performance in neural prostheses. The delay element is implemented with a low-power continuous-time filter using a ninth-order equiripple allpass transfer function. All circuit building blocks use subthreshold OTAs employing dedicated circuit techniques for achieving ultra low-power and high dynamic range. The proposed circuit function in the submicrowatt range as the implemented CMOS 0.18- microm chip dissipates 780 nW, and it features a size of 0.07 mm(2). So it is suitable for massive integration in a multichannel device with modest overhead. The fabricated detector succeeds to automatically detect APs from underlying background activity. Testbench validation results obtained with synthetic neural waveforms are presented.

  15. Differences in delineation guidelines for head and neck cancer result in inconsistent reported dose and corresponding NTCP.

    PubMed

    Brouwer, Charlotte L; Steenbakkers, Roel J H M; Gort, Elske; Kamphuis, Marije E; van der Laan, Hans Paul; Van't Veld, Aart A; Sijtsema, Nanna M; Langendijk, Johannes A

    2014-04-01

    To test the hypothesis that delineation of swallowing organs at risk (SWOARs) based on different guidelines results in differences in dose-volume parameters and subsequent normal tissue complication probability (NTCP) values for dysphagia-related endpoints. Nine different SWOARs were delineated according to five different delineation guidelines in 29 patients. Reference delineation was performed according to the guidelines and NTCP-models of Christianen et al. Concordance Index (CI), dosimetric consequences, as well as differences in the subsequent NTCPs were calculated. The median CI of the different delineation guidelines with the reference guidelines was 0.54 for the pharyngeal constrictor muscles, 0.56 for the laryngeal structures and 0.07 for the cricopharyngeal muscle and esophageal inlet muscle. The average difference in mean dose to the SWOARs between the guidelines with the largest difference (maxΔD) was 3.5±3.2Gy. A mean ΔNTCP of 2.3±2.7% was found. For two patients, ΔNTCP exceeded 10%. The majority of the patients showed little differences in NTCPs between the different delineation guidelines. However, large NTCP differences >10% were found in 7% of the patients. For correct use of NTCP models in individual patients, uniform delineation guidelines are of great importance. Copyright © 2014 The Authors. Published by Elsevier Ireland Ltd.. All rights reserved.

  16. [Delineation of ecological security pattern based on ecological network].

    PubMed

    Fu, Qiang; Gu, Chao Lin

    2017-03-18

    Ecological network can be used to describe and assess the relationship between spatial organization of landscapes and species survival under the condition of the habitat fragmentation. Taking Qingdao City as the research area, woodland and wetland ecological networks in 2005 were simulated based on least cost path method, and the ecological networks were classified by their corridors' cumulative cost value. We made importance distinction of ecological network structure elements such as patches and corridors using betweenness centrality index and correlation length-percentage of importance of omitted patches index, and then created the structure system of ecological network. Considering the effects brought by the newly-added construction land from 2005 to 2013, we proposed the ecological security pattern for construction land change of Qingdao City. The results showed that based on ecological network framework, graph theory based methods could be used to quantify both attributes of specific ecological land (e.g., the area of an ecological network patch) and functional connection between ecological lands. Between 2005 and 2013, large area of wetlands had been destroyed by newly-added construction land, while the role of specific woodland and wetland played in the connection of the whole network had not been considered. The delineation of ecological security pattern based on ecological network could optimize regional ecological basis, provide accurate spatial explicit decision for ecological conservation and restoration, and meanwhile provide scientific and reasonable space guidance for urban spatial expansion.

  17. Automatic correction of intensity nonuniformity from sparseness of gradient distribution in medical images.

    PubMed

    Zheng, Yuanjie; Grossman, Murray; Awate, Suyash P; Gee, James C

    2009-01-01

    We propose to use the sparseness property of the gradient probability distribution to estimate the intensity nonuniformity in medical images, resulting in two novel automatic methods: a non-parametric method and a parametric method. Our methods are easy to implement because they both solve an iteratively re-weighted least squares problem. They are remarkably accurate as shown by our experiments on images of different imaged objects and from different imaging modalities.

  18. Automatic Segmentation and Quantification of Filamentous Structures in Electron Tomography

    PubMed Central

    Loss, Leandro A.; Bebis, George; Chang, Hang; Auer, Manfred; Sarkar, Purbasha; Parvin, Bahram

    2016-01-01

    Electron tomography is a promising technology for imaging ultrastructures at nanoscale resolutions. However, image and quantitative analyses are often hindered by high levels of noise, staining heterogeneity, and material damage either as a result of the electron beam or sample preparation. We have developed and built a framework that allows for automatic segmentation and quantification of filamentous objects in 3D electron tomography. Our approach consists of three steps: (i) local enhancement of filaments by Hessian filtering; (ii) detection and completion (e.g., gap filling) of filamentous structures through tensor voting; and (iii) delineation of the filamentous networks. Our approach allows for quantification of filamentous networks in terms of their compositional and morphological features. We first validate our approach using a set of specifically designed synthetic data. We then apply our segmentation framework to tomograms of plant cell walls that have undergone different chemical treatments for polysaccharide extraction. The subsequent compositional and morphological analyses of the plant cell walls reveal their organizational characteristics and the effects of the different chemical protocols on specific polysaccharides. PMID:28090597

  19. Automatic Segmentation and Quantification of Filamentous Structures in Electron Tomography.

    PubMed

    Loss, Leandro A; Bebis, George; Chang, Hang; Auer, Manfred; Sarkar, Purbasha; Parvin, Bahram

    2012-10-01

    Electron tomography is a promising technology for imaging ultrastructures at nanoscale resolutions. However, image and quantitative analyses are often hindered by high levels of noise, staining heterogeneity, and material damage either as a result of the electron beam or sample preparation. We have developed and built a framework that allows for automatic segmentation and quantification of filamentous objects in 3D electron tomography. Our approach consists of three steps: (i) local enhancement of filaments by Hessian filtering; (ii) detection and completion (e.g., gap filling) of filamentous structures through tensor voting; and (iii) delineation of the filamentous networks. Our approach allows for quantification of filamentous networks in terms of their compositional and morphological features. We first validate our approach using a set of specifically designed synthetic data. We then apply our segmentation framework to tomograms of plant cell walls that have undergone different chemical treatments for polysaccharide extraction. The subsequent compositional and morphological analyses of the plant cell walls reveal their organizational characteristics and the effects of the different chemical protocols on specific polysaccharides.

  20. FlowerMorphology: fully automatic flower morphometry software.

    PubMed

    Rozov, Sergey M; Deineko, Elena V; Deyneko, Igor V

    2018-05-01

    The software FlowerMorphology is designed for automatic morphometry of actinomorphic flowers. The novel complex parameters of flowers calculated by FlowerMorphology allowed us to quantitatively characterize a polyploid series of tobacco. Morphological differences of plants representing closely related lineages or mutants are mostly quantitative. Very often, there are only very fine variations in plant morphology. Therefore, accurate and high-throughput methods are needed for their quantification. In addition, new characteristics are necessary for reliable detection of subtle changes in morphology. FlowerMorphology is an all-in-one software package to automatically image and analyze five-petal actinomorphic flowers of the dicotyledonous plants. Sixteen directly measured parameters and ten calculated complex parameters of a flower allow us to characterize variations with high accuracy. The program was developed for the needs of automatic characterization of Nicotiana tabacum flowers, but is applicable to many other plants with five-petal actinomorphic flowers and can be adopted for flowers of other merosity. A genetically similar polyploid series of N. tabacum plants was used to investigate differences in flower morphology. For the first time, we could quantify the dependence between ploidy and size and form of the tobacco flowers. We found that the radius of inner petal incisions shows a persistent positive correlation with the chromosome number. In contrast, a commonly used parameter-radius of outer corolla-does not discriminate 2n and 4n plants. Other parameters show that polyploidy leads to significant aberrations in flower symmetry and are also positively correlated with chromosome number. Executables of FlowerMorphology, source code, documentation, and examples are available at the program website: https://github.com/Deyneko/FlowerMorphology .

  1. A Semi-Automated Machine Learning Algorithm for Tree Cover Delineation from 1-m Naip Imagery Using a High Performance Computing Architecture

    NASA Astrophysics Data System (ADS)

    Basu, S.; Ganguly, S.; Nemani, R. R.; Mukhopadhyay, S.; Milesi, C.; Votava, P.; Michaelis, A.; Zhang, G.; Cook, B. D.; Saatchi, S. S.; Boyda, E.

    2014-12-01

    Accurate tree cover delineation is a useful instrument in the derivation of Above Ground Biomass (AGB) density estimates from Very High Resolution (VHR) satellite imagery data. Numerous algorithms have been designed to perform tree cover delineation in high to coarse resolution satellite imagery, but most of them do not scale to terabytes of data, typical in these VHR datasets. In this paper, we present an automated probabilistic framework for the segmentation and classification of 1-m VHR data as obtained from the National Agriculture Imagery Program (NAIP) for deriving tree cover estimates for the whole of Continental United States, using a High Performance Computing Architecture. The results from the classification and segmentation algorithms are then consolidated into a structured prediction framework using a discriminative undirected probabilistic graphical model based on Conditional Random Field (CRF), which helps in capturing the higher order contextual dependencies between neighboring pixels. Once the final probability maps are generated, the framework is updated and re-trained by incorporating expert knowledge through the relabeling of misclassified image patches. This leads to a significant improvement in the true positive rates and reduction in false positive rates. The tree cover maps were generated for the state of California, which covers a total of 11,095 NAIP tiles and spans a total geographical area of 163,696 sq. miles. Our framework produced correct detection rates of around 85% for fragmented forests and 70% for urban tree cover areas, with false positive rates lower than 3% for both regions. Comparative studies with the National Land Cover Data (NLCD) algorithm and the LiDAR high-resolution canopy height model shows the effectiveness of our algorithm in generating accurate high-resolution tree cover maps.

  2. Automatic respiration tracking for radiotherapy using optical 3D camera

    NASA Astrophysics Data System (ADS)

    Li, Tuotuo; Geng, Jason; Li, Shidong

    2013-03-01

    Rapid optical three-dimensional (O3D) imaging systems provide accurate digitized 3D surface data in real-time, with no patient contact nor radiation. The accurate 3D surface images offer crucial information in image-guided radiation therapy (IGRT) treatments for accurate patient repositioning and respiration management. However, applications of O3D imaging techniques to image-guided radiotherapy have been clinically challenged by body deformation, pathological and anatomical variations among individual patients, extremely high dimensionality of the 3D surface data, and irregular respiration motion. In existing clinical radiation therapy (RT) procedures target displacements are caused by (1) inter-fractional anatomy changes due to weight, swell, food/water intake; (2) intra-fractional variations from anatomy changes within any treatment session due to voluntary/involuntary physiologic processes (e.g. respiration, muscle relaxation); (3) patient setup misalignment in daily reposition due to user errors; and (4) changes of marker or positioning device, etc. Presently, viable solution is lacking for in-vivo tracking of target motion and anatomy changes during the beam-on time without exposing patient with additional ionized radiation or high magnet field. Current O3D-guided radiotherapy systems relay on selected points or areas in the 3D surface to track surface motion. The configuration of the marks or areas may change with time that makes it inconsistent in quantifying and interpreting the respiration patterns. To meet the challenge of performing real-time respiration tracking using O3D imaging technology in IGRT, we propose a new approach to automatic respiration motion analysis based on linear dimensionality reduction technique based on PCA (principle component analysis). Optical 3D image sequence is decomposed with principle component analysis into a limited number of independent (orthogonal) motion patterns (a low dimension eigen-space span by eigen-vectors). New

  3. SHIELD: FITGALAXY -- A Software Package for Automatic Aperture Photometry of Extended Sources

    NASA Astrophysics Data System (ADS)

    Marshall, Melissa

    2013-01-01

    Determining the parameters of extended sources, such as galaxies, is a common but time-consuming task. Finding a photometric aperture that encompasses the majority of the flux of a source and identifying and excluding contaminating objects is often done by hand - a lengthy and difficult to reproduce process. To make extracting information from large data sets both quick and repeatable, I have developed a program called FITGALAXY, written in IDL. This program uses minimal user input to automatically fit an aperture to, and perform aperture and surface photometry on, an extended source. FITGALAXY also automatically traces the outlines of surface brightness thresholds and creates surface brightness profiles, which can then be used to determine the radial properties of a source. Finally, the program performs automatic masking of contaminating sources. Masks and apertures can be applied to multiple images (regardless of the WCS solution or plate scale) in order to accurately measure the same source at different wavelengths. I present the fluxes, as measured by the program, of a selection of galaxies from the Local Volume Legacy Survey. I then compare these results with the fluxes given by Dale et al. (2009) in order to assess the accuracy of FITGALAXY.

  4. Automatic segmentation of nine retinal layer boundaries in OCT images of non-exudative AMD patients using deep learning and graph search.

    PubMed

    Fang, Leyuan; Cunefare, David; Wang, Chong; Guymer, Robyn H; Li, Shutao; Farsiu, Sina

    2017-05-01

    We present a novel framework combining convolutional neural networks (CNN) and graph search methods (termed as CNN-GS) for the automatic segmentation of nine layer boundaries on retinal optical coherence tomography (OCT) images. CNN-GS first utilizes a CNN to extract features of specific retinal layer boundaries and train a corresponding classifier to delineate a pilot estimate of the eight layers. Next, a graph search method uses the probability maps created from the CNN to find the final boundaries. We validated our proposed method on 60 volumes (2915 B-scans) from 20 human eyes with non-exudative age-related macular degeneration (AMD), which attested to effectiveness of our proposed technique.

  5. Automatic Measurement of Fetal Brain Development from Magnetic Resonance Imaging: New Reference Data.

    PubMed

    Link, Daphna; Braginsky, Michael B; Joskowicz, Leo; Ben Sira, Liat; Harel, Shaul; Many, Ariel; Tarrasch, Ricardo; Malinger, Gustavo; Artzi, Moran; Kapoor, Cassandra; Miller, Elka; Ben Bashat, Dafna

    2018-01-01

    Accurate fetal brain volume estimation is of paramount importance in evaluating fetal development. The aim of this study was to develop an automatic method for fetal brain segmentation from magnetic resonance imaging (MRI) data, and to create for the first time a normal volumetric growth chart based on a large cohort. A semi-automatic segmentation method based on Seeded Region Growing algorithm was developed and applied to MRI data of 199 typically developed fetuses between 18 and 37 weeks' gestation. The accuracy of the algorithm was tested against a sub-cohort of ground truth manual segmentations. A quadratic regression analysis was used to create normal growth charts. The sensitivity of the method to identify developmental disorders was demonstrated on 9 fetuses with intrauterine growth restriction (IUGR). The developed method showed high correlation with manual segmentation (r2 = 0.9183, p < 0.001) as well as mean volume and volume overlap differences of 4.77 and 18.13%, respectively. New reference data on 199 normal fetuses were created, and all 9 IUGR fetuses were at or below the third percentile of the normal growth chart. The proposed method is fast, accurate, reproducible, user independent, applicable with retrospective data, and is suggested for use in routine clinical practice. © 2017 S. Karger AG, Basel.

  6. Estimating patient dose from CT exams that use automatic exposure control: Development and validation of methods to accurately estimate tube current values.

    PubMed

    McMillan, Kyle; Bostani, Maryam; Cagnon, Christopher H; Yu, Lifeng; Leng, Shuai; McCollough, Cynthia H; McNitt-Gray, Michael F

    2017-08-01

    The vast majority of body CT exams are performed with automatic exposure control (AEC), which adapts the mean tube current to the patient size and modulates the tube current either angularly, longitudinally or both. However, most radiation dose estimation tools are based on fixed tube current scans. Accurate estimates of patient dose from AEC scans require knowledge of the tube current values, which is usually unavailable. The purpose of this work was to develop and validate methods to accurately estimate the tube current values prescribed by one manufacturer's AEC system to enable accurate estimates of patient dose. Methods were developed that took into account available patient attenuation information, user selected image quality reference parameters and x-ray system limits to estimate tube current values for patient scans. Methods consistent with AAPM Report 220 were developed that used patient attenuation data that were: (a) supplied by the manufacturer in the CT localizer radiograph and (b) based on a simulated CT localizer radiograph derived from image data. For comparison, actual tube current values were extracted from the projection data of each patient. Validation of each approach was based on data collected from 40 pediatric and adult patients who received clinically indicated chest (n = 20) and abdomen/pelvis (n = 20) scans on a 64 slice multidetector row CT (Sensation 64, Siemens Healthcare, Forchheim, Germany). For each patient dataset, the following were collected with Institutional Review Board (IRB) approval: (a) projection data containing actual tube current values at each projection view, (b) CT localizer radiograph (topogram) and (c) reconstructed image data. Tube current values were estimated based on the actual topogram (actual-topo) as well as the simulated topogram based on image data (sim-topo). Each of these was compared to the actual tube current values from the patient scan. In addition, to assess the accuracy of each method in estimating

  7. Delineating wetland catchments and modeling hydrologic connectivity using lidar data and aerial imagery

    NASA Astrophysics Data System (ADS)

    Wu, Qiusheng; Lane, Charles R.

    2017-07-01

    In traditional watershed delineation and topographic modeling, surface depressions are generally treated as spurious features and simply removed from a digital elevation model (DEM) to enforce flow continuity of water across the topographic surface to the watershed outlets. In reality, however, many depressions in the DEM are actual wetland landscape features with seasonal to permanent inundation patterning characterized by nested hierarchical structures and dynamic filling-spilling-merging surface-water hydrological processes. Differentiating and appropriately processing such ecohydrologically meaningful features remains a major technical terrain-processing challenge, particularly as high-resolution spatial data are increasingly used to support modeling and geographic analysis needs. The objectives of this study were to delineate hierarchical wetland catchments and model their hydrologic connectivity using high-resolution lidar data and aerial imagery. The graph-theory-based contour tree method was used to delineate the hierarchical wetland catchments and characterize their geometric and topological properties. Potential hydrologic connectivity between wetlands and streams were simulated using the least-cost-path algorithm. The resulting flow network delineated potential flow paths connecting wetland depressions to each other or to the river network on scales finer than those available through the National Hydrography Dataset. The results demonstrated that our proposed framework is promising for improving overland flow simulation and hydrologic connectivity analysis.

  8. Automatic tissue image segmentation based on image processing and deep learning

    NASA Astrophysics Data System (ADS)

    Kong, Zhenglun; Luo, Junyi; Xu, Shengpu; Li, Ting

    2018-02-01

    Image segmentation plays an important role in multimodality imaging, especially in fusion structural images offered by CT, MRI with functional images collected by optical technologies or other novel imaging technologies. Plus, image segmentation also provides detailed structure description for quantitative visualization of treating light distribution in the human body when incorporated with 3D light transport simulation method. Here we used image enhancement, operators, and morphometry methods to extract the accurate contours of different tissues such as skull, cerebrospinal fluid (CSF), grey matter (GM) and white matter (WM) on 5 fMRI head image datasets. Then we utilized convolutional neural network to realize automatic segmentation of images in a deep learning way. We also introduced parallel computing. Such approaches greatly reduced the processing time compared to manual and semi-automatic segmentation and is of great importance in improving speed and accuracy as more and more samples being learned. Our results can be used as a criteria when diagnosing diseases such as cerebral atrophy, which is caused by pathological changes in gray matter or white matter. We demonstrated the great potential of such image processing and deep leaning combined automatic tissue image segmentation in personalized medicine, especially in monitoring, and treatments.

  9. Fully Automatic Speech-Based Analysis of the Semantic Verbal Fluency Task.

    PubMed

    König, Alexandra; Linz, Nicklas; Tröger, Johannes; Wolters, Maria; Alexandersson, Jan; Robert, Phillipe

    2018-06-08

    Semantic verbal fluency (SVF) tests are routinely used in screening for mild cognitive impairment (MCI). In this task, participants name as many items as possible of a semantic category under a time constraint. Clinicians measure task performance manually by summing the number of correct words and errors. More fine-grained variables add valuable information to clinical assessment, but are time-consuming. Therefore, the aim of this study is to investigate whether automatic analysis of the SVF could provide these as accurate as manual and thus, support qualitative screening of neurocognitive impairment. SVF data were collected from 95 older people with MCI (n = 47), Alzheimer's or related dementias (ADRD; n = 24), and healthy controls (HC; n = 24). All data were annotated manually and automatically with clusters and switches. The obtained metrics were validated using a classifier to distinguish HC, MCI, and ADRD. Automatically extracted clusters and switches were highly correlated (r = 0.9) with manually established values, and performed as well on the classification task separating HC from persons with ADRD (area under curve [AUC] = 0.939) and MCI (AUC = 0.758). The results show that it is possible to automate fine-grained analyses of SVF data for the assessment of cognitive decline. © 2018 S. Karger AG, Basel.

  10. Fully automatic region of interest selection in glomerular filtration rate estimation from 99mTc-DTPA renogram.

    PubMed

    Lin, Kun-Ju; Huang, Jia-Yann; Chen, Yung-Sheng

    2011-12-01

    Glomerular filtration rate (GFR) is a common accepted standard estimation of renal function. Gamma camera-based methods for estimating renal uptake of (99m)Tc-diethylenetriaminepentaacetic acid (DTPA) without blood or urine sampling have been widely used. Of these, the method introduced by Gates has been the most common method. Currently, most of gamma cameras are equipped with a commercial program for GFR determination, a semi-quantitative analysis by manually drawing region of interest (ROI) over each kidney. Then, the GFR value can be computed from the scintigraphic determination of (99m)Tc-DTPA uptake within the kidney automatically. Delineating the kidney area is difficult when applying a fixed threshold value. Moreover, hand-drawn ROIs are tedious, time consuming, and dependent highly on operator skill. Thus, we developed a fully automatic renal ROI estimation system based on the temporal changes in intensity counts, intensity-pair distribution image contrast enhancement method, adaptive thresholding, and morphological operations that can locate the kidney area and obtain the GFR value from a (99m)Tc-DTPA renogram. To evaluate the performance of the proposed approach, 30 clinical dynamic renograms were introduced. The fully automatic approach failed in one patient with very poor renal function. Four patients had a unilateral kidney, and the others had bilateral kidneys. The automatic contours from the remaining 54 kidneys were compared with the contours of manual drawing. The 54 kidneys were included for area error and boundary error analyses. There was high correlation between two physicians' manual contours and the contours obtained by our approach. For area error analysis, the mean true positive area overlap is 91%, the mean false negative is 13.4%, and the mean false positive is 9.3%. The boundary error is 1.6 pixels. The GFR calculated using this automatic computer-aided approach is reproducible and may be applied to help nuclear medicine physicians in

  11. Population delineation of polar bears using satellite collar data

    USGS Publications Warehouse

    Bethke, R.; Taylor, Mitchell K.; Amstrup, Steven C.; Messier, François

    1996-01-01

    To produce reliable estimates of the size or vital rates of a given population, it is important that the boundaries of the population under study are clearly defined. This is particularly critical for large, migratory animals where levels of sustainable harvest are based on these estimates, and where small errors may have serious long-term consequences for the population. Once populations are delineated, rates of exchange between adjacent populations can be determined and accounted/corrected for when calculating abundance (e.g., based on mark-recapture data). Using satellite radio-collar locations for polar bears in the western Canadian Arctic, we illustrate one approach to delineating wildlife populations that integrates cluster analysis methods for determining group membership with home range plotting procedures to define spatial utilization. This approach is flexible with respect to the specific procedures used and provides an objective and quantitative basis for defining population boundaries.

  12. SU-F-R-25: Automatic Identification of Suspicious Recurrent/residual Disease Regions After Prostatectomy

    DOE Office of Scientific and Technical Information (OSTI.GOV)

    Parra, N A; Abramowitz, M; Pollack, A

    2016-06-15

    Purpose: To automatically identify and outline suspicious regions of recurrent or residual disease in the prostate bed using Dynamic Contrast Enhanced-MRI (DCE-MRI) in patients after prostatectomy. Methods: Twenty-two patients presenting for salvage radiotherapy and with identified Gross Tumor Volume (GTV) in the prostate bed were retrospectively analyzed. The MRI data consisted of Axial T2weighted-MRI (T2w) of the pelvis: resolution 1.25×1.25×2.5 mm; Field of View (FOV): 320×320 mm; slice thickness=2.5mm; 72 slices; and Dynamic Contrast Enhanced MRI (DCE-MRI)–12 series of T1w with identical spatial resolution to T2w and at 30–34s temporal resolution. Unsupervised pattern recognition was used to decompose the 4Dmore » DCE data as the product W.H of weights W of k patterns H. A well-perfused pattern Hwp was identified and the weight map Wwp associated to Hwp was used to delineate suspicious volumes. Threshold of Wwp set at mean(Wwp)+S*std(Wwp), S=1,1.5,2 and 2.5 defined four volumes labeled as DCE1.0 to DCE2.5. These volumes were displayed on T2w and, along with GTV, were correlated with the highest pre-treatment PSA values, and with pharmacokinetic analysis constants. Results: GTV was significantly correlated with DCE2.0(ρ= 0.60, p<0.003), and DCE 2.5 (ρ=0.58, p=0.004)). Significant correlation was found between highest pre-treatment PSA and GTV(ρ=0.42, p<0.049), DCE2.0(ρ= 0.52, p<0.012), and DCE 2.5 (ρ=0.67, p<<0.01)). Kruskal-Wallis analysis showed that Ktrans median value was statistically different between non-specific prostate bed tissue NSPBT and both GTV (p<<0.001) and DCE2.5 (p<<0.001), but while median Ve was statistically different between DCE2.5 and NSPBT (p=0.002), it was not statistically different between GTV and NSPBT (p=0.054), suggesting that automatic volumes capture more accurately the area of malignancy. Conclusion: Software developed for identification and visualization of suspicions regions in DCE-MRI from post

  13. Application of a semi-automatic cartilage segmentation method for biomechanical modeling of the knee joint.

    PubMed

    Liukkonen, Mimmi K; Mononen, Mika E; Tanska, Petri; Saarakkala, Simo; Nieminen, Miika T; Korhonen, Rami K

    2017-10-01

    Manual segmentation of articular cartilage from knee joint 3D magnetic resonance images (MRI) is a time consuming and laborious task. Thus, automatic methods are needed for faster and reproducible segmentations. In the present study, we developed a semi-automatic segmentation method based on radial intensity profiles to generate 3D geometries of knee joint cartilage which were then used in computational biomechanical models of the knee joint. Six healthy volunteers were imaged with a 3T MRI device and their knee cartilages were segmented both manually and semi-automatically. The values of cartilage thicknesses and volumes produced by these two methods were compared. Furthermore, the influences of possible geometrical differences on cartilage stresses and strains in the knee were evaluated with finite element modeling. The semi-automatic segmentation and 3D geometry construction of one knee joint (menisci, femoral and tibial cartilages) was approximately two times faster than with manual segmentation. Differences in cartilage thicknesses, volumes, contact pressures, stresses, and strains between segmentation methods in femoral and tibial cartilage were mostly insignificant (p > 0.05) and random, i.e. there were no systematic differences between the methods. In conclusion, the devised semi-automatic segmentation method is a quick and accurate way to determine cartilage geometries; it may become a valuable tool for biomechanical modeling applications with large patient groups.

  14. The automatic component of habit in health behavior: habit as cue-contingent automaticity.

    PubMed

    Orbell, Sheina; Verplanken, Bas

    2010-07-01

    Habit might be usefully characterized as a form of automaticity that involves the association of a cue and a response. Three studies examined habitual automaticity in regard to different aspects of the cue-response relationship characteristic of unhealthy and healthy habits. In each study, habitual automaticity was assessed by the Self-Report Habit Index (SRHI). In Study 1 SRHI scores correlated with attentional bias to smoking cues in a Stroop task. Study 2 examined the ability of a habit cue to elicit an unwanted habit response. In a prospective field study, habitual automaticity in relation to smoking when drinking alcohol in a licensed public house (pub) predicted the likelihood of cigarette-related action slips 2 months later after smoking in pubs had become illegal. In Study 3 experimental group participants formed an implementation intention to floss in response to a specified situational cue. Habitual automaticity of dental flossing was rapidly enhanced compared to controls. The studies provided three different demonstrations of the importance of cues in the automatic operation of habits. Habitual automaticity assessed by the SRHI captured aspects of a habit that go beyond mere frequency or consistency of the behavior. PsycINFO Database Record (c) 2010 APA, all rights reserved.

  15. High-order space charge effects using automatic differentiation

    DOE Office of Scientific and Technical Information (OSTI.GOV)

    Reusch, M.F.; Bruhwiler, D.L.

    1997-02-01

    The Northrop Grumman Topkark code has been upgraded to Fortran 90, making use of operator overloading, so the same code can be used to either track an array of particles or construct a Taylor map representation of the accelerator lattice. We review beam optics and beam dynamics simulations conducted with TOPKARK in the past and we present a new method for modeling space charge forces to high-order with automatic differentiation. This method generates an accurate, high-order, 6-D Taylor map of the phase space variable trajectories for a bunched, high-current beam. The spatial distribution is modeled as the product of amore » Taylor Series times a Gaussian. The variables in the argument of the Gaussian are normalized to the respective second moments of the distribution. This form allows for accurate representation of a wide range of realistic distributions, including any asymmetries, and allows for rapid calculation of the space charge fields with free space boundary conditions. An example problem is presented to illustrate our approach. {copyright} {ital 1997 American Institute of Physics.}« less

  16. High-order space charge effects using automatic differentiation

    DOE Office of Scientific and Technical Information (OSTI.GOV)

    Reusch, Michael F.; Bruhwiler, David L.; Computer Accelerator Physics Conference Williamsburg, Virginia 1996

    1997-02-01

    The Northrop Grumman Topkark code has been upgraded to Fortran 90, making use of operator overloading, so the same code can be used to either track an array of particles or construct a Taylor map representation of the accelerator lattice. We review beam optics and beam dynamics simulations conducted with TOPKARK in the past and we present a new method for modeling space charge forces to high-order with automatic differentiation. This method generates an accurate, high-order, 6-D Taylor map of the phase space variable trajectories for a bunched, high-current beam. The spatial distribution is modeled as the product of amore » Taylor Series times a Gaussian. The variables in the argument of the Gaussian are normalized to the respective second moments of the distribution. This form allows for accurate representation of a wide range of realistic distributions, including any asymmetries, and allows for rapid calculation of the space charge fields with free space boundary conditions. An example problem is presented to illustrate our approach.« less

  17. Automatic MRI 2D brain segmentation using graph searching technique.

    PubMed

    Pedoia, Valentina; Binaghi, Elisabetta

    2013-09-01

    Accurate and efficient segmentation of the whole brain in magnetic resonance (MR) images is a key task in many neuroscience and medical studies either because the whole brain is the final anatomical structure of interest or because the automatic extraction facilitates further analysis. The problem of segmenting brain MRI images has been extensively addressed by many researchers. Despite the relevant achievements obtained, automated segmentation of brain MRI imagery is still a challenging problem whose solution has to cope with critical aspects such as anatomical variability and pathological deformation. In the present paper, we describe and experimentally evaluate a method for segmenting brain from MRI images basing on two-dimensional graph searching principles for border detection. The segmentation of the whole brain over the entire volume is accomplished slice by slice, automatically detecting frames including eyes. The method is fully automatic and easily reproducible by computing the internal main parameters directly from the image data. The segmentation procedure is conceived as a tool of general applicability, although design requirements are especially commensurate with the accuracy required in clinical tasks such as surgical planning and post-surgical assessment. Several experiments were performed to assess the performance of the algorithm on a varied set of MRI images obtaining good results in terms of accuracy and stability. Copyright © 2012 John Wiley & Sons, Ltd.

  18. An Automatic Critical Care Urine Meter

    PubMed Central

    Otero, Abraham; Fernández, Roemi; Apalkov, Andrey; Armada, Manuel

    2012-01-01

    Nowadays patients admitted to critical care units have most of their physiological parameters measured automatically by sophisticated commercial monitoring devices. More often than not, these devices supervise whether the values of the parameters they measure lie within a pre-established range, and issue warning of deviations from this range by triggering alarms. The automation of measuring and supervising tasks not only discharges the healthcare staff of a considerable workload but also avoids human errors in these repetitive and monotonous tasks. Arguably, the most relevant physiological parameter that is still measured and supervised manually by critical care unit staff is urine output (UO). In this paper we present a patent-pending device that provides continuous and accurate measurements of patient's UO. The device uses capacitive sensors to take continuous measurements of the height of the column of liquid accumulated in two chambers that make up a plastic container. The first chamber, where the urine inputs, has a small volume. Once it has been filled it overflows into a second bigger chamber. The first chamber provides accurate UO measures of patients whose UO has to be closely supervised, while the second one avoids the need for frequent interventions by the nursing staff to empty the container. PMID:23201988

  19. TU-AB-BRA-11: Evaluation of Fully Automatic Volumetric GBM Segmentation in the TCGA-GBM Dataset: Prognosis and Correlation with VASARI Features

    DOE Office of Scientific and Technical Information (OSTI.GOV)

    Rios Velazquez, E; Meier, R; Dunn, W

    Purpose: Reproducible definition and quantification of imaging biomarkers is essential. We evaluated a fully automatic MR-based segmentation method by comparing it to manually defined sub-volumes by experienced radiologists in the TCGA-GBM dataset, in terms of sub-volume prognosis and association with VASARI features. Methods: MRI sets of 67 GBM patients were downloaded from the Cancer Imaging archive. GBM sub-compartments were defined manually and automatically using the Brain Tumor Image Analysis (BraTumIA), including necrosis, edema, contrast enhancing and non-enhancing tumor. Spearman’s correlation was used to evaluate the agreement with VASARI features. Prognostic significance was assessed using the C-index. Results: Auto-segmented sub-volumes showedmore » high agreement with manually delineated volumes (range (r): 0.65 – 0.91). Also showed higher correlation with VASARI features (auto r = 0.35, 0.60 and 0.59; manual r = 0.29, 0.50, 0.43, for contrast-enhancing, necrosis and edema, respectively). The contrast-enhancing volume and post-contrast abnormal volume showed the highest C-index (0.73 and 0.72), comparable to manually defined volumes (p = 0.22 and p = 0.07, respectively). The non-enhancing region defined by BraTumIA showed a significantly higher prognostic value (CI = 0.71) than the edema (CI = 0.60), both of which could not be distinguished by manual delineation. Conclusion: BraTumIA tumor sub-compartments showed higher correlation with VASARI data, and equivalent performance in terms of prognosis compared to manual sub-volumes. This method can enable more reproducible definition and quantification of imaging based biomarkers and has a large potential in high-throughput medical imaging research.« less

  20. Automatic segmentation of the bone and extraction of the bone cartilage interface from magnetic resonance images of the knee

    NASA Astrophysics Data System (ADS)

    Fripp, Jurgen; Crozier, Stuart; Warfield, Simon K.; Ourselin, Sébastien

    2007-03-01

    The accurate segmentation of the articular cartilages from magnetic resonance (MR) images of the knee is important for clinical studies and drug trials into conditions like osteoarthritis. Currently, segmentations are obtained using time-consuming manual or semi-automatic algorithms which have high inter- and intra-observer variabilities. This paper presents an important step towards obtaining automatic and accurate segmentations of the cartilages, namely an approach to automatically segment the bones and extract the bone-cartilage interfaces (BCI) in the knee. The segmentation is performed using three-dimensional active shape models, which are initialized using an affine registration to an atlas. The BCI are then extracted using image information and prior knowledge about the likelihood of each point belonging to the interface. The accuracy and robustness of the approach was experimentally validated using an MR database of fat suppressed spoiled gradient recall images. The (femur, tibia, patella) bone segmentation had a median Dice similarity coefficient of (0.96, 0.96, 0.89) and an average point-to-surface error of 0.16 mm on the BCI. The extracted BCI had a median surface overlap of 0.94 with the real interface, demonstrating its usefulness for subsequent cartilage segmentation or quantitative analysis.

  1. Automatic Correction of Intensity Nonuniformity from Sparseness of Gradient Distribution in Medical Images

    PubMed Central

    Zheng, Yuanjie; Grossman, Murray; Awate, Suyash P.; Gee, James C.

    2013-01-01

    We propose to use the sparseness property of the gradient probability distribution to estimate the intensity nonuniformity in medical images, resulting in two novel automatic methods: a non-parametric method and a parametric method. Our methods are easy to implement because they both solve an iteratively re-weighted least squares problem. They are remarkably accurate as shown by our experiments on images of different imaged objects and from different imaging modalities. PMID:20426191

  2. Automatic and manual segmentation of healthy retinas using high-definition optical coherence tomography.

    PubMed

    Golbaz, Isabelle; Ahlers, Christian; Goesseringer, Nina; Stock, Geraldine; Geitzenauer, Wolfgang; Prünte, Christian; Schmidt-Erfurth, Ursula Margarethe

    2011-03-01

    This study compared automatic- and manual segmentation modalities in the retina of healthy eyes using high-definition optical coherence tomography (HD-OCT). Twenty retinas in 20 healthy individuals were examined using an HD-OCT system (Carl Zeiss Meditec, Inc.). Three-dimensional imaging was performed with an axial resolution of 6 μm at a maximum scanning speed of 25,000 A-scans/second. Volumes of 6 × 6 × 2 mm were scanned. Scans were analysed using a matlab-based algorithm and a manual segmentation software system (3D-Doctor). The volume values calculated by the two methods were compared. Statistical analysis revealed a high correlation between automatic and manual modes of segmentation. The automatic mode of measuring retinal volume and the corresponding three-dimensional images provided similar results to the manual segmentation procedure. Both methods were able to visualize retinal and subretinal features accurately. This study compared two methods of assessing retinal volume using HD-OCT scans in healthy retinas. Both methods were able to provide realistic volumetric data when applied to raster scan sets. Manual segmentation methods represent an adequate tool with which to control automated processes and to identify clinically relevant structures, whereas automatic procedures will be needed to obtain data in larger patient populations. © 2009 The Authors. Journal compilation © 2009 Acta Ophthalmol.

  3. Evaluation of automated threshold selection methods for accurately sizing microscopic fluorescent cells by image analysis.

    PubMed Central

    Sieracki, M E; Reichenbach, S E; Webb, K L

    1989-01-01

    The accurate measurement of bacterial and protistan cell biomass is necessary for understanding their population and trophic dynamics in nature. Direct measurement of fluorescently stained cells is often the method of choice. The tedium of making such measurements visually on the large numbers of cells required has prompted the use of automatic image analysis for this purpose. Accurate measurements by image analysis require an accurate, reliable method of segmenting the image, that is, distinguishing the brightly fluorescing cells from a dark background. This is commonly done by visually choosing a threshold intensity value which most closely coincides with the outline of the cells as perceived by the operator. Ideally, an automated method based on the cell image characteristics should be used. Since the optical nature of edges in images of light-emitting, microscopic fluorescent objects is different from that of images generated by transmitted or reflected light, it seemed that automatic segmentation of such images may require special considerations. We tested nine automated threshold selection methods using standard fluorescent microspheres ranging in size and fluorescence intensity and fluorochrome-stained samples of cells from cultures of cyanobacteria, flagellates, and ciliates. The methods included several variations based on the maximum intensity gradient of the sphere profile (first derivative), the minimum in the second derivative of the sphere profile, the minimum of the image histogram, and the midpoint intensity. Our results indicated that thresholds determined visually and by first-derivative methods tended to overestimate the threshold, causing an underestimation of microsphere size. The method based on the minimum of the second derivative of the profile yielded the most accurate area estimates for spheres of different sizes and brightnesses and for four of the five cell types tested. A simple model of the optical properties of fluorescing objects and

  4. Accurately tracking single-cell movement trajectories in microfluidic cell sorting devices.

    PubMed

    Jeong, Jenny; Frohberg, Nicholas J; Zhou, Enlu; Sulchek, Todd; Qiu, Peng

    2018-01-01

    Microfluidics are routinely used to study cellular properties, including the efficient quantification of single-cell biomechanics and label-free cell sorting based on the biomechanical properties, such as elasticity, viscosity, stiffness, and adhesion. Both quantification and sorting applications require optimal design of the microfluidic devices and mathematical modeling of the interactions between cells, fluid, and the channel of the device. As a first step toward building such a mathematical model, we collected video recordings of cells moving through a ridged microfluidic channel designed to compress and redirect cells according to cell biomechanics. We developed an efficient algorithm that automatically and accurately tracked the cell trajectories in the recordings. We tested the algorithm on recordings of cells with different stiffness, and showed the correlation between cell stiffness and the tracked trajectories. Moreover, the tracking algorithm successfully picked up subtle differences of cell motion when passing through consecutive ridges. The algorithm for accurately tracking cell trajectories paves the way for future efforts of modeling the flow, forces, and dynamics of cell properties in microfluidics applications.

  5. DOE Office of Scientific and Technical Information (OSTI.GOV)

    Pasquier, David; Lacornerie, Thomas; Vermandel, Maximilien

    Purpose: Target-volume and organ-at-risk delineation is a time-consuming task in radiotherapy planning. The development of automated segmentation tools remains problematic, because of pelvic organ shape variability. We evaluate a three-dimensional (3D), deformable-model approach and a seeded region-growing algorithm for automatic delineation of the prostate and organs-at-risk on magnetic resonance images. Methods and Materials: Manual and automatic delineation were compared in 24 patients using a sagittal T2-weighted (T2-w) turbo spin echo (TSE) sequence and an axial T1-weighted (T1-w) 3D fast-field echo (FFE) or TSE sequence. For automatic prostate delineation, an organ model-based method was used. Prostates without seminal vesicles were delineatedmore » as the clinical target volume (CTV). For automatic bladder and rectum delineation, a seeded region-growing method was used. Manual contouring was considered the reference method. The following parameters were measured: volume ratio (Vr) (automatic/manual), volume overlap (Vo) (ratio of the volume of intersection to the volume of union; optimal value = 1), and correctly delineated volume (Vc) (percent ratio of the volume of intersection to the manually defined volume; optimal value 100). Results: For the CTV, the Vr, Vo, and Vc were 1.13 ({+-}0.1 SD), 0.78 ({+-}0.05 SD), and 94.75 ({+-}3.3 SD), respectively. For the rectum, the Vr, Vo, and Vc were 0.97 ({+-}0.1 SD), 0.78 ({+-}0.06 SD), and 86.52 ({+-}5 SD), respectively. For the bladder, the Vr, Vo, and Vc were 0.95 ({+-}0.03 SD), 0.88 ({+-}0.03 SD), and 91.29 ({+-}3.1 SD), respectively. Conclusions: Our results show that the organ-model method is robust, and results in reproducible prostate segmentation with minor interactive corrections. For automatic bladder and rectum delineation, magnetic resonance imaging soft-tissue contrast enables the use of region-growing methods.« less

  6. Intelligent vision guide for automatic ventilation grommet insertion into the tympanic membrane.

    PubMed

    Gao, Wenchao; Tan, Kok Kiong; Liang, Wenyu; Gan, Chee Wee; Lim, Hsueh Yee

    2016-03-01

    Otitis media with effusion is a worldwide ear disease. The current treatment is to surgically insert a ventilation grommet into the tympanic membrane. A robotic device allowing automatic grommet insertion has been designed in a previous study; however, the part of the membrane where the malleus bone is attached to the inner surface is to be avoided during the insertion process. This paper proposes a synergy of optical flow technique and a gradient vector flow active contours algorithm to achieve an online tracking of the malleus under endoscopic vision, to guide the working channel to move efficiently during the surgery. The proposed method shows a more stable and accurate tracking performance than the current tracking methods in preclinical tests. With satisfactory tracking results, vision guidance of a suitable insertion spot can be provided to the device to perform the surgery in an automatic way. Copyright © 2015 John Wiley & Sons, Ltd.

  7. Automatic extraction of blocks from 3D point clouds of fractured rock

    NASA Astrophysics Data System (ADS)

    Chen, Na; Kemeny, John; Jiang, Qinghui; Pan, Zhiwen

    2017-12-01

    This paper presents a new method for extracting blocks and calculating block size automatically from rock surface 3D point clouds. Block size is an important rock mass characteristic and forms the basis for several rock mass classification schemes. The proposed method consists of four steps: 1) the automatic extraction of discontinuities using an improved Ransac Shape Detection method, 2) the calculation of discontinuity intersections based on plane geometry, 3) the extraction of block candidates based on three discontinuities intersecting one another to form corners, and 4) the identification of "true" blocks using an improved Floodfill algorithm. The calculated block sizes were compared with manual measurements in two case studies, one with fabricated cardboard blocks and the other from an actual rock mass outcrop. The results demonstrate that the proposed method is accurate and overcomes the inaccuracies, safety hazards, and biases of traditional techniques.

  8. Cognitive learning: a machine learning approach for automatic process characterization from design

    NASA Astrophysics Data System (ADS)

    Foucher, J.; Baderot, J.; Martinez, S.; Dervilllé, A.; Bernard, G.

    2018-03-01

    Cutting edge innovation requires accurate and fast process-control to obtain fast learning rate and industry adoption. Current tools available for such task are mainly manual and user dependent. We present in this paper cognitive learning, which is a new machine learning based technique to facilitate and to speed up complex characterization by using the design as input, providing fast training and detection time. We will focus on the machine learning framework that allows object detection, defect traceability and automatic measurement tools.

  9. First- and Second-Order Sensitivity Analysis of a P-Version Finite Element Equation Via Automatic Differentiation

    NASA Technical Reports Server (NTRS)

    Hou, Gene

    1998-01-01

    Sensitivity analysis is a technique for determining derivatives of system responses with respect to design parameters. Among many methods available for sensitivity analysis, automatic differentiation has been proven through many applications in fluid dynamics and structural mechanics to be an accurate and easy method for obtaining derivatives. Nevertheless, the method can be computational expensive and can require a high memory space. This project will apply an automatic differentiation tool, ADIFOR, to a p-version finite element code to obtain first- and second- order then-nal derivatives, respectively. The focus of the study is on the implementation process and the performance of the ADIFOR-enhanced codes for sensitivity analysis in terms of memory requirement, computational efficiency, and accuracy.

  10. Automatic processing of high-rate, high-density multibeam echosounder data

    NASA Astrophysics Data System (ADS)

    Calder, B. R.; Mayer, L. A.

    2003-06-01

    Multibeam echosounders (MBES) are currently the best way to determine the bathymetry of large regions of the seabed with high accuracy. They are becoming the standard instrument for hydrographic surveying and are also used in geological studies, mineral exploration and scientific investigation of the earth's crustal deformations and life cycle. The significantly increased data density provided by an MBES has significant advantages in accurately delineating the morphology of the seabed, but comes with the attendant disadvantage of having to handle and process a much greater volume of data. Current data processing approaches typically involve (computer aided) human inspection of all data, with time-consuming and subjective assessment of all data points. As data rates increase with each new generation of instrument and required turn-around times decrease, manual approaches become unwieldy and automatic methods of processing essential. We propose a new method for automatically processing MBES data that attempts to address concerns of efficiency, objectivity, robustness and accuracy. The method attributes each sounding with an estimate of vertical and horizontal error, and then uses a model of information propagation to transfer information about the depth from each sounding to its local neighborhood. Embedded in the survey area are estimation nodes that aim to determine the true depth at an absolutely defined location, along with its associated uncertainty. As soon as soundings are made available, the nodes independently assimilate propagated information to form depth hypotheses which are then tracked and updated on-line as more data is gathered. Consequently, we can extract at any time a "current-best" estimate for all nodes, plus co-located uncertainties and other metrics. The method can assimilate data from multiple surveys, multiple instruments or repeated passes of the same instrument in real-time as data is being gathered. The data assimilation scheme is

  11. Automatically monitoring driftwood in large rivers: preliminary results

    NASA Astrophysics Data System (ADS)

    Piegay, H.; Lemaire, P.; MacVicar, B.; Mouquet-Noppe, C.; Tougne, L.

    2014-12-01

    Driftwood in rivers impact sediment transport, riverine habitat and human infrastructures. Quantifying it, in particular large woods on fairly large rivers where it can move easily, would allow us to improve our knowledge on fluvial transport processes. There are several means of studying this phenomenon, amongst which RFID sensors tracking, photo and video monitoring. In this abstract, we are interested in the latter, being easier and cheaper to deploy. However, video monitoring of driftwood generates a huge amount of images and manually labeling it is tedious. It is essential to automate such a monitoring process, which is a difficult task in the field of computer vision, and more specifically automatic video analysis. Detecting foreground into dynamic background remains an open problem to date. We installed a video camera at the riverside of a gauging station on the Ain River, a 3500 km² Piedmont River in France. Several floods were manually annotated by a human operator. We developed software that automatically extracts and characterizes wood blocks within a video stream. This algorithm is based upon a statistical model and combines static, dynamic and spatial data. Segmented wood objects are further described with the help of a skeleton-based approach that helps us to automatically determine its shape, diameter and length. The first detailed comparisons between manual annotations and automatically extracted data show that we can fairly well detect large wood until a given size (approximately 120 cm in length or 15 cm in diameter) whereas smaller ones are difficult to detect and tend to be missed by either the human operator, either the algorithm. Detection is fairly accurate in high flow conditions where the water channel is usually brown because of suspended sediment transport. In low flow context, our algorithm still needs improvement to reduce the number of false positive so as to better distinguish shadow or turbulence structures from wood pieces.

  12. Delineating Biophysical Environments of the Sunda Banda Seascape, Indonesia

    PubMed Central

    Wang, Mingshu; Ahmadia, Gabby N.; Chollett, Iliana; Huang, Charles; Fox, Helen; Wijonarno, Anton; Madden, Marguerite

    2015-01-01

    The Sunda Banda Seascape (SBS), located in the center of the Coral Triangle, is a global center of marine biodiversity and a conservation priority. We proposed the first biophysical environmental delineation of the SBS using globally available satellite remote sensing and model-assimilated data to categorize this area into unique and meaningful biophysical classes. Specifically, the SBS was partitioned into eight biophysical classes characterized by similar sea surface temperature, chlorophyll a concentration, currents, and salinity patterns. Areas within each class were expected to have similar habitat types and ecosystem functions. Our work supplemented prevailing global marine management schemes by focusing in on a regional scale with finer spatial resolution. It also provided a baseline for academic research, ecological assessments and will facilitate marine spatial planning and conservation activities in the area. In addition, the framework and methods of delineating biophysical environments we presented can be expanded throughout the whole Coral Triangle to support research and conservation activities in this important region. PMID:25648170

  13. Improving the delineation of hydrocarbon-impacted soils and water through induced polarization (IP) tomographies: a field study at an industrial waste land.

    PubMed

    Deceuster, John; Kaufmann, Olivier

    2012-08-01

    Without a good estimation of samples representativeness, the delineation of the contaminated plume extent and the evaluation of volumes of hydrocarbon-impacted soils may remain difficult. To contribute to this question, a time domain induced polarization (IP) field experiment was conducted on an industrial waste land. Boreholes were drilled to specify the local geological context. Cross-hole seismic tomographies were performed to extend borehole logs and to draw an interpreted geological cross-section. Soil samples taken during drillings were analysed in laboratory. A preliminary survey was conducted to locate the IP profile. The polarization signatures linked to the presence of clayey sediments were filtered out from the data set. Chargeability and resistivity depth soundings were computed and compared to mean concentrations of total organic products to overcome the data support issue between the geophysical models and the spot samples of soils. A logarithmic relation between chargeabilities and smoothed hydrocarbon concentrations in soils was found. Taking into account contaminant's concentration thresholds defined in local codes and regulations allows defining chargeability classes to delineate hotspots on this site. This showed that IP tomography can be an accurate screening methodology. A statistical methodology is proposed to assess the efficiency of the investigation strategy. Copyright © 2012 Elsevier B.V. All rights reserved.

  14. Management of natural resources through automatic cartographic inventory. [France

    NASA Technical Reports Server (NTRS)

    Rey, P.; Gourinard, Y.; Cambou, F. (Principal Investigator)

    1974-01-01

    The author has identified the following significant results. (1) Accurate recognition of previously known ground features from ERTS-1 imagery has been confirmed and a probable detection range for the major signatures can be given. (2) Unidentified elements, however, must be decoded by means of the equal densitometric value zone method. (3) Determination of these zonings involves an analogical treatment of images using the color equidensity methods (pseudo-color), color composites and especially temporal color composite (repetitive superposition). (4) After this analogical preparation, the digital equidensities can be processed by computer in the four MSS bands, according to a series of transfer operations from imagery and automatic cartography.

  15. Comparison of manual and automatic segmentation methods for brain structures in the presence of space-occupying lesions: a multi-expert study

    PubMed Central

    Deeley, M A; Chen, A; Datteri, R; Noble, J; Cmelak, A; Donnelly, E; Malcolm, A; Moretti, L; Jaboin, J; Niermann, K; Yang, Eddy S; Yu, David S; Yei, F; Koyama, T; Ding, G X; Dawant, B M

    2011-01-01

    The purpose of this work was to characterize expert variation in segmentation of intracranial structures pertinent to radiation therapy, and to assess a registration-driven atlas-based segmentation algorithm in that context. Eight experts were recruited to segment the brainstem, optic chiasm, optic nerves, and eyes, of 20 patients who underwent therapy for large space-occupying tumors. Performance variability was assessed through three geometric measures: volume, Dice similarity coefficient, and Euclidean distance. In addition, two simulated ground truth segmentations were calculated via the simultaneous truth and performance level estimation (STAPLE) algorithm and a novel application of probability maps. The experts and automatic system were found to generate structures of similar volume, though the experts exhibited higher variation with respect to tubular structures. No difference was found between the mean Dice coefficient (DSC) of the automatic and expert delineations as a group at a 5% significance level over all cases and organs. The larger structures of the brainstem and eyes exhibited mean DSC of approximately 0.8–0.9, whereas the tubular chiasm and nerves were lower, approximately 0.4–0.5. Similarly low DSC have been reported previously without the context of several experts and patient volumes. This study, however, provides evidence that experts are similarly challenged. The average maximum distances (maximum inside, maximum outside) from a simulated ground truth ranged from (−4.3, +5.4) mm for the automatic system to (−3.9, +7.5) mm for the experts considered as a group. Over all the structures in a rank of true positive rates at a 2 mm threshold from the simulated ground truth, the automatic system ranked second of the nine raters. This work underscores the need for large scale studies utilizing statistically robust numbers of patients and experts in evaluating quality of automatic algorithms. PMID:21725140

  16. Comparison of manual and automatic segmentation methods for brain structures in the presence of space-occupying lesions: a multi-expert study

    NASA Astrophysics Data System (ADS)

    Deeley, M. A.; Chen, A.; Datteri, R.; Noble, J. H.; Cmelak, A. J.; Donnelly, E. F.; Malcolm, A. W.; Moretti, L.; Jaboin, J.; Niermann, K.; Yang, Eddy S.; Yu, David S.; Yei, F.; Koyama, T.; Ding, G. X.; Dawant, B. M.

    2011-07-01

    The purpose of this work was to characterize expert variation in segmentation of intracranial structures pertinent to radiation therapy, and to assess a registration-driven atlas-based segmentation algorithm in that context. Eight experts were recruited to segment the brainstem, optic chiasm, optic nerves, and eyes, of 20 patients who underwent therapy for large space-occupying tumors. Performance variability was assessed through three geometric measures: volume, Dice similarity coefficient, and Euclidean distance. In addition, two simulated ground truth segmentations were calculated via the simultaneous truth and performance level estimation algorithm and a novel application of probability maps. The experts and automatic system were found to generate structures of similar volume, though the experts exhibited higher variation with respect to tubular structures. No difference was found between the mean Dice similarity coefficient (DSC) of the automatic and expert delineations as a group at a 5% significance level over all cases and organs. The larger structures of the brainstem and eyes exhibited mean DSC of approximately 0.8-0.9, whereas the tubular chiasm and nerves were lower, approximately 0.4-0.5. Similarly low DSCs have been reported previously without the context of several experts and patient volumes. This study, however, provides evidence that experts are similarly challenged. The average maximum distances (maximum inside, maximum outside) from a simulated ground truth ranged from (-4.3, +5.4) mm for the automatic system to (-3.9, +7.5) mm for the experts considered as a group. Over all the structures in a rank of true positive rates at a 2 mm threshold from the simulated ground truth, the automatic system ranked second of the nine raters. This work underscores the need for large scale studies utilizing statistically robust numbers of patients and experts in evaluating quality of automatic algorithms.

  17. 356. Delineator Unknown March 1946 STATE OF CALIFORNIA; DEPARTMENT OF ...

    Library of Congress Historic Buildings Survey, Historic Engineering Record, Historic Landscapes Survey

    356. Delineator Unknown March 1946 STATE OF CALIFORNIA; DEPARTMENT OF PUBLIC WORKS; SAN FRANCISCO - OAKLAND BAY BRIDGE; GENERAL DATA; PLAT III - San Francisco Oakland Bay Bridge, Spanning San Francisco Bay, San Francisco, San Francisco County, CA

  18. Automatic yield-line analysis of slabs using discontinuity layout optimization

    PubMed Central

    Gilbert, Matthew; He, Linwei; Smith, Colin C.; Le, Canh V.

    2014-01-01

    The yield-line method of analysis is a long established and extremely effective means of estimating the maximum load sustainable by a slab or plate. However, although numerous attempts to automate the process of directly identifying the critical pattern of yield-lines have been made over the past few decades, to date none has proved capable of reliably analysing slabs of arbitrary geometry. Here, it is demonstrated that the discontinuity layout optimization (DLO) procedure can successfully be applied to such problems. The procedure involves discretization of the problem using nodes inter-connected by potential yield-line discontinuities, with the critical layout of these then identified using linear programming. The procedure is applied to various benchmark problems, demonstrating that highly accurate solutions can be obtained, and showing that DLO provides a truly systematic means of directly and reliably automatically identifying yield-line patterns. Finally, since the critical yield-line patterns for many problems are found to be quite complex in form, a means of automatically simplifying these is presented. PMID:25104905

  19. Integration of multi-disciplinary geospatial data for delineating agroecosystem uniform management zones.

    PubMed

    Liu, Huanjun; Huffman, Ted; Liu, Jiangui; Li, Zhe; Daneshfar, Bahram; Zhang, Xinle

    2015-01-01

    Understanding agricultural ecosystems and their complex interactions with the environment is important for improving agricultural sustainability and environmental protection. Developing the necessary understanding requires approaches that integrate multi-source geospatial data and interdisciplinary relationships at different spatial scales. In order to identify and delineate landscape units representing relatively homogenous biophysical properties and eco-environmental functions at different spatial scales, a hierarchical system of uniform management zones (UMZ) is proposed. The UMZ hierarchy consists of seven levels of units at different spatial scales, namely site-specific, field, local, regional, country, continent, and globe. Relatively few studies have focused on the identification of the two middle levels of units in the hierarchy, namely the local UMZ (LUMZ) and the regional UMZ (RUMZ), which prevents true eco-environmental studies from being carried out across the full range of scales. This study presents a methodology to delineate LUMZ and RUMZ spatial units using land cover, soil, and remote sensing data. A set of objective criteria were defined and applied to evaluate the within-zone homogeneity and between-zone separation of the delineated zones. The approach was applied in a farming and forestry region in southeastern Ontario, Canada, and the methodology was shown to be objective, flexible, and applicable with commonly available spatial data. The hierarchical delineation of UMZs can be used as a tool to organize the spatial structure of agricultural landscapes, to understand spatial relationships between cropping practices and natural resources, and to target areas for application of specific environmental process models and place-based policy interventions.

  20. Determination of land use in Minnesota by automatic interpretation of ERTS MSS data

    NASA Technical Reports Server (NTRS)

    Zirkle, R. E.; Pile, D. R.

    1973-01-01

    This program aims to determine the feasibility of identifying land use in Minnesota by automatic interpretation of ERTS-MSS data. Ultimate objectives include establishment of land use delineation and quantification by computer processing with a minimum of human operator interaction. This implies not only that reflectivity as a function of calendar time can be catalogued effectively, but also that the effects of uncontrolled variables can be identified and compensated. Clouds are the major uncontrollable data pollutant, so part of the initial effort is devoted to determining their effect and the construction of a model to help correct or justifiably ignore affected data. Other short range objectives are to identify and verify measurements giving results of importance to land managers. Lake-counting is a prominent example. Open water is easily detected in band 7 data with some support from either band 4 or band 5 to remove ambiguities. Land managers and conservationists commission studies periodically to measure water bodies and total water count within specified areas.

  1. Motor automaticity in Parkinson’s disease

    PubMed Central

    Wu, Tao; Hallett, Mark; Chan, Piu

    2017-01-01

    Bradykinesia is the most important feature contributing to motor difficulties in Parkinson’s disease (PD). However, the pathophysiology underlying bradykinesia is not fully understood. One important aspect is that PD patients have difficulty in performing learned motor skills automatically, but this problem has been generally overlooked. Here we review motor automaticity associated motor deficits in PD, such as reduced arm swing, decreased stride length, freezing of gait, micrographia and reduced facial expression. Recent neuroimaging studies have revealed some neural mechanisms underlying impaired motor automaticity in PD, including less efficient neural coding of movement, failure to shift automated motor skills to the sensorimotor striatum, instability of the automatic mode within the striatum, and use of attentional control and/or compensatory efforts to execute movements usually performed automatically in healthy people. PD patients lose previously acquired automatic skills due to their impaired sensorimotor striatum, and have difficulty in acquiring new automatic skills or restoring lost motor skills. More investigations on the pathophysiology of motor automaticity, the effect of L-dopa or surgical treatments on automaticity, and the potential role of using measures of automaticity in early diagnosis of PD would be valuable. PMID:26102020

  2. High-throughput image analysis of tumor spheroids: a user-friendly software application to measure the size of spheroids automatically and accurately.

    PubMed

    Chen, Wenjin; Wong, Chung; Vosburgh, Evan; Levine, Arnold J; Foran, David J; Xu, Eugenia Y

    2014-07-08

    The increasing number of applications of three-dimensional (3D) tumor spheroids as an in vitro model for drug discovery requires their adaptation to large-scale screening formats in every step of a drug screen, including large-scale image analysis. Currently there is no ready-to-use and free image analysis software to meet this large-scale format. Most existing methods involve manually drawing the length and width of the imaged 3D spheroids, which is a tedious and time-consuming process. This study presents a high-throughput image analysis software application - SpheroidSizer, which measures the major and minor axial length of the imaged 3D tumor spheroids automatically and accurately; calculates the volume of each individual 3D tumor spheroid; then outputs the results in two different forms in spreadsheets for easy manipulations in the subsequent data analysis. The main advantage of this software is its powerful image analysis application that is adapted for large numbers of images. It provides high-throughput computation and quality-control workflow. The estimated time to process 1,000 images is about 15 min on a minimally configured laptop, or around 1 min on a multi-core performance workstation. The graphical user interface (GUI) is also designed for easy quality control, and users can manually override the computer results. The key method used in this software is adapted from the active contour algorithm, also known as Snakes, which is especially suitable for images with uneven illumination and noisy background that often plagues automated imaging processing in high-throughput screens. The complimentary "Manual Initialize" and "Hand Draw" tools provide the flexibility to SpheroidSizer in dealing with various types of spheroids and diverse quality images. This high-throughput image analysis software remarkably reduces labor and speeds up the analysis process. Implementing this software is beneficial for 3D tumor spheroids to become a routine in vitro model

  3. Delineation of peatland lagg boundaries from airborne LiDAR

    NASA Astrophysics Data System (ADS)

    Langlois, Melanie N.; Richardson, Murray C.; Price, Jonathan S.

    2017-09-01

    In Canada, peatlands are the most common type of wetland, but boundary delineation in peatland complexes has received little attention in the scientific literature. Typically, peatland boundaries are mapped as crisp, absolute features, and the transitional lagg zone—the ecotone found between a raised bog and the surrounding mineral land—is often overlooked. In this study, we aim (1) to advance existing approaches for detecting and locating laggs and lagg boundaries using airborne LiDAR surveys and (2) to describe the spatial distribution of laggs around raised bog peatlands. Two contrasting spatial analytical approaches for lagg detection were tested using five LiDAR-derived topographic and vegetation indices: topography, vegetation height, topographic wetness index, the standard deviation of the vegetation's height (as a proxy for the complexity of the vegetation's structure), and local indices of elevation variance. Using a dissimilarity approach (edge-detection, split-moving window analysis), no one variable accurately depicted both the lagg-mineral land and bog-lagg boundaries. Some indicators were better at predicting the bog-lagg boundary (i.e., vegetation height) and others at finding the lagg-mineral land boundary (i.e., topography). Dissimilarity analysis reinforces the usefulness of derived variables (e.g., wetness indices) in locating laggs, especially for those with weak topographic and vegetation gradients. When the lagg was confined between the bog and the adjacent upland, it took a linear form, parallel to the peatland's edge and was easier to predict. When the adjacent mineral land was flat or sloping away from the peatland, the lagg was discontinuous and intermittent and more difficult to predict.

  4. An evolutionary computation based algorithm for calculating solar differential rotation by automatic tracking of coronal bright points

    NASA Astrophysics Data System (ADS)

    Shahamatnia, Ehsan; Dorotovič, Ivan; Fonseca, Jose M.; Ribeiro, Rita A.

    2016-03-01

    Developing specialized software tools is essential to support studies of solar activity evolution. With new space missions such as Solar Dynamics Observatory (SDO), solar images are being produced in unprecedented volumes. To capitalize on that huge data availability, the scientific community needs a new generation of software tools for automatic and efficient data processing. In this paper a prototype of a modular framework for solar feature detection, characterization, and tracking is presented. To develop an efficient system capable of automatic solar feature tracking and measuring, a hybrid approach combining specialized image processing, evolutionary optimization, and soft computing algorithms is being followed. The specialized hybrid algorithm for tracking solar features allows automatic feature tracking while gathering characterization details about the tracked features. The hybrid algorithm takes advantages of the snake model, a specialized image processing algorithm widely used in applications such as boundary delineation, image segmentation, and object tracking. Further, it exploits the flexibility and efficiency of Particle Swarm Optimization (PSO), a stochastic population based optimization algorithm. PSO has been used successfully in a wide range of applications including combinatorial optimization, control, clustering, robotics, scheduling, and image processing and video analysis applications. The proposed tool, denoted PSO-Snake model, was already successfully tested in other works for tracking sunspots and coronal bright points. In this work, we discuss the application of the PSO-Snake algorithm for calculating the sidereal rotational angular velocity of the solar corona. To validate the results we compare them with published manual results performed by an expert.

  5. Anatomical brain images alone can accurately diagnose chronic neuropsychiatric illnesses.

    PubMed

    Bansal, Ravi; Staib, Lawrence H; Laine, Andrew F; Hao, Xuejun; Xu, Dongrong; Liu, Jun; Weissman, Myrna; Peterson, Bradley S

    2012-01-01

    Diagnoses using imaging-based measures alone offer the hope of improving the accuracy of clinical diagnosis, thereby reducing the costs associated with incorrect treatments. Previous attempts to use brain imaging for diagnosis, however, have had only limited success in diagnosing patients who are independent of the samples used to derive the diagnostic algorithms. We aimed to develop a classification algorithm that can accurately diagnose chronic, well-characterized neuropsychiatric illness in single individuals, given the availability of sufficiently precise delineations of brain regions across several neural systems in anatomical MR images of the brain. We have developed an automated method to diagnose individuals as having one of various neuropsychiatric illnesses using only anatomical MRI scans. The method employs a semi-supervised learning algorithm that discovers natural groupings of brains based on the spatial patterns of variation in the morphology of the cerebral cortex and other brain regions. We used split-half and leave-one-out cross-validation analyses in large MRI datasets to assess the reproducibility and diagnostic accuracy of those groupings. In MRI datasets from persons with Attention-Deficit/Hyperactivity Disorder, Schizophrenia, Tourette Syndrome, Bipolar Disorder, or persons at high or low familial risk for Major Depressive Disorder, our method discriminated with high specificity and nearly perfect sensitivity the brains of persons who had one specific neuropsychiatric disorder from the brains of healthy participants and the brains of persons who had a different neuropsychiatric disorder. Although the classification algorithm presupposes the availability of precisely delineated brain regions, our findings suggest that patterns of morphological variation across brain surfaces, extracted from MRI scans alone, can successfully diagnose the presence of chronic neuropsychiatric disorders. Extensions of these methods are likely to provide biomarkers

  6. Automatic liver tumor segmentation on computed tomography for patient treatment planning and monitoring

    PubMed Central

    Moghbel, Mehrdad; Mashohor, Syamsiah; Mahmud, Rozi; Saripan, M. Iqbal Bin

    2016-01-01

    Segmentation of liver tumors from Computed Tomography (CT) and tumor burden analysis play an important role in the choice of therapeutic strategies for liver diseases and treatment monitoring. In this paper, a new segmentation method for liver tumors from contrast-enhanced CT imaging is proposed. As manual segmentation of tumors for liver treatment planning is both labor intensive and time-consuming, a highly accurate automatic tumor segmentation is desired. The proposed framework is fully automatic requiring no user interaction. The proposed segmentation evaluated on real-world clinical data from patients is based on a hybrid method integrating cuckoo optimization and fuzzy c-means algorithm with random walkers algorithm. The accuracy of the proposed method was validated using a clinical liver dataset containing one of the highest numbers of tumors utilized for liver tumor segmentation containing 127 tumors in total with further validation of the results by a consultant radiologist. The proposed method was able to achieve one of the highest accuracies reported in the literature for liver tumor segmentation compared to other segmentation methods with a mean overlap error of 22.78 % and dice similarity coefficient of 0.75 in 3Dircadb dataset and a mean overlap error of 15.61 % and dice similarity coefficient of 0.81 in MIDAS dataset. The proposed method was able to outperform most other tumor segmentation methods reported in the literature while representing an overlap error improvement of 6 % compared to one of the best performing automatic methods in the literature. The proposed framework was able to provide consistently accurate results considering the number of tumors and the variations in tumor contrast enhancements and tumor appearances while the tumor burden was estimated with a mean error of 0.84 % in 3Dircadb dataset. PMID:27540353

  7. 1. Photocopy of measured drawing (original delineated by the Royal ...

    Library of Congress Historic Buildings Survey, Historic Engineering Record, Historic Landscapes Survey

    1. Photocopy of measured drawing (original delineated by the Royal Danish Academy of Fine Arts (Kunstakademiets), Copenhagen, Denmark, 1961) Photographer and date of photograph unknown HOSPITALSGADE (FRONT) ELEVATION - Hospitalsgade 23 (House), 23 Hospital Street, Christiansted, St. Croix, VI

  8. Automatic imitation: A meta-analysis.

    PubMed

    Cracco, Emiel; Bardi, Lara; Desmet, Charlotte; Genschow, Oliver; Rigoni, Davide; De Coster, Lize; Radkova, Ina; Deschrijver, Eliane; Brass, Marcel

    2018-05-01

    Automatic imitation is the finding that movement execution is facilitated by compatible and impeded by incompatible observed movements. In the past 15 years, automatic imitation has been studied to understand the relation between perception and action in social interaction. Although research on this topic started in cognitive science, interest quickly spread to related disciplines such as social psychology, clinical psychology, and neuroscience. However, important theoretical questions have remained unanswered. Therefore, in the present meta-analysis, we evaluated seven key questions on automatic imitation. The results, based on 161 studies containing 226 experiments, revealed an overall effect size of g z = 0.95, 95% CI [0.88, 1.02]. Moderator analyses identified automatic imitation as a flexible, largely automatic process that is driven by movement and effector compatibility, but is also influenced by spatial compatibility. Automatic imitation was found to be stronger for forced choice tasks than for simple response tasks, for human agents than for nonhuman agents, and for goalless actions than for goal-directed actions. However, it was not modulated by more subtle factors such as animacy beliefs, motion profiles, or visual perspective. Finally, there was no evidence for a relation between automatic imitation and either empathy or autism. Among other things, these findings point toward actor-imitator similarity as a crucial modulator of automatic imitation and challenge the view that imitative tendencies are an indicator of social functioning. The current meta-analysis has important theoretical implications and sheds light on longstanding controversies in the literature on automatic imitation and related domains. (PsycINFO Database Record (c) 2018 APA, all rights reserved).

  9. An algorithm for automatic parameter adjustment for brain extraction in BrainSuite

    NASA Astrophysics Data System (ADS)

    Rajagopal, Gautham; Joshi, Anand A.; Leahy, Richard M.

    2017-02-01

    Brain Extraction (classification of brain and non-brain tissue) of MRI brain images is a crucial pre-processing step necessary for imaging-based anatomical studies of the human brain. Several automated methods and software tools are available for performing this task, but differences in MR image parameters (pulse sequence, resolution) and instrumentand subject-dependent noise and artefacts affect the performance of these automated methods. We describe and evaluate a method that automatically adapts the default parameters of the Brain Surface Extraction (BSE) algorithm to optimize a cost function chosen to reflect accurate brain extraction. BSE uses a combination of anisotropic filtering, Marr-Hildreth edge detection, and binary morphology for brain extraction. Our algorithm automatically adapts four parameters associated with these steps to maximize the brain surface area to volume ratio. We evaluate the method on a total of 109 brain volumes with ground truth brain masks generated by an expert user. A quantitative evaluation of the performance of the proposed algorithm showed an improvement in the mean (s.d.) Dice coefficient from 0.8969 (0.0376) for default parameters to 0.9509 (0.0504) for the optimized case. These results indicate that automatic parameter optimization can result in significant improvements in definition of the brain mask.

  10. Automatic quantification framework to detect cracks in teeth

    PubMed Central

    Shah, Hina; Hernandez, Pablo; Budin, Francois; Chittajallu, Deepak; Vimort, Jean-Baptiste; Walters, Rick; Mol, André; Khan, Asma; Paniagua, Beatriz

    2018-01-01

    Studies show that cracked teeth are the third most common cause for tooth loss in industrialized countries. If detected early and accurately, patients can retain their teeth for a longer time. Most cracks are not detected early because of the discontinuous symptoms and lack of good diagnostic tools. Currently used imaging modalities like Cone Beam Computed Tomography (CBCT) and intraoral radiography often have low sensitivity and do not show cracks clearly. This paper introduces a novel method that can detect, quantify, and localize cracks automatically in high resolution CBCT (hr-CBCT) scans of teeth using steerable wavelets and learning methods. These initial results were created using hr-CBCT scans of a set of healthy teeth and of teeth with simulated longitudinal cracks. The cracks were simulated using multiple orientations. The crack detection was trained on the most significant wavelet coefficients at each scale using a bagged classifier of Support Vector Machines. Our results show high discriminative specificity and sensitivity of this method. The framework aims to be automatic, reproducible, and open-source. Future work will focus on the clinical validation of the proposed techniques on different types of cracks ex-vivo. We believe that this work will ultimately lead to improved tracking and detection of cracks allowing for longer lasting healthy teeth. PMID:29769755

  11. SU-C-BRA-02: Gradient Based Method of Target Delineation On PET/MR Image of Head and Neck Cancer Patients

    DOE Office of Scientific and Technical Information (OSTI.GOV)

    Dance, M; Chera, B; Falchook, A

    2015-06-15

    Purpose: Validate the consistency of a gradient-based segmentation tool to facilitate accurate delineation of PET/CT-based GTVs in head and neck cancers by comparing against hybrid PET/MR-derived GTV contours. Materials and Methods: A total of 18 head and neck target volumes (10 primary and 8 nodal) were retrospectively contoured using a gradient-based segmentation tool by two observers. Each observer independently contoured each target five times. Inter-observer variability was evaluated via absolute percent differences. Intra-observer variability was examined by percentage uncertainty. All target volumes were also contoured using the SUV percent threshold method. The thresholds were explored case by case so itsmore » derived volume matched with the gradient-based volume. Dice similarity coefficients (DSC) were calculated to determine overlap of PET/CT GTVs and PET/MR GTVs. Results: The Levene’s test showed there was no statistically significant difference of the variances between the observer’s gradient-derived contours. However, the absolute difference between the observer’s volumes was 10.83%, with a range from 0.39% up to 42.89%. PET-avid regions with qualitatively non-uniform shapes and intensity levels had a higher absolute percent difference near 25%, while regions with uniform shapes and intensity levels had an absolute percent difference of 2% between observers. The average percentage uncertainty between observers was 4.83% and 7%. As the volume of the gradient-derived contours increased, the SUV threshold percent needed to match the volume decreased. Dice coefficients showed good agreement of the PET/CT and PET/MR GTVs with an average DSC value across all volumes at 0.69. Conclusion: Gradient-based segmentation of PET volume showed good consistency in general but can vary considerably for non-uniform target shapes and intensity levels. PET/CT-derived GTV contours stemming from the gradient-based tool show good agreement with the anatomically and

  12. Automatic Text Structuring and Summarization.

    ERIC Educational Resources Information Center

    Salton, Gerard; And Others

    1997-01-01

    Discussion of the use of information retrieval techniques for automatic generation of semantic hypertext links focuses on automatic text summarization. Topics include World Wide Web links, text segmentation, and evaluation of text summarization by comparing automatically generated abstracts with manually prepared abstracts. (Author/LRW)

  13. Automatic segmentation of nine retinal layer boundaries in OCT images of non-exudative AMD patients using deep learning and graph search

    PubMed Central

    Fang, Leyuan; Cunefare, David; Wang, Chong; Guymer, Robyn H.; Li, Shutao; Farsiu, Sina

    2017-01-01

    We present a novel framework combining convolutional neural networks (CNN) and graph search methods (termed as CNN-GS) for the automatic segmentation of nine layer boundaries on retinal optical coherence tomography (OCT) images. CNN-GS first utilizes a CNN to extract features of specific retinal layer boundaries and train a corresponding classifier to delineate a pilot estimate of the eight layers. Next, a graph search method uses the probability maps created from the CNN to find the final boundaries. We validated our proposed method on 60 volumes (2915 B-scans) from 20 human eyes with non-exudative age-related macular degeneration (AMD), which attested to effectiveness of our proposed technique. PMID:28663902

  14. Automatic laser beam alignment using blob detection for an environment monitoring spectroscopy

    NASA Astrophysics Data System (ADS)

    Khidir, Jarjees; Chen, Youhua; Anderson, Gary

    2013-05-01

    This paper describes a fully automated system to align an infra-red laser beam with a small retro-reflector over a wide range of distances. The component development and test were especially used for an open-path spectrometer gas detection system. Using blob detection under OpenCV library, an automatic alignment algorithm was designed to achieve fast and accurate target detection in a complex background environment. Test results are presented to show that the proposed algorithm has been successfully applied to various target distances and environment conditions.

  15. Automatic segmentation and volumetry of multiple sclerosis brain lesions from MR images

    PubMed Central

    Jain, Saurabh; Sima, Diana M.; Ribbens, Annemie; Cambron, Melissa; Maertens, Anke; Van Hecke, Wim; De Mey, Johan; Barkhof, Frederik; Steenwijk, Martijn D.; Daams, Marita; Maes, Frederik; Van Huffel, Sabine; Vrenken, Hugo; Smeets, Dirk

    2015-01-01

    The location and extent of white matter lesions on magnetic resonance imaging (MRI) are important criteria for diagnosis, follow-up and prognosis of multiple sclerosis (MS). Clinical trials have shown that quantitative values, such as lesion volumes, are meaningful in MS prognosis. Manual lesion delineation for the segmentation of lesions is, however, time-consuming and suffers from observer variability. In this paper, we propose MSmetrix, an accurate and reliable automatic method for lesion segmentation based on MRI, independent of scanner or acquisition protocol and without requiring any training data. In MSmetrix, 3D T1-weighted and FLAIR MR images are used in a probabilistic model to detect white matter (WM) lesions as an outlier to normal brain while segmenting the brain tissue into grey matter, WM and cerebrospinal fluid. The actual lesion segmentation is performed based on prior knowledge about the location (within WM) and the appearance (hyperintense on FLAIR) of lesions. The accuracy of MSmetrix is evaluated by comparing its output with expert reference segmentations of 20 MRI datasets of MS patients. Spatial overlap (Dice) between the MSmetrix and the expert lesion segmentation is 0.67 ± 0.11. The intraclass correlation coefficient (ICC) equals 0.8 indicating a good volumetric agreement between the MSmetrix and expert labelling. The reproducibility of MSmetrix' lesion volumes is evaluated based on 10 MS patients, scanned twice with a short interval on three different scanners. The agreement between the first and the second scan on each scanner is evaluated through the spatial overlap and absolute lesion volume difference between them. The spatial overlap was 0.69 ± 0.14 and absolute total lesion volume difference between the two scans was 0.54 ± 0.58 ml. Finally, the accuracy and reproducibility of MSmetrix compare favourably with other publicly available MS lesion segmentation algorithms, applied on the same data using default parameter

  16. Photocopy of site plan, Dene Hendrick, delineator, 1977, for the ...

    Library of Congress Historic Buildings Survey, Historic Engineering Record, Historic Landscapes Survey

    Photocopy of site plan, Dene Hendrick, delineator, 1977, for the City of San Jose in cooperative agreement with the California Department of Transportation (from the San Jose Historical Museum) - Stevens Ranch Complex, State Route 101, Coyote, Santa Clara County, CA

  17. Acute Alcohol Consumption Impairs Controlled but Not Automatic Processes in a Psychophysical Pointing Paradigm

    PubMed Central

    Johnston, Kevin; Timney, Brian; Goodale, Melvyn A.

    2013-01-01

    Numerous studies have investigated the effects of alcohol consumption on controlled and automatic cognitive processes. Such studies have shown that alcohol impairs performance on tasks requiring conscious, intentional control, while leaving automatic performance relatively intact. Here, we sought to extend these findings to aspects of visuomotor control by investigating the effects of alcohol in a visuomotor pointing paradigm that allowed us to separate the influence of controlled and automatic processes. Six male participants were assigned to an experimental “correction” condition in which they were instructed to point at a visual target as quickly and accurately as possible. On a small percentage of trials, the target “jumped” to a new location. On these trials, the participants’ task was to amend their movement such that they pointed to the new target location. A second group of 6 participants were assigned to a “countermanding” condition, in which they were instructed to terminate their movements upon detection of target “jumps”. In both the correction and countermanding conditions, participants served as their own controls, taking part in alcohol and no-alcohol conditions on separate days. Alcohol had no effect on participants’ ability to correct movements “in flight”, but impaired the ability to withhold such automatic corrections. Our data support the notion that alcohol selectively impairs controlled processes in the visuomotor domain. PMID:23861934

  18. Automatic identification of cochlear implant electrode arrays for post-operative assessment

    NASA Astrophysics Data System (ADS)

    Noble, Jack H.; Schuman, Theodore A.; Wright, Charles G.; Labadie, Robert F.; Dawant, Benoit M.

    2011-03-01

    Cochlear implantation is a procedure performed to treat profound hearing loss. Accurately determining the postoperative position of the implant in vivo would permit studying the correlations between implant position and hearing restoration. To solve this problem, we present an approach based on parametric Gradient Vector Flow snakes to segment the electrode array in post-operative CT. By combining this with existing methods for localizing intra-cochlear anatomy, we have developed a system that permits accurate assessment of the implant position in vivo. The system is validated using a set of seven temporal bone specimens. The algorithms were run on pre- and post-operative CTs of the specimens, and the results were compared to histological images. It was found that the position of the arrays observed in the histological images is in excellent agreement with the position of their automatically generated 3D reconstructions in the CT scans.

  19. Automatic Marker-free Longitudinal Infrared Image Registration by Shape Context Based Matching and Competitive Winner-guided Optimal Corresponding

    PubMed Central

    Lee, Chia-Yen; Wang, Hao-Jen; Lai, Jhih-Hao; Chang, Yeun-Chung; Huang, Chiun-Sheng

    2017-01-01

    Long-term comparisons of infrared image can facilitate the assessment of breast cancer tissue growth and early tumor detection, in which longitudinal infrared image registration is a necessary step. However, it is hard to keep markers attached on a body surface for weeks, and rather difficult to detect anatomic fiducial markers and match them in the infrared image during registration process. The proposed study, automatic longitudinal infrared registration algorithm, develops an automatic vascular intersection detection method and establishes feature descriptors by shape context to achieve robust matching, as well as to obtain control points for the deformation model. In addition, competitive winner-guided mechanism is developed for optimal corresponding. The proposed algorithm is evaluated in two ways. Results show that the algorithm can quickly lead to accurate image registration and that the effectiveness is superior to manual registration with a mean error being 0.91 pixels. These findings demonstrate that the proposed registration algorithm is reasonably accurate and provide a novel method of extracting a greater amount of useful data from infrared images. PMID:28145474

  20. Testing interactive effects of automatic and conflict control processes during response inhibition - A system neurophysiological study.

    PubMed

    Chmielewski, Witold X; Beste, Christian

    2017-02-01

    In everyday life successful acting often requires to inhibit automatic responses that might not be appropriate in the current situation. These response inhibition processes have been shown to become aggravated with increasing automaticity of pre-potent response tendencies. Likewise, it has been shown that inhibitory processes are complicated by a concurrent engagement in additional cognitive control processes (e.g. conflicting monitoring). Therefore, opposing processes (i.e. automaticity and cognitive control) seem to strongly impact response inhibition. However, possible interactive effects of automaticity and cognitive control for the modulation of response inhibition processes have yet not been examined. In the current study we examine this question using a novel experimental paradigm combining a Go/NoGo with a Simon task in a system neurophysiological approach combining EEG recordings with source localization analyses. The results show that response inhibition is less accurate in non-conflicting than in conflicting stimulus-response mappings. Thus it seems that conflicts and the resulting engagement in conflict monitoring processes, as reflected in the N2 amplitude, may foster response inhibition processes. This engagement in conflict monitoring processes leads to an increase in cognitive control, as reflected by an increased activity in the anterior and posterior cingulate areas, while simultaneously the automaticity of response tendencies is decreased. Most importantly, this study suggests that the quality of conflict processes in anterior cingulate areas and especially the resulting interaction of cognitive control and automaticity of pre-potent response tendencies are important factors to consider, when it comes to the modulation of response inhibition processes. Copyright © 2016 Elsevier Inc. All rights reserved.

  1. Surface-region context in optimal multi-object graph-based segmentation: robust delineation of pulmonary tumors.

    PubMed

    Song, Qi; Chen, Mingqing; Bai, Junjie; Sonka, Milan; Wu, Xiaodong

    2011-01-01

    Multi-object segmentation with mutual interaction is a challenging task in medical image analysis. We report a novel solution to a segmentation problem, in which target objects of arbitrary shape mutually interact with terrain-like surfaces, which widely exists in the medical imaging field. The approach incorporates context information used during simultaneous segmentation of multiple objects. The object-surface interaction information is encoded by adding weighted inter-graph arcs to our graph model. A globally optimal solution is achieved by solving a single maximum flow problem in a low-order polynomial time. The performance of the method was evaluated in robust delineation of lung tumors in megavoltage cone-beam CT images in comparison with an expert-defined independent standard. The evaluation showed that our method generated highly accurate tumor segmentations. Compared with the conventional graph-cut method, our new approach provided significantly better results (p < 0.001). The Dice coefficient obtained by the conventional graph-cut approach (0.76 +/- 0.10) was improved to 0.84 +/- 0.05 when employing our new method for pulmonary tumor segmentation.

  2. Delineating Floodplain in North Korea using Remote Sensing and Geographic Information System

    NASA Astrophysics Data System (ADS)

    Lim, J.; Lee, K. S.

    2015-12-01

    Korea has been divided into two countries after World War II. So environmental studies about North Korean are not easy and very limited. There were several flood damages every summer in North Korea since 1995, which induces lots of economic loss and agricultural production decrease. Delineating floodplain is indispensable to estimate the magnitude of flood damage and restore the flooded paddy field after unification. Remote Sensing (RS) can provide opportunity to study inaccessible area. In addition, flooding detection is possible. Several research groups study about flooding disaster using RS. Optical images and microwave images have been used in that field. Also, Digital topographic data have been used for flooding detection. Therefore, the purpose of this study is to investigate the land characteristics of floodplain by delineating floodplain in inaccessible North Korea using Landsat and digital topographic data. Landsat TM 5 images were used in this study. North Korea had severe flooding disaster since 1995. Among them 1995, 2007 and 2012 flooding are known for serious damages. Two Landsat images before and after flooding of each year were used to delineate floodplain. Study areas are Pyongyang City, Nampo City, North and South Hwanghae Province and South Pyongan Province. Floodplain are derived from overlaid classification image and flood-depth map. 1:25,000 scale digital topographic data were used to make flood-depth map. For land cover classification image enhancement and supervised classification with maximum likelihood classifier were used. Training areas were selected by visual interpretation using Daum-map which provides high resolution image of whole North Korea. The spatial characteristics of the floodplain were discussed based on floodplain map delineated in this study.

  3. Automatic radiation dose monitoring for CT of trauma patients with different protocols: feasibility and accuracy.

    PubMed

    Higashigaito, K; Becker, A S; Sprengel, K; Simmen, H-P; Wanner, G; Alkadhi, H

    2016-09-01

    To demonstrate the feasibility and accuracy of automatic radiation dose monitoring software for computed tomography (CT) of trauma patients in a clinical setting over time, and to evaluate the potential of radiation dose reduction using iterative reconstruction (IR). In a time period of 18 months, data from 378 consecutive thoraco-abdominal CT examinations of trauma patients were extracted using automatic radiation dose monitoring software, and patients were split into three cohorts: cohort 1, 64-section CT with filtered back projection, 200 mAs tube current-time product; cohort 2, 128-section CT with IR and identical imaging protocol; cohort 3, 128-section CT with IR, 150 mAs tube current-time product. Radiation dose parameters from the software were compared with the individual patient protocols. Image noise was measured and image quality was semi-quantitatively determined. Automatic extraction of radiation dose metrics was feasible and accurate in all (100%) patients. All CT examinations were of diagnostic quality. There were no differences between cohorts 1 and 2 regarding volume CT dose index (CTDIvol; p=0.62), dose-length product (DLP), and effective dose (ED, both p=0.95), while noise was significantly lower (chest and abdomen, both -38%, p<0.017). Compared to cohort 1, CTDIvol, DLP, and ED in cohort 3 were significantly lower (all -25%, p<0.017), similar to the noise in the chest (-32%) and abdomen (-27%, both p<0.017). Compared to cohort 2, CTDIvol (-28%), DLP, and ED (both -26%) in cohort 3 was significantly lower (all, p<0.017), while noise in the chest (+9%) and abdomen (+18%) was significantly higher (all, p<0.017). Automatic radiation dose monitoring software is feasible and accurate, and can be implemented in a clinical setting for evaluating the effects of lowering radiation doses of CT protocols over time. Copyright © 2016 The Royal College of Radiologists. Published by Elsevier Ltd. All rights reserved.

  4. Comparison of automatic control systems

    NASA Technical Reports Server (NTRS)

    Oppelt, W

    1941-01-01

    This report deals with a reciprocal comparison of an automatic pressure control, an automatic rpm control, an automatic temperature control, and an automatic directional control. It shows the difference between the "faultproof" regulator and the actual regulator which is subject to faults, and develops this difference as far as possible in a parallel manner with regard to the control systems under consideration. Such as analysis affords, particularly in its extension to the faults of the actual regulator, a deep insight into the mechanism of the regulator process.

  5. Clinical evaluation of pacemaker automatic capture management and atrioventricular interval extension algorithm.

    PubMed

    Chen, Ke-ping; Xu, Geng; Wu, Shulin; Tang, Baopeng; Wang, Li; Zhang, Shu

    2013-03-01

    The present study was to assess the accuracy of automatic atrial and ventricular capture management (ACM and VCM) in determining pacing threshold and the performance of a second-generation automatic atrioventricular (AV) interval extension algorithm for reducing unnecessary ventricular pacing. A total of 398 patients at 32 centres who received an EnPulse dual-chamber pacing/dual-chamber adaptive rate pacing pacemaker (Medtronic, Minneapolis, MN, USA) were enrolled. The last amplitude thresholds as measured by ACM and VCM prior to the 6-month follow-up were compared with manually measured thresholds. Device diagnostics were used to evaluate ACM and VCM and the percentage of ventricular pacing with and without the AV extension algorithm. Modelling was performed to assess longevity gains relating to the use of automaticity features. Atrial and ventricular capture management performed accurately and reliably provided complete capture management in 97% of studied patients. The AV interval extension algorithm reduced the median per cent of right ventricular pacing in patients with sinus node dysfunction from 99.7 to 1.5% at 6-month follow-up and in patients with intermittent AV block (excluding persistent 3° AV block) from 99.9 to 50.2%. On the basis of validated modelling, estimated device longevity could potentially be extended by 1.9 years through the use of the capture management and AV interval extension features. Both ACM and VCM features reliably measured thresholds in nearly all patients; the AV extension algorithm significantly reduced ventricular pacing; and the use of pacemaker automaticity features potentially extends device longevity.

  6. Automatic segmentation of bones from digital hand radiographs

    NASA Astrophysics Data System (ADS)

    Liu, Brent J.; Taira, Ricky K.; Shim, Hyeonjoon; Keaton, Patricia

    1995-05-01

    The purpose of this paper is to develop a robust and accurate method that automatically segments phalangeal and epiphyseal bones from digital pediatric hand radiographs exhibiting various stages of growth. The algorithm uses an object-oriented approach comprising several stages beginning with the most general objects to be segmented, such as the outline of the hand from background, and proceeding in a succession of stages to the most specific object, such as a specific phalangeal bone from a digit of the hand. Each stage carries custom operators unique to the needs of that specific stage which will aid in more accurate results. The method is further aided by a knowledge base where all model contours and other information such as age, race, and sex, are stored. Shape models, 1-D wrist profiles, as well as an interpretation tree are used to map model and data contour segments. Shape analysis is performed using an arc-length orientation transform. The method is tested on close to 340 phalangeal and epiphyseal objects to be segmented from 17 cases of pediatric hand images obtained from our clinical PACS. Patient age ranges from 2 - 16 years. A pediatric radiologist preliminarily assessed the results of the object contours and were found to be accurate to within 95% for cases with non-fused bones and to within 85% for cases with fused bones. With accurate and robust results, the method can be applied toward areas such as the determination of bone age, the development of a normal hand atlas, and the characterization of many congenital and acquired growth diseases. Furthermore, this method's architecture can be applied to other image segmentation problems.

  7. 396. Delineator Unknown Date Unknown STATE OF CALIFORNIA; DEPARTMENT OF ...

    Library of Congress Historic Buildings Survey, Historic Engineering Record, Historic Landscapes Survey

    396. Delineator Unknown Date Unknown STATE OF CALIFORNIA; DEPARTMENT OF PUBLIC WORKS; SAN FRANCISCO - OAKLAND BAY BRIDGE; EAST BAY CROSSING; CANTILEVER STRUCTURE; DETAILS I; DRG. NO. 68 - San Francisco Oakland Bay Bridge, Spanning San Francisco Bay, San Francisco, San Francisco County, CA

  8. 393. Delineator Unknown Date Unknown STATE OF CALIFORNIA; DEPARTMENT OF ...

    Library of Congress Historic Buildings Survey, Historic Engineering Record, Historic Landscapes Survey

    393. Delineator Unknown Date Unknown STATE OF CALIFORNIA; DEPARTMENT OF PUBLIC WORKS; SAN FRANCISCO - OAKLAND BAY BRIDGE; EAST BAY CROSSING; PIER-E3; GENERAL DETAILS; DRG. NO. 47 - San Francisco Oakland Bay Bridge, Spanning San Francisco Bay, San Francisco, San Francisco County, CA

  9. 397. Delineator Unknown Date Unknown STATE OF CALIFORNIA; DEPARTMENT OF ...

    Library of Congress Historic Buildings Survey, Historic Engineering Record, Historic Landscapes Survey

    397. Delineator Unknown Date Unknown STATE OF CALIFORNIA; DEPARTMENT OF PUBLIC WORKS; SAN FRANCISCO - OAKLAND BAY BRIDGE; EAST BAY CROSSING; CANTILEVER STRUCTURE; DETAILS II; DRG. NO. 69 - San Francisco Oakland Bay Bridge, Spanning San Francisco Bay, San Francisco, San Francisco County, CA

  10. 398. Delineator Unknown Date Unknown STATE OF CALIFORNIA; DEPARTMENT OF ...

    Library of Congress Historic Buildings Survey, Historic Engineering Record, Historic Landscapes Survey

    398. Delineator Unknown Date Unknown STATE OF CALIFORNIA; DEPARTMENT OF PUBLIC WORKS; SAN FRANCISCO - OAKLAND BAY BRIDGE; EAST BAY CROSSING; GENERAL PLAN; TOWER E-9; DRG. NO. 59 - San Francisco Oakland Bay Bridge, Spanning San Francisco Bay, San Francisco, San Francisco County, CA

  11. A generalized methodology for identification of threshold for HRU delineation in SWAT model

    NASA Astrophysics Data System (ADS)

    M, J.; Sudheer, K.; Chaubey, I.; Raj, C.

    2016-12-01

    The distributed hydrological model, Soil and Water Assessment Tool (SWAT) is a comprehensive hydrologic model widely used for making various decisions. The simulation accuracy of the distributed hydrological model differs due to the mechanism involved in the subdivision of the watershed. Soil and Water Assessment Tool (SWAT) considers sub-dividing the watershed and the sub-basins into small computing units known as 'hydrologic response units (HRU). The delineation of HRU is done based on unique combinations of land use, soil types, and slope within the sub-watersheds, which are not spatially defined. The computations in SWAT are done at HRU level and are then aggregated up to the sub-basin outlet, which is routed through the stream system. Generally, the HRUs are delineated by considering a threshold percentage of land use, soil and slope are to be given by the modeler to decrease the computation time of the model. The thresholds constrain the minimum area for constructing an HRU. In the current HRU delineation practice in SWAT, the land use, soil and slope of the watershed within a sub-basin, which is less than the predefined threshold, will be surpassed by the dominating land use, soil and slope, and introduce some level of ambiguity in the process simulations in terms of inappropriate representation of the area. But the loss of information due to variation in the threshold values depends highly on the purpose of the study. Therefore this research studies the effects of threshold values of HRU delineation on the hydrological modeling of SWAT on sediment simulations and suggests guidelines for selecting the appropriate threshold values considering the sediment simulation accuracy. The preliminary study was done on Illinois watershed by assigning different thresholds for land use and soil. A general methodology was proposed for identifying an appropriate threshold for HRU delineation in SWAT model that considered computational time and accuracy of the simulation

  12. Evaluation of methodology for delineation of protection zones around public-supply wells in west-central Florida

    USGS Publications Warehouse

    Vecchioli, John; Hunn, J.D.; Aucott, W.R.

    1989-01-01

    Public-supply wells in the west-central Florida area of Citrus, Hernando, Pasco, Hillsborough, and Pinellas Counties derive their supply solely from the Floridan aquifer system. In much of this area, the Floridan is at or near land surface and vulnerable to contamination. Recognizing this potential threat to the aquifer, the Florida Department of Environmental Regulation (FDER) recently promulgated regulations providing for the delineation of protection zones around public-supply wells that tap vulnerable aquifers, such as the Floridan in west-central Florida. This report evaluates the methodology for delineation of protection zones for public supply wells in west-central Florida in accordance with the methods detailed in the FDER regulations. Protection zones were delineated for public supply wells or well fields that are permitted an average daily withdrawal of 100,000 gal or more from the Floridan aquifer system where it is unconfined or leaky confined. Leaky confined, as used in FDER regulations describe conditions such that the time for a particle of water to travel vertically from the water table to the top of the Floridan is 5 years or less. Protection zones were delineated by using a radial volumetric-displacement model that simulated 5 years of permitted-rate withdrawal. Where zones overlapped, such as for well fields, composite protection zones in shapes that varied according to the configuration of well arrays were delineated on maps. (USGS)

  13. Learning-based automatic detection of severe coronary stenoses in CT angiographies

    NASA Astrophysics Data System (ADS)

    Melki, Imen; Cardon, Cyril; Gogin, Nicolas; Talbot, Hugues; Najman, Laurent

    2014-03-01

    3D cardiac computed tomography angiography (CCTA) is becoming a standard routine for non-invasive heart diseases diagnosis. Thanks to its high negative predictive value, CCTA is increasingly used to decide whether or not the patient should be considered for invasive angiography. However, an accurate assessment of cardiac lesions using this modality is still a time consuming task and needs a high degree of clinical expertise. Thus, providing automatic tool to assist clinicians during the diagnosis task is highly desirable. In this work, we propose a fully automatic approach for accurate severe cardiac stenoses detection. Our algorithm uses the Random Forest classi cation to detect stenotic areas. First, the classi er is trained on 18 CT cardiac exams with CTA reference standard. Then, then classi cation result is used to detect severe stenoses (with a narrowing degree higher than 50%) in a 30 cardiac CT exam database. Features that best captures the di erent stenoses con guration are extracted along the vessel centerlines at di erent scales. To ensure the accuracy against the vessel direction and scale changes, we extract features inside cylindrical patterns with variable directions and radii. Thus, we make sure that the ROIs contains only the vessel walls. The algorithm is evaluated using the Rotterdam Coronary Artery Stenoses Detection and Quantication Evaluation Framework. The evaluation is performed using reference standard quanti cations obtained from quantitative coronary angiography (QCA) and consensus reading of CTA. The obtained results show that we can reliably detect severe stenosis with a sensitivity of 64%.

  14. Automatic atlas-based three-label cartilage segmentation from MR knee images

    PubMed Central

    Shan, Liang; Zach, Christopher; Charles, Cecil; Niethammer, Marc

    2016-01-01

    Osteoarthritis (OA) is the most common form of joint disease and often characterized by cartilage changes. Accurate quantitative methods are needed to rapidly screen large image databases to assess changes in cartilage morphology. We therefore propose a new automatic atlas-based cartilage segmentation method for future automatic OA studies. Atlas-based segmentation methods have been demonstrated to be robust and accurate in brain imaging and therefore also hold high promise to allow for reliable and high-quality segmentations of cartilage. Nevertheless, atlas-based methods have not been well explored for cartilage segmentation. A particular challenge is the thinness of cartilage, its relatively small volume in comparison to surrounding tissue and the difficulty to locate cartilage interfaces – for example the interface between femoral and tibial cartilage. This paper focuses on the segmentation of femoral and tibial cartilage, proposing a multi-atlas segmentation strategy with non-local patch-based label fusion which can robustly identify candidate regions of cartilage. This method is combined with a novel three-label segmentation method which guarantees the spatial separation of femoral and tibial cartilage, and ensures spatial regularity while preserving the thin cartilage shape through anisotropic regularization. Our segmentation energy is convex and therefore guarantees globally optimal solutions. We perform an extensive validation of the proposed method on 706 images of the Pfizer Longitudinal Study. Our validation includes comparisons of different atlas segmentation strategies, different local classifiers, and different types of regularizers. To compare to other cartilage segmentation approaches we validate based on the 50 images of the SKI10 dataset. PMID:25128683

  15. Realizing parameterless automatic classification of remote sensing imagery using ontology engineering and cyberinfrastructure techniques

    NASA Astrophysics Data System (ADS)

    Sun, Ziheng; Fang, Hui; Di, Liping; Yue, Peng

    2016-09-01

    It was an untouchable dream for remote sensing experts to realize total automatic image classification without inputting any parameter values. Experts usually spend hours and hours on tuning the input parameters of classification algorithms in order to obtain the best results. With the rapid development of knowledge engineering and cyberinfrastructure, a lot of data processing and knowledge reasoning capabilities become online accessible, shareable and interoperable. Based on these recent improvements, this paper presents an idea of parameterless automatic classification which only requires an image and automatically outputs a labeled vector. No parameters and operations are needed from endpoint consumers. An approach is proposed to realize the idea. It adopts an ontology database to store the experiences of tuning values for classifiers. A sample database is used to record training samples of image segments. Geoprocessing Web services are used as functionality blocks to finish basic classification steps. Workflow technology is involved to turn the overall image classification into a total automatic process. A Web-based prototypical system named PACS (Parameterless Automatic Classification System) is implemented. A number of images are fed into the system for evaluation purposes. The results show that the approach could automatically classify remote sensing images and have a fairly good average accuracy. It is indicated that the classified results will be more accurate if the two databases have higher quality. Once the experiences and samples in the databases are accumulated as many as an expert has, the approach should be able to get the results with similar quality to that a human expert can get. Since the approach is total automatic and parameterless, it can not only relieve remote sensing workers from the heavy and time-consuming parameter tuning work, but also significantly shorten the waiting time for consumers and facilitate them to engage in image

  16. Delineation of the Pahute Mesa–Oasis Valley groundwater basin, Nevada

    USGS Publications Warehouse

    Fenelon, Joseph M.; Halford, Keith J.; Moreo, Michael T.

    2016-01-22

    This report delineates the Pahute Mesa–Oasis Valley (PMOV) groundwater basin, where recharge occurs, moves downgradient, and discharges to Oasis Valley, Nevada. About 5,900 acre-feet of water discharges annually from Oasis Valley, an area of springs and seeps near the town of Beatty in southern Nevada. Radionuclides in groundwater beneath Pahute Mesa, an area of historical underground nuclear testing at the Nevada National Security Site, are believed to be migrating toward Oasis Valley. Delineating the boundary of the PMOV groundwater basin is necessary to adequately assess the potential for transport of radionuclides from Pahute Mesa to Oasis Valley.The PMOV contributing area is defined based on regional water-level contours, geologic controls, and knowledge of adjacent flow systems. The viability of this area as the contributing area to Oasis Valley and the absence of significant interbasin flow between the PMOV groundwater basin and adjacent basins are shown regionally and locally. Regional constraints on the location of the contributing area boundary and on the absence of interbasin groundwater flow are shown by balancing groundwater discharges in the PMOV groundwater basin and adjacent basins against available water from precipitation. Internal consistency for the delineated contributing area is shown by matching measured water levels, groundwater discharges, and transmissivities with simulated results from a single-layer, steady-state, groundwater-flow model. An alternative basin boundary extending farther north than the final boundary was rejected based on a poor chloride mass balance and a large imbalance in the northern area between preferred and simulated recharge.

  17. Automatic generation of natural language nursing shift summaries in neonatal intensive care: BT-Nurse.

    PubMed

    Hunter, James; Freer, Yvonne; Gatt, Albert; Reiter, Ehud; Sripada, Somayajulu; Sykes, Cindy

    2012-11-01

    Our objective was to determine whether and how a computer system could automatically generate helpful natural language nursing shift summaries solely from an electronic patient record system, in a neonatal intensive care unit (NICU). A system was developed which automatically generates partial NICU shift summaries (for the respiratory and cardiovascular systems), using data-to-text technology. It was evaluated for 2 months in the NICU at the Royal Infirmary of Edinburgh, under supervision. In an on-ward evaluation, a substantial majority of the summaries was found by outgoing and incoming nurses to be understandable (90%), and a majority was found to be accurate (70%), and helpful (59%). The evaluation also served to identify some outstanding issues, especially with regard to extra content the nurses wanted to see in the computer-generated summaries. It is technically possible automatically to generate limited natural language NICU shift summaries from an electronic patient record. However, it proved difficult to handle electronic data that was intended primarily for display to the medical staff, and considerable engineering effort would be required to create a deployable system from our proof-of-concept software. Copyright © 2012 Elsevier B.V. All rights reserved.

  18. Automatic anterior chamber angle assessment for HD-OCT images.

    PubMed

    Tian, Jing; Marziliano, Pina; Baskaran, Mani; Wong, Hong-Tym; Aung, Tin

    2011-11-01

    Angle-closure glaucoma is a major blinding eye disease and could be detected by measuring the anterior chamber angle in the human eyes. High-definition OCT (Cirrus HD-OCT) is an emerging noninvasive, high-speed, and high-resolution imaging modality for the anterior segment of the eye. Here, we propose a novel algorithm which automatically detects a new landmark, Schwalbe's line, and measures the anterior chamber angle in the HD-OCT images. The distortion caused by refraction is corrected by dewarping the HD-OCT images, and three biometric measurements are defined to quantitatively assess the anterior chamber angle. The proposed algorithm was tested on 40 HD-OCT images of the eye and provided accurate measurements in about 1 second.

  19. Automatic Collision Avoidance Technology (ACAT)

    NASA Technical Reports Server (NTRS)

    Swihart, Donald E.; Skoog, Mark A.

    2007-01-01

    This document represents two views of the Automatic Collision Avoidance Technology (ACAT). One viewgraph presentation reviews the development and system design of Automatic Collision Avoidance Technology (ACAT). Two types of ACAT exist: Automatic Ground Collision Avoidance (AGCAS) and Automatic Air Collision Avoidance (AACAS). The AGCAS Uses Digital Terrain Elevation Data (DTED) for mapping functions, and uses Navigation data to place aircraft on map. It then scans DTED in front of and around aircraft and uses future aircraft trajectory (5g) to provide automatic flyup maneuver when required. The AACAS uses data link to determine position and closing rate. It contains several canned maneuvers to avoid collision. Automatic maneuvers can occur at last instant and both aircraft maneuver when using data link. The system can use sensor in place of data link. The second viewgraph presentation reviews the development of a flight test and an evaluation of the test. A review of the operation and comparison of the AGCAS and a pilot's performance are given. The same review is given for the AACAS is given.

  20. 359. Delineator Unknown April 1935 STATE OF CALIFORNIA; DEPARTMENT OF ...

    Library of Congress Historic Buildings Survey, Historic Engineering Record, Historic Landscapes Survey

    359. Delineator Unknown April 1935 STATE OF CALIFORNIA; DEPARTMENT OF PUBLIC WORKS; SAN FRANCISCO - OAKLAND BAY BRIDGE; BRIDGE RAILWAY SAN FRANCISCO LOOP; DETAILS OF VIADUCT; FINAL REPORT; DRG. NO. 92 - San Francisco Oakland Bay Bridge, Spanning San Francisco Bay, San Francisco, San Francisco County, CA

  1. 403. Delineator Unknown May 2, 1933 STUDY FOR SUSPENSION TOWERS; ...

    Library of Congress Historic Buildings Survey, Historic Engineering Record, Historic Landscapes Survey

    403. Delineator Unknown May 2, 1933 STUDY FOR SUSPENSION TOWERS; SAN FRANCISCO - OAKLAND BAY BRIDGE; TIMOTHY L. PFLUEGER, ARTHUR BROWN JR., JOHN J. DONOVAN; BOARD OF CONSULTING ARCHITECTS; SCHEME 2 - San Francisco Oakland Bay Bridge, Spanning San Francisco Bay, San Francisco, San Francisco County, CA

  2. Delineating Tree Types in a Complex Tropical Forest Setting Using High Resolution Multispectral Satellite Imagery

    NASA Astrophysics Data System (ADS)

    Cross, M.

    2016-12-01

    An improved process for the identification of tree types from satellite imagery for tropical forests is needed for more accurate assessments of the impact of forests on the global climate. La Selva Biological Station in Costa Rica was the tropical forest area selected for this particular study. WorldView-3 imagery was utilized because of its high spatial, spectral and radiometric resolution, its availability, and its potential to differentiate species in a complex forest setting. The first-step was to establish confidence in the high spatial and high radiometric resolution imagery from WorldView-3 in delineating tree types within a complex forest setting. In achieving this goal, ASD field spectrometer data were collected of specific tree species to establish solid ground control within the study site. The spectrometer data were collected from the top of each specific tree canopy utilizing established towers located at La Selva Biological Station so as to match the near-nadir view of the WorldView-3 imagery. The ASD data was processed utilizing the spectral response functions for each of the WorldView-3 bands to convert the ASD data into a band specific reflectivity. This allowed direct comparison of the ASD spectrometer reflectance data to the WorldView-3 multispectral imagery. The WorldView-3 imagery was processed to surface reflectance using two standard atmospheric correction procedures and the proprietary DigitalGlobe Atmospheric Compensation (AComp) product. The most accurate correction process was identified through comparison to the spectrometer data collected. A series of statistical measures were then utilized to access the accuracy of the processed imagery and which imagery bands are best suited for tree type identification. From this analysis, a segmentation/classification process was performed to identify individual tree type locations within the study area. It is envisioned the results of this study will improve traditional forest classification

  3. A fully automatic tool to perform accurate flood mapping by merging remote sensing imagery and ancillary data

    NASA Astrophysics Data System (ADS)

    D'Addabbo, Annarita; Refice, Alberto; Lovergine, Francesco; Pasquariello, Guido

    2016-04-01

    Flooding is one of the most frequent and expansive natural hazard. High-resolution flood mapping is an essential step in the monitoring and prevention of inundation hazard, both to gain insight into the processes involved in the generation of flooding events, and from the practical point of view of the precise assessment of inundated areas. Remote sensing data are recognized to be useful in this respect, thanks to the high resolution and regular revisit schedules of state-of-the-art satellites, moreover offering a synoptic overview of the extent of flooding. In particular, Synthetic Aperture Radar (SAR) data present several favorable characteristics for flood mapping, such as their relative insensitivity to the meteorological conditions during acquisitions, as well as the possibility of acquiring independently of solar illumination, thanks to the active nature of the radar sensors [1]. However, flood scenarios are typical examples of complex situations in which different factors have to be considered to provide accurate and robust interpretation of the situation on the ground: the presence of many land cover types, each one with a particular signature in presence of flood, requires modelling the behavior of different objects in the scene in order to associate them to flood or no flood conditions [2]. Generally, the fusion of multi-temporal, multi-sensor, multi-resolution and/or multi-platform Earth observation image data, together with other ancillary information, seems to have a key role in the pursuit of a consistent interpretation of complex scenes. In the case of flooding, distance from the river, terrain elevation, hydrologic information or some combination thereof can add useful information to remote sensing data. Suitable methods, able to manage and merge different kind of data, are so particularly needed. In this work, a fully automatic tool, based on Bayesian Networks (BNs) [3] and able to perform data fusion, is presented. It supplies flood maps

  4. Maritime Route Delineation using AIS Data from the Atlantic Coast of the US

    DOE Office of Scientific and Technical Information (OSTI.GOV)

    Breithaupt, Stephen A.; Copping, Andrea; Tagestad, Jerry

    2016-09-28

    This study examines maritime routes between ports along the Atlantic coast of the US, utilising Automated Identification System (AIS) data for the years 2010 through 2012. The delineation of vessel routes conducted in this study was motivated by development planned for offshore Wind Energy Areas (WEAs) along the Atlantic coast of the US and the need to evaluate the effect of these development areas on commercial shipping. To this end, available AIS data were processed to generate commercial vessel tracks for individual vessels, though cargo vessels are the focus in this study. The individual vessel tracks were sampled at transectsmore » placed along the Atlantic coast. The transect samples were analysed and partitioned by voyages between Atlantic ports to facilitate computation of vessel routes between ports. The route boundary analysis utilised a definition from UK guidance in which routes' boundaries encompassed 95% of the vessel traffic between ports. In addition to delineating route boundaries, we found multi-modal transverse distributions of vessels for well-travelled routes, which indicated preference for lanes of travel within the delineated routes.« less

  5. Automatically identifying, counting, and describing wild animals in camera-trap images with deep learning.

    PubMed

    Norouzzadeh, Mohammad Sadegh; Nguyen, Anh; Kosmala, Margaret; Swanson, Alexandra; Palmer, Meredith S; Packer, Craig; Clune, Jeff

    2018-06-19

    Having accurate, detailed, and up-to-date information about the location and behavior of animals in the wild would improve our ability to study and conserve ecosystems. We investigate the ability to automatically, accurately, and inexpensively collect such data, which could help catalyze the transformation of many fields of ecology, wildlife biology, zoology, conservation biology, and animal behavior into "big data" sciences. Motion-sensor "camera traps" enable collecting wildlife pictures inexpensively, unobtrusively, and frequently. However, extracting information from these pictures remains an expensive, time-consuming, manual task. We demonstrate that such information can be automatically extracted by deep learning, a cutting-edge type of artificial intelligence. We train deep convolutional neural networks to identify, count, and describe the behaviors of 48 species in the 3.2 million-image Snapshot Serengeti dataset. Our deep neural networks automatically identify animals with >93.8% accuracy, and we expect that number to improve rapidly in years to come. More importantly, if our system classifies only images it is confident about, our system can automate animal identification for 99.3% of the data while still performing at the same 96.6% accuracy as that of crowdsourced teams of human volunteers, saving >8.4 y (i.e., >17,000 h at 40 h/wk) of human labeling effort on this 3.2 million-image dataset. Those efficiency gains highlight the importance of using deep neural networks to automate data extraction from camera-trap images, reducing a roadblock for this widely used technology. Our results suggest that deep learning could enable the inexpensive, unobtrusive, high-volume, and even real-time collection of a wealth of information about vast numbers of animals in the wild. Copyright © 2018 the Author(s). Published by PNAS.

  6. Automatic first-arrival picking based on extended super-virtual interferometry with quality control procedure

    NASA Astrophysics Data System (ADS)

    An, Shengpei; Hu, Tianyue; Liu, Yimou; Peng, Gengxin; Liang, Xianghao

    2017-12-01

    Static correction is a crucial step of seismic data processing for onshore play, which frequently has a complex near-surface condition. The effectiveness of the static correction depends on an accurate determination of first-arrival traveltimes. However, it is difficult to accurately auto-pick the first arrivals for data with low signal-to-noise ratios (SNR), especially for those measured in the area of the complex near-surface. The technique of the super-virtual interferometry (SVI) has the potential to enhance the SNR of first arrivals. In this paper, we develop the extended SVI with (1) the application of the reverse correlation to improve the capability of SNR enhancement at near-offset, and (2) the usage of the multi-domain method to partially overcome the limitation of current method, given insufficient available source-receiver combinations. Compared to the standard SVI, the SNR enhancement of the extended SVI can be up to 40%. In addition, we propose a quality control procedure, which is based on the statistical characteristics of multichannel recordings of first arrivals. It can auto-correct the mispicks, which might be spurious events generated by the SVI. This procedure is very robust, highly automatic and it can accommodate large data in batches. Finally, we develop one automatic first-arrival picking method to combine the extended SVI and the quality control procedure. Both the synthetic and the field data examples demonstrate that the proposed method is able to accurately auto-pick first arrivals in seismic traces with low SNR. The quality of the stacked seismic sections obtained from this method is much better than those obtained from an auto-picking method, which is commonly employed by the commercial software.

  7. Fast left ventricle tracking in CMR images using localized anatomical affine optical flow

    NASA Astrophysics Data System (ADS)

    Queirós, Sandro; Vilaça, João. L.; Morais, Pedro; Fonseca, Jaime C.; D'hooge, Jan; Barbosa, Daniel

    2015-03-01

    In daily cardiology practice, assessment of left ventricular (LV) global function using non-invasive imaging remains central for the diagnosis and follow-up of patients with cardiovascular diseases. Despite the different methodologies currently accessible for LV segmentation in cardiac magnetic resonance (CMR) images, a fast and complete LV delineation is still limitedly available for routine use. In this study, a localized anatomically constrained affine optical flow method is proposed for fast and automatic LV tracking throughout the full cardiac cycle in short-axis CMR images. Starting from an automatically delineated LV in the end-diastolic frame, the endocardial and epicardial boundaries are propagated by estimating the motion between adjacent cardiac phases using optical flow. In order to reduce the computational burden, the motion is only estimated in an anatomical region of interest around the tracked boundaries and subsequently integrated into a local affine motion model. Such localized estimation enables to capture complex motion patterns, while still being spatially consistent. The method was validated on 45 CMR datasets taken from the 2009 MICCAI LV segmentation challenge. The proposed approach proved to be robust and efficient, with an average distance error of 2.1 mm and a correlation with reference ejection fraction of 0.98 (1.9 +/- 4.5%). Moreover, it showed to be fast, taking 5 seconds for the tracking of a full 4D dataset (30 ms per image). Overall, a novel fast, robust and accurate LV tracking methodology was proposed, enabling accurate assessment of relevant global function cardiac indices, such as volumes and ejection fraction

  8. 402. Delineator Unknown May 2, 1933 STUDY FOR SUSPENSION TOWERS; ...

    Library of Congress Historic Buildings Survey, Historic Engineering Record, Historic Landscapes Survey

    402. Delineator Unknown May 2, 1933 STUDY FOR SUSPENSION TOWERS; SAN FRANCISCO - OAKLAND BAY BRIDGE; TIMOTHY L. PFLUEGER, ARTHUR BROWN JR., JOHN J. DONOVAN; BOARD OF CONSULTING ARCHITECTS; SCHEME 1A - San Francisco Oakland Bay Bridge, Spanning San Francisco Bay, San Francisco, San Francisco County, CA

  9. 380. Delineator Unknown December 1932 STATE OF CALIFORNIA; DEPARTMENT OF ...

    Library of Congress Historic Buildings Survey, Historic Engineering Record, Historic Landscapes Survey

    380. Delineator Unknown December 1932 STATE OF CALIFORNIA; DEPARTMENT OF PUBLIC WORKS; SAN FRANCISCO - OAKLAND BAY BRIDGE; CABLES & ATTACHMENTS WEST BAY CROSSING; CABLE BANDS; CONTRACT NO. 6A; DRAWING NO. 3 - San Francisco Oakland Bay Bridge, Spanning San Francisco Bay, San Francisco, San Francisco County, CA

  10. 385. Delineator Unknown December 1932 STATE OF CALIFORNIA; DEPARTMENT OF ...

    Library of Congress Historic Buildings Survey, Historic Engineering Record, Historic Landscapes Survey

    385. Delineator Unknown December 1932 STATE OF CALIFORNIA; DEPARTMENT OF PUBLIC WORKS; SAN FRANCISCO - OAKLAND BAY BRIDGE; SUPERSTRUCTURE - WEST BAY CROSSING; ROCKER POSTS AND BEARING; CONTRACT NO. 6; DRAWINGS NO. 42 - San Francisco Oakland Bay Bridge, Spanning San Francisco Bay, San Francisco, San Francisco County, CA

  11. 379. Delineator Unknown December 1932 STATE OF CALIFORNIA; DEPARTMENT OF ...

    Library of Congress Historic Buildings Survey, Historic Engineering Record, Historic Landscapes Survey

    379. Delineator Unknown December 1932 STATE OF CALIFORNIA; DEPARTMENT OF PUBLIC WORKS; SAN FRANCISCO - OAKLAND BAY BRIDGE; CABLES AND ATTACHMENTS - WEST BAY CROSSING; SPLAY CASTINGS; CONTRACT NO. 6A; DRAWING NO. 4 - San Francisco Oakland Bay Bridge, Spanning San Francisco Bay, San Francisco, San Francisco County, CA

  12. 400. Delineator Unknown Date Unknown STATE OF CALIFORNIA; DEPARTMENT OF ...

    Library of Congress Historic Buildings Survey, Historic Engineering Record, Historic Landscapes Survey

    400. Delineator Unknown Date Unknown STATE OF CALIFORNIA; DEPARTMENT OF PUBLIC WORKS; SAN FRANCISCO - OAKLAND BAY BRIDGE; EAST BAY CROSSING; PIER E-6 TO E-23; TYPICAL DETAILS; DRG. NO. 52 - San Francisco Oakland Bay Bridge, Spanning San Francisco Bay, San Francisco, San Francisco County, CA

  13. Robust automatic P-phase picking: an on-line implementation in the analysis of broadband seismogram recordings

    NASA Astrophysics Data System (ADS)

    Sleeman, Reinoud; van Eck, Torild

    1999-06-01

    The onset of a seismic signal is determined through joint AR modeling of the noise and the seismic signal, and the application of the Akaike Information Criterion (AIC) using the onset time as parameter. This so-called AR-AIC phase picker has been tested successfully and implemented on the Z-component of the broadband station HGN to provide automatic P-phase picks for a rapid warning system. The AR-AIC picker is shown to provide accurate and robust automatic picks on a large experimental database. Out of 1109 P-phase onsets with signal-to-noise ratio (SNR) above 1 from local, regional and teleseismic earthquakes, our implementation detects 71% and gives a mean difference with manual picks of 0.1 s. An optimal version of the well-established picker of Baer and Kradolfer [Baer, M., Kradolfer, U., An automatic phase picker for local and teleseismic events, Bull. Seism. Soc. Am. 77 (1987) 1437-1445] detects less than 41% and gives a mean difference with manual picks of 0.3 s using the same dataset.

  14. Automatic Coal-Mining System

    NASA Technical Reports Server (NTRS)

    Collins, E. R., Jr.

    1985-01-01

    Coal cutting and removal done with minimal hazard to people. Automatic coal mine cutting, transport and roof-support movement all done by automatic machinery. Exposure of people to hazardous conditions reduced to inspection tours, maintenance, repair, and possibly entry mining.

  15. Image Based Hair Segmentation Algorithm for the Application of Automatic Facial Caricature Synthesis

    PubMed Central

    Peng, Zhenyun; Zhang, Yaohui

    2014-01-01

    Hair is a salient feature in human face region and are one of the important cues for face analysis. Accurate detection and presentation of hair region is one of the key components for automatic synthesis of human facial caricature. In this paper, an automatic hair detection algorithm for the application of automatic synthesis of facial caricature based on a single image is proposed. Firstly, hair regions in training images are labeled manually and then the hair position prior distributions and hair color likelihood distribution function are estimated from these labels efficiently. Secondly, the energy function of the test image is constructed according to the estimated prior distributions of hair location and hair color likelihood. This energy function is further optimized according to graph cuts technique and initial hair region is obtained. Finally, K-means algorithm and image postprocessing techniques are applied to the initial hair region so that the final hair region can be segmented precisely. Experimental results show that the average processing time for each image is about 280 ms and the average hair region detection accuracy is above 90%. The proposed algorithm is applied to a facial caricature synthesis system. Experiments proved that with our proposed hair segmentation algorithm the facial caricatures are vivid and satisfying. PMID:24592182

  16. A PC-based computer package for automatic detection and location of earthquakes: Application to a seismic network in eastern sicity (Italy)

    NASA Astrophysics Data System (ADS)

    Patanè, Domenico; Ferrari, Ferruccio; Giampiccolo, Elisabetta; Gresta, Stefano

    Few automated data acquisition and processing systems operate on mainframes, some run on UNIX-based workstations and others on personal computers, equipped with either DOS/WINDOWS or UNIX-derived operating systems. Several large and complex software packages for automatic and interactive analysis of seismic data have been developed in recent years (mainly for UNIX-based systems). Some of these programs use a variety of artificial intelligence techniques. The first operational version of a new software package, named PC-Seism, for analyzing seismic data from a local network is presented in Patanè et al. (1999). This package, composed of three separate modules, provides an example of a new generation of visual object-oriented programs for interactive and automatic seismic data-processing running on a personal computer. In this work, we mainly discuss the automatic procedures implemented in the ASDP (Automatic Seismic Data-Processing) module and real time application to data acquired by a seismic network running in eastern Sicily. This software uses a multi-algorithm approach and a new procedure MSA (multi-station-analysis) for signal detection, phase grouping and event identification and location. It is designed for an efficient and accurate processing of local earthquake records provided by single-site and array stations. Results from ASDP processing of two different data sets recorded at Mt. Etna volcano by a regional network are analyzed to evaluate its performance. By comparing the ASDP pickings with those revised manually, the detection and subsequently the location capabilities of this software are assessed. The first data set is composed of 330 local earthquakes recorded in the Mt. Etna erea during 1997 by the telemetry analog seismic network. The second data set comprises about 970 automatic locations of more than 2600 local events recorded at Mt. Etna during the last eruption (July 2001) at the present network. For the former data set, a comparison of the

  17. Machine Learning of Parameters for Accurate Semiempirical Quantum Chemical Calculations

    DOE Office of Scientific and Technical Information (OSTI.GOV)

    Dral, Pavlo O.; von Lilienfeld, O. Anatole; Thiel, Walter

    2015-05-12

    We investigate possible improvements in the accuracy of semiempirical quantum chemistry (SQC) methods through the use of machine learning (ML) models for the parameters. For a given class of compounds, ML techniques require sufficiently large training sets to develop ML models that can be used for adapting SQC parameters to reflect changes in molecular composition and geometry. The ML-SQC approach allows the automatic tuning of SQC parameters for individual molecules, thereby improving the accuracy without deteriorating transferability to molecules with molecular descriptors very different from those in the training set. The performance of this approach is demonstrated for the semiempiricalmore » OM2 method using a set of 6095 constitutional isomers C7H10O2, for which accurate ab initio atomization enthalpies are available. The ML-OM2 results show improved average accuracy and a much reduced error range compared with those of standard OM2 results, with mean absolute errors in atomization enthalpies dropping from 6.3 to 1.7 kcal/mol. They are also found to be superior to the results from specific OM2 reparameterizations (rOM2) for the same set of isomers. The ML-SQC approach thus holds promise for fast and reasonably accurate high-throughput screening of materials and molecules.« less

  18. Machine learning of parameters for accurate semiempirical quantum chemical calculations

    DOE PAGES

    Dral, Pavlo O.; von Lilienfeld, O. Anatole; Thiel, Walter

    2015-04-14

    We investigate possible improvements in the accuracy of semiempirical quantum chemistry (SQC) methods through the use of machine learning (ML) models for the parameters. For a given class of compounds, ML techniques require sufficiently large training sets to develop ML models that can be used for adapting SQC parameters to reflect changes in molecular composition and geometry. The ML-SQC approach allows the automatic tuning of SQC parameters for individual molecules, thereby improving the accuracy without deteriorating transferability to molecules with molecular descriptors very different from those in the training set. The performance of this approach is demonstrated for the semiempiricalmore » OM2 method using a set of 6095 constitutional isomers C 7H 10O 2, for which accurate ab initio atomization enthalpies are available. The ML-OM2 results show improved average accuracy and a much reduced error range compared with those of standard OM2 results, with mean absolute errors in atomization enthalpies dropping from 6.3 to 1.7 kcal/mol. They are also found to be superior to the results from specific OM2 reparameterizations (rOM2) for the same set of isomers. The ML-SQC approach thus holds promise for fast and reasonably accurate high-throughput screening of materials and molecules.« less

  19. Automatic segmentation of the facial nerve and chorda tympani in pediatric CT scans.

    PubMed

    Reda, Fitsum A; Noble, Jack H; Rivas, Alejandro; McRackan, Theodore R; Labadie, Robert F; Dawant, Benoit M

    2011-10-01

    Cochlear implant surgery is used to implant an electrode array in the cochlea to treat hearing loss. The authors recently introduced a minimally invasive image-guided technique termed percutaneous cochlear implantation. This approach achieves access to the cochlea by drilling a single linear channel from the outer skull into the cochlea via the facial recess, a region bounded by the facial nerve and chorda tympani. To exploit existing methods for computing automatically safe drilling trajectories, the facial nerve and chorda tympani need to be segmented. The goal of this work is to automatically segment the facial nerve and chorda tympani in pediatric CT scans. The authors have proposed an automatic technique to achieve the segmentation task in adult patients that relies on statistical models of the structures. These models contain intensity and shape information along the central axes of both structures. In this work, the authors attempted to use the same method to segment the structures in pediatric scans. However, the authors learned that substantial differences exist between the anatomy of children and that of adults, which led to poor segmentation results when an adult model is used to segment a pediatric volume. Therefore, the authors built a new model for pediatric cases and used it to segment pediatric scans. Once this new model was built, the authors employed the same segmentation method used for adults with algorithm parameters that were optimized for pediatric anatomy. A validation experiment was conducted on 10 CT scans in which manually segmented structures were compared to automatically segmented structures. The mean, standard deviation, median, and maximum segmentation errors were 0.23, 0.17, 0.18, and 1.27 mm, respectively. The results indicate that accurate segmentation of the facial nerve and chorda tympani in pediatric scans is achievable, thus suggesting that safe drilling trajectories can also be computed automatically.

  20. Automatically measuring brain ventricular volume within PACS using artificial intelligence.

    PubMed

    Yepes-Calderon, Fernando; Nelson, Marvin D; McComb, J Gordon

    2018-01-01

    The picture archiving and communications system (PACS) is currently the standard platform to manage medical images but lacks analytical capabilities. Staying within PACS, the authors have developed an automatic method to retrieve the medical data and access it at a voxel level, decrypted and uncompressed that allows analytical capabilities while not perturbing the system's daily operation. Additionally, the strategy is secure and vendor independent. Cerebral ventricular volume is important for the diagnosis and treatment of many neurological disorders. A significant change in ventricular volume is readily recognized, but subtle changes, especially over longer periods of time, may be difficult to discern. Clinical imaging protocols and parameters are often varied making it difficult to use a general solution with standard segmentation techniques. Presented is a segmentation strategy based on an algorithm that uses four features extracted from the medical images to create a statistical estimator capable of determining ventricular volume. When compared with manual segmentations, the correlation was 94% and holds promise for even better accuracy by incorporating the unlimited data available. The volume of any segmentable structure can be accurately determined utilizing the machine learning strategy presented and runs fully automatically within the PACS.

  1. Defect inspection in hot slab surface: multi-source CCD imaging based fuzzy-rough sets method

    NASA Astrophysics Data System (ADS)

    Zhao, Liming; Zhang, Yi; Xu, Xiaodong; Xiao, Hong; Huang, Chao

    2016-09-01

    To provide an accurate surface defects inspection method and make the automation of robust image region of interests(ROI) delineation strategy a reality in production line, a multi-source CCD imaging based fuzzy-rough sets method is proposed for hot slab surface quality assessment. The applicability of the presented method and the devised system are mainly tied to the surface quality inspection for strip, billet and slab surface etcetera. In this work we take into account the complementary advantages in two common machine vision (MV) systems(line array CCD traditional scanning imaging (LS-imaging) and area array CCD laser three-dimensional (3D) scanning imaging (AL-imaging)), and through establishing the model of fuzzy-rough sets in the detection system the seeds for relative fuzzy connectedness(RFC) delineation for ROI can placed adaptively, which introduces the upper and lower approximation sets for RIO definition, and by which the boundary region can be delineated by RFC region competitive classification mechanism. For the first time, a Multi-source CCD imaging based fuzzy-rough sets strategy is attempted for CC-slab surface defects inspection that allows an automatic way of AI algorithms and powerful ROI delineation strategies to be applied to the MV inspection field.

  2. Automatic Lumbar Spondylolisthesis Measurement in CT Images.

    PubMed

    Liao, Shu; Zhan, Yiqiang; Dong, Zhongxing; Yan, Ruyi; Gong, Liyan; Zhou, Xiang Sean; Salganicoff, Marcos; Fei, Jun

    2016-07-01

    Lumbar spondylolisthesis is one of the most common spinal diseases. It is caused by the anterior shift of a lumbar vertebrae relative to subjacent vertebrae. In current clinical practices, staging of spondylolisthesis is often conducted in a qualitative way. Although meyerding grading opens the door to stage spondylolisthesis in a more quantitative way, it relies on the manual measurement, which is time consuming and irreproducible. Thus, an automatic measurement algorithm becomes desirable for spondylolisthesis diagnosis and staging. However, there are two challenges. 1) Accurate detection of the most anterior and posterior points on the superior and inferior surfaces of each lumbar vertebrae. Due to the small size of the vertebrae, slight errors of detection may lead to significant measurement errors, hence, wrong disease stages. 2) Automatic localize and label each lumbar vertebrae is required to provide the semantic meaning of the measurement. It is difficult since different lumbar vertebraes have high similarity of both shape and image appearance. To resolve these challenges, a new auto measurement framework is proposed with two major contributions: First, a learning based spine labeling method that integrates both the image appearance and spine geometry information is designed to detect lumbar vertebrae. Second, a hierarchical method using both the population information from atlases and domain-specific information in the target image is proposed for most anterior and posterior points positioning. Validated on 258 CT spondylolisthesis patients, our method shows very similar results to manual measurements by radiologists and significantly increases the measurement efficiency.

  3. MDOT's successful road delineation program will become even better : research spotlight.

    DOT National Transportation Integrated Search

    2017-03-01

    When the Wayne County Road Commission painted a centerline on River Road near Trenton, Michigan, in 1911, it was this countrys first painted centerline. MDOT maintains its leadership in road delineation practices with annual restriping and delinea...

  4. Automatic Spatio-Temporal Flow Velocity Measurement in Small Rivers Using Thermal Image Sequences

    NASA Astrophysics Data System (ADS)

    Lin, D.; Eltner, A.; Sardemann, H.; Maas, H.-G.

    2018-05-01

    An automatic spatio-temporal flow velocity measurement approach, using an uncooled thermal camera, is proposed in this paper. The basic principle of the method is to track visible thermal features at the water surface in thermal camera image sequences. Radiometric and geometric calibrations are firstly implemented to remove vignetting effects in thermal imagery and to get the interior orientation parameters of the camera. An object-based unsupervised classification approach is then applied to detect the interest regions for data referencing and thermal feature tracking. Subsequently, GCPs are extracted to orient the river image sequences and local hot points are identified as tracking features. Afterwards, accurate dense tracking outputs are obtained using pyramidal Lucas-Kanade method. To validate the accuracy potential of the method, measurements obtained from thermal feature tracking are compared with reference measurements taken by a propeller gauge. Results show a great potential of automatic flow velocity measurement in small rivers using imagery from a thermal camera.

  5. 1. Photocopy of site plan, Dene Hendrick, delineator, 1977, for ...

    Library of Congress Historic Buildings Survey, Historic Engineering Record, Historic Landscapes Survey

    1. Photocopy of site plan, Dene Hendrick, delineator, 1977, for the City of San Jose in cooperative agreement with the California Department of Transportation (from the San Jose Historical Museum). - Twin Oaks Dairy, Northwest of Metcalfe Road, off State Route 101 (Monterey Road), Coyote, Santa Clara County, CA

  6. 404. Delineator Unknown June 1, 1933 STUDY FOR TOP OF ...

    Library of Congress Historic Buildings Survey, Historic Engineering Record, Historic Landscapes Survey

    404. Delineator Unknown June 1, 1933 STUDY FOR TOP OF SUSPENSION TOWERS; SAN FRANCISCO OAKLAND BAY BRIDGE; TIMOTHY L. PFLUEGER, ARTHUR BROWN JR., JOHN J. DONOVAN; BOARD OF CONSULTING ARCHITECTS; SCHEME 7-A - San Francisco Oakland Bay Bridge, Spanning San Francisco Bay, San Francisco, San Francisco County, CA

  7. Automatic non-proliferative diabetic retinopathy screening system based on color fundus image.

    PubMed

    Xiao, Zhitao; Zhang, Xinpeng; Geng, Lei; Zhang, Fang; Wu, Jun; Tong, Jun; Ogunbona, Philip O; Shan, Chunyan

    2017-10-26

    Non-proliferative diabetic retinopathy is the early stage of diabetic retinopathy. Automatic detection of non-proliferative diabetic retinopathy is significant for clinical diagnosis, early screening and course progression of patients. This paper introduces the design and implementation of an automatic system for screening non-proliferative diabetic retinopathy based on color fundus images. Firstly, the fundus structures, including blood vessels, optic disc and macula, are extracted and located, respectively. In particular, a new optic disc localization method using parabolic fitting is proposed based on the physiological structure characteristics of optic disc and blood vessels. Then, early lesions, such as microaneurysms, hemorrhages and hard exudates, are detected based on their respective characteristics. An equivalent optical model simulating human eyes is designed based on the anatomical structure of retina. Main structures and early lesions are reconstructed in the 3D space for better visualization. Finally, the severity of each image is evaluated based on the international criteria of diabetic retinopathy. The system has been tested on public databases and images from hospitals. Experimental results demonstrate that the proposed system achieves high accuracy for main structures and early lesions detection. The results of severity classification for non-proliferative diabetic retinopathy are also accurate and suitable. Our system can assist ophthalmologists for clinical diagnosis, automatic screening and course progression of patients.

  8. Research on large spatial coordinate automatic measuring system based on multilateral method

    NASA Astrophysics Data System (ADS)

    Miao, Dongjing; Li, Jianshuan; Li, Lianfu; Jiang, Yuanlin; Kang, Yao; He, Mingzhao; Deng, Xiangrui

    2015-10-01

    To measure the spatial coordinate accurately and efficiently in large size range, a manipulator automatic measurement system which based on multilateral method is developed. This system is divided into two parts: The coordinate measurement subsystem is consists of four laser tracers, and the trajectory generation subsystem is composed by a manipulator and a rail. To ensure that there is no laser beam break during the measurement process, an optimization function is constructed by using the vectors between the laser tracers measuring center and the cat's eye reflector measuring center, then an orientation automatically adjust algorithm for the reflector is proposed, with this algorithm, the laser tracers are always been able to track the reflector during the entire measurement process. Finally, the proposed algorithm is validated by taking the calibration of laser tracker for instance: the actual experiment is conducted in 5m × 3m × 3.2m range, the algorithm is used to plan the orientations of the reflector corresponding to the given 24 points automatically. After improving orientations of some minority points with adverse angles, the final results are used to control the manipulator's motion. During the actual movement, there are no beam break occurs. The result shows that the proposed algorithm help the developed system to measure the spatial coordinates over a large range with efficiency.

  9. 18F-fluorocholine PET-guided target volume delineation techniques for partial prostate re-irradiation in local recurrent prostate cancer.

    PubMed

    Wang, Hui; Vees, Hansjörg; Miralbell, Raymond; Wissmeyer, Michael; Steiner, Charles; Ratib, Osman; Senthamizhchelvan, Srinivasan; Zaidi, Habib

    2009-11-01

    We evaluate the contribution of (18)F-choline PET/CT in the delineation of gross tumour volume (GTV) in local recurrent prostate cancer after initial irradiation using various PET image segmentation techniques. Seventeen patients with local-only recurrent prostate cancer (median=5.7 years) after initial irradiation were included in the study. Rebiopsies were performed in 10 patients that confirmed the local recurrence. Following injection of 300 MBq of (18)F-fluorocholine, dynamic PET frames (3 min each) were reconstructed from the list-mode acquisition. Five PET image segmentation techniques were used to delineate the (18)F-choline-based GTVs. These included manual delineation of contours (GTV(man)) by two teams consisting of a radiation oncologist and a nuclear medicine physician each, a fixed threshold of 40% and 50% of the maximum signal intensity (GTV(40%) and GTV(50%)), signal-to-background ratio-based adaptive thresholding (GTV(SBR)), and a region growing (GTV(RG)) algorithm. Geographic mismatches between the GTVs were also assessed using overlap analysis. Inter-observer variability for manual delineation of GTVs was high but not statistically significant (p=0.459). In addition, the volumes and shapes of GTVs delineated using semi-automated techniques were significantly higher than those of GTVs defined manually. Semi-automated segmentation techniques for (18)F-choline PET-guided GTV delineation resulted in substantially higher GTVs compared to manual delineation and might replace the latter for determination of recurrent prostate cancer for partial prostate re-irradiation. The selection of the most appropriate segmentation algorithm still needs to be determined.

  10. Automatic anatomy recognition on CT images with pathology

    NASA Astrophysics Data System (ADS)

    Huang, Lidong; Udupa, Jayaram K.; Tong, Yubing; Odhner, Dewey; Torigian, Drew A.

    2016-03-01

    Body-wide anatomy recognition on CT images with pathology becomes crucial for quantifying body-wide disease burden. This, however, is a challenging problem because various diseases result in various abnormalities of objects such as shape and intensity patterns. We previously developed an automatic anatomy recognition (AAR) system [1] whose applicability was demonstrated on near normal diagnostic CT images in different body regions on 35 organs. The aim of this paper is to investigate strategies for adapting the previous AAR system to diagnostic CT images of patients with various pathologies as a first step toward automated body-wide disease quantification. The AAR approach consists of three main steps - model building, object recognition, and object delineation. In this paper, within the broader AAR framework, we describe a new strategy for object recognition to handle abnormal images. In the model building stage an optimal threshold interval is learned from near-normal training images for each object. This threshold is optimally tuned to the pathological manifestation of the object in the test image. Recognition is performed following a hierarchical representation of the objects. Experimental results for the abdominal body region based on 50 near-normal images used for model building and 20 abnormal images used for object recognition show that object localization accuracy within 2 voxels for liver and spleen and 3 voxels for kidney can be achieved with the new strategy.

  11. Automatic switching matrix

    DOEpatents

    Schlecht, Martin F.; Kassakian, John G.; Caloggero, Anthony J.; Rhodes, Bruce; Otten, David; Rasmussen, Neil

    1982-01-01

    An automatic switching matrix that includes an apertured matrix board containing a matrix of wires that can be interconnected at each aperture. Each aperture has associated therewith a conductive pin which, when fully inserted into the associated aperture, effects electrical connection between the wires within that particular aperture. Means is provided for automatically inserting the pins in a determined pattern and for removing all the pins to permit other interconnecting patterns.

  12. Breast cancer margin delineation with fluorescence lifetime imaging (Conference Presentation)

    NASA Astrophysics Data System (ADS)

    Phipps, Jennifer E.; Gorpas, Dimitris; Darrow, Morgan; Unger, Jakob; Bold, Richard; Marcu, Laura

    2017-02-01

    The current standard of care for early stages of breast cancer is breast-conserving surgery (BCS). BCS involves a lumpectomy procedure, during which the tumor is removed with a rim of normal tissue-if cancer cells found in that rim of tissue, it is called a positive margin and means part of the tumor remains in the breast. Currently there is no method to determine if cancer cells exist at the margins of lumpectomy specimens aside from time-intensive histology methods that result in reoperations in up to 38% of cases. We used fluorescence lifetime imaging (FLIm) to measure time-resolved autofluorescence from N=13 ex vivo human breast cancer specimens (N=10 patients undergoing lumpectomy or mastectomy) and compared our results to histology. Tumor (both invasive and ductal carcinoma in situ), fibrous tissue, fat and fat necrosis have unique fluorescence signatures. For instance, between 500-580 nm, fluorescence lifetime of tumor was shortest (4.7 +/- 0.4 ns) compared to fibrous tissue (5.5 +/- 0.7 ns) and fat (7.0 +/- 0.1 ns), P<0.05 (ANOVA). These differences are due to the biochemical properties of lipid, nicotineamide adenine dinucleotide (NADH) and collagen fibers in the fat, tumor and fibrous tissue, respectively. Additionally, the FLIm data is augmented to video of the breast tissue with image processing algorithms that track a blue (450 nm) aiming beam used in parallel with the 355 nm excitation beam. This allows for accurate histologic co-registration and in the future will allow for three-dimensional lumpectomy surfaces to be imaged for cancer margin delineation.

  13. Automatic Classification of Time-variable X-Ray Sources

    NASA Astrophysics Data System (ADS)

    Lo, Kitty K.; Farrell, Sean; Murphy, Tara; Gaensler, B. M.

    2014-05-01

    To maximize the discovery potential of future synoptic surveys, especially in the field of transient science, it will be necessary to use automatic classification to identify some of the astronomical sources. The data mining technique of supervised classification is suitable for this problem. Here, we present a supervised learning method to automatically classify variable X-ray sources in the Second XMM-Newton Serendipitous Source Catalog (2XMMi-DR2). Random Forest is our classifier of choice since it is one of the most accurate learning algorithms available. Our training set consists of 873 variable sources and their features are derived from time series, spectra, and other multi-wavelength contextual information. The 10 fold cross validation accuracy of the training data is ~97% on a 7 class data set. We applied the trained classification model to 411 unknown variable 2XMM sources to produce a probabilistically classified catalog. Using the classification margin and the Random Forest derived outlier measure, we identified 12 anomalous sources, of which 2XMM J180658.7-500250 appears to be the most unusual source in the sample. Its X-ray spectra is suggestive of a ultraluminous X-ray source but its variability makes it highly unusual. Machine-learned classification and anomaly detection will facilitate scientific discoveries in the era of all-sky surveys.

  14. Automatic classification of time-variable X-ray sources

    DOE Office of Scientific and Technical Information (OSTI.GOV)

    Lo, Kitty K.; Farrell, Sean; Murphy, Tara

    2014-05-01

    To maximize the discovery potential of future synoptic surveys, especially in the field of transient science, it will be necessary to use automatic classification to identify some of the astronomical sources. The data mining technique of supervised classification is suitable for this problem. Here, we present a supervised learning method to automatically classify variable X-ray sources in the Second XMM-Newton Serendipitous Source Catalog (2XMMi-DR2). Random Forest is our classifier of choice since it is one of the most accurate learning algorithms available. Our training set consists of 873 variable sources and their features are derived from time series, spectra, andmore » other multi-wavelength contextual information. The 10 fold cross validation accuracy of the training data is ∼97% on a 7 class data set. We applied the trained classification model to 411 unknown variable 2XMM sources to produce a probabilistically classified catalog. Using the classification margin and the Random Forest derived outlier measure, we identified 12 anomalous sources, of which 2XMM J180658.7–500250 appears to be the most unusual source in the sample. Its X-ray spectra is suggestive of a ultraluminous X-ray source but its variability makes it highly unusual. Machine-learned classification and anomaly detection will facilitate scientific discoveries in the era of all-sky surveys.« less

  15. Automatic cloud coverage assessment of Formosat-2 image

    NASA Astrophysics Data System (ADS)

    Hsu, Kuo-Hsien

    2011-11-01

    Formosat-2 satellite equips with the high-spatial-resolution (2m ground sampling distance) remote sensing instrument. It has been being operated on the daily-revisiting mission orbit by National Space organization (NSPO) of Taiwan since May 21 2004. NSPO has also serving as one of the ground receiving stations for daily processing the received Formosat- 2 images. The current cloud coverage assessment of Formosat-2 image for NSPO Image Processing System generally consists of two major steps. Firstly, an un-supervised K-means method is used for automatically estimating the cloud statistic of Formosat-2 image. Secondly, manual estimation of cloud coverage from Formosat-2 image is processed by manual examination. Apparently, a more accurate Automatic Cloud Coverage Assessment (ACCA) method certainly increases the efficiency of processing step 2 with a good prediction of cloud statistic. In this paper, mainly based on the research results from Chang et al, Irish, and Gotoh, we propose a modified Formosat-2 ACCA method which considered pre-processing and post-processing analysis. For pre-processing analysis, cloud statistic is determined by using un-supervised K-means classification, Sobel's method, Otsu's method, non-cloudy pixels reexamination, and cross-band filter method. Box-Counting fractal method is considered as a post-processing tool to double check the results of pre-processing analysis for increasing the efficiency of manual examination.

  16. Reliable clarity automatic-evaluation method for optical remote sensing images

    NASA Astrophysics Data System (ADS)

    Qin, Bangyong; Shang, Ren; Li, Shengyang; Hei, Baoqin; Liu, Zhiwen

    2015-10-01

    Image clarity, which reflects the sharpness degree at the edge of objects in images, is an important quality evaluate index for optical remote sensing images. Scholars at home and abroad have done a lot of work on estimation of image clarity. At present, common clarity-estimation methods for digital images mainly include frequency-domain function methods, statistical parametric methods, gradient function methods and edge acutance methods. Frequency-domain function method is an accurate clarity-measure approach. However, its calculation process is complicate and cannot be carried out automatically. Statistical parametric methods and gradient function methods are both sensitive to clarity of images, while their results are easy to be affected by the complex degree of images. Edge acutance method is an effective approach for clarity estimate, while it needs picking out the edges manually. Due to the limits in accuracy, consistent or automation, these existing methods are not applicable to quality evaluation of optical remote sensing images. In this article, a new clarity-evaluation method, which is based on the principle of edge acutance algorithm, is proposed. In the new method, edge detection algorithm and gradient search algorithm are adopted to automatically search the object edges in images. Moreover, The calculation algorithm for edge sharpness has been improved. The new method has been tested with several groups of optical remote sensing images. Compared with the existing automatic evaluation methods, the new method perform better both in accuracy and consistency. Thus, the new method is an effective clarity evaluation method for optical remote sensing images.

  17. Automatically detect and track infrared small targets with kernel Fukunaga-Koontz transform and Kalman prediction.

    PubMed

    Liu, Ruiming; Liu, Erqi; Yang, Jie; Zeng, Yong; Wang, Fanglin; Cao, Yuan

    2007-11-01

    Fukunaga-Koontz transform (FKT), stemming from principal component analysis (PCA), is used in many pattern recognition and image-processing fields. It cannot capture the higher-order statistical property of natural images, so its detection performance is not satisfying. PCA has been extended into kernel PCA in order to capture the higher-order statistics. However, thus far there have been no researchers who have definitely proposed kernel FKT (KFKT) and researched its detection performance. For accurately detecting potential small targets from infrared images, we first extend FKT into KFKT to capture the higher-order statistical properties of images. Then a framework based on Kalman prediction and KFKT, which can automatically detect and track small targets, is developed. Results of experiments show that KFKT outperforms FKT and the proposed framework is competent to automatically detect and track infrared point targets.

  18. Automatically detect and track infrared small targets with kernel Fukunaga-Koontz transform and Kalman prediction

    NASA Astrophysics Data System (ADS)

    Liu, Ruiming; Liu, Erqi; Yang, Jie; Zeng, Yong; Wang, Fanglin; Cao, Yuan

    2007-11-01

    Fukunaga-Koontz transform (FKT), stemming from principal component analysis (PCA), is used in many pattern recognition and image-processing fields. It cannot capture the higher-order statistical property of natural images, so its detection performance is not satisfying. PCA has been extended into kernel PCA in order to capture the higher-order statistics. However, thus far there have been no researchers who have definitely proposed kernel FKT (KFKT) and researched its detection performance. For accurately detecting potential small targets from infrared images, we first extend FKT into KFKT to capture the higher-order statistical properties of images. Then a framework based on Kalman prediction and KFKT, which can automatically detect and track small targets, is developed. Results of experiments show that KFKT outperforms FKT and the proposed framework is competent to automatically detect and track infrared point targets.

  19. Automated Delineation of Lung Tumors from CT Images Using a Single Click Ensemble Segmentation Approach

    PubMed Central

    Gu, Yuhua; Kumar, Virendra; Hall, Lawrence O; Goldgof, Dmitry B; Li, Ching-Yen; Korn, René; Bendtsen, Claus; Velazquez, Emmanuel Rios; Dekker, Andre; Aerts, Hugo; Lambin, Philippe; Li, Xiuli; Tian, Jie; Gatenby, Robert A; Gillies, Robert J

    2012-01-01

    A single click ensemble segmentation (SCES) approach based on an existing “Click&Grow” algorithm is presented. The SCES approach requires only one operator selected seed point as compared with multiple operator inputs, which are typically needed. This facilitates processing large numbers of cases. Evaluation on a set of 129 CT lung tumor images using a similarity index (SI) was done. The average SI is above 93% using 20 different start seeds, showing stability. The average SI for 2 different readers was 79.53%. We then compared the SCES algorithm with the two readers, the level set algorithm and the skeleton graph cut algorithm obtaining an average SI of 78.29%, 77.72%, 63.77% and 63.76% respectively. We can conclude that the newly developed automatic lung lesion segmentation algorithm is stable, accurate and automated. PMID:23459617

  20. MIDAS robust trend estimator for accurate GPS station velocities without step detection

    NASA Astrophysics Data System (ADS)

    Blewitt, Geoffrey; Kreemer, Corné; Hammond, William C.; Gazeaux, Julien

    2016-03-01

    Automatic estimation of velocities from GPS coordinate time series is becoming required to cope with the exponentially increasing flood of available data, but problems detectable to the human eye are often overlooked. This motivates us to find an automatic and accurate estimator of trend that is resistant to common problems such as step discontinuities, outliers, seasonality, skewness, and heteroscedasticity. Developed here, Median Interannual Difference Adjusted for Skewness (MIDAS) is a variant of the Theil-Sen median trend estimator, for which the ordinary version is the median of slopes vij = (xj-xi)/(tj-ti) computed between all data pairs i > j. For normally distributed data, Theil-Sen and least squares trend estimates are statistically identical, but unlike least squares, Theil-Sen is resistant to undetected data problems. To mitigate both seasonality and step discontinuities, MIDAS selects data pairs separated by 1 year. This condition is relaxed for time series with gaps so that all data are used. Slopes from data pairs spanning a step function produce one-sided outliers that can bias the median. To reduce bias, MIDAS removes outliers and recomputes the median. MIDAS also computes a robust and realistic estimate of trend uncertainty. Statistical tests using GPS data in the rigid North American plate interior show ±0.23 mm/yr root-mean-square (RMS) accuracy in horizontal velocity. In blind tests using synthetic data, MIDAS velocities have an RMS accuracy of ±0.33 mm/yr horizontal, ±1.1 mm/yr up, with a 5th percentile range smaller than all 20 automatic estimators tested. Considering its general nature, MIDAS has the potential for broader application in the geosciences.